From 99c9232ff1797b24078cd4be54a57590a4c3c4e2 Mon Sep 17 00:00:00 2001 From: Matteo Pilz Date: Mon, 15 Apr 2024 23:11:04 +0200 Subject: [PATCH] Major docs restructure (#229) * fix broken links * fix broken links * relink developer quickstart guide * update TOPPView intensity mode description * update tutorials * remove duplicated tutorial * reordered sections * download fix * major restructure * fixed installation cross ref * updated knime tutorial references * fixed warnings * fixed download link * fixed links * fixed links --------- Co-authored-by: Samuel Wein --- .../additional-resources/click-fork.png | Bin .../Existing-Projects-into-Workspace.png | Bin .../advanced-resources/KNIME-Build-System.png | Bin .../KNIME-Desktop-Update-Site.png | Bin .../KNIME-File-Handling-Nodes.png | Bin .../eclipse-application.png | Bin .../advanced-resources/new-configuration.png | Bin .../select-root-directory.png | Bin .../installations/Windows_logo.svg | 0 .../installations/linux_logo.svg | 100 +-- 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    +

    **Video**

    +For more information on isobaric labeling, view the following links: +
    + +OpenMS contains tools that analyze data from isobaric labeling experiments. + +### Metabolic labeling + +During metabolic labeling, the organism is 'fed' with labeled metabolites. Metabolites include but are not limited to amino acids, nitrogen sources and glucose. Unlike chemical labeling, metabolic labeling occurs early in the study. Therefore, experiments that incorporate metabolic labeling are highly reproducible. + +#### Stable Isotope Labeling with Amino Aids in Cell Culture (SILAC) + +In SILAC, the labeled amino acids are fed to the cell culture. The labels are integrated into the proteins after a period. The labeled sample is then compared with the unlabeled sample. + +OpenMS contains tools that analyze data from SILAC experiments. + +
    +

    **Video**

    +For more information on SILAC, view the following links: + +
    + +## Label-free quantification (LFQ) + +LFQ is a cheap and natural method of quantifying molecules of interest. As the name suggests, no labeling of molecules is involved. + +LFQ includes the following steps: + +1. **Conduct replicate experiments**. +2. **Generate LC-MS maps** for each experiment. +3. **Find features** in all LC-MS maps. A {term}`feature` is a collection of peaks that belong to a chemical compound. +4. **Align maps** to address shifts in retention times. +5. **Match corresponding features** in different maps. We refer to this as **grouping** or **linking**. +6. **Identify feature groups**, called {term}`consensus features `. +7. **Quantify consensus features**. + +
    +

    **Video**

    +For more information on LFQ, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141118_002_cpm_0001?t=2115.00). +For more information on the steps involved in LFQ, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141118_002_cpm_0001?t=2230.18) +
    + +### Feature finding + +Feature finding is method for identifying all peaks belonging to a chemical compound. Feature finding involves the following steps: + +1. **Extension** where we collect all data points we think belong to the peptide. +2. **Refinement** where we remove peaks that we think do not belong to the peptide. +3. **Fit an optimal model** to the isolated peaks. + +The above steps are iterative; we repeat these steps until no improvement can be made to the model. + +OpenMS contains a number of feature finding algorithms. + +
    +

    **Video**

    +For more information on feature finding, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141118_002_cpm_0001?t=2670.44). +
    \ No newline at end of file diff --git a/docs/about/learning/lc-chromatography.md b/docs/about/learning/lc-chromatography.md new file mode 100644 index 00000000..c8cab830 --- /dev/null +++ b/docs/about/learning/lc-chromatography.md @@ -0,0 +1,59 @@ +Liquid chromatography (LC) +========================== + +Chromatography is a technique used by life scientists to separate molecules based on a specific physical or chemical property. + +
    +

    **Video**

    +For more information on chromatography, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_001_cpm_0001?t=210.00). +
    + +There are many types of chromatography, but this section focuses on LC as it is widely used in proteomics and metabolomics. + +LC separates molecules based on a specific physical or chemical property by mixing a sample containing the molecules of interest (otherwise known as **analytes**) in a liquid solution. + +## Key components of LC + +An LC setup is made up of the following components: +- **A liquid solution**, known as the **mobile phase**, containing the analytes. +- **A pump** which transports the liquid solution. +- **A stationary phase** which is a solid, homogeneous substance. +- **A column** that contains the stationary phase. +- **A detector** that plots the time it takes for the analyte to escape the column (retention time) against the analyte's concentration. This plot is called a **chromatogram**. + +Refer to the image below for a diagrammatic representation of an LC setup. + +![schematic illustration of an LC setup](/_images/introduction/lc-components.png) + +## How does LC work? + +The liquid solution containing the analytes is pumped through a column that is attached to the stationary phase. Analytes are separated based on how strongly they interact with each phase. Some analytes will interact strongly with the mobile phase while others will be strongly attracted to the stationary phase, depending on their physical or chemical properties. The stronger an analyte's attraction is to the mobile phase, the faster it will leave the column. The time it takes for an analyte to escape from the column is called the analyte's {term}`retention time`. As a result of their differing attractions to the mobile and stationary phases, different analytes will have different retention times, which is how separation occurs. + +The retention times for each analyte are recorded by a detector. The most common detector used is the mass spectrometer, which we discuss later. However, other detection methods exist, such as: +- Light absorption (photometric detector) +- Fluorescence +- Change in diffraction index + +## High performance liquid chromatography (HPLC) + +HPLC is the most commonly used technique for separating proteins and metabolites. In HPLC, a high-pressured pump is used to transport a liquid (solvent) containing the molecules of interest through a thin capillary column. The stationary phase is ‘packed’ into the column. + +
    +

    **Video**

    +For more information on HPLC, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_001_cpm_0001?t=699.69). +
    + +Several variations of HPLC exist such as: +- Reversed-phase (RP) chromatography +- Strong cation/anion exchange (SCX/SAX) chromatography +- Affinity chromatography +- Size exclusion chromatography + +### Special case of HPLC: Reversed-phase (RP) chromatography + +RP chromatography is the most commony type of HPLC with biological samples. In reversed-phase liquid chromatography, the solid phase is modified to become hydrophobic, when it is originally hydrophilic, hence the term ‘reversed-phase’. The liquid phase is a mixture of water and an organic solvent. The separation of molecules happens based on the following behavior: hydrophilic analytes have a high affinity to the mobile phase and escape the column quickly while hydrophobic analytes have a high affinity towards the organic solvent and therefore, take a longer time to escape the column. + +
    +

    **Video**

    +For more information on RP chromatography, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_001_cpm_0001?t=1399.85). +
    \ No newline at end of file diff --git a/docs/about/learning/mass-spectrometry.md b/docs/about/learning/mass-spectrometry.md new file mode 100644 index 00000000..04d9b3f0 --- /dev/null +++ b/docs/about/learning/mass-spectrometry.md @@ -0,0 +1,195 @@ +Mass spectrometry (MS) +====================== + +Mass spectrometry is an analytical technique used to determine the abundance of molecules in a sample. + +## Key components of MS + +There are three key components in a mass spectrometer: +- An **ion source**, which generates {term}`ions ` from the incoming sample. All mass spectrometry techniques rely on ionized molecules to control their movement in an electric field. +- A **mass analyzer**, which separates the ions according to their mass-to-charge (m/z) ratio. There are several types such as time of flight (TOF), orbitrap and quadrupole mass analyzers. Depending on the mass analyzer, OpenMS offers calibration tools, so that highly accurate results can be achieved. +- A **detector**, which scans ions at a given time point producing a {term}`mass spectrum`, where the intensity is plotted against the m/z. + +Refer to the image below for a diagrammatic representation of the key components in MS. + +![schematic illustration of a mass spectrometer](/_images/introduction/mass-spectrometry-components.png) + +### Ion source + +We want the analytes to move through the electrostatic and electromagnetic fields in the mass analyzer. To achieve this objective, we need to convert them to ions by charging them. There are a number of ways to charge our analytes including: +- Electrospray Ionization (ESI) +- Matrix Assisted Laser Desorption/Ionization (MALDI) +- Electron Impact Ionization (EI) + +In proteomics and metabolomics, ESI and MALDI are used because they are soft ionization techniques. A soft ionization technique is one which charges analytes while keeping the molecules of interest largely intact, so that they can be characterized easily at a later stage. Hard ionization techniques such as EI shatter analytes in smaller fragments, making it difficult to characterize large molecules. + +Given that OpenMS focuses on proteomic and metabolomic applications, we will describe ESI and MALDI in further detail. + +#### Electrospray Ionization (ESI) +ESI can be broken down into the following steps. + +1. The sample is dissolved in a polar, volatile buffer. +2. The sample - dissolved in the buffer - is pumped through a thin, stainless steel capillary. +3. The sample is converted to small, charged, stable droplets (aerosolized) by applying high voltage. +4. The aerosol is directed through regions of high vacuum until the droplets evaporate until only the charged molecules are left. +5. The particles are fed to the mass analyzer. + +Refer to the image below for a diagrammatic representation of the steps in ESI. + +![a simplified, schematic representation of ESI](/_images/introduction/electrospray-ionization.png) + +
    +

    **Video**

    +For more information on ESI, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=624.28). +
    + +#### Matrix Assisted Laser Desorption/Ionization (MALDI) + +MALDI can be broken down into the following steps: +1. The analytes are mixed with a small organic molecule known as a matrix. +2. The mixture is exposed to radiation with short pulses of laser light, charging the matrix. +3. The matrix transfers its charge to the analytes because the wavelength of the laser light is the same as the absorbance maximum of the matrix. +4. The analytes become charged and are fed to the mass analyzer. + +Refer to the image below for a diagrammatic representation of the steps in MALDI. + +![a simplified, schematic representation of MALDI](/_images/introduction/MALDI.png) + +
    +

    **Video**

    +For more information on MALDI, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=838.40). +
    + +### Mass analyzer + +Once the analytes have been charged by the ion source, we want to now sort the analytes by their mass-to-charge ratio for easy identification. + +A number of mass analyzers exists. These include: +- Quadrupole analyzer +- Time-of-Flight analyzer +- Orbitrap analyzer + +The next sections describe each analyzer type in detail. + +#### Quadrupole + +In a quadropole analyzer, you can set the quadropole voltage so that ions with a specific m/z ratio travel through. The oscillating electrostatic fields stabilize the flight path for the ions so that they can pass through the quadropole. Other ions will be accelerated out of the quadropole and will not make it to the end. + +Refer to the image below for a diagrammatic representation of the quadrupole analyzer. + +![a simplified, schematic representation of the quadrupole analyzer](/_images/introduction/quadrupole-analyzer.png) + +
    +

    **Video**

    +For more information on quadrupole analyzers, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=1477.00). +
    + +#### Time-of-Flight (TOF) + +In a time-of-flight analyzer, ions are extracted from the ion source through an electrostatic field in pulses in a field-free drift zone. An electrostatic mirror called a reflectron reflects the ions back onto the next component of mass spectrometry, the detector. The detector counts the particles and records the time of flight from extraction to the moment the particle hits the detector. + +Refer to the image below for a diagrammatic representation of the TOF analyzer. + +![a simplified, schematic representation of TOF](/_images/introduction/TOF.png) + +Lighter ions fly faster than heavier ions of the same charge and will arrive earlier at the detector. Therefore, an ion’s time of flight depends on the ion’s mass. The ion's time of flight is also dependant on the ion's charge. This can be demonstrated by using the following equations: +1. Potential energy is transferred to an ion with charge **q** accelerated by an electrostatic field with voltage. +```{math} + \begin{equation} E_p = qU_a + \end{equation} +``` +2. The potential energy is converted to kinetic energy as the ion accelerates. + +```{math} + \begin{equation} E_p = E_k = \frac{1}{2}mv^2 + \end{equation} +``` + +3. We know that for a given path,**s**, from extraction to the detector, the time of flight, **t** is equal to: + +```{math} + \begin{equation} t = \frac{s}{v} + \end{equation} +``` + +Therefore,, **t**, for a given instrument's path length, **s**, depends on an ion's charge and mass. + +```{math} + \begin{equation} t = \frac{s}{v} = \frac{s}{\sqrt{\frac{2qU_a}{m}}} + \end{equation} +``` +
    +

    **Video**

    +For more information on TOF analyzers, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=1262.00). +
    + +#### Orbitrap + +The orbitrap analyzer is the most frequently used analyzer in mass spectrometry for proteomic and metabolomic applications. It consists of two outer electrodes and a central electrode. Ions are captured inside the analyzer because of an applied electrostatic field. The ions in the orbitrap analyzer oscillate around the central electrode along the axis of the electrostatic field at a set frequency, ω. This frequency is used to determine the mass-to-charge ratio using the following formula: + +```{math} + \begin{equation} ω = \sqrt{\frac{kz}{m}} + \end{equation} +``` +, where *k* is a constant. + +The following diagram is a conceptual representation of the orbitrap analyzer. + +![schematic illustration of a mass spectrometer](/_images/introduction/orbitrap.png) + +
    +

    **Video**

    +For more information on orbitrap analyzers, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=1572.96). +
    + +## Identifying molecules with Tandem Mass Spectrometry (MS2) + +To get better results, we can use two mass analyzers sequentially to generate and analyze ions. This technique is called **tandem mass spectrometry** or MS/MS (MS2). Tandem mass spectrometry is especially useful for linear polymers like proteins, RNA and DNA. + +With MS2, ions called **precursor ions** are isolated and fragmented into ion fragments or **product ions**. A {term}`mass spectrum` is recorded for both the precursor and the product ions. + +
    +

    **Video**

    +For more information on MS2, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=1650.00). +
    + +Different fragmentation techniques to fragment peptides exist: + +- Collision-Induced Dissociation (CID) +- Pulsed Q Dissociation (PQD) +- Electron transfer dissociation (ETD) +- Electron capture dissociation (ECD) +- Higher energy collision dissociation (HCD) + +CID is the most frequently used fragmentation technique and will therefore be discussed in more detail in the following section. + +### Collision-induced dissociation + +Collision-induced dissociation is a method to fragment peptides using an inert gas such as argon or helium. Selected primary or precursor ions enter a collision cell filled with the inert gas. The application of the inert gas on the precursor ions causes the precursor ions that reach the energy threshold to fragment into smaller, product ions and or neutral losses. A {term}`mass spectrum` is recorded for both the precursor ions and the product ions. The {term}`mass spectrum` for the precursor ions will give you the mass for the entire peptide while the product ions will inform you about it’s amino acid composition. + +
    +

    **Video**

    +For more information on CID, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=1757.45). +
    + + +LC-MS +----- + +Liquid chromatography is often coupled with mass spectrometry to reduce complexity in the mass spectra. If complex samples were directly fed to a mass spectrometer, you would not be able to detect the less abundant analyte ions. The separated analytes from the liquid chromatography setup are directly injected into the ion source from the mass spectrometry setup. Multiple analytes that escape the column at the same time are separated by their mass-to-charge ratio using the mass spectrometer. + +Refer to the image below for a diagrammatic representation of the LC-MS setup. + +![lc-ms setup](/_images/introduction/lc-ms-setup.png) + +From the LC-MS setup, a set of spectra called a peak map is produced. In a peak map, each spectrum represents the ions detected at a particular retention time. Each peak in a spectrum has a retention time, mass-to-charge and intensity dimension. + +From the LC-MS setup, a series of spectra are 'stacked' together to form what is known as a peak map. Each spectrum in a peak map is a collection of data points called {term}`peaks ` which indicate the retention time, mass-to-charge and intensity of each detected ion. Analyzing peak maps is difficult as different compounds can elute at the same time which means that peaks can overlap. Therefore, sophisticated techniques are required for the accurate identification and quantification of molecules. + +The image below includes a spectrum at a given retention time (left) and a peak map (right). + +![peak map](/_images/introduction/spectrum_peakmap.png) + +
    +

    **Video**

    +For more information on a *specific* application of LC-MS, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141014_002_cpm_0001?t=946.20). \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index 63c84fb5..d28cdf24 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -14,7 +14,6 @@ # import sys # sys.path.insert(0, os.path.abspath('.')) - # -- Project information ----------------------------------------------------- project = 'OpenMS' @@ -70,6 +69,7 @@ "substitution", ] +root_doc = 'index' myst_heading_anchors = 3 # do not start footnotes with transition, gives warnings/errors when @@ -139,7 +139,6 @@ #pathicon = 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' pathicon = 'fa fa-folder-open' -root_doc = 'index' variables_to_export = [ "project", diff --git a/docs/contribute-to-openms/advanced.md b/docs/contribute-to-openms/advanced.md deleted file mode 100644 index 50b1de97..00000000 --- a/docs/contribute-to-openms/advanced.md +++ /dev/null @@ -1,9 +0,0 @@ -Advanced -======== -```{toctree} -:maxdepth: 1 - -advanced/developer-guidelines-for-adding-new-dependent-libraries.md -advanced/custom-compilation.md -advanced/build-custom-knime-plugin.md -``` diff --git a/docs/contribute-to-openms/advanced/build-custom-knime-plugin.md b/docs/contribute-to-openms/advanced/build-custom-knime-plugin.md deleted file mode 100644 index 61508a4e..00000000 --- a/docs/contribute-to-openms/advanced/build-custom-knime-plugin.md +++ /dev/null @@ -1,4 +0,0 @@ -Build Custom KNIME Plugin -========================= - -This page is under construction. diff --git a/docs/contribute-to-openms/reporting-bugs-and-issues.md b/docs/contribute-to-openms/reporting-bugs-and-issues.md deleted file mode 100644 index 2d75884a..00000000 --- a/docs/contribute-to-openms/reporting-bugs-and-issues.md +++ /dev/null @@ -1,19 +0,0 @@ -Reporting Bugs and Issues -========================= - -A list of known issues in the current OpenMS release can be found [here](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/known_dev_bugs.html). -Please check if your OpenMS version matches the current version and if the bug has already been reported. - -In order to report a new bug, please create a [GitHub issue](write-and-label-github-issues.md) or [contact us](/quick-reference/contact-us.md). - -Include the following information in your bug report: - -1. The command line (i.e. call) including the TOPP tool and the arguments you used, or the steps you followed in a GUI - tool (e.g. TOPPView) - e.g. `FeatureFinderCentroided -in myfile.mzML -out myfile.featureXML`. -2. The output of OpenMS/TOPP (or a screenshot in case of a GUI problem). -3. Operating system (e.g. "Windows XP 32 bit", "Win 7 64 bit", "Fedora 8 32 bit", "macOS 10.6 64 bit"). -4. OpenMS version (e.g. "OpenMS 1.11.1", "Revision 63082 from the SVN repository"). -5. OpenMS architecture ("32 bit" or "64 bit") - -Please provide files that we need to reproduce the bug (e.g. `TOPP INI` files, data files — usually mzML) via a download -link, via the mailing list or by directly contacting one of the developers. diff --git a/docs/contribute-to-openms/write-and-label-github-issues.md b/docs/contribute-to-openms/write-and-label-github-issues.md deleted file mode 100644 index 59b12918..00000000 --- a/docs/contribute-to-openms/write-and-label-github-issues.md +++ /dev/null @@ -1,25 +0,0 @@ -Write and Label GitHub Issues -============================= - -## Create an Issue - -To create an issue: - -1. Go to the [OpenMS codebase](https://github.com/OpenMS/OpenMS). -2. Submit an [issue](https://github.com/OpenMS/OpenMS/issues/new). - -The issue will be listed under **Issues**. - -## Label an Issue - -To label an issue: - -1. On the right of the screen, select the cog icon under **Labels**. -2. Choose a label from the list. Normally, an issue can have one or more of the following labels: - - **defect**: A defect refers to a bug in OpenMS. This is a high priority issue. - - **enhancement**: An enhancement refers to a feature idea to enhance the current OpenMS code. This is a medium - priority issue. - - **task**: A task refers to a single piece of work that a developer can undertake. This is a medium priority issue. - - **refactoring**: A refactoring issue refers to a suggestion to streamline the code without changing how the code - function. - - **question**: A question could trigger to a discussion about tools, parameters and scientific tasks. diff --git a/docs/develop-with-openms/build-openms-from-source.md b/docs/develop-with-openms/build-openms-from-source.md deleted file mode 100644 index 6b2e0c1c..00000000 --- a/docs/develop-with-openms/build-openms-from-source.md +++ /dev/null @@ -1,19 +0,0 @@ -Build From Source -================= - -## GNU Linux - -To build OpenMS from source on a GNU Linux machine, follow the build instructions for [Linux](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/install_linux.html). - -Dockerfiles to build different kind of images (corresponding to build instructions, e.g. on ArchLinux) can be found on -GitHub in [OpenMS/dockerfiles](https://github.com/OpenMS/dockerfiles) repository. - - -## macOS - -To build OpenMS from source on a macOS machine, follow the build instructions for [macOS](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/install_mac.html). - - -## Windows - -To build OpenMS from source on a Windows machine, follow the build instructions for [Windows](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/install_win.html). diff --git a/docs/develop-with-openms/openms-core-cplusplus-library.md b/docs/develop-with-openms/openms-core-cplusplus-library.md deleted file mode 100644 index c76fa72a..00000000 --- a/docs/develop-with-openms/openms-core-cplusplus-library.md +++ /dev/null @@ -1,4 +0,0 @@ -OpenMS C++ Core Library -======================= - -This page is under construction. diff --git a/docs/downloads.md b/docs/downloads.md deleted file mode 100644 index 932a5eb2..00000000 --- a/docs/downloads.md +++ /dev/null @@ -1,48 +0,0 @@ -# OpenMS Installers -::::{tab-set} - -:::{tab-item} Release -:sync: Release -| Platform | Name | -|----------|------| -| Windows | {{ '[OpenMS-{0}-Win64.exe](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/release/{0}/OpenMS-{0}-Win64.exe)'.format(version) }} | -| macOS | {{ '[OpenMS-{0}-macOS.dmg](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/release/{0}/OpenMS-{0}-macOS-Intel.dmg)'.format(version) }} | -| GNU/Linux | {{ '[OpenMS-{0}-Debian-Linux-x86_64.deb](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/release/{0}/OpenMS-{0}-Debian-Linux-x86_64.deb)'.format(version) }} | `d203985c7042b885ac1085c30a2d9f36d7609b47` | -| Source | {{ '[OpenMS-{0}-src.tar.gz](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/release/{0}/OpenMS-{0}-src.tar.gz)'.format(version) }} | -::: - -:::{tab-item} Nightly -:sync: Nightly -| Platform | Name | -|----------|------| -| Windows | {{ '[OpenMS-3.1.0-Win64.exe](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/nightly/OpenMS-3.1.0-pre-nightly-latest-Win64.exe)'.format(version) }} | -| macOS | [OpenMS-3.1.0-macOS.dmg](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/nightly/OpenMS-3.1.0-pre-nightly-latest-macOS-Intel.dmg) | -| GNU/Linux | [OpenMS-3.1.0-Linux-x86_64.deb](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/nightly/OpenMS-3.1.0-pre-nightly-latest-Linux-x86_64.deb) | -| Source | [OpenMS-3.1.0-src.tar.gz](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/nightly/OpenMS-3.1.0-src.tar.gz) | -::: - -:::: - -# Workflows - -| Workflow | Description | Download Link | -|----------|-------------|---------------| -|`ProteomicsLFQ_tool_and_MSstats_postprocessing` | Label-free identification and quantification using the comet search engine, the ProteomicsLFQ tool and statistical down-stream processing using MSstats. Compared to the other proteomics LFQ workflows, it is less complex as it combines quantification and inference steps in a single ProtemicLFQ tool. | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/ProteomicsLFQ_tool_and_MSstats_postprocessing.knwf?raw=true) | -|`DIAMetAlyzer` | Metabolomics assay library construction with decoy generation from DDA data and targeted DIA analysis using OpenSWATH and pyprophet for statistical validation. | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/DIAMetAlyzer.knwf?raw=true) | -|`Identification_quantification_with_inference_isobaric_epifany_MSstatsTMT` | Identification and quantification for isobaric experiments using MSGFPlus as search engine, epifany for inference and MSstatsTMT for statistical down-stream analysis. | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/Identification_quantification_with_inference_isobaric_epifany_MSstatsTMT.knwf?raw=true) | -|`labelfree_with_protein_quantification` | Label-free with protein quantification steps implemented using individual OpenMS tools | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/labelfree_with_protein_quantification.knwf?raw=true) | -|`Metabolite_Adduct_Grouping` | Quantification and identification via accurate mass based on multiple adduct grouping steps (adducts, neutral losses). | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/Metabolite_Adduct_Grouping.knwf?raw=true) | -|`Metabolite_DeNovoID` | Quantification and identification via adduct grouping and de-novo identification using SIRIUS/CSI:FingerID. | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/Metabolite_DeNovoID.knwf?raw=true) | -|`Metabolite_ID` | Quantification and identification via accurate mass based with downstream processing and visualisation. | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/Metabolite_ID.knwf?raw=true) | -|`Metabolite_SpectralID` | Identification via spectral library search for small molecules. | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/Metabolite_SpectralID.knwf?raw=true) | -|`MSstats_statPostProcessing_iPRG2015` | Post processing workflow for using MSstats based on "Example_OneTool_ProteomicsLFQ_MSstats.knwf" | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/MSstats_statPostProcessing_iPRG2015.knwf?raw=true) | -|`MSstatsTMT` | Post processing workflow for using MSstatsTMT based on "Identification_quantification_with_inference_isobaric_epifany_MSstatsTMT". | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/MSstatsTMT.knwf?raw=true) | -|`OpenSWATH` | Targeted extraction and scoring of transitions in DIA data based on an (iRT) assay library. | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/OpenSWATH.knwf?raw=true) | -|`Phosphoproteomics_ID` | Identification of Phosphorilation sites. | [Download](https://github.com/OpenMS/Tutorials/blob/master/Workflows/Phosphoproteomics_ID.knwf?raw=true) | - -# Other Resources - -| Name | Description | Download Link | -|------|-------------|---------------| -| Schemas | Documented schemas of the OpenMS formats | [Download](https://github.com/OpenMS/OpenMS/tree/develop/share/OpenMS/SCHEMAS)| -| iPRG2016 data | Dataset mxMLs, Fasta database, Identification file (idXML), Big Data (idXML) | [Download](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Data/iPRG2016/) | diff --git a/docs/run-workflows-with-openms-tools/openms-on-galaxy.md b/docs/getting-started/galaxy-get-started.md similarity index 81% rename from docs/run-workflows-with-openms-tools/openms-on-galaxy.md rename to docs/getting-started/galaxy-get-started.md index 0645b7be..9face5d5 100644 --- a/docs/run-workflows-with-openms-tools/openms-on-galaxy.md +++ b/docs/getting-started/galaxy-get-started.md @@ -1,5 +1,5 @@ -OpenMS on Galaxy -================ +Galaxy +====== Galaxy is an open-source web platform designed for processing and analyzing large quantities of biomedical data. @@ -13,6 +13,6 @@ To use TOPP tools on Galaxy: 4. Search for "OpenMS". 5. You will see a list of TOPP tools. - ![topp tool list](/images/run-workflows-with-openms-tools/galaxy/topp-tool-list.png) + ![topp tool list](/_images/run-workflows-with-openms-tools/galaxy/topp-tool-list.png) Choose one of the TOPP tools from the list. You will be able to run it in isolation or use it to create a workflow. diff --git a/docs/getting-started/introduction.rst b/docs/getting-started/introduction.rst new file mode 100644 index 00000000..85b7c4e0 --- /dev/null +++ b/docs/getting-started/introduction.rst @@ -0,0 +1,103 @@ +Introduction +============ + +OpenMS offers a wide range of tools you can use for your mass spectrometry analysis. +Users without programming experience or those who want to quickly run common pipelines and analyses on their data can check out our webapps. + +Those who want to have more control over the settings in their pipeline can pick a pre-defined workflow or create a new one themselves with one +of our supported workflow systems. + +If you want to develop high performance pipelines including a large amount of data, you can use our TOPP +tools either in the command line or create a new Nextflow workflow. + +Should you be missing something you can also add new functions or classes to the OpenMS C++ core library. + + +.. grid:: 1 + :gutter: 0 + + .. grid-item:: + + .. grid:: + :gutter: 4 + + .. grid-item:: **Users** + + + .. grid-item:: **Developers** + + + .. grid-item:: + + .. grid:: + :gutter: 4 + :padding: 0 + :margin: 0 + + .. grid-item-card:: :octicon:`browser;5em;sd-text-info` **WebApps** + :link: webapps + :link-type: doc + + **Run pre-built analysis applications in your browser.** + + Run FLASHTaggerViewer, NuXL or UmetaFlow directly in your browser or host them locally. + + + .. grid-item-card:: :fab:`python;sd-text-info fa-xl` **pyOpenMS** + :link: https://pyopenms.readthedocs.io/en/latest/user_guide/installation.html + :link-type: url + + **Use the pyOpenMS python library to rapidly prototype methods and scripts.** + + Quickly prototype new methods and scripts or interface with other prominent data science, machine learning or visualization libraries in Python. + + + .. grid-item:: + + .. grid:: 2 + :gutter: 4 + :padding: 0 + :margin: 0 + + .. grid-item-card:: :material-regular:`account_tree;5em;sd-text-info` **Workflow Editor** + :link: /getting-started/webapps + :link-type: doc + + **Use a supported workflow editor to create or run predefined workflows.** + + Use applications such as KNIME, Nextflow, Galaxy or our tool TOPPAS, to apply predefined workflows or custom workflows you have designed on your data. + + + .. grid-item-card:: :octicon:`terminal;5em;sd-text-info` **TOPP Tools** + :link: /getting-started/topp-tools + :link-type: doc + + **Use over 100 command-line tools to automate pre-defined tasks efficiently.** + + Automate tasks and create workflows that can be saved, stored and used on multiple datasets. + + + .. grid-item:: + + .. grid:: 2 + :gutter: 4 + :padding: 0 + :margin: 0 + + .. grid-item-card:: :material-regular:`insert_chart;5em;sd-text-info` **TOPPView** + :link: /getting-started/visualize-with-openms + :link-type: doc + + **Use the OpenMS graphical user interface to inspect your results.** + + Visualize your mass spectrometry data in 1D, 2D and 3D with TOPPView. + + + .. grid-item-card:: :octicon:`cpu;5em;sd-text-info` **OpenMS C++ core library** + :link: /manual/develop + :link-type: doc + + **Develop your own efficient tools and methods with the OpenMS C++ core library.** + + Using the OpenMS C++ core library directly provides faster access to tools and shorter run-times. + diff --git a/docs/getting-started/knime-get-started.md b/docs/getting-started/knime-get-started.md new file mode 100644 index 00000000..388b0a22 --- /dev/null +++ b/docs/getting-started/knime-get-started.md @@ -0,0 +1,34 @@ +KNIME +===== + +## Installation +Click [here](knime/knime-installation.md) to install KNIME, the OpenMS plugin and its required packages. + +## Ready-made KNIME workflows + +You can get ready-made KNIME workflows and workflow components with OpenMS nodes from our +[community hub](https://hub.knime.com/openms-team>). +You can easily drag-and-drop workflows into your opened KNIME analytics platform. +For more, e.g., thirdparty OpenMS workflows use the search bar on the hub and search for +["openms"](https://hub.knime.com/search?q=openms). + + +## Creating workflows with KNIME + +Download Introduction to OpenMS in KNIME [user tutorial](/tutorials/knime-user-tutorial.md) containing hands-on training material covering also basic +usage of KNIME. See the official [KNIME Getting Started Guide](https://www.knime.com/getting-started-guide) for a more in-depth view of +the KNIME functionality besides OpenMS. + +If you face any issues, please [contact us](/about/communication.md) and specifically for the usage of OpenMS in KNIME, the KNIME community contribution [forum](https://forum.knime.com/tag/openms). + +Click [here](knime/minimal-workflow.md) to create your own minimal workflow. + +```{toctree} +--- +maxdepth: 1 +hidden: True +--- + +Install +knime/minimal-workflow.md +``` \ No newline at end of file diff --git a/docs/run-workflows-with-openms-tools/knime/installation.md b/docs/getting-started/knime/knime-installation.md similarity index 71% rename from docs/run-workflows-with-openms-tools/knime/installation.md rename to docs/getting-started/knime/knime-installation.md index deed7eb7..05197572 100644 --- a/docs/run-workflows-with-openms-tools/knime/installation.md +++ b/docs/getting-started/knime/knime-installation.md @@ -1,4 +1,5 @@ -# Installation of KNIME and its OpenMS plugin +KNIME - Installation +==================== Installation of OpenMS in {term}`KNIME` is platform-independent across Windows, MacOSX, and Linux. @@ -6,7 +7,7 @@ Installation of OpenMS in {term}`KNIME` is platform-independent across Windows, 2. In the full install of {term}`KNIME` skip the following installation routine since all required plugins should be installed by default. For the standard (core) installation, follow the instructions here or in the extended [user-tutorial](../../tutorials-and-quickstart-guides/openms-user-tutorial.md). - ![](/images/tutorials/knime/KNIME_Install.png) + ![](/_images/tutorials/knime/KNIME_Install.png) 3. In KNIME click on **Help** > **Install new Software**. 4. Install the required KNIME File Handling nodes from the official KNIME Update Site (a standard entry in the update @@ -18,7 +19,7 @@ Installation of OpenMS in {term}`KNIME` is platform-independent across Windows, 5. Filter the results for **File handling** and select the {term}`KNIME` File Handling Nodes. Click **Next** and install. - ![](/images/tutorials/knime/KNIME_update_site.jpeg) + ![](/_images/tutorials/knime/KNIME_update_site.jpeg) 6. Now, install the actual OpenMS plugin. Next to the **Work with:** dropdown menu, click on **Add...**. In the opening dialog fill in at least one of the following additional Update Sites (if not already present): @@ -47,15 +48,3 @@ Installation of OpenMS in {term}`KNIME` is platform-independent across Windows, software ProteoWizard that is packaged with our plugin. 8. After a restart of KNIME the OpenMS nodes will be available in your Node Repository (panel on the lower left) under **Community Nodes**. - -## Creating workflows with KNIME - -Download Introduction to OpenMS in KNIME [user tutorial](../../tutorials-and-quickstart-guides/openms-user-tutorial.md) containing hands-on training material covering also basic -usage of KNIME. See the official [KNIME Getting Started Guide](https://www.knime.com/getting-started-guide) for a more in-depth view of -the KNIME functionality besides OpenMS. - -If you face any issues, please [contact us](/quick-reference/contact-us.md) and specifically for the usage of OpenMS in KNIME, the KNIME community contribution [forum](https://forum.knime.com/tag/openms). - -## Creating your own Generic KNIME Nodes - -To create your own generic KNIME nodes, refer to their [documentation](https://docs.knime.com/latest/pure_python_node_extensions_guide/index.html#introduction). diff --git a/docs/getting-started/knime/minimal-workflow.md b/docs/getting-started/knime/minimal-workflow.md new file mode 100644 index 00000000..01ffc2b3 --- /dev/null +++ b/docs/getting-started/knime/minimal-workflow.md @@ -0,0 +1,6 @@ + + +```{include} /tutorials/knime-user-tutorial/minimal-workflow.md +``` \ No newline at end of file diff --git a/docs/getting-started/nextflow-get-started.md b/docs/getting-started/nextflow-get-started.md new file mode 100644 index 00000000..c18eabe8 --- /dev/null +++ b/docs/getting-started/nextflow-get-started.md @@ -0,0 +1,36 @@ +NextFlow +======== + +Nextflow is a workflow system for creating scalable, portable, and reproducible workflows. +It is based on the dataflow programming model, which greatly simplifies the writing of parallel and distributed pipelines, +allowing you to focus on the flow of data and computation. +Nextflow can deploy workflows on a variety of execution platforms, including your local machine, HPC schedulers, +AWS Batch, Azure Batch, Google Cloud Batch, and Kubernetes. +Additionally, it supports many ways to manage your software dependencies, including Conda, Spack, Docker, Podman, Singularity, and more.[^1] + +## Installation +Click [here](https://www.nextflow.io/docs/latest/getstarted.html#installation) to install Nextflow only. +Alternatively click [here](https://nf-co.re/docs/usage/installation) to follow the instructions for using nf-core curated pipelines in Nextflow. + +## Ready-made OpenMS nextflow workflows + +**SCALABLE [NF-CORE](https://nf-co.re/) COMPATIBLE [NEXTFLOW](https://nextflow.io/) PIPELINES** + +Click on “Launch” to configure the pipeline for your data online and launch it via nextflow’s [tower app](https://tower.nf/) (by registering a compute environment there) or by copying a configuration token for your local computer or HPC head node. + +*** + + +Launch "https://nf-co.re/launch?pipeline=quantms" +*** + + +Launch "https://nf-co.re/launch?pipeline=mhcquant" +*** + + +Launch "https://nf-co.re/launch?pipeline=diaproteomics" + + +## References +[^1]:P. Di Tommaso, et al. Nextflow enables reproducible computational workflows. Nature Biotechnology 35, 316–319 (2017), doi:10.1038/nbt.3820. \ No newline at end of file diff --git a/docs/getting-started/topp-tools.md b/docs/getting-started/topp-tools.md new file mode 100644 index 00000000..79b0752d --- /dev/null +++ b/docs/getting-started/topp-tools.md @@ -0,0 +1,179 @@ +TOPP Tools +========== + + +**TOPP - The OpenMS Pipeline** is a set of tools for the analysis of HPLC-MS data. These tools can be either: + +- [Executed from the command line](/getting-started/topp-tools.md#command-line-interface) or, +- Applied individually using OpenMS graphical applications. +- Applied in sequence as a workflow using a workflow editor such as KNIME, Nextflow or Galaxy. + +Before you choose one of the above options, there are few concepts that need to be understood. + +File formats +------------ + +OpenMS only accepts files in certain formats, including but not limited to: + +- **mzML**: The HUPO-PSI standard format for mass spectrometry data. +- **featureXML**: The OpenMS format for quantitation results. +- **consensusXML**: The OpenMS format for grouping features in one map or across several maps. +- **idXML**: The OpenMS format for protein and peptide identification. + +Documented schemas of the OpenMS formats can be found [here](https://github.com/OpenMS/OpenMS/tree/develop/share/OpenMS/SCHEMAS). + +If your data is not in the above formats, you may need to use a file conversion TOPP tool. + +Command Line Interface +---------------------- + +Command line calls will depend on the TOPP tools used, as each TOPP tool has its own set of parameters. However, the following arguments are typically used: + +- `-in` + + Specify an input file in the command line using the `-in` argument. The input file should be in a supported format. If not, use the file converter to convert the file to one of the supported formats. For more information, view the file handling documentation. +- `-out` + + Specify an output file in the command line using the `-out` argument. +- `-ini` + + Specify an INI file in the command line using the `-ini` argument. TOPP uses INI files to set parameters specific to the command line tool being called. +- `-write_ini` + + Create an INI file using the `-write_ini` file argument. + Create an INI file with this call: + ` -write_ini ` + If you want a visual tool to assist setting parameters, use the [INIFileEditor](/manual/additional/ini-file-editor.md), an application provided when you download OpenMS. Otherwise, you can set the parameters from the command line. +- `-help` + + Get information about basic options related to the tool using the -help parameter. For more advanced options (algorithmic parameters), use `--help`. +- `--help` + + Get detailed information about algorithmic parameters using the `--help` parameter. + +Many (but not all) command line calls will have the following structure: + +```bash + -in -out -ini +``` + +The following command line call uses the FileFilter tool to extract data from an mzML file. Note, that this call directly specifies the tool-specific parameters and doesn’t rely on an INI file: + +![break down of example command line call](/_images/topp/command-line-call.png) + +TOPP INI files +-------------- + +TOPP INI files are XML-based files with an `.ini` extension. OpenMS uses TOPP INI files to set parameters for one or more TOPP tools. Here is an example of a TOPP INI file: + + ```xml + + + + + + + + + + + + + + + + + + + + + ``` + +Features, feature maps and featureXML files +------------------------------------------- + +An LC-MS feature is a construct in OpenMS that is used to describe a 2D peak caused by an analyte interacting with the stationary phase. Each feature contains the following metadata: an id, retention time, mass-to-charge ratio, intensity, overall quality and one or more convex hulls. + +A feature map is a container for features. One feature map can contain many features. + +A featureXML file is an XML based file which contains one feature map. + +FeatureXML files can be created from mzML files using OpenMS’s feature detection algorithms. + +Consensus feature, consensus maps, consensusXML files +----------------------------------------------------- + +A consensus feature is a special type of LC-MS feature that is quantified across multiple experiments. A consensus feature is formed by linking or grouping features with similar mass-to-charge ratios and intensities from various experiment runs. Each consensus feature references the features used to form the consensus feature. + +Similar to a feature map, a consensus map is a container for consensus features. One consensus map can contain many consensus features. + +ConsensusXML files can be created from featureXML files using OpenMS's feature grouping algorithms. + + +Types of TOPP Tools +------------------- + +The following tools are offered: + +- **File conversion** + + TOPP file conversion tools can be used to convert files into a supported format. + +- **File handling** + + TOPP file handling tools are largely used to extract or merge data. For more information, view the [File handling](types-of-topp-tools/file-handling.md). + +- **Centroiding** + + The conversion of the "raw" ion count data acquired by the machine into peak lists for further processing is usually called peak picking or centroiding. The choice of the algorithm should mainly depend on the resolution of the data. OpenMS provides different algorithms for centroiding depending on the resolution of the data. For more information, view the [Picking peaks](types-of-topp-tools/picking-peaks.md) section. + +- **Spectrum processing** + + A number of spectrum processing tools are available. These include peak filtering and peak normalization tools, as well as other miscellaneous tools. + +- **Mass correction and calibration** + + To ensure that your data is sound, OpenMS have provided a number of mass correction and calibration tools. The types of tools used will depend on the type of equipment you have employed. For more information, view the [Calibration](types-of-topp-tools/calibration.md) section. + +- **Spectrum clustering** + + Spectrum clustering is the grouping of spectra that have many peaks in common. OpenMS provides tools for spectrum clustering to identify molecules in large datasets more efficiently. + +- **Map alignment** + + When looking to identify molecules, it is common to run multiple experiments, where each experiment produces a set of data. In OpenMS, every set of data is represented by a feature map. Before combining feature maps to create a consensus map, it is advised to use OpenMS’s map alignment tools so that all your datasets are comparable and based on a common retention time axis. For more information, view the [Map alignment](types-of-topp-tools/map-alignment.md) section. + +- **Feature linking** + + OpenMS provides a number of algorithms for feature grouping or linking. For more information, view the [Feature grouping](types-of-topp-tools/feature-grouping.md) section. + +- **Quantitation** + + A number of tools are available that allow for the identification and quantification of features. The tools you use will depend on the type of mass spectrometry experiment you have set up, and the type of molecules you wish to identify. For more information, view the [Feature detection](types-of-topp-tools/feature-detection.md) section. + +- **Protein/Peptide identification** +- **Protein/Peptide processing** +- **Targeted experiments and OpenSWATH** + +- **Cross-linking** + + Cross-linking is a technique where substances are chemically treated to create covalent bonds between different molecules. The strength of the covalent bonds can be quantified to indicate the proximity of certain molecules within a 3D structure. +- **Quality control** + + OpenMS provides tools to measure the quality of LC-MS data. For more information, view the [Quality control](types-of-topp-tools/quality-control.md) section. + +For the full list of TOPP tools, visit the [API reference](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/TOPP_documentation.html) website. + + +```{toctree} +:maxdepth: 1 + +types-of-topp-tools/file-handling.md +types-of-topp-tools/picking-peaks.md +types-of-topp-tools/calibration.md +types-of-topp-tools/map-alignment.md +types-of-topp-tools/feature-grouping.md +types-of-topp-tools/feature-detection.md +types-of-topp-tools/quality-control.md + +``` \ No newline at end of file diff --git a/docs/run-workflows-with-openms-tools/openms-in-toppas.md b/docs/getting-started/toppas-get-started.md similarity index 80% rename from docs/run-workflows-with-openms-tools/openms-in-toppas.md rename to docs/getting-started/toppas-get-started.md index c871f3e5..7cd4d317 100644 --- a/docs/run-workflows-with-openms-tools/openms-in-toppas.md +++ b/docs/getting-started/toppas-get-started.md @@ -1,5 +1,5 @@ -OpenMS on TOPPAS -================ +TOPPAS +====== TOPPAS is the build-in workflow editor of OpenMS. All TOPP tools can be chained, configured and executed. @@ -8,7 +8,7 @@ TOPPAS workflows run on the local machine where TOPPAS is executed and thus only Also, external tools (anything other than OpenMS TOPP tools), can only be called using a special 'GenericWrapper' node. We generally recommend to run only OpenMS-specific tools in TOPPAS and hand the resulting data to other tools, or use other workflow systems, such as KNIME which can fully integrate other tools. -The strong point of TOPPAS is that it ships with OpenMS natively, thus requires no extra installation effort. +The strong point of TOPPAS is that it ships with OpenMS natively in the graphical and command-line tools. It also has a very shallow learning curve, making it very intuitive to create workflows. -See [TOPPAS tutorial](https://openms.de/doxygen/nightly/html/TOPPAS_tutorial.html) for more details. \ No newline at end of file +See the [TOPPAS tutorial](https://openms.de/doxygen/nightly/html/TOPPAS_tutorial.html) for more details. \ No newline at end of file diff --git a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/calibration.md b/docs/getting-started/types-of-topp-tools/calibration.md similarity index 98% rename from docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/calibration.md rename to docs/getting-started/types-of-topp-tools/calibration.md index ab6d0d7f..fa65e17b 100644 --- a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/calibration.md +++ b/docs/getting-started/types-of-topp-tools/calibration.md @@ -1,6 +1,3 @@ ---- -orphan: true ---- Calibration =========== diff --git a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/feature-detection.md b/docs/getting-started/types-of-topp-tools/feature-detection.md similarity index 65% rename from docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/feature-detection.md rename to docs/getting-started/types-of-topp-tools/feature-detection.md index 35a77ded..c5a443c2 100644 --- a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/feature-detection.md +++ b/docs/getting-started/types-of-topp-tools/feature-detection.md @@ -1,6 +1,3 @@ ---- -orphan: true ---- Feature Detection ================= @@ -23,7 +20,7 @@ coded by the wavelet itself. The algorithm also works for 2D maps (in combinatio technique (Schulz-Trieglaff et al.: "A Fast and Accurate Algorithm for the Quantification of Peptides from Mass Spectrometry Data" (2007))). The algorithm could originally be executed on (several) high-speed CUDA graphics cards. Tests on real-world data sets revealed potential speedups beyond factors of 200 (using 2 NVIDIA Tesla cards in parallel). -Support for CUDA was removed in [OpenMS]() due to maintenance overhead. Please refer to Hussong et al.: "Highly +Support for CUDA was removed in OpenMS due to maintenance overhead. Please refer to Hussong et al.: "Highly accelerated feature detection in proteomics data sets using modern graphics processing units" (2009) for more details on the implementation. ``` @@ -42,9 +39,9 @@ The extension is based on the sweep-line paradigm and is done on the fly after t None (explicitly done by the wavelet). -See the `FeatureFinderAlgorithmIsotopeWavelet` class documentation for a parameter list. -## FeatureFinderCentroided +FeatureFinderCentroided +----------------------- ### Description @@ -71,4 +68,29 @@ In model fitting, the retention time profile (Gaussian) of all mass traces is fi fitting, the data is truncated in RT and m/z dimension. The reported feature intensity is based on the fitted model, rather than on the (noisy) data. -See the `FeatureFinderAlgorithmPicked` class documentation for a parameter list. +#### Example +For this example the file `LCMS-centroided.mzML` from the examples data is used (**File** > **Open example data**). In order +to adapt the algorithm to the data, some parameters have to be set. + +##### Intensity + +The algorithm estimates the significance of peak intensities in a local environment. Therefore, the HPLC-MS map is +divided into `n` times `n` regions. Set the `intensity:bins` parameter to `10` for the whole map. For a small region, set +it to `1`. + +##### Mass trace + +For the mass traces, define the number of adjacent spectra in which a mass has to occur (`mass_trace:min_spectra`). In +order to compensate for peak picking errors, missing peaks can be allowed (`mass_trace:max_missing`) and a tolerated +mass deviation must be set (`mass_trace:mz_tolerance`). + +##### Isotope pattern + +The expected isotopic intensity pattern is estimated from an averagene amino acid composition. The algorithm searches +all charge states in a defined range (`isotopic_pattern:change_min` to `isotopic_pattern:change_max`). Just as for mass +traces, a tolerated mass deviation between isotopic peaks has to be set (`isotopic_pattern:mz_tolerance`). + +The image shows the centroided peak data and the found peptide features. The used parameters can be found in the TOPP +tools dialog. + +![TOPPView Tools FF\_Centrioided](/_images/tutorials/topp/TOPPView_tools_ff_centroided.png) diff --git a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/feature-grouping.md b/docs/getting-started/types-of-topp-tools/feature-grouping.md similarity index 91% rename from docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/feature-grouping.md rename to docs/getting-started/types-of-topp-tools/feature-grouping.md index 2dd76ba5..0baacd03 100644 --- a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/feature-grouping.md +++ b/docs/getting-started/types-of-topp-tools/feature-grouping.md @@ -1,6 +1,3 @@ ---- -orphan: true ---- Feature Grouping ================ @@ -24,13 +21,13 @@ To differentially quantify the features of an isotope-labeled HPLC-MS map, follo - feature quality of feature 2 - quality measure for the shift (how near is it to the optimal shift) - ![TOPP labeled quant](/images/tutorials/topp/TOPP_labeled_quant.png) + ![TOPP labeled quant](/_images/tutorials/topp/TOPP_labeled_quant.png) ## Label-free quantitation To differentially quantify the features of two or more label-free HPLC-MS map. -![TOPP labelfree quant](/images/tutorials/topp/TOPP_labelfree_quant.png) +![TOPP labelfree quant](/_images/tutorials/topp/TOPP_labelfree_quant.png) ```{tip} This algorithm assumes that the retention time axes of all input maps are very similar. To correct for diff --git a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/file-handling.md b/docs/getting-started/types-of-topp-tools/file-handling.md similarity index 55% rename from docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/file-handling.md rename to docs/getting-started/types-of-topp-tools/file-handling.md index b53b28d5..83324433 100644 --- a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/file-handling.md +++ b/docs/getting-started/types-of-topp-tools/file-handling.md @@ -1,6 +1,3 @@ ---- -orphan: true ---- File Handling ============= @@ -59,3 +56,67 @@ intensity range or for MS level. To extract the MS/MS scans between retention ti command: `FileFilter -in infile.mzML -levels 2 -rt 100:1500 -out outfile.mzML` + +## Conversion Between OpenMS XML Formats and Text Formats + +### Export of OpenMS XML formats + +As TOPP offers no functionality for statistical analysis, this step is normally done using external statistics packages. +In order to export the OpenMS XML formats into an appropriate format for these packages the TOPP **TextExporter** can be +used. + +It converts the the following OpenMS XML formats to text files: + +- featureXML +- idXML +- consensusXML + +The use of the `TextExporter` is is very simple: + +`TextExporter -in infile.idXML -out outfile.txt` + +### Import of feature data to OpenMS + +OpenMS offers a lot of visualization and analysis functionality for feature data. +Feature data in text format, e.g. from other analysis tools, can be imported using the `TextImporter`. The default +mode accepts comma separated values containing the following columns: RT, m/z, intensity. Additionally meta data +columns may follow. If meta data is used, meta data column names have to be specified in a header line. Without headers: + +```bash +1201 503.123 1435000 +1201 1006.246 1235200 +``` + +Or with headers: + +```bash +RT m/z Int isHeavy myMeta +1201 503.123 1435000 true 2 +1201 1006.246 1235200 maybe 1 +``` + +Example invocation: + +`TextImporter -in infile.txt -out outfile.featureXML` + +The tool also supports data from msInspect,SpecArray and Kroenik(Hardkloer sibling), just specify the `-mode` option +accordingly. + +### Import of protein/peptide identification data to OpenMS + +Peptide/protein identification data from several identification engines can be converted to idXML format using the +**IDFileConverter** tool. + +It can currently read the following formats: +- Sequest output folder +- pepXML file +- idXML file + +It can currently write the following formats: + +- pepXML +- idXML + +This example shows how to convert pepXML to idXML: + +`IDFileConverter -in infile.pepXML -out outfile.idXML` \ No newline at end of file diff --git a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/map-alignment.md b/docs/getting-started/types-of-topp-tools/map-alignment.md similarity index 96% rename from docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/map-alignment.md rename to docs/getting-started/types-of-topp-tools/map-alignment.md index 1fa427f4..0fbc57e8 100644 --- a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/map-alignment.md +++ b/docs/getting-started/types-of-topp-tools/map-alignment.md @@ -1,6 +1,3 @@ ---- -orphan: true ---- Map Alignment ============= @@ -12,7 +9,7 @@ corrects for shifted and scaled retention times, which may result from changes o The different **MapAligner** tools take `n` input maps, de-warp them and store the `n` de-warped maps. The following image shows the general procedure: -![TOPP Alignment](/images/tutorials/topp/TOPP_alignment.png) +![TOPP Alignment](/_images/tutorials/topp/TOPP_alignment.png) There are different map alignment tools available. The following table gives a rough overview of them: diff --git a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/picking-peaks.md b/docs/getting-started/types-of-topp-tools/picking-peaks.md similarity index 71% rename from docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/picking-peaks.md rename to docs/getting-started/types-of-topp-tools/picking-peaks.md index 7f07d8e9..2e3dfbed 100644 --- a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/picking-peaks.md +++ b/docs/getting-started/types-of-topp-tools/picking-peaks.md @@ -1,11 +1,8 @@ ---- -orphan: true ---- Picking Peaks ============ -For low resolution data, consider to smooth the data first ([Smoothing raw data](../../../topp-command-line-tools/smoothing-raw-data.md)) and subtract -the baseline ([Subtracting a baseline from a spectrum](../../../topp-command-line-tools/subtracting-a-baseline-from-a-spectrum.md)) before peak picking. +For low resolution data, consider to smooth the data first ([Smoothing raw data](/tutorials/toppview-user-tutorial/smoothing-raw-data.md)) and subtract +the baseline ([Subtracting a baseline from a spectrum](/tutorials/toppview-user-tutorial/subtracting-a-baseline-from-a-spectrum.md)) before peak picking. There are two types of PeakPickers: the **PeakPickerWavelet** and one especially suited for high resolution data (**PeakPickerHiRes**). This tutorial explains the PeakPickerWavelet. Use the file `peakpicker_tutorial_2.mzML` from the examples data (select **File** > **Open example data**). @@ -14,7 +11,7 @@ the approximate `fwhm` of peaks, use the estimation included in the PeakPickerWa to `true`. After applying the PeakPickerWavelet, observe which peak width was estimated and used for peak picking in the log window. -To estimate the peak width, use the measuring tool Action Modes and Their Uses to determine +To estimate the peak width, use the [measuring too](/getting-started/visualize-with-openms/views-in-toppview.md#action-modes-and-their-uses) to determine the fwhm of one or several representative peaks. If the peak picker delivers only a few peaks even though the `peak_with` and `signal_to_noise` parameters are set to @@ -24,4 +21,4 @@ good values, consider changing the advanced parameter `fwhm_lower_bound_factor` The following image shows a part of the spectrum with the picked peaks shown in green, the estimated peak width in the log window and the measured peak width. -![TOPPView tools pp\_picked](/images/tutorials/topp/TOPPView_tools_pp_picked.png) +![TOPPView tools pp\_picked](/_images/tutorials/topp/TOPPView_tools_pp_picked.png) diff --git a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/quality-control.md b/docs/getting-started/types-of-topp-tools/quality-control.md similarity index 98% rename from docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/quality-control.md rename to docs/getting-started/types-of-topp-tools/quality-control.md index e194a6bd..eff2a632 100644 --- a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/quality-control.md +++ b/docs/getting-started/types-of-topp-tools/quality-control.md @@ -1,6 +1,3 @@ ---- -orphan: true ---- Quality Control =============== @@ -13,7 +10,7 @@ then be processed using custom scripts or via the R package (see [PTXQC](https:/ ## Workflow -![](/images/tutorials/topp/TOPP_qualitycontrol.png) +![](/_images/tutorials/topp/TOPP_qualitycontrol.png) Find an example workflow in `OpenMS/share/OpenMS/examples/TOPPAS/QualityControl.toppas`. diff --git a/docs/openms-applications-and-tools/visualize-with-openms.md b/docs/getting-started/visualize-with-openms.md similarity index 84% rename from docs/openms-applications-and-tools/visualize-with-openms.md rename to docs/getting-started/visualize-with-openms.md index 98792d15..a7ec89cd 100644 --- a/docs/openms-applications-and-tools/visualize-with-openms.md +++ b/docs/getting-started/visualize-with-openms.md @@ -1,5 +1,5 @@ -TOPPView: Visualize with OpenMS -=============================== +TOPPView +======== ## Introduction @@ -15,7 +15,7 @@ developers. The following image illustrates different components of TOPPView's user interface. -![toppview user interface](/images/tutorials/toppview/user-interface.png) +![toppview user interface](/_images/tutorials/toppview/user-interface.png) Components include: @@ -37,7 +37,7 @@ To import data into TOPPView: 2. Choose a file from the file importer and click **Open**. 3. Select options from the following panel and click **Ok**: - ![file import options](/images/tutorials/toppview/file-import-options.png) + ![file import options](/_images/tutorials/toppview/file-import-options.png) You can choose to open the new dataset as a **new window** or **new layer**. Choosing **new window** will open a new tab. If you are planning on comparing multiple datasets and want to view them all at once, choose **new layer**. @@ -57,13 +57,13 @@ To apply a TOPP tool to your dataset: 1. Select a layer in the **Layers window**. The selected layer will be highlighted blue. - ![display selected layer](/images/tutorials/toppview/layers-window.png) + ![display selected layer](/_images/tutorials/toppview/layers-window.png) 2. Go to **Tools** > **Apply TOPP tool to whole layer**. This will open a panel to select and configure your TOPP tool. 3. Select a **TOPP tool** from the dropdown menu. A description of the TOPP tool will be displayed on the right. You may have to also specify the **input argument** as well though TOPPView might automatically select this option for you. To save the output to a file, specify the **output argument**. - ![display selected layer](/images/tutorials/toppview/topp-tool-arguments-and-description.png) + ![display selected layer](/_images/tutorials/toppview/topp-tool-arguments-and-description.png) 4. Specify the TOPP tool parameters by either:
      @@ -72,14 +72,14 @@ To apply a TOPP tool to your dataset:
    1. Editing the parameters shown in the table and then saving the INI file. To edit a parameter, double click a row in the table and enter a value or choose from the options available. The modified value will be highlighted yellow. To save the parameters, click Store and enter a file name for the INI file.
    - ![topp tool parameters](/images/tutorials/toppview/topp-tool-parameters.png) + ![topp tool parameters](/_images/tutorials/toppview/topp-tool-parameters.png) 5. Click **Ok**. You will be prompted to load the new dataset as a **new window** or a **new layer**. Choose an option and click **Ok**. - ![data import options](/images/tutorials/toppview/data-import-options.png) + ![data import options](/_images/tutorials/toppview/data-import-options.png) 6. If you chose to load the data in a new window, a new tab will appear. To view that data, select the tab. If you chose to load the data as a new layer, the data will be visualized in the **Viewer**. You can also see the new layer wihout a name in the **Layers window**. - ![layer loaded in Viewer](/images/tutorials/toppview/layer-loaded-in-viewer.png) + ![layer loaded in Viewer](/_images/tutorials/toppview/layer-loaded-in-viewer.png) 7. (Optional) If you did choose to import the data as a new layer, give the new layer a name. To do this, right-click the layer in the **Layers window** and select **Rename**. Enter a name and click OK. @@ -91,17 +91,17 @@ To filter your data: 1. Select a layer from the **Layers window**. - ![display selected layer](/images/tutorials/toppview/layers-window.png) + ![display selected layer](/_images/tutorials/toppview/layers-window.png) 2. Open the **Data filters window** by clicking the tab at the bottom of the screen. - ![select data filters window](/images/tutorials/toppview/select-data-filters-window.png) + ![select data filters window](/_images/tutorials/toppview/select-data-filters-window.png) 3. Add a filter to the **Data filters window** by right-clicking the window and then selecting **Add filter** from the context menu. 4. Select a field, select an operation and enter a value. For example, to exclude all peaks with an intensity of less than 6999, set **field** to **Intensity**, **operation** to **=>** and set the value to 7000. Click **Ok** on the panel to apply the changes. - ![filtering options](/images/tutorials/toppview/filtering-options.png) + ![filtering options](/_images/tutorials/toppview/filtering-options.png) 5. You should see only see data that satisfies the specified criteria. diff --git a/docs/openms-applications-and-tools/visualize-with-openms/data-analysis-in-toppview.md b/docs/getting-started/visualize-with-openms/data-analysis-in-toppview.md similarity index 82% rename from docs/openms-applications-and-tools/visualize-with-openms/data-analysis-in-toppview.md rename to docs/getting-started/visualize-with-openms/data-analysis-in-toppview.md index 70258605..24455924 100644 --- a/docs/openms-applications-and-tools/visualize-with-openms/data-analysis-in-toppview.md +++ b/docs/getting-started/visualize-with-openms/data-analysis-in-toppview.md @@ -4,14 +4,14 @@ Data Analysis in TOPPView {term}`TOPPView` also offers limited data analysis capabilities for single layers, which will be illustrated in the following sections. The functionality presented here can be found in the **Tools** menu: -![TOPPView Tools Menu](/images/tutorials/topp/TOPPView_tools_menu.png) +![TOPPView Tools Menu](/_images/tutorials/topp/TOPPView_tools_menu.png) ## TOPP Tools Single {term}`TOPP tools` can be applied to the data of the currently selected layer or to the visible data of the current layer. The following example image shows the TOPP tools dialog: -![TOPPView Tools](/images/tutorials/topp/TOPPView_tools.png) +![TOPPView Tools](/_images/tutorials/topp/TOPPView_tools.png) To apply a {term}`TOPP tool`, follow the instructions below: @@ -25,7 +25,7 @@ To apply a {term}`TOPP tool`, follow the instructions below: One can access the metadata, the layer is annotated with. This data comprises e.g. contact person, instrument description and sample description. -![Meta Data Browser](/images/tutorials/topp/MetaDataBrowser.png) +![Meta Data Browser](/_images/tutorials/topp/MetaDataBrowser.png) ```{tip} Identification data, e.g. from a {term}`Mascot` run, can be annotated to the spectra or features, too. After @@ -37,4 +37,4 @@ annotation, this data is listed in the metadata. Statistics about peak/feature intensities and peak meta information can be displayed. For intensities, it is possible to display an additional histogram view. -![TOPPView Statistics](/images/tutorials/topp/TOPPView_statistics.png) +![TOPPView Statistics](/_images/tutorials/topp/TOPPView_statistics.png) diff --git a/docs/openms-applications-and-tools/visualize-with-openms/data-editing-in-toppview.md b/docs/getting-started/visualize-with-openms/data-editing-in-toppview.md similarity index 100% rename from docs/openms-applications-and-tools/visualize-with-openms/data-editing-in-toppview.md rename to docs/getting-started/visualize-with-openms/data-editing-in-toppview.md diff --git a/docs/openms-applications-and-tools/visualize-with-openms/display-modes-and-view-options.md b/docs/getting-started/visualize-with-openms/display-modes-and-view-options.md similarity index 95% rename from docs/openms-applications-and-tools/visualize-with-openms/display-modes-and-view-options.md rename to docs/getting-started/visualize-with-openms/display-modes-and-view-options.md index 50335fe9..e3ec4b6f 100644 --- a/docs/openms-applications-and-tools/visualize-with-openms/display-modes-and-view-options.md +++ b/docs/getting-started/visualize-with-openms/display-modes-and-view-options.md @@ -4,7 +4,7 @@ Display Modes and View Options in TOPPView All of the views support several display modes and view options. Display modes determine how intensities are displayed. View options configure the view. -![TOPPView Icons](/images/tutorials/topp/TOPPView_icons.png) +![TOPPView Icons](/_images/tutorials/topp/TOPPView_icons.png) ## Display Modes diff --git a/docs/openms-applications-and-tools/visualize-with-openms/hotkeys-table.md b/docs/getting-started/visualize-with-openms/hotkeys-table.md similarity index 100% rename from docs/openms-applications-and-tools/visualize-with-openms/hotkeys-table.md rename to docs/getting-started/visualize-with-openms/hotkeys-table.md diff --git a/docs/openms-applications-and-tools/visualize-with-openms/views-in-toppview.md b/docs/getting-started/visualize-with-openms/views-in-toppview.md similarity index 96% rename from docs/openms-applications-and-tools/visualize-with-openms/views-in-toppview.md rename to docs/getting-started/visualize-with-openms/views-in-toppview.md index 67576d59..2c707b09 100644 --- a/docs/openms-applications-and-tools/visualize-with-openms/views-in-toppview.md +++ b/docs/getting-started/visualize-with-openms/views-in-toppview.md @@ -51,7 +51,7 @@ annotations and text labels have been added by right-clicking peaks and selectin clicking anywhere and selecting **Add Label**, respectively. Multiple annotations can be selected by holding down the CTRL key while clicking them. They can be moved around by dragging the mouse and deleted by pressing DEL. -![TOPPView 1D](/images/tutorials/topp/TOPPView_1D.png) +![TOPPView 1D](/_images/tutorials/topp/TOPPView_1D.png) Through the **context menu**: of the 1D view you can: @@ -70,7 +70,7 @@ layer individually. The following example image shows a small section of a peak map and the detected features in a second layer. -![Plot 2D Widget](/images/tutorials/topp/Plot2DWidget.png) +![Plot 2D Widget](/_images/tutorials/topp/Plot2DWidget.png) In addition to the normal top-down view, the 2D view can display the projections of the data to the `m/z` and `RT` axis. This feature is mainly used to assess the quality of a feature without opening the data region in 3D view. @@ -93,7 +93,7 @@ layer individually. The following example image shows a small region of a peak map: -![Plot 3D Widget](/images/tutorials/topp/Plot3DWidget.png) +![Plot 3D Widget](/_images/tutorials/topp/Plot3DWidget.png) Through the **context menu**: of the 3D view you can: diff --git a/docs/getting-started/webapps.rst b/docs/getting-started/webapps.rst new file mode 100644 index 00000000..8cc14314 --- /dev/null +++ b/docs/getting-started/webapps.rst @@ -0,0 +1,31 @@ +WebApps +======= + +.. grid:: + :gutter: 2 + + .. grid-item-card:: FLASHTaggerViewer + :img-top: /_images/webapps/flashtaggerviewer.png + :link: https://abi-services.cs.uni-tuebingen.de/flashtaggerviewer/ + :link-type: url + :columns: 12 + + Visualizes outputs from FLASH* tools + + + .. grid-item-card:: NuXL + :img-top: /_images/webapps/nuxl.jpg + :link: https://abi-services.cs.uni-tuebingen.de/nuxl/ + :link-type: url + :columns: 12 + + A specialized protein nucleic-acid crosslink search engine + + + .. grid-item-card:: UmetaFlow + :img-top: /_images/webapps/umetaflow.png + :link: https://abi-services.cs.uni-tuebingen.de/umetaflow/ + :link-type: url + :columns: 12 + + A universal metabolomics tool \ No newline at end of file diff --git a/docs/getting-started/workflows.rst b/docs/getting-started/workflows.rst new file mode 100644 index 00000000..f5516cde --- /dev/null +++ b/docs/getting-started/workflows.rst @@ -0,0 +1,112 @@ +.. _workflows: + +Workflow Editor +=============== + +Which workflow environment to choose for running OpenMS tools? + +You can run OpenMS TOPP tools from the command line using your custom scripts, or use powerful +workflow systems designed to make workflow creation and maintenance more fun: + + +.. grid:: 2 + :gutter: 2 + + .. grid-item-card:: KNIME + :img-top: /_images/introduction/KNIMELogoTM.svg + :link: /getting-started/knime-get-started + :link-type: doc + :columns: 12 + :class-card: only-light + :class-img-top: halfwidth + + Free, open source, desktop app. An analytics platform with workflow editor and a nice drag-and-drop user interface. + Very interactive and has built-in nodes for related tasks like working with chemical structures, databases, machine learning, scripting. Distributed computing is best achieved with a KNIME server (License required) which also allows user management + and a web interface to interact with workflows. In KNIME you can easily construct your own workflows or just download our + ready-made creations for the most common analysis tasks. + + .. grid-item-card:: KNIME + :img-top: /_images/introduction/KNIMELogoTM_white.svg + :link: /getting-started/knime-get-started + :link-type: doc + :columns: 12 + :class-card: only-dark + :class-img-top: halfwidth + + Free, open source, desktop app. An analytics platform with workflow editor and a nice drag-and-drop user interface. + Very interactive and has built-in nodes for related tasks like working with chemical structures, databases, machine learning, scripting. Distributed computing is best achieved with a KNIME server (License required) which also allows user management + and a web interface to interact with workflows. In KNIME you can easily construct your own workflows or just download our + ready-made creations for the most common analysis tasks. + + + .. grid-item-card:: Nextflow + :img-top: /_images/introduction/nextflow2014_no-bg.png + :link: /getting-started/nextflow-get-started + :link-type: doc + :class-card: only-light + :class-img-top: halfwidth + :columns: 12 + + Script/DSL-based workflow language, executor and utilities (such as the browser based launcher and supervisor nf-tower). Automatically runs on various different cloud (AWS, Google, ...) and HPC environments (SLURM, LFS, Kubernetes, ...). It is recommended to use our ready-made nf-core compatible workflows for ease of use via the browser-based configuration and launcher. + + .. grid-item-card:: Nextflow + :img-top: /_images/introduction/nextflow2014_no-bg-bright.png + :link: /getting-started/nextflow-get-started + :link-type: doc + :class-card: only-dark + :class-img-top: halfwidth + :columns: 12 + + Script/DSL-based workflow language, executor and utilities (such as the browser based launcher and supervisor nf-tower). Automatically runs on various different cloud (AWS, Google, ...) and HPC environments (SLURM, LFS, Kubernetes, ...). It is recommended to use our ready-made nf-core compatible workflows for ease of use via the browser-based configuration and launcher. + + + .. grid-item-card:: Galaxy + :img-top: /_images/introduction/galaxy_project_logo.png + :link: /getting-started/galaxy-get-started + :link-type: doc + :class-card: only-light + :class-img-top: halfwidth + :columns: 12 + + Server and browser-based interactive workflow editor and runner. A public server instance can be used for testing and smaller experiments. Provides nice guided tutorials. + + .. grid-item-card:: Galaxy + :img-top: /_images/introduction/galaxy_project_logo_white.png + :link: /getting-started/galaxy-get-started + :link-type: doc + :class-card: only-dark + :class-img-top: halfwidth + :columns: 12 + + Server and browser-based interactive workflow editor and runner. A public server instance can be used for testing and smaller experiments. Provides nice guided tutorials. + + + .. grid-item-card:: TOPPAS + :img-top: /_images/introduction/TOPPAS_logo_white.png + :link: /getting-started/toppas-get-started + :link-type: doc + :class-card: only-light + :class-img-top: halfwidth + :columns: 12 + + OpenMS' build-in workflow system, with limited capabilities but easy to use and tailored to TOPP tools. + + .. grid-item-card:: TOPPAS + :img-top: /_images/introduction/TOPPAS_logo_dark.png + :link: /manual/develop + :link-type: doc + :class-card: only-dark + :class-img-top: halfwidth + :columns: 12 + + OpenMS' build-in workflow system, with limited capabilities but easy to use and tailored to TOPP tools. + + +.. toctree:: + :maxdepth: 1 + :hidden: + + /getting-started/knime-get-started.md + /getting-started/nextflow-get-started.md + /getting-started/galaxy-get-started.md + /getting-started/toppas-get-started.md \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index e10d4c0e..2c5f6ad1 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,118 +1,75 @@ About OpenMS ============ -`OpenMS `_ -is an open-source software C++ library for :term:`LC-MS` data management and -analyses. It offers an infrastructure for rapid development of mass -spectrometry related software. OpenMS is free software available under the -three clause BSD license and runs under Windows, macOS, and Linux. - -It comes with a vast variety of pre-built and ready-to-use tools for proteomics -and metabolomics data analysis (:term:`TOPP Tools`) as well as powerful 1D, 2D and 3D -visualization (:term:`TOPPView`). - -OpenMS offers analyses for various quantitation protocols, including label-free -quantitation, :term:`SILAC`, :term:`iTRAQ`, :term:`TMT`, :term:`SRM`, :term:`SWATH`, etc. - -It provides built-in algorithms for de-novo identification and database search, -as well as adapters to other state-of-the art tools like X!Tandem, :term:`Mascot`, etc. It supports easy integration of OpenMS built tools into workflow -engines like :term:`KNIME`, Galaxy, WS-Pgrade, and :term:`TOPPAS` via the :term:`TOPP tools` concept and -a unified parameter handling via a 'common tool description' (CTD) scheme. - -.. important:: - As part of the **Center for Integrative Bioinformatics** (CiBi) in the **German Network for Bioinformatics** - `deNBI `_, - OpenMS is currently focusing the development efforts on the integration of OpenMS into KNIME. KNIME is a well-established - data analysis framework that supports the generation of workflows for data analysis. Using a Common Tool Description - (CTD) file which is writeable by every TOPP tool and a node generator program (`Generic KNIME Nodes `_), all :term:`TOPP tools` can be made available to run in KNIME. - -With :term:`pyOpenMS`, OpenMS offers Python bindings to a large part of the :term:`OpenMS API` -to enable rapid algorithm development. OpenMS supports the Proteomics Standard -Initiative (PSI) formats for MS data. The main contributors of OpenMS are -currently the Eberhard-Karls-Universität in Tübingen, the Freie Universität -Berlin, and the University of Toronto. +What is OpenMS +-------------- -Contents --------- +`OpenMS `_ is a free, open-source framework based on a C++ library with Python bindings. It is commonly used for liquid +chromatography-mass spectrometry (:term:`LC-MS`) data management and analyses. OpenMS provides an infrastructure for the rapid +development of mass spectrometry related software as well as a rich toolset built on top of it. OpenMS is available +under the `three clause BSD licence `_ and runs under Windows, macOS, and Linux operating systems. -.. toctree:: - :maxdepth: 2 - :caption: Introduction - :titlesonly: +.. figure:: _images/introduction/openms-overview.jpg + :target: https://www.nature.com/articles/s41592-024-02197-7 + :align: left + :alt: OpenMS overview - introduction/about-open-ms.md - introduction/extended-background.md - introduction/entry-points-to-openms.md - - -.. toctree:: - :maxdepth: 2 - :caption: OpenMS Applications and Tools +OpenMS developers can create new C++ algorithms and tools, while users can execute tools or implement new algorithms or scripts in Python. +Workflows integrate pyOpenMS scripts and OpenMS tools with third-party tools and external Python libraries to create scalable data-processing pipelines. +For deployment, users can use pyOpenMS with web frameworks or deploy workflows on desktop, high-performance computing (HPC) or cloud infrastructure using one of the community-supported workflow systems. - openms-applications-and-tools/installation.md - openms-applications-and-tools/openms-graphical-user-interfaces.md - openms-applications-and-tools/topp-tools.md - openms-applications-and-tools/command-line-interface.md - openms-applications-and-tools/visualize-with-openms.md +OpenMS supports the Proteomics Standard Initiative (PSI) formats for MS data. The main contributors of OpenMS are +currently the Eberhard-Karls-Universität in Tübingen, the Freie Universität +Berlin, and the University of Toronto. -.. toctree:: - :maxdepth: 2 - :caption: Run Workflows with OpenMS Tools - run-workflows-with-openms-tools/recommended-workflow-systems.md - run-workflows-with-openms-tools/openms-in-knime.md - run-workflows-with-openms-tools/openms-in-nextflow.md - run-workflows-with-openms-tools/openms-on-galaxy.md - run-workflows-with-openms-tools/openms-in-toppas.md +Get involved +------------ +OpenMS is developed by a group of `core developers `_ and the community. You can help spreading the idea of open source mass spectrometry analysis by: -.. toctree:: - :maxdepth: 2 - :caption: Tutorials and Quick Start Guides +* Contribute to the development by giving us your feedback about the OpenMS project on `Discord `_ or become active by `developing new tools yourself `_. +* Donate to the OpenMS project using our `opencollective `_ account. All donations will be used strictly to fund the development of Openms’s open source software, documentation, and community. +* Promote OpenMS either online (`e.g. on X `_) or in your work group. - tutorials-and-quickstart-guides/guides/user-quickstart-guide.md - tutorials-and-quickstart-guides/openms-user-tutorial.md - tutorials-and-quickstart-guides/tutorials.md .. toctree:: - :maxdepth: 2 - :caption: Develop with OpenMS + :maxdepth: 1 + :caption: About + :hidden: - Developer Quickstart Guide - develop-with-openms/openms-core-cplusplus-library.md - OpenMS API Reference - pyOpenMS - develop-with-openms/build-openms-from-source.md - develop-with-openms/link-external-code-to-openms.md + about/installation.md + about/communication.md + about/learning/background.md .. toctree:: - :maxdepth: 2 - :caption: Contribute to OpenMS + :maxdepth: 1 + :caption: Getting Started + :hidden: - tutorials-and-quickstart-guides/guides/contributors-quickstart-guide.md - contribute-to-openms/openms-git-workflow.md - contribute-to-openms/write-and-label-github-issues.md - contribute-to-openms/adding-new-tool-to-topp.md - contribute-to-openms/pull-request-checklist.md - contribute-to-openms/reporting-bugs-and-issues.md - contribute-to-openms/advanced.md + getting-started/introduction.rst + getting-started/webapps.md + getting-started/workflows.md + getting-started/visualize-with-openms.md + getting-started/topp-tools.md + pyOpenMS .. toctree:: - :maxdepth: 2 - :caption: Downloads + :maxdepth: 1 + :caption: Manual + :hidden: - downloads.md + manual/contribute.md + manual/develop.md + manual/additional.md + API Reference + manual/glossary.md .. toctree:: - :maxdepth: 2 - :caption: Quick Reference + :maxdepth: 1 + :caption: Tutorials + :hidden: - quick-reference/contributor-faq.md - quick-reference/developer-faq.md - quick-reference/contact-us.md - quick-reference/glossary.md + tutorials/knime-user-tutorial.rst + tutorials/toppview-user-tutorial.md -Indices and tables -================== -* :ref:`genindex` -* :ref:`search` diff --git a/docs/introduction.md b/docs/introduction.md deleted file mode 100644 index fdca55e1..00000000 --- a/docs/introduction.md +++ /dev/null @@ -1,96 +0,0 @@ ---- -orphan: true ---- -Introduction -============ - -[OpenMS](http://www.openms.org/) -is an open-source software C++ library for {term}`LC-MS` data management and -analyses. It offers an infrastructure for rapid development of {term}`mass` -spectrometry related software. OpenMS is free software available under the -three clause BSD license and runs under Windows, macOS, and Linux. - -```{note} -This introduction is aimed at users new to the field of LC-MS data analysis and will introduce some basics terms -and concepts. How to handle the data analysis, available data structures, algorithms and more are covered in the various -subsections of this documentation. -``` - -# Background - -Proteomics and metabolomics are interdisciplinary research fields that study structure, function, and interaction of -proteins and metabolites. They employ large-scale experimental techniques that allow acquiring data at the level of -cellular systems to whole organisms. {term}`Mass` spectrometry combined with chromatographic separation is commonly used to -identify, characterize or quantify the amount of proteins and metabolites. - -In {term}`mass` spectrometry-based proteomics and metabolomics, biological samples are extracted, prepared, and separated to -reduce sample complexity. The separated analytes are ionized and measured in the {term}`mass` spectrometer. {term}`Mass` and abundance -of ions are stored in {term}`mass` spectra and used to identify and quantify the analytes in the sample using computational -methods. The quantity and identity of analytes can then be used, for instance, in biomarker discovery, medical diagnostics, -or basic research. - -## Liquid Chromatography(LC) - -LC aims to reduce the complexity of the measured sample by separating analytes based on their physicochemical properties. -Separating analytes in time ensures that a manageable amount of analytes elute at the same time. In {term}`mass` -spectrometry-based proteomics, (high-pressure) liquid chromatographic separation techniques (HPLC) are methods of choice -to achieve a high degree of separation. In HPLC, {term}`peptides` are separated on a column. Solved in a pressurized liquid -(mobile phase) they are pumped through a solid adsorbent material (stationary phase) packet into a capillary column. -Physicochemical properties of each peptide determine how strongly it interacts with the stationary phase. The most -common HPLC technique in proteomics and metabolomics uses reversed-phase chromatography (RPC) columns. RPC employs a -hydrophobic stationary phase like {term}`octadecyl (C18)`, a nonpolar carbon chain bonded to a silica base, and a polar mobile -phase. Polar molecules interact weakly with the stationary phase and elute earlier, while non-polar molecules are retained. -Interaction can be further modulated by changing the gradient of solvent concentration in the mobile phase over time. -Elution times in LC are inherently prone to variation, for example, due to fluctuations in the flow rate of the mobile -phase or change of column. Retention time shifts between runs may be compensated using computational chromatographic -retention time alignment methods. In the {term}`LC-MS` setup, the column is directly coupled to the ion source of the {term}`mass` -spectrometer. - - -![](/images/introduction/introduction_LC.png) - -## Mass Spectrometry - -MS is an analytical technique used to determine the {term}`mass` of molecules. In order to achieve highly accurate and sensitive -{term}`mass` measurements at the atomic scale, {term}`mass` spectrometers manipulate charged particles using magnetic and electrostatic -fields. - -![](/images/introduction/introduction_MS.png) - -In a typical {term}`mass` spectrometer, three principal components can be identified: - -- **Ion Source**: A {term}`mass` spectrometer only handles {term}`ions `. Thus, charge needs first be transferred to uncharged - particles. The component responsible for the ionization is the ion source. Different types of ion sources and ionization - techniques exist with {term}`electrospray ionization (ESI)` being currently the most widely used ionization technique - for {term}`mass` spectrometry-based proteomics. - -- **Mass Analyzer**: Most commonly used {term}`mass` analyzer in proteomics are {term}`time-of-flight (TOF)` {term}`mass` analyzers, - {term}`quadrupole mass filters`, and {term}`orbitrap analyzers`. In TOF {term}`mass` analyzers, the ions are accelerated in an electric field. - The flight time of an ion allows calculating the velocity which in turn is used to calculate the mass-to-charge ratio - (m/z). Varying the electric field allows filtering certain mass-to-charge ratios before they enter the detector. In - quadrupole mass filters, ions pass through an oscillating electric field created by four parallel rods. For a - particular voltage, only ions in a certain mass-to-charge range will reach the detector. The orbitrap is an ion trap - mass analyzer (and detector) that traps ions in orbital motion between a barrel-like outer electrode and a spindle-like -central electrode allowing for prolonged {term}`mass` measurement. As a result of the prolonged {term}`mass` measurements, a high {term}`mass` - resolution can be achieved. - -- **Detector**: The last component of the mass spectrometer is the detector. It determines the abundance of ions that -passed through the {term}`mass` analyzer. Ion intensities (a value that relates to its abundance) and the mass-to-charge ratio -are recorded in a {term}`mass` spectrum. - -A sample is measured over the retention time of the chromatography typically resulting in tens of thousands of spectra. -The measurement of one sample is called an MS run and the set of spectra called an {term}`MS(1)` map or peak map. - -![](/images/introduction/spectrum_peakmap.png) - -The left image displays spectrum with peaks (m/z and intensity values) and the right image shows spectra stacked in -retention time yielding a peak map. - - -In proteomics and metabolomics, the {term}`MS(1)` intensity is often used for the quantification of an analyte. Identification based on the {term}`MS(1)` mass-to-charge and the isotope pattern is highly ambiguous. To improve identification, tandem mass spectrometry {term}`MS/MS` can be applied to assess the analyte substructure. To this end, the precursor ion is isolated and -kinetically fragmented using an inert gas (e.g., Argon). Fragments produced by {term}`collision-induced dissociation (CID)` -are stored in an {term}`MS^2` (or {term}`MS/MS`) spectrum and provide information that helps to resolve the ambiguities in identification. -Alternatively, {term}`MS/MS` spectra can be used for quantification. - - -Get started with installing OpenMS using the [installers available for different operating systems](/openms-applications-and-tools/installation.md). diff --git a/docs/introduction/about-open-ms.md b/docs/introduction/about-open-ms.md deleted file mode 100644 index abbff286..00000000 --- a/docs/introduction/about-open-ms.md +++ /dev/null @@ -1,9 +0,0 @@ -What is OpenMS? -=============== - -OpenMS is an open-source software platform designed for the analysis and visualization of high-throughput mass spectrometry data. OpenMS has been designed to operate on all platforms, and provides a flexible framework for users to access a wide range of built-in tools or build their own tools using the existing functionality. These tools can be applied separately to data or be applied in sequence (as a workflow or pipeline) to mass spectrometry data. OpenMS is well established and has been used widely in [literature](https://openms.github.io/publications/), particularly in the life sciences. - -Fields such as proteomics and metabolomics require the rapid, large-scale identification, quantification and characterization of biomolecules which traditional analytical techniques struggle to offer. OpenMS has been created by a team of biologists and computer scientists to create a completely open-source solution that offers customisable tools for high-throughput processing of mass spectrometry data. - -OpenMS provides a number of tools built from a C++ core library. These tools are collectively referred to as “The OpenMS PiPeline (TOPP) (formerly known as The OpenMS Proteomic Pipeline) tools. TOPP tools can be chained in a sequence to form workflows and can be applied to mass spectrometry data. -Note: TOPP’s capabilities have been expanded to apply to a wide range of areas in the life sciences. diff --git a/docs/introduction/entry-points-to-openms.md b/docs/introduction/entry-points-to-openms.md deleted file mode 100644 index de0a5989..00000000 --- a/docs/introduction/entry-points-to-openms.md +++ /dev/null @@ -1,57 +0,0 @@ -Entry Points to OpenMS -====================== - -OpenMS has been structured so that users from a wide range of fields can access what they need to solve their particular problem, depending on their skillset. - -![openms architecture](/images/introduction/openms-architecture.png) - -The following entry points for OpenMS and its TOPP tools are available for users (click the card for more information): - -::::{grid} 1 1 2 2 - -:::{grid-item-card} {material-regular}`account_tree;5em;sd-text-info` **Workflows**
    Use a supported workflow editor to create or run predefined workflows - :link: /run-workflows-with-openms-tools/recommended-workflow-systems - :link-type: doc - - Suppose you want to run the same sequence of TOPP tools on a number of data sets. You can use applications such as KNIME, Nextflow and Galaxy (where TOPP tools are available as a plugin), to apply predefined workflows or custom workflows you have designed on your data. - -::: - -:::{grid-item-card} {material-regular}`insert_chart;5em;sd-text-info` **Graphical apps**
    Use OpenMS graphical user interfaces to easily process data and inspect results - :link: /openms-applications-and-tools/openms-graphical-user-interfaces - :link-type: doc - When OpenMS is installed, a number of graphical user interfaces are available. Life science experts that want to quickly process their mass spectrometry data with the TOPP tools available can use this option. - -::: - -:::: - -::::{grid} 1 1 2 2 - -:::{grid-item-card} {octicon}`terminal;5em;sd-text-info` **Command-line tools**
    Use over 100 command-line tools to automate pre-defined tasks efficiently - :link: /openms-applications-and-tools/command-line-interface - :link-type: doc - - All TOPP tools can be executed from a Command Line Interface (CLI) directly or using a shell script. By using a CLI, users can easily automate tasks and create workflows that can be saved, stored and used on multiple datasets. Command line interfaces include, but are not limited to PowerShell in Windows or Terminal in Linux or macOS. -::: - -:::{grid-item-card} {fab}`python;sd-text-info fa-xl` **pyOpenMS**
    Use the pyOpenMS python library to rapidly prototype methods and scripts - :link: https://pyopenms.readthedocs.io/en/latest/user_guide/installation.html - :link-type: url - :class-title: flex - - Classes and methods originally written in C++ have been exposed to a Python interface (pyOpenMS) using Python bindings (via Cython). Central data structures even provide fast export to pandas dataframes or numpy arrays. pyOpenMS was created for users with Python knowledge who want to quickly prototype new methods and scripts or interface with other prominent data science, machine learning or visualization libraries such as tensorflow or plotly. -::: - -:::: - - -:::{card} {octicon}`cpu;5em;sd-text-info` **OpenMS C++ core library**
    Build the OpenMS C++ core library from source to develop your own efficient tools and methods - :link: /develop-with-openms/openms-core-cplusplus-library - :link-type: doc - - As shown in the image above, TOPP tools have been created using the OpenMS core library and some external libraries, which are written in C++. Using the OpenMS core library directly provides faster access to tools and shorter run-times. Additional TOPP tools can also be developed, customized or extended based on the user’s needs. -::: - - - diff --git a/docs/introduction/extended-background.md b/docs/introduction/extended-background.md deleted file mode 100644 index 270296bd..00000000 --- a/docs/introduction/extended-background.md +++ /dev/null @@ -1,358 +0,0 @@ -Background -========== - -Proteomics and metabolomics focus on complex interactions within biological systems; the former is centered on proteins while the latter is based on metabolites. To understand these interactions, we need to accurately identify the different biological components involved. - -{term}`Liquid chromatography` (LC) and {term}`mass spectrometry` (MS) are the analytical techniques used to isolate and identify biological components in proteomics and metabolomics. LC-MS data can be difficult to analyze manually given its amount and complexity. Therefore, we need specialized software that can analyze high-throughput LC-MS data quickly and accurately. - -## Why use OpenMS - -OpenMS is an open-source, C++ framework for analyzing large volumes of mass spectrometry data. It has been specially designed for analyzing high performance LC-MS data but over recent times, has been extended to analyze data generated by other techniques. - -```{note} -OpenMS in recent times has been expanded to support a wide variety of mass spectrometry experiments. To design your analysis solution, [contact the OpenMS team](https://openms.github.io/community/) today. -``` - -To use OpenMS effectively, an understanding of chromatography and mass spectrometry is required as many of the algorithms are based on these techniques. -This section provides a detailed explanation on LC and MS, and how they are combined to identify and quantify substances. - -## Liquid chromatography (LC) - -Chromatography is a technique used by life scientists to separate molecules based on a specific physical or chemical property. - -
    -

    **Video**

    -For more information on chromatography, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_001_cpm_0001?t=210.00). -
    - -There are many types of chromatography, but this section focuses on LC as it is widely used in proteomics and metabolomics. - -LC separates molecules based on a specific physical or chemical property by mixing a sample containing the molecules of interest (otherwise known as **analytes**) in a liquid solution. - -### Key components of LC - -An LC setup is made up of the following components: -- **A liquid solution**, known as the **mobile phase**, containing the analytes. -- **A pump** which transports the liquid solution. -- **A stationary phase** which is a solid, homogeneous substance. -- **A column** that contains the stationary phase. -- **A detector** that plots the time it takes for the analyte to escape the column (retention time) against the analyte's concentration. This plot is called a **chromatogram**. - -Refer to the image below for a diagrammatic representation of an LC setup. - -![schematic illustration of an LC setup](/images/introduction/lc-components.png) - -### How does LC work? - -The liquid solution containing the analytes is pumped through a column that is attached to the stationary phase. Analytes are separated based on how strongly they interact with each phase. Some analytes will interact strongly with the mobile phase while others will be strongly attracted to the stationary phase, depending on their physical or chemical properties. The stronger an analyte's attraction is to the mobile phase, the faster it will leave the column. The time it takes for an analyte to escape from the column is called the analyte's {term}`retention time`. As a result of their differing attractions to the mobile and stationary phases, different analytes will have different retention times, which is how separation occurs. - -The retention times for each analyte are recorded by a detector. The most common detector used is the mass spectrometer, which we discuss later. However, other detection methods exist, such as: -- Light absorption (photometric detector) -- Fluorescence -- Change in diffraction index - -### High performance liquid chromatography (HPLC) - -HPLC is the most commonly used technique for separating proteins and metabolites. In HPLC, a high-pressured pump is used to transport a liquid (solvent) containing the molecules of interest through a thin capillary column. The stationary phase is ‘packed’ into the column. - -
    -

    **Video**

    -For more information on HPLC, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_001_cpm_0001?t=699.69). -
    - -Several variations of HPLC exist such as: -- Reversed-phase (RP) chromatography -- Strong cation/anion exchange (SCX/SAX) chromatography -- Affinity chromatography -- Size exclusion chromatography - -#### Special case of HPLC: Reversed-phase (RP) chromatography - -RP chromatography is the most commony type of HPLC with biological samples. In reversed-phase liquid chromatography, the solid phase is modified to become hydrophobic, when it is originally hydrophilic, hence the term ‘reversed-phase’. The liquid phase is a mixture of water and an organic solvent. The separation of molecules happens based on the following behavior: hydrophilic analytes have a high affinity to the mobile phase and escape the column quickly while hydrophobic analytes have a high affinity towards the organic solvent and therefore, take a longer time to escape the column. - -
    -

    **Video**

    -For more information on RP chromatography, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_001_cpm_0001?t=1399.85). -
    - -## Mass spectrometry (MS) - -Mass spectrometry is an analytical technique used to determine the abundance of molecules in a sample. - -### Key components of MS - -There are three key components in a mass spectrometer: -- An **ion source**, which generates {term}`ions ` from the incoming sample. All mass spectrometry techniques rely on ionized molecules to control their movement in an electric field. -- A **mass analyzer**, which separates the ions according to their mass-to-charge (m/z) ratio. There are several types such as time of flight (TOF), orbitrap and quadrupole mass analyzers. Depending on the mass analyzer, OpenMS offers calibration tools, so that highly accurate results can be achieved. -- A **detector**, which scans ions at a given time point producing a {term}`mass spectrum`, where the intensity is plotted against the m/z. - -Refer to the image below for a diagrammatic representation of the key components in MS. - -![schematic illustration of a mass spectrometer](/images/introduction/mass-spectrometry-components.png) - -#### Ion source - -We want the analytes to move through the electrostatic and electromagnetic fields in the mass analyzer. To achieve this objective, we need to convert them to ions by charging them. There are a number of ways to charge our analytes including: -- Electrospray Ionization (ESI) -- Matrix Assisted Laser Desorption/Ionization (MALDI) -- Electron Impact Ionization (EI) - -In proteomics and metabolomics, ESI and MALDI are used because they are soft ionization techniques. A soft ionization technique is one which charges analytes while keeping the molecules of interest largely intact, so that they can be characterized easily at a later stage. Hard ionization techniques such as EI shatter analytes in smaller fragments, making it difficult to characterize large molecules. - -Given that OpenMS focuses on proteomic and metabolomic applications, we will describe ESI and MALDI in further detail. - -##### Electrospray Ionization (ESI) -ESI can be broken down into the following steps. - -1. The sample is dissolved in a polar, volatile buffer. -2. The sample - dissolved in the buffer - is pumped through a thin, stainless steel capillary. -3. The sample is converted to small, charged, stable droplets (aerosolized) by applying high voltage. -4. The aerosol is directed through regions of high vacuum until the droplets evaporate until only the charged molecules are left. -5. The particles are fed to the mass analyzer. - -Refer to the image below for a diagrammatic representation of the steps in ESI. - -![a simplified, schematic representation of ESI](/images/introduction/electrospray-ionization.png) - -
    -

    **Video**

    -For more information on ESI, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=624.28). -
    - -##### Matrix Assisted Laser Desorption/Ionization (MALDI) - -MALDI can be broken down into the following steps: -1. The analytes are mixed with a small organic molecule known as a matrix. -2. The mixture is exposed to radiation with short pulses of laser light, charging the matrix. -3. The matrix transfers its charge to the analytes because the wavelength of the laser light is the same as the absorbance maximum of the matrix. -4. The analytes become charged and are fed to the mass analyzer. - -Refer to the image below for a diagrammatic representation of the steps in MALDI. - -![a simplified, schematic representation of MALDI](/images/introduction/MALDI.png) - -
    -

    **Video**

    -For more information on MALDI, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=838.40). -
    - -#### Mass analyzer - -Once the analytes have been charged by the ion source, we want to now sort the analytes by their mass-to-charge ratio for easy identification. - -A number of mass analyzers exists. These include: -- Quadrupole analyzer -- Time-of-Flight analyzer -- Orbitrap analyzer - -The next sections describe each analyzer type in detail. - -##### Quadrupole - -In a quadropole analyzer, you can set the quadropole voltage so that ions with a specific m/z ratio travel through. The oscillating electrostatic fields stabilize the flight path for the ions so that they can pass through the quadropole. Other ions will be accelerated out of the quadropole and will not make it to the end. - -Refer to the image below for a diagrammatic representation of the quadrupole analyzer. - -![a simplified, schematic representation of the quadrupole analyzer](/images/introduction/quadrupole-analyzer.png) - -
    -

    **Video**

    -For more information on quadrupole analyzers, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=1477.00). -
    - -##### Time-of-Flight (TOF) - -In a time-of-flight analyzer, ions are extracted from the ion source through an electrostatic field in pulses in a field-free drift zone. An electrostatic mirror called a reflectron reflects the ions back onto the next component of mass spectrometry, the detector. The detector counts the particles and records the time of flight from extraction to the moment the particle hits the detector. - -Refer to the image below for a diagrammatic representation of the TOF analyzer. - -![a simplified, schematic representation of TOF](/images/introduction/TOF.png) - -Lighter ions fly faster than heavier ions of the same charge and will arrive earlier at the detector. Therefore, an ion’s time of flight depends on the ion’s mass. The ion's time of flight is also dependant on the ion's charge. This can be demonstrated by using the following equations: -1. Potential energy is transferred to an ion with charge **q** accelerated by an electrostatic field with voltage. -```{math} - \begin{equation} E_p = qU_a - \end{equation} -``` -2. The potential energy is converted to kinetic energy as the ion accelerates. - -```{math} - \begin{equation} E_p = E_k = \frac{1}{2}mv^2 - \end{equation} -``` - -3. We know that for a given path,**s**, from extraction to the detector, the time of flight, **t** is equal to: - -```{math} - \begin{equation} t = \frac{s}{v} - \end{equation} -``` - -Therefore,, **t**, for a given instrument's path length, **s**, depends on an ion's charge and mass. - -```{math} - \begin{equation} t = \frac{s}{v} = \frac{s}{\sqrt{\frac{2qU_a}{m}}} - \end{equation} -``` -
    -

    **Video**

    -For more information on TOF analyzers, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=1262.00). -
    - -##### Orbitrap - -The orbitrap analyzer is the most frequently used analyzer in mass spectrometry for proteomic and metabolomic applications. It consists of two outer electrodes and a central electrode. Ions are captured inside the analyzer because of an applied electrostatic field. The ions in the orbitrap analyzer oscillate around the central electrode along the axis of the electrostatic field at a set frequency, ω. This frequency is used to determine the mass-to-charge ratio using the following formula: - -```{math} - \begin{equation} ω = \sqrt{\frac{kz}{m}} - \end{equation} -``` -, where *k* is a constant. - -The following diagram is a conceptual representation of the orbitrap analyzer. - -![schematic illustration of a mass spectrometer](/images/introduction/orbitrap.png) - -
    -

    **Video**

    -For more information on orbitrap analyzers, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=1572.96). -
    - -### Identifying molecules with Tandem Mass Spectrometry (MS2) - -To get better results, we can use two mass analyzers sequentially to generate and analyze ions. This technique is called **tandem mass spectrometry** or MS/MS (MS2). Tandem mass spectrometry is especially useful for linear polymers like proteins, RNA and DNA. - -With MS2, ions called **precursor ions** are isolated and fragmented into ion fragments or **product ions**. A {term}`mass spectrum` is recorded for both the precursor and the product ions. - -
    -

    **Video**

    -For more information on MS2, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=1650.00). -
    - -Different fragmentation techniques to fragment peptides exist: - -- Collision-Induced Dissociation (CID) -- Pulsed Q Dissociation (PQD) -- Electron transfer dissociation (ETD) -- Electron capture dissociation (ECD) -- Higher energy collision dissociation (HCD) - -CID is the most frequently used fragmentation technique and will therefore be discussed in more detail in the following section. - -#### Collision-induced dissociation - -Collision-induced dissociation is a method to fragment peptides using an inert gas such as argon or helium. Selected primary or precursor ions enter a collision cell filled with the inert gas. The application of the inert gas on the precursor ions causes the precursor ions that reach the energy threshold to fragment into smaller, product ions and or neutral losses. A {term}`mass spectrum` is recorded for both the precursor ions and the product ions. The {term}`mass spectrum` for the precursor ions will give you the mass for the entire peptide while the product ions will inform you about it’s amino acid composition. - -
    -

    **Video**

    -For more information on CID, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141028_002_cpm_0001?t=1757.45). -
    - -## LC-MS - -Liquid chromatography is often coupled with mass spectrometry to reduce complexity in the mass spectra. If complex samples were directly fed to a mass spectrometer, you would not be able to detect the less abundant analyte ions. The separated analytes from the liquid chromatography setup are directly injected into the ion source from the mass spectrometry setup. Multiple analytes that escape the column at the same time are separated by their mass-to-charge ratio using the mass spectrometer. - -Refer to the image below for a diagrammatic representation of the LC-MS setup. - -![lc-ms setup](/images/introduction/lc-ms-setup.png) - -From the LC-MS setup, a set of spectra called a peak map is produced. In a peak map, each spectrum represents the ions detected at a particular retention time. Each peak in a spectrum has a retention time, mass-to-charge and intensity dimension. - -From the LC-MS setup, a series of spectra are 'stacked' together to form what is known as a peak map. Each spectrum in a peak map is a collection of data points called {term}`peaks ` which indicate the retention time, mass-to-charge and intensity of each detected ion. Analyzing peak maps is difficult as different compounds can elute at the same time which means that peaks can overlap. Therefore, sophisticated techniques are required for the accurate identification and quantification of molecules. - -The image below includes a spectrum at a given retention time (left) and a peak map (right). - -![peak map](/images/introduction/spectrum_peakmap.png) - -
    -

    **Video**

    -For more information on a *specific* application of LC-MS, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141014_002_cpm_0001?t=946.20). - -## Improving identification and quantification - -While the combination of liquid chromatography and mass spectrometry can ease the process of characterising molecules of interest, further techniques are required to easily identify and quantify these molecules. This section discusses both labeled and label-free quantification techniques. - -### Labeling - -Relative quantification is one strategy where one sample is chemically treated and compared to another sample without treatment. This section discusses a particular relative quanitification technique called **labeling** or **stable isotope labeling** which involves the addition of isotopes to one sample. An isotope of an element behaves the same chemically but has a different mass. Stable isotope labeling is used in mass spectrometry so that scientists can easily identify proteins and metabolites. - -Two types of stable isotope labeling exist: chemical labeling and metabolic labeling. - -#### Chemical labeling - -During chemical labeling, the label is attached at specific functional groups in a molecule like the N-terminus of a peptide or specific side chains. - -Chemical labeling occurs late in the process, therefore experiments that incorporate this technique are not highly reproducible. - -##### Isobaric labeling - -Isobaric labeling, is a technique where peptides and proteins are labeled with chemical groups that have an identical mass, but vary in terms of of distribution of heavy isotopes in their structure. - -
    -

    **Video**

    -For more information on isobaric labeling, view the following links: -
    - -OpenMS contains tools that analyze data from isobaric labeling experiments. - -#### Metabolic labeling - -During metabolic labeling, the organism is 'fed' with labeled metabolites. Metabolites include but are not limited to amino acids, nitrogen sources and glucose. Unlike chemical labeling, metabolic labeling occurs early in the study. Therefore, experiments that incorporate metabolic labeling are highly reproducible. - -##### Stable Isotope Labeling with Amino Aids in Cell Culture (SILAC) - -In SILAC, the labeled amino acids are fed to the cell culture. The labels are integrated into the proteins after a period. The labeled sample is then compared with the unlabeled sample. - -OpenMS contains tools that analyze data from SILAC experiments. - -
    -

    **Video**

    -For more information on SILAC, view the following links: - -
    - -### Label-free quantification (LFQ) - -LFQ is a cheap and natural method of quantifying molecules of interest. As the name suggests, no labeling of molecules is involved. - -LFQ includes the following steps: - -1. **Conduct replicate experiments**. -2. **Generate LC-MS maps** for each experiment. -3. **Find features** in all LC-MS maps. A {term}`feature` is a collection of peaks that belong to a chemical compound. -4. **Align maps** to address shifts in retention times. -5. **Match corresponding features** in different maps. We refer to this as **grouping** or **linking**. -6. **Identify feature groups**, called {term}`consensus features `. -7. **Quantify consensus features**. - -
    -

    **Video**

    -For more information on LFQ, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141118_002_cpm_0001?t=2115.00). -For more information on the steps involved in LFQ, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141118_002_cpm_0001?t=2230.18) -
    - -#### Feature finding - -Feature finding is method for identifying all peaks belonging to a chemical compound. Feature finding involves the following steps: - -1. **Extension** where we collect all data points we think belong to the peptide. -2. **Refinement** where we remove peaks that we think do not belong to the peptide. -3. **Fit an optimal model** to the isolated peaks. - -The above steps are iterative; we repeat these steps until no improvement can be made to the model. - -OpenMS contains a number of feature finding algorithms. - -
    -

    **Video**

    -For more information on feature finding, [view this video](https://timms.uni-tuebingen.de:/tp/UT_20141118_002_cpm_0001?t=2670.44). -
    \ No newline at end of file diff --git a/docs/manual/additional.md b/docs/manual/additional.md new file mode 100644 index 00000000..accbbf1f --- /dev/null +++ b/docs/manual/additional.md @@ -0,0 +1,32 @@ +Additional +========== + +## Graphical User Interfaces + +OpenMS provides additional graphical user interfaces besides TOPPAS and TOPPView, designed for users who want easy access to TOPP tools. These interfaces include: + +- **INIFileEditor** + + A GUI application used to edit TOPP INI files. TOPP INI files are used to configure TOPP tool parameters. TOPP INI files are files with the extension `.ini`. For mor information, read our [INIFileEditor](additional/ini-file-editor.md) section. + +- **SwathWizard** + An application for SWATH analysis. SwathWizard is used to analyze DIA swath data. For more information, read our [SwathWizard](additional/swathwizard.md) section. + + +A possible workflow would consist of the following steps: + +1. Generate a TOPP INI file from the [command line](/getting-started/topp-tools.md#command-line-interface). +2. Edit the TOPP INI file in the INIFile Editor. +3. Import data into TOPPView. +4. Apply TOPP tool to data in TOPPView. You will need to load the TOPP INI file edited in step 1. + + +```{toctree} +--- +maxdepth: 1 +hidden: True +--- + +additional/swathwizard.md +additional/ini-file-editor.md +``` \ No newline at end of file diff --git a/docs/topp-and-utils/ini-file-editor.md b/docs/manual/additional/ini-file-editor.md similarity index 77% rename from docs/topp-and-utils/ini-file-editor.md rename to docs/manual/additional/ini-file-editor.md index 4e77931f..83698dbd 100644 --- a/docs/topp-and-utils/ini-file-editor.md +++ b/docs/manual/additional/ini-file-editor.md @@ -1,6 +1,3 @@ ---- -orphan: true ---- INIFileEditor ============ @@ -10,4 +7,4 @@ The values can be edited by double-clicking or pressing F2. The documentation of each value is shown in the text area on the bottom of the widget. -![INIFileEditor](/images/topp/INIFileEditor.png) +![INIFileEditor](/_images/topp/INIFileEditor.png) \ No newline at end of file diff --git a/docs/openms-applications-and-tools/openms-applications/swathwizard.md b/docs/manual/additional/swathwizard.md similarity index 95% rename from docs/openms-applications-and-tools/openms-applications/swathwizard.md rename to docs/manual/additional/swathwizard.md index ed2ffebd..169d8f30 100644 --- a/docs/openms-applications-and-tools/openms-applications/swathwizard.md +++ b/docs/manual/additional/swathwizard.md @@ -1,6 +1,3 @@ ---- -orphan: true ---- SwathWizard ============ @@ -19,7 +16,7 @@ both the intermediate files from OpenSWATH (e.g. the XIC data in `.sqMass` forma This is how the wizard looks like: -![SwathWizard](/images/topp/SwathWizard.png) +![SwathWizard](/_images/topp/SwathWizard.png) A schematic of the internal data flow (all tools are called by SwathWizard in the background) can be found in the [TOPP Documentation: SwathWizard](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/TOPP_SwathWizard.html). diff --git a/docs/manual/contribute.md b/docs/manual/contribute.md new file mode 100644 index 00000000..4f2497ae --- /dev/null +++ b/docs/manual/contribute.md @@ -0,0 +1,57 @@ +Contribute +========== + +## Reporting Bugs and Issues + +A list of known issues in the current OpenMS release can be found [here](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/known_dev_bugs.html). +Please check if your OpenMS version matches the current version and if the bug has already been reported. + +In order to report a new bug, please create a [GitHub issue](manual/contribute.md#Write and Label GitHub Issues) or [contact us](/about/communication.md). + +Include the following information in your bug report: + +1. The command line (i.e. call) including the TOPP tool and the arguments you used, or the steps you followed in a GUI + tool (e.g. TOPPView) - e.g. `FeatureFinderCentroided -in myfile.mzML -out myfile.featureXML`. +2. The output of OpenMS/TOPP (or a screenshot in case of a GUI problem). +3. Operating system (e.g. "Windows XP 32 bit", "Win 7 64 bit", "Fedora 8 32 bit", "macOS 10.6 64 bit"). +4. OpenMS version (e.g. "OpenMS 1.11.1", "Revision 63082 from the SVN repository"). +5. OpenMS architecture ("32 bit" or "64 bit") + +Please provide files that we need to reproduce the bug (e.g. `TOPP INI` files, data files — usually mzML) via a download +link, via the mailing list or by directly contacting one of the developers. + + +## Write and Label GitHub Issues + +### Create an Issue + +To create an issue: + +1. Go to the [OpenMS codebase](https://github.com/OpenMS/OpenMS). +2. Submit an [issue](https://github.com/OpenMS/OpenMS/issues/new). + +The issue will be listed under **Issues**. + +### Label an Issue + +To label an issue: + +1. On the right of the screen, select the cog icon under **Labels**. +2. Choose a label from the list. Normally, an issue can have one or more of the following labels: + - **defect**: A defect refers to a bug in OpenMS. This is a high priority issue. + - **enhancement**: An enhancement refers to a feature idea to enhance the current OpenMS code. This is a medium + priority issue. + - **task**: A task refers to a single piece of work that a developer can undertake. This is a medium priority issue. + - **refactoring**: A refactoring issue refers to a suggestion to streamline the code without changing how the code + function. + - **question**: A question could trigger to a discussion about tools, parameters and scientific tasks. + + + +```{toctree} +:maxdepth: 1 + +contribute/pull-request-checklist.md +contribute/openms-git-workflow.md + +``` \ No newline at end of file diff --git a/docs/contribute-to-openms/openms-git-workflow.md b/docs/manual/contribute/openms-git-workflow.md similarity index 99% rename from docs/contribute-to-openms/openms-git-workflow.md rename to docs/manual/contribute/openms-git-workflow.md index 56987125..b322c5e7 100644 --- a/docs/contribute-to-openms/openms-git-workflow.md +++ b/docs/manual/contribute/openms-git-workflow.md @@ -24,7 +24,7 @@ Start by [forking](https://docs.github.com/en/get-started/quickstart/fork-a-repo To create a fork, click **Fork** under the main menu as shown below. -![image info](/images/additional-resources/click-fork.png) +![image info](/_images/additional-resources/click-fork.png) ## Clone your fork diff --git a/docs/contribute-to-openms/pull-request-checklist.md b/docs/manual/contribute/pull-request-checklist.md similarity index 96% rename from docs/contribute-to-openms/pull-request-checklist.md rename to docs/manual/contribute/pull-request-checklist.md index 56281366..3731713d 100644 --- a/docs/contribute-to-openms/pull-request-checklist.md +++ b/docs/manual/contribute/pull-request-checklist.md @@ -9,7 +9,7 @@ Before opening a pull request, check the following: 2. **Do all tests pass?** To check if all tests have passed, execute `ctest`. If a test that is unrelated to your changes fails, check the [nightly builds](https://cdash.seqan.de/index.php?project=OpenMS) - to see if the error is also in `develop`. If the error is in develop, [create a github issue](write-and-label-github-issues.md). + to see if the error is also in `develop`. If the error is in develop, [create a github issue](/manual/contribute.md#Write and Label GitHub Issues). 3. **Is the code documented?** Document all new classes, including their methods and parameters. It is also recommended to document non-public members and methods. diff --git a/docs/tutorials-and-quickstart-guides/guides/contributors-quickstart-guide.md b/docs/manual/develop.md similarity index 79% rename from docs/tutorials-and-quickstart-guides/guides/contributors-quickstart-guide.md rename to docs/manual/develop.md index 10bdbdde..0b59471a 100644 --- a/docs/tutorials-and-quickstart-guides/guides/contributors-quickstart-guide.md +++ b/docs/manual/develop.md @@ -1,13 +1,13 @@ -Contributor's Quick Start Guide -=============================== +Developers +========== To contribute to OpenMS: -- Familiarise yourself with the [OpenMS online documentation](../../index.rst). -- Learn how to [build OpenMS](../../develop-with-openms/build-openms-from-source.md). +- Familiarise yourself with the OpenMS online documentation. +- Learn how to [build OpenMS](/about/installation.rst). - Check out the [OpenMS tutorial for developers](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/tutorial.html#tutorial_developing). -For any questions, please [contact us](/quick-reference/contact-us.md). +For any questions, please [contact us](/about/communication.md). ## Technical documentation @@ -36,7 +36,7 @@ To create a fork: 1. Follow the documentation on [forking](https://help.github.com/articles/fork-a-repo). 2. Keep your fork [up-to-date](https://help.github.com/articles/syncing-a-fork). 3. Create a [pull request](https://help.github.com/articles/using-pull-requests). Before opening the pull request, please - view the [pull request guidelines](../../contribute-to-openms/pull-request-checklist.md). + view the [pull request guidelines](/manual/contribute/pull-request-checklist.md). ### Coding conventions @@ -67,8 +67,19 @@ Nightly tests: [CDASH](https://cdash.seqan.de/index.php?project=OpenMS). Consider the following resources for further information: -- **Guidelines for adding new dependency libraries**: View the guidelines for [adding new dependency libraries](../../contribute-to-openms/advanced/developer-guidelines-for-adding-new-dependent-libraries.md). +- **Guidelines for adding new dependency libraries**: View the guidelines for [adding new dependency libraries](/manual/develop/developer-guidelines-for-adding-new-dependent-libraries.md). - **Experimental installers**: We automatically build installers for different platforms. These usually contain unstable or partially untested code. The nightly (unstable) installers are available at the [build archive](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/nightly/). -- **Developer FAQ**: Visit the [Developer FAQ](/quick-reference/developer-faq.md) to get answers to frequently asked questions. +- **Developer FAQ**: Visit the [Developer FAQ](/manual/develop/developer-faq.md) to get answers to frequently asked questions. + +```{toctree} +:maxdepth: 1 + +develop/adding-new-tool-to-topp.md +develop/custom-compilation.md +develop/developer-guidelines-for-adding-new-dependent-libraries.md +develop/link-external-code-to-openms.md +develop/developer-faq.md + +``` \ No newline at end of file diff --git a/docs/contribute-to-openms/adding-new-tool-to-topp.md b/docs/manual/develop/adding-new-tool-to-topp.md similarity index 100% rename from docs/contribute-to-openms/adding-new-tool-to-topp.md rename to docs/manual/develop/adding-new-tool-to-topp.md diff --git a/docs/contribute-to-openms/advanced/custom-compilation.md b/docs/manual/develop/custom-compilation.md similarity index 100% rename from docs/contribute-to-openms/advanced/custom-compilation.md rename to docs/manual/develop/custom-compilation.md diff --git a/docs/quick-reference/developer-faq.md b/docs/manual/develop/developer-faq.md similarity index 96% rename from docs/quick-reference/developer-faq.md rename to docs/manual/develop/developer-faq.md index ea4f63ce..b9cdd854 100644 --- a/docs/quick-reference/developer-faq.md +++ b/docs/manual/develop/developer-faq.md @@ -10,12 +10,12 @@ The following section provides general information to new contributors. ### I am new to OpenMS. What should I do first? * Check out the development version of OpenMS (see website). -* Build OpenMS by following the installation instructions or [from source](../develop-with-openms/build-openms-from-source.md). +* Build OpenMS by following the installation instructions or [from source](/about/installation.rst). * Read the [OpenMS Coding Conventions](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/coding_conventions.html) -* Read the [OpenMS User Tutorial](../tutorials-and-quickstart-guides/openms-user-tutorial.md). +* Read the [OpenMS User Tutorial](/tutorials/knime-user-tutorial.md). * Create a GitHub account. * Subscribe to the [open-ms-general](https://sourceforge.net/projects/open-ms/lists/open-ms-general) - or [contact-us](/quick-reference/contact-us.md). + or [contact-us](/about/communication.md). ### I have written a class for OpenMS. What should I do? @@ -26,7 +26,7 @@ Coding style (brackets, variable names, etc.) must conform to the conventions. * The class and all the members should be properly documented. * Check your code with the tool `tools/checker.php`. Call `php tools/checker.php` for detailed instructions. -Please open a pull request and follow the [pull request guidelines](../contribute-to-openms/pull-request-checklist.md). +Please open a pull request and follow the [pull request guidelines](/manual/contribute/pull-request-checklist.md). ### Can I use QT designer to create GUI widgets? @@ -236,6 +236,30 @@ Hints to resolve the OPENMS_DLLAPI issue using the ``cmake`` generator are welco The following section provides information about how to debug your code. +### How do I run a single test? + +Execute an OpenMS class test using the CTest regular expressions: + +```bash + +$ ctest -V -R "^_test" + +# To build a class test, call the respective make target in ./source/TEST: + +$ make _test +``` +To run a TOPP test, use: + +```bash + +$ ctest -V -R "TOPP_" +``` + +To build the tool, use: + +```bash +$ make +``` ### How do I debug uncaught exceptions? Dump a core if an uncaught exception occurs, by setting the environment variable `OPENMS_DUMP_CORE`. @@ -367,7 +391,7 @@ Test if everything worked by calling `make doc_param_internal`. The command line ### How to contribute a bug fix? -Read [contributor quickstart guide](../tutorials-and-quickstart-guides/guides/contributors-quickstart-guide.md). +Read [contributor quickstart guide](/manual/contribute.md). ### How can I profile my code? diff --git a/docs/contribute-to-openms/advanced/developer-guidelines-for-adding-new-dependent-libraries.md b/docs/manual/develop/developer-guidelines-for-adding-new-dependent-libraries.md similarity index 100% rename from docs/contribute-to-openms/advanced/developer-guidelines-for-adding-new-dependent-libraries.md rename to docs/manual/develop/developer-guidelines-for-adding-new-dependent-libraries.md diff --git a/docs/develop-with-openms/link-external-code-to-openms.md b/docs/manual/develop/link-external-code-to-openms.md similarity index 100% rename from docs/develop-with-openms/link-external-code-to-openms.md rename to docs/manual/develop/link-external-code-to-openms.md diff --git a/docs/quick-reference/glossary.md b/docs/manual/glossary.md similarity index 95% rename from docs/quick-reference/glossary.md rename to docs/manual/glossary.md index 5fa30c8c..abc36a99 100644 --- a/docs/quick-reference/glossary.md +++ b/docs/manual/glossary.md @@ -1,5 +1,5 @@ -OpenMS Glossary -============== +Glossary +======== A glossary of common terms used throughout OpenMS documentation. @@ -92,7 +92,7 @@ TOPP tool see {term}`TOPP Tools` TOPPView - TOPPView is a viewer for MS and HPLC-MS data which ships with OpenMS. More information is available in [TOPPView documentation](../openms-applications-and-tools/visualize-with-openms.md). + TOPPView is a viewer for MS and HPLC-MS data which ships with OpenMS. More information is available in [TOPPView documentation](/getting-started/visualize-with-openms.md). [Nightly Snapshot](https://openms.de/current_doxygen/html/index.html) Untested installers and containers are known as the nightly snapshot. @@ -145,19 +145,19 @@ de novo peptide sequencing A peptide’s amino acid sequence is inferred directly from the precursor peptide mass and tandem mass spectrum ({term}`MS/MS` or {term}`MS^3`) fragment ions, without comparison to a reference proteome. TOPPAS - An graphical user interface (GUI), which is shipped with OpenMS, to create and execute worflows using {term}`TOPP tools`; see [Recommended Workflow Systems](./../run-workflows-with-openms-tools/recommended-workflow-systems.md). + An graphical user interface (GUI), which is shipped with OpenMS, to create and execute worflows using {term}`TOPP tools`; see [Workflow Editor](/getting-started/workflows.rst). chromatogram A two-dimensional plot that describes the amount of analyte eluted from a chromatography versus the analyte's retention time. OpenMS represents a chromatogram using the class [MSChromatogram](https://openms.de/current_doxygen/html/structOpenMS_1_1Interfaces_1_1Chromatogram.html) KNIME - An advanced workflow editor which OpenMS provides a plugin for; see [Recommended Workflow Systems](./../run-workflows-with-openms-tools/recommended-workflow-systems.md). + An advanced workflow editor which OpenMS provides a plugin for; see [Workflow Editor](/getting-started/workflows.rst). Nextflow - Script/DSL-based workflow language, executor and utilities; see [Recommended Workflow Systems](./../run-workflows-with-openms-tools/recommended-workflow-systems.md). + Script/DSL-based workflow language, executor and utilities; see [Workflow Editor](/getting-started/workflows.rst). Galaxy - Server and browser-based interactive workflow editor and runner; see [Recommended Workflow Systems](./../run-workflows-with-openms-tools/recommended-workflow-systems.md). + Server and browser-based interactive workflow editor and runner; see [Workflow Editor](/getting-started/workflows.rst). SILAC Stands for 'Stable isotope labeling using amino acids in cell culture'. diff --git a/docs/openms-applications-and-tools/command-line-interface.md b/docs/openms-applications-and-tools/command-line-interface.md deleted file mode 100644 index 03f8a882..00000000 --- a/docs/openms-applications-and-tools/command-line-interface.md +++ /dev/null @@ -1,38 +0,0 @@ -Command Line Interface -====================== - -TOPP tools are designed to be called from the command line. OpenMS provides a Command Line Interface (CLI) called TOPP shell to easily execute TOPP tools on mass spectrometry data. However, you can configure the CLI of your choice to run TOPP tools. - -Command line calls will depend on the TOPP tools used, as each TOPP tool has its own set of parameters. However, the following arguments are typically used: - -- `-in` - - Specify an input file in the command line using the `-in` argument. The input file should be in a supported format. If not, use the file converter to convert the file to one of the supported formats. For more information, view the file handling documentation. -- `-out` - - Specify an output file in the command line using the `-out` argument. -- `-ini` - - Specify an INI file in the command line using the `-ini` argument. TOPP uses INI files to set parameters specific to the command line tool being called. -- `-write_ini` - - Create an INI file using the `-write_ini` file argument. - Create an INI file with this call: - ` -write_ini ` - If you want a visual tool to assist setting parameters, use [INIFileEditor](../openms-applications-and-tools/openms-applications/ini-file-editor.md), an application provided when you download OpenMS. Otherwise, you can set the parameters from the command line. -- `-help` - - Get information about basic options related to the tool using the -help parameter. For more advanced options (algorithmic parameters), use `--help`. -- `--help` - - Get detailed information about algorithmic parameters using the `--help` parameter. - -Many (but not all) command line calls will have the following structure: - -```bash - -in -out -ini -``` - -The following command line call uses the FileFilter tool to extract data from an mzML file. Note, that this call directly specifies the tool-specific parameters and doesn’t rely on an INI file: - -![break down of example command line call](/images/topp/command-line-call.png) diff --git a/docs/openms-applications-and-tools/installation.md b/docs/openms-applications-and-tools/installation.md deleted file mode 100644 index 89b583bc..00000000 --- a/docs/openms-applications-and-tools/installation.md +++ /dev/null @@ -1,46 +0,0 @@ -# Installation - -```{toctree} ---- -hidden: true ---- - -installation/installation-on-gnu-linux.md -installation/installation-on-macos.md -installation/installation-on-windows.md -``` - -For instructions on how to install the OpenMS graphical and command-line tools, choose your operating system -from the items below. - - -::::{grid} 1 3 3 3 - -:::{grid-item-card} GNU/Linux -:img-top: /images/installations/linux_logo.svg -:link: installation/installation-on-gnu-linux -:link-type: doc - -Installation on Linux - -::: - -:::{grid-item-card} macOS -:img-top: /images/installations/macos_logo.svg -:link: installation/installation-on-macos -:link-type: doc - -Installation on macOS - -::: - -:::{grid-item-card} Windows -:img-top: /images/installations/Windows_logo.svg -:link: installation/installation-on-windows -:link-type: doc - -Installation on Windows - -::: - -:::: diff --git a/docs/openms-applications-and-tools/openms-applications/ini-file-editor.md b/docs/openms-applications-and-tools/openms-applications/ini-file-editor.md deleted file mode 100644 index baf43b2a..00000000 --- a/docs/openms-applications-and-tools/openms-applications/ini-file-editor.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -orphan: true ---- -INIFileEditor -============ - -The INIFileEditor is an application used to visually edit INI files of TOPP tools. - -The values can be edited by double-clicking or pressing F2. - -The documentation of each value is shown in the text area on the bottom of the widget. - -![INIFileEditor](/images/topp/INIFileEditor.png) diff --git a/docs/openms-applications-and-tools/openms-graphical-user-interfaces.md b/docs/openms-applications-and-tools/openms-graphical-user-interfaces.md deleted file mode 100644 index 8e89d49d..00000000 --- a/docs/openms-applications-and-tools/openms-graphical-user-interfaces.md +++ /dev/null @@ -1,27 +0,0 @@ -OpenMS Graphical User Interfaces -================================ - -OpenMS provides a suite of graphical user interfaces, designed for users who want easy access to TOPP tools. These interfaces include: - -- **INIFileEditor** - - A GUI application used to edit TOPP INI files. TOPP INI files are used to configure TOPP tool parameters. TOPP INI files are files with the extension `.ini`. For more information, read our [INIFile Editor](openms-applications/ini-file-editor.md) section. - -- **TOPPView** - - A GUI application used to inspect, visualize and compare mass spectrometry data. For more information, read our [TOPPView: Visualize with OpenMS](visualize-with-openms.md) section. - -- **TOPPAS** - - A simple GUI application shipped with OpenMS used to apply multiple TOPP tools sequentially on mass spectrometry data. Applying multiple tools in a sequence is referred to as a workflow or a pipeline. Interacting with external tools (e.g. downstream MSStats) is limited, but all OpenMS TOPP tools are natively supported. - -- **SwathWizard** - An application for SWATH analysis. SwathWizard is used to analyze DIA swath data. For more information, read our [SwathWizard](openms-applications/swathwizard.md) section. - - -A possible workflow would consist of the following steps: - -1. Generate a TOPP INI file from the [command line](command-line-interface.md). -2. Edit the TOPP INI file in the INIFile Editor. -3. Import data into TOPPView. -4. Apply TOPP tool to data in TOPPView. You will need to load the TOPP INI file edited in step 1. diff --git a/docs/openms-applications-and-tools/topp-tools.md b/docs/openms-applications-and-tools/topp-tools.md deleted file mode 100644 index a4d3d2fb..00000000 --- a/docs/openms-applications-and-tools/topp-tools.md +++ /dev/null @@ -1,12 +0,0 @@ -TOPP Tools -========== - -The following sections introduce TOPP, some general concepts and more specific information regarding each tool. - -```{toctree} -:maxdepth: 1 - -topp-tools/introduction-to-topp.md -topp-tools/types-of-topp-tools.md - -``` diff --git a/docs/openms-applications-and-tools/topp-tools/introduction-to-topp.md b/docs/openms-applications-and-tools/topp-tools/introduction-to-topp.md deleted file mode 100644 index 902b34f9..00000000 --- a/docs/openms-applications-and-tools/topp-tools/introduction-to-topp.md +++ /dev/null @@ -1,68 +0,0 @@ -Introduction to TOPP -==================== - -**TOPP - The OpenMS Pipeline** is a set of tools for the analysis of HPLC-MS data. These tools can be either: - -- [Executed from the command line](../../openms-applications-and-tools/command-line-interface.md) or, -- Applied individually using OpenMS graphical applications. -- Applied in sequence as a workflow using a workflow editor such as KNIME, Nextflow or Galaxy. - -Before you choose one of the above options, there are few concepts that need to be understood. - -## File formats - -OpenMS only accepts files in certain formats, including but not limited to: - -- **mzML**: The HUPO-PSI standard format for mass spectrometry data. -- **featureXML**: The OpenMS format for quantitation results. -- **consensusXML**: The OpenMS format for grouping features in one map or across several maps. -- **idXML**: The OpenMS format for protein and peptide identification. - -Documented schemas of the OpenMS formats can be found [here](https://github.com/OpenMS/OpenMS/tree/develop/share/OpenMS/SCHEMAS). - -If your data is not in the above formats, you may need to use a file conversion TOPP tool. - -## TOPP INI files - -TOPP INI files are XML-based files with an `.ini` extension. OpenMS uses TOPP INI files to set parameters for one or more TOPP tools. Alternatively, the command line can be used to set TOPP tool parameters. -Here is an example of a TOPP INI file: - - ```xml - - - - - - - - - - - - - - - - - - - - - ``` -## Features, feature maps and featureXML files - -An LC-MS feature is a construct in OpenMS that is used to describe a 2D peak caused by an analyte interacting with the stationary phase. Each feature contains the following metadata: an id, retention time, mass-to-charge ratio, intensity, overall quality and one or more convex hulls. - -A feature map is a container for features. One feature map can contain many features. - -A featureXML file is an XML based file which contains one feature map. - -FeatureXML files can be created from mzML files using OpenMS’s feature detection algorithms. - -## Consensus feature, consensus maps, consensusXML files - -A consensus feature is a special type of LC-MS feature that is quantified across multiple experiments. A consensus feature is formed by linking or grouping features with similar mass-to-charge ratios and intensities from various experiment runs. Each consensus feature references the features used to form the consensus feature. - -Similar to a feature map, a consensus map is a container for consensus features. One consensus map can contain many consensus features. - -ConsensusXML files can be created from featureXML files using OpenMS's feature grouping algorithms. diff --git a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools.md b/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools.md deleted file mode 100644 index 94c93573..00000000 --- a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools.md +++ /dev/null @@ -1,52 +0,0 @@ -Types of TOPP Tools -=================== - -The following tools are offered: - -- **File conversion** - - TOPP file conversion tools can be used to convert files into a supported format. - -- **File handling** - - TOPP file handling tools are largely used to extract or merge data. For more information, view the [File handling](types-of-topp-tools/file-handling.md). - -- **Centroiding** - - The conversion of the "raw" ion count data acquired by the machine into peak lists for further processing is usually called peak picking or centroiding. The choice of the algorithm should mainly depend on the resolution of the data. OpenMS provides different algorithms for centroiding depending on the resolution of the data. For more information, view the [Picking peaks](types-of-topp-tools/picking-peaks.md) section. - -- **Spectrum processing** - - A number of spectrum processing tools are available. These include peak filtering and peak normalization tools, as well as other miscellaneous tools. - -- **Mass correction and calibration** - - To ensure that your data is sound, OpenMS have provided a number of mass correction and calibration tools. The types of tools used will depend on the type of equipment you have employed. For more information, view the [Calibration](types-of-topp-tools/calibration.md) section. - -- **Spectrum clustering** - - Spectrum clustering is the grouping of spectra that have many peaks in common. OpenMS provides tools for spectrum clustering to identify molecules in large datasets more efficiently. - -- **Map alignment** - - When looking to identify molecules, it is common to run multiple experiments, where each experiment produces a set of data. In OpenMS, every set of data is represented by a feature map. Before combining feature maps to create a consensus map, it is advised to use OpenMS’s map alignment tools so that all your datasets are comparable and based on a common retention time axis. For more information, view the [Map alignment](types-of-topp-tools/map-alignment.md) section. - -- **Feature linking** - - OpenMS provides a number of algorithms for feature grouping or linking. For more information, view the [Feature grouping](types-of-topp-tools/feature-grouping.md) section. -- **Quantitation** - - A number of tools are available that allow for the identification and quantification of features. The tools you use will depend on the type of mass spectrometry experiment you have set up, and the type of molecules you wish to identify. For more information, view the [Feature detection](types-of-topp-tools/feature-detection.md) and [Feature detection on centroided data](types-of-topp-tools/feature-detection-on-centroided-data.md) sections. - -- **Protein/Peptide identification** -- **Protein/Peptide processing** -- **Targeted experiments and OpenSWATH** - -- **Cross-linking** - - Cross-linking is a technique where substances are chemically treated to create covalent bonds between different molecules. The strength of the covalent bonds can be quantified to indicate the proximity of certain molecules within a 3D structure. -- **Quality control** - - OpenMS provides tools to measure the quality of LC-MS data. For more information, view the [Quality control](types-of-topp-tools/quality-control.md) section. - -For the full list of TOPP tools, visit the [API reference](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/index.html) website. diff --git a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/feature-detection-on-centroided-data.md b/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/feature-detection-on-centroided-data.md deleted file mode 100644 index b3ea53b7..00000000 --- a/docs/openms-applications-and-tools/topp-tools/types-of-topp-tools/feature-detection-on-centroided-data.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -orphan: true ---- -Feature Detection on Centroided Data -==================================== - -To quantify peptide features, TOPP offers the **FeatureFinder** tools. In this section the `FeatureFinderCentroided` -is used, which works only on centroided data. There are other FeatureFinders available that also work on profile data. - -For this example the file `LCMS-centroided.mzML` from the examples data is used (**File** > **Open example data**). In order -to adapt the algorithm to the data, some parameters have to be set. - -## Intensity - -The algorithm estimates the significance of peak intensities in a local environment. Therefore, the HPLC-MS map is -divided into `n` times `n` regions. Set the `intensity:bins` parameter to `10` for the whole map. For a small region, set -it to `1`. - -## Mass trace - -For the mass traces, define the number of adjacent spectra in which a mass has to occur (`mass_trace:min_spectra`). In -order to compensate for peak picking errors, missing peaks can be allowed (`mass_trace:max_missing`) and a tolerated -mass deviation must be set (`mass_trace:mz_tolerance`). - -## Isotope pattern - -The expected isotopic intensity pattern is estimated from an averagene amino acid composition. The algorithm searches -all charge states in a defined range (`isotopic_pattern:change_min` to `isotopic_pattern:change_max`). Just as for mass -traces, a tolerated mass deviation between isotopic peaks has to be set (`isotopic_pattern:mz_tolerance`). - -The image shows the centroided peak data and the found peptide features. The used parameters can be found in the TOPP -tools dialog. - -![TOPPView Tools FF\_Centrioided](/images/tutorials/topp/TOPPView_tools_ff_centroided.png) diff --git a/docs/quick-reference/contact-us.md b/docs/quick-reference/contact-us.md deleted file mode 100644 index 869dd1dc..00000000 --- a/docs/quick-reference/contact-us.md +++ /dev/null @@ -1,13 +0,0 @@ -Contact Us -========= - -Join us on [Discord](https://discord.gg/WxynEEsf5X)! - -You can also contact us: - -1. On the user and contributor real time [Gitter chat](https://gitter.im/OpenMS/OpenMS). -2. Drop us an email at user support [open-ms-general](https://sourceforge.net/projects/open-ms/lists/open-ms-general) - mailing list. -3. To stay updated of new versions of OpenMS and releases, subscribe to - [openms-announcements mailing list](https://sourceforge.net/projects/open-ms/lists/open-ms-announcements). -4. To report a new bug, create an issue on GitHub [OpenMS](https://github.com/OpenMS/OpenMS/issues) repository. diff --git a/docs/quick-reference/contributor-faq.md b/docs/quick-reference/contributor-faq.md deleted file mode 100644 index 79fcf683..00000000 --- a/docs/quick-reference/contributor-faq.md +++ /dev/null @@ -1,199 +0,0 @@ -Contributor FAQ -=============== - -The following contains answers to typical questions from contributors about OpenMS. - -## General - -The following section provides general information to new contributors. - -### I am new to OpenMS. What should I do first? - -* Check out the development version of OpenMS (see OpenMS releases and installers). -* Build OpenMS according to the installation instructions. -* Read the [OpenMS Coding Conventions](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/coding_conventions.html). -* Read the [OpenMS User Tutorial](../tutorials-and-quickstart-guides/openms-user-tutorial.md). -* Create a GitHub account. -* Subscribe to the [open-ms-general](https://sourceforge.net/projects/open-ms/lists/open-ms-general) -or [contact us](/quick-reference/contact-us.md). - -### I have written a class for OpenMS. What should I do? - -Follow the [OpenMS coding conventions](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/coding_conventions.html). - -Coding style (brackets, variable names, etc.) must conform to the conventions. - -* The class and all the members must be documented thoroughly. -* Check your code with the tool `tools/checker.php`. Call `php tools/checker.php` for detailed instructions. - -Please open a pull request and follow the [pull request guidelines](../contribute-to-openms/pull-request-checklist.md). - -## Troubleshooting - -The following section provides information about how to troubleshoot common OpenMS issues. - -### OpenMS complains about boost not being found - -`CMake` got confused. Set up a new build directory and try again. Build from source, deleting the `CMakeCache.txt` -and `cmake` directory might help. - -## Build System - -The following questions are related to the build system. - -### What is CMake? - -`CMake` builds BuildSystems for different platforms, e.g. VisualStudio Solutions on Windows, Makefiles on Linux etc. -This allows us to define in one central location (namely `CMakeLists.txt`) how OpenMS is build and have the platform -specific stuff handled by `CMake`. View the [cmake website](http://www.cmake.org) for more information. - -### How do I use CMake? - -See Installation instructions for your platform. -In general, call `CMake(.exe)` with some parameters to create the native build-system. -Afterwards, (but usually) don't have to edit the current configuration using a GUI named `ccmake` -(or `CMake-GUI` in Windows), which ships with `CMake`). - -```{note} -Whenever `ccmake` is mentioned in this document, substitute this by `CMake-GUI` if your OS is Windows. Edit -the `CMakeCache.txt` file directly. -``` - -### How do I generate a build-system for Eclipse, KDevelop, CodeBlocks etc? - -Type `cmake` into a console. This will list the available code generators available on your platform, -pass them to `CMake` using the `-G` option. - -### How do I add a new class to the build system? - -1. Create the new class in the corresponding sub-folder of the sub-project. The header has to be created in - `src//include/OpenMS` and the cpp file in `src//source`, e.g., - `src/openms/include/OpenMS/FORMAT/NewFileFormat.h` and `src/openms/source/FORMAT/NewFileFormat.cpp`. -2. Add both to the respective `sources.cmake` file in the same directory (e.g., `src/openms/source/FORMAT/` and - `src/openms/include/OpenMS/FORMAT/`). -3. Add the corresponding class test to `src/tests/class_tests//` - (e.g., `src/tests/class_tests/openms/source/NewFileFormat_test.cpp`). -4. Add the test to the `executables.cmake` file in the test folder - (e.g., `src/tests/class_tests/openms/executables.cmake`). -5. Add them to git by using the command `git add`. - -### How do I add a new directory to the build system? - -1. Create two new `sources.cmake` files (one for `src//include/OpenMS/MYDIR`, one for - `src//source/MYDIR`), using existing `sources.cmake` files as template. -2. Add the new `sources.cmake` files to `src//includes.cmake` -3. If you created a new directory directly under `src/openms/source`, then have a look - at `src/tests/class_tests/openms/executables.cmake`. -4. Add a new section that makes the unit testing system aware of the new (upcoming) tests. -5. Look at the very bottom and augment `TEST_executables`. -6. Add a new group target to `src/tests/class_tests/openms/CMakeLists.txt`. - -## Debugging - -The following section provides information about how to debug your code. - -### How do I run a single test? - -Execute an OpenMS class test using the CTest regular expressions: - -```bash - -$ ctest -V -R "^_test" - -# To build a class test, call the respective make target in ./source/TEST: - -$ make _test -``` -To run a TOPP test, use: - -```bash - -$ ctest -V -R "TOPP_" -``` - -To build the tool, use: - -```bash -$ make -``` -### How do I debug uncaught exceptions? - -Dump a core if an uncaught exception occurs, by setting the environment variable `OPENMS_DUMP_CORE`. - -Each time an uncaught exception occurs, the `OPENMS_DUMP_CORE` variable is checked and a segmentation fault is caused, -if it is set. - -### (Linux) How can I set breakpoints in gdb to debug OpenMS? - -Debug the TOPPView application to stop at line 341 of SpectrumMDIWindow.C. - -1. Enter the following in your terminal: - - ```bash - Run gdb: - shell> gdb TOPPView -``` - -2. Start the application (and close it): - - ```bash - gdb> run [arguments] -``` - -3. Set the breakpoint: - ```bash - gdb> break SpectrumMDIWindow.C:341 -``` - -4. Start the application again (with the same arguments): - - ```bash - gdb> run - ``` - -## Doxygen Documentation - -### Where can I find the definition of the main page? - -Find a definition of the main page [here](https://github.com/OpenMS/OpenMS/edit/develop/doc/doxygen/public/Main.doxygen). - -### Where can I add a new module? - -Add a new module [here](https://github.com/OpenMS/OpenMS/edit/develop/doc/doxygen/public/Modules.doxygen). - - -### How is the command line documentation for TOPP tools created? - -The program `OpenMS/doc/doxygen/parameters/TOPPDocumenter.cpp` creates the command line documentation for all classes -that are included in the static `ToolHandler.cpp` tools list. It can be included in the documentation using -the following `doxygen` command: - -`@verbinclude TOPP_.cli` - -Test if everything worked by calling `make doc_param_internal`. The command line documentation is written to -`OpenMS/doc/doxygen/parameters/output/`. - -### What are the important files for adding a new tutorial section? - -View the following OpenMS tutorials: - -* `OpenMS/doc/OpenMS_tutorial/refman_overwrite.tex.in` (for PDF tutorials) -* `OpenMS/doc/doxygen/public/OpenMS_Tutorial_html.doxygen` (for html tutorials) - -View the following TOPP and TOPPView tutorials: - -* `OpenMS/doc/TOPP_tutorial/refman_overwrite.tex.in` (for PDF tutorials) -* `OpenMS/doc/doxygen/public/TOPP_Tutorial_html.doxygen` (for html tutorials) - -## Bug Fixes - -### How do I contribute to a bug fix? - -To contribute to a bug fix: - -1. Submit the bug as a GitHub issue. -2. Create a feature branch (e.g. `feature/fix_missing_filename_issue_615`) from your (up-to-date) - develop branch in your fork of OpenMS. -3. Fix the bug and add a test. -4. Create a pull request for your branch. -5. After approval and merge make sure the issue is closed. diff --git a/docs/run-workflows-with-openms-tools/knime/workflows.md b/docs/run-workflows-with-openms-tools/knime/workflows.md deleted file mode 100644 index b1616f37..00000000 --- a/docs/run-workflows-with-openms-tools/knime/workflows.md +++ /dev/null @@ -1,7 +0,0 @@ -# Ready-made OpenMS KNIME workflows - -You can get ready-made KNIME workflows and workflow components with OpenMS nodes from our -[community hub](https://hub.knime.com/openms-team). -You can easily drag-and-drop workflows into your opened KNIME analytics platform. -For more, e.g., thirdparty OpenMS workflows use the search bar on the hub and [search for -"openms"](https://hub.knime.com/search?q=openms). diff --git a/docs/run-workflows-with-openms-tools/nextflow/installation.md b/docs/run-workflows-with-openms-tools/nextflow/installation.md deleted file mode 100644 index 31108046..00000000 --- a/docs/run-workflows-with-openms-tools/nextflow/installation.md +++ /dev/null @@ -1,3 +0,0 @@ -# Installation of nextflow and nf-core - -under construction \ No newline at end of file diff --git a/docs/run-workflows-with-openms-tools/nextflow/tutorial.md b/docs/run-workflows-with-openms-tools/nextflow/tutorial.md deleted file mode 100644 index 694c6e29..00000000 --- a/docs/run-workflows-with-openms-tools/nextflow/tutorial.md +++ /dev/null @@ -1,3 +0,0 @@ -# Tutorial - -under construction \ No newline at end of file diff --git a/docs/run-workflows-with-openms-tools/nextflow/workflows.md b/docs/run-workflows-with-openms-tools/nextflow/workflows.md deleted file mode 100644 index ebe3ab71..00000000 --- a/docs/run-workflows-with-openms-tools/nextflow/workflows.md +++ /dev/null @@ -1,18 +0,0 @@ -# Ready-made OpenMS nextflow workflows - -**SCALABLE [NF-CORE](https://nf-co.re/) COMPATIBLE [NEXTFLOW](https://nextflow.io/) PIPELINES** - -Click on “Launch” to configure the pipeline for your data online and launch it via nextflow’s [tower app](https://tower.nf/) (by registering a compute environment there) or by copying a configuration token for your local computer or HPC head node. - -*** - - -Launch "https://nf-co.re/launch?pipeline=quantms" -*** - - -Launch "https://nf-co.re/launch?pipeline=mhcquant" -*** - - -Launch "https://nf-co.re/launch?pipeline=diaproteomics" diff --git a/docs/run-workflows-with-openms-tools/openms-in-knime.md b/docs/run-workflows-with-openms-tools/openms-in-knime.md deleted file mode 100644 index 59b8c0e2..00000000 --- a/docs/run-workflows-with-openms-tools/openms-in-knime.md +++ /dev/null @@ -1,15 +0,0 @@ -OpenMS in KNIME -=============== - -KNIME is a free graphical application for creating and executing workflows. OpenMS provides a plugin, which exposes TOPP tools to the KNIME interface. - - -```{toctree} ---- -maxdepth: 1 ---- - -knime/installation.md -knime/workflows.md -OpenMS KNIME User Tutorial <../tutorials-and-quickstart-guides/openms-user-tutorial.md> -``` \ No newline at end of file diff --git a/docs/run-workflows-with-openms-tools/openms-in-nextflow.md b/docs/run-workflows-with-openms-tools/openms-in-nextflow.md deleted file mode 100644 index a62a1b8a..00000000 --- a/docs/run-workflows-with-openms-tools/openms-in-nextflow.md +++ /dev/null @@ -1,11 +0,0 @@ -OpenMS in Nextflow -================== - -This page is under construction. - -```{toctree} - -nextflow/installation.md -nextflow/workflows.md -nextflow/tutorial.md -``` \ No newline at end of file diff --git a/docs/run-workflows-with-openms-tools/recommended-workflow-systems.md b/docs/run-workflows-with-openms-tools/recommended-workflow-systems.md deleted file mode 100644 index 59f06cf4..00000000 --- a/docs/run-workflows-with-openms-tools/recommended-workflow-systems.md +++ /dev/null @@ -1,102 +0,0 @@ -# Recommended Workflow Systems - -Which workflow environment to choose for running OpenMS tools? - -You can run OpenMS TOPP tools from the command line using your custom scripts, or use powerful -workflow systems designed to make workflow creation and maintenance more fun: - -:::{card} KNIME -:img-top: /images/introduction/KNIMELogoTM.svg -:link: openms-in-knime -:link-type: doc -:class-card: only-light -:class-img-top: halfwidth - -Free, open source, desktop app. An analytics platform with workflow editor and a nice drag-and-drop user interface. -Very interactive and has built-in nodes for related tasks like working with chemical structures, databases, machine learning, scripting. Distributed computing is best achieved with a KNIME server (License required) which also allows user management -and a web interface to interact with workflows. In KNIME you can easily construct your own workflows or just download our -ready-made creations for the most common analysis tasks. - -::: - -:::{card} KNIME -:img-top: /images/introduction/KNIMELogoTM_white.svg -:link: openms-in-knime -:link-type: doc -:class-card: only-dark -:class-img-top: halfwidth - -Free, open source, desktop app. An analytics platform with workflow editor and a nice drag-and-drop user interface. -Very interactive and has built-in nodes for related tasks like working with chemical structures, databases, machine learning, scripting. Distributed computing is best achieved with a KNIME server (License required) which also allows user management -and a web interface to interact with workflows. In KNIME you can easily construct your own workflows or just download our -ready-made creations for the most common analysis tasks. - -::: - -:::{card} Nextflow -:img-top: /images/introduction/nextflow2014_no-bg.png -:link: openms-in-nextflow -:link-type: doc -:class-card: only-light -:class-img-top: halfwidth - -Script/DSL-based workflow language, executor and utilities (such as the browser based launcher and supervisor nf-tower). Automatically runs on various different cloud (AWS, Google, ...) and HPC environments (SLURM, LFS, Kubernetes, ...). It is recommended to use our ready-made nf-core compatible workflows for ease of use via the browser-based configuration and launcher. - -::: - -:::{card} Nextflow -:img-top: /images/introduction/nextflow2014_no-bg-bright.png -:link: openms-in-nextflow -:link-type: doc -:class-card: only-dark -:class-img-top: halfwidth - -Script/DSL-based workflow language, executor and utilities (such as the browser based launcher and supervisor nf-tower). Automatically runs on various different cloud (AWS, Google, ...) and HPC environments (SLURM, LFS, Kubernetes, ...). It is recommended to use our ready-made nf-core compatible workflows for ease of use via the browser-based configuration and launcher. - -::: - -:::{card} Galaxy -:img-top: /images/introduction/galaxy_project_logo.png -:link: openms-on-galaxy -:link-type: doc -:class-card: only-light -:class-img-top: halfwidth - -Server and browser-based interactive workflow editor and runner. A public server instance can be used for testing and smaller experiments. Provides nice guided tutorials. - -::: - -:::{card} Galaxy -:img-top: /images/introduction/galaxy_project_logo_white.png -:link: openms-on-galaxy -:link-type: doc -:class-card: only-dark -:class-img-top: halfwidth - -Server and browser-based interactive workflow editor and runner. A public server instance can be used for testing and smaller experiments. Provides nice guided tutorials. - - -::: - -:::{card} TOPPAS -:img-top: /images/introduction/TOPPAS_logo_white.png -:link: openms-in-toppas -:link-type: doc -:class-card: only-light -:class-img-top: halfwidth - -OpenMS' build-in workflow system, with limited capabilities but easy to use and tailored to TOPP tools. - -::: - -:::{card} TOPPAS -:img-top: /images/introduction/TOPPAS_logo_dark.png -:link: openms-in-toppas -:link-type: doc -:class-card: only-dark -:class-img-top: halfwidth - -OpenMS' build-in workflow system, with limited capabilities but easy to use and tailored to TOPP tools. - - -::: diff --git a/docs/topp-and-utils/proteomicslfq.md b/docs/topp-and-utils/proteomicslfq.md deleted file mode 100644 index c8280b88..00000000 --- a/docs/topp-and-utils/proteomicslfq.md +++ /dev/null @@ -1,51 +0,0 @@ ---- -orphan: true ---- -ProteomicsLFQ -============= - -ProteomicsLFQ performs label-free quantification of peptides and proteins. - -## Input - -- Spectra in mzML format -- Identifications in idXML or mzIdentML format with posterior error probabilities as score type. To generate those we - suggest to run: - - PeptideIndexer to annotate target and decoy information. - - PSMFeatureExtractor to annotate percolator features. - - PercolatorAdapter tool (score_type = 'q-value', -post-processing-tdc). - - IDFilter (pep:score = 0.01) to filter PSMs at 1% FDR. -- An experimental design file: - - ```{seealso} - [Experimental Design](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/classOpenMS_1_1ExperimentalDesign.html) for details. - ``` -- A protein database in with appended decoy sequences in FASTA format. - (e.g., generated by the [OpenMS DecoyDatabase tool](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/namespaceOpenMS.html)). - Processing: - ProteomicsLFQ has different methods to extract features: ID-based (targeted only), or both ID-based and untargeted. - - The first method uses targeted feature dectection using RT and m/z information derived from identification data to - extract features. - ```{note} - Only identifications found in a particular MS run are used to extract features in the same run. No transfer of IDs - (match between runs) is performed. - ``` - - The second method adds untargeted feature detection to obtain quantities from unidentified features. Transfer of Ids - (match between runs) is performed by transfering feature identifications to coeluting, unidentified features with - similar mass and RT in other runs. - -## Requantification - -Optionally, a requantification step is performed that tries to fill NA values. If a peptide has been quantified in -more than half of all maps, the peptide is selected for requantification. In that case, the mean observed RT -(and theoretical m/z) of the peptide is used to perform a second round of targeted extraction. - -## Output - -Output format are as follows: - -- mzTab file with analysis results. -- MSstats file with analysis results for statistical downstream analysis in MSstats. -- ConsensusXML file for visualization and further processing in OpenMS. - -Potential scripts to perform the search can be found under `src/tests/topp/ProteomicsLFQTestScripts`. diff --git a/docs/topp-and-utils/swathwizard.md b/docs/topp-and-utils/swathwizard.md deleted file mode 100644 index 9c049dd8..00000000 --- a/docs/topp-and-utils/swathwizard.md +++ /dev/null @@ -1,27 +0,0 @@ ---- -orphan: true ---- -SwathWizard -========== - -An assistant for Swath analysis. - -The Wizard takes the user through the whole analysis pipeline for SWATH proteomics data analysis, i.e. the -[TOPP Documentation: OpenSwathWorkflow](https://openms.readthedocs.io/en/latest/tutorials-and-quickstart-guides/openms-user-tutorial.html#id44) tool, including downstream tools such as [GitHub:PyProphet/pyProphet](https://github.com/PyProphet/pyprophet) and the [GitHub:msproteomicstools/TRIC alignment](https://github.com/msproteomicstools/msproteomicstools) tool. - -Since the downstream tools require Python and the respective modules, the Wizard will check their proper installation -status and warn the user if a component is missing. - -Users can enter the required input data (mzML MS/MS data, configuration files) in dedicated fields, usually by drag and -droping files from the operating systems' file explorer (Explorer, Nautilus, Finder...). The output of the Wizard is -both the intermediate files from OpenSWATH (e.g. the XIC data in `.sqMass` format) and the tab-separated table format -(`.tsv`) from pyProphet and TRIC. - -This is how the wizard looks like: - -![SwathWizard](/images/topp/SwathWizard.png) - -A schematic of the internal data flow (all tools are called by SwathWizard in the background) can be found in the -[TOPP Documentation: SwathWizard](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Documentation/nightly/html/TOPP_SwathWizard.html). - -A recommended test data for the Wizard is the [PASS00779](https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/PASS_View?identifier=PASS00779) dataset. diff --git a/docs/topp-command-line-tools/consensus-peptide-identification.md b/docs/topp-command-line-tools/consensus-peptide-identification.md deleted file mode 100644 index b34f1055..00000000 --- a/docs/topp-command-line-tools/consensus-peptide-identification.md +++ /dev/null @@ -1,25 +0,0 @@ ---- -orphan: true ---- -Consensus Peptide Identification -=============================== - -In order to compute a consensus identification for a HPLC-MS\MS experiment use several identification engines. - -OpenMS offers adapters for the following commercial and free peptide identification engines: Sequest, Mascot, -PepNovo, XTandem and Inspect. The adapters allow setting the input parameters and data for the identification engine -and return the result in the OpenMS idXML format. - -In order to improve the identification accuracy, several identification engines can be used and a consensus -identification can be calculated from the results. The image below shows an example where Mascot results are -fed to the **ConsensusID** tool (ConsensusID is currently usable for Mascot and XTandem). - -![TOPP Consensus ID](/images/tutorials/topp/TOPP_consensus_id.png) - - -To combine quantitation and identification results: - -Protein/peptide identifications can be annotated to quantitation results (featureXML, consensusXML) by the `IDMapper` -tool. The combined results can then be exported by the **TextExporter** tool: - -[Conversion between OpenMS XML formats and text formats](conversion-between-openms-xml-formats-and-text-formats.md). diff --git a/docs/topp-command-line-tools/conversion-between-openms-xml-formats-and-text-formats.md b/docs/topp-command-line-tools/conversion-between-openms-xml-formats-and-text-formats.md deleted file mode 100644 index e325f623..00000000 --- a/docs/topp-command-line-tools/conversion-between-openms-xml-formats-and-text-formats.md +++ /dev/null @@ -1,67 +0,0 @@ ---- -orphan: true ---- -Conversion Between OpenMS XML Formats and Text Formats -===================================================== - -## Export of OpenMS XML formats - -As TOPP offers no functionality for statistical analysis, this step is normally done using external statistics packages. -In order to export the OpenMS XML formats into an appropriate format for these packages the TOPP **TextExporter** can be -used. - -It converts the the following OpenMS XML formats to text files: - -- featureXML -- idXML -- consensusXML - -The use of the `TextExporter` is is very simple: - -`TextExporter -in infile.idXML -out outfile.txt` - -## Import of feature data to OpenMS - -OpenMS offers a lot of visualization and analysis functionality for feature data. -Feature data in text format, e.g. from other analysis tools, can be imported using the `TextImporter`. The default -mode accepts comma separated values containing the following columns: RT, m/z, intensity. Additionally meta data -columns may follow. If meta data is used, meta data column names have to be specified in a header line. Without headers: - -```bash -1201 503.123 1435000 -1201 1006.246 1235200 -``` - -Or with headers: - -```bash -RT m/z Int isHeavy myMeta -1201 503.123 1435000 true 2 -1201 1006.246 1235200 maybe 1 -``` - -Example invocation: - -`TextImporter -in infile.txt -out outfile.featureXML` - -The tool also supports data from msInspect,SpecArray and Kroenik(Hardkloer sibling), just specify the `-mode` option -accordingly. - -## Import of protein/peptide identification data to OpenMS - -Peptide/protein identification data from several identification engines can be converted to idXML format using the -**IDFileConverter** tool. - -It can currently read the following formats: -- Sequest output folder -- pepXML file -- idXML file - -It can currently write the following formats: - -- pepXML -- idXML - -This example shows how to convert pepXML to idXML: - -`IDFileConverter -in infile.pepXML -out outfile.idXML` diff --git a/docs/tutorials-and-quickstart-guides/guides/user-quickstart-guide.md b/docs/tutorials-and-quickstart-guides/guides/user-quickstart-guide.md deleted file mode 100644 index a382a851..00000000 --- a/docs/tutorials-and-quickstart-guides/guides/user-quickstart-guide.md +++ /dev/null @@ -1,65 +0,0 @@ -User Quickstart Guide -==================== - -Read the User Quickstart guide to gain a brief understanding of key concepts and how to use the tools. For more in-depth -information, consult [OpenMS API Reference](https://openms.de/current_doxygen/). - -## What is OpenMS - -[OpenMS](https://www.openms.de/) is a free, open-source C++ library with Python bindings. It is commonly used for liquid -chromatography-mass spectrometry ({term}`LC-MS`) data management and analyses. OpenMS provides an infrastructure for the rapid -development of mass spectrometry related software as well as a rich toolset built on top of it. OpenMS is available -under the [three clause BSD licence](https://github.com/OpenMS/OpenMS/blob/develop/LICENSE) and runs under Windows, macOS, and Linux operating systems. - -## Background - -Before using OpenMS, become familiar with the following terms: - -| Tool and Utilities | Description | -|--------------------|-------------| -|**TOPPView** |A tool that is used to view and explore {term}`LC-MS` data, alignments, groups, peptide identifications, and more.| -|**TOPPAS** |A graphical workflow design tool that is used to create pipelines from all {term}`TOPP tools`.| -|**TOPP tools** |A set of command line tools. Each of these command line tools is a building block of an analysis pipeline and are chained together in a way that fits the requirements of the user. The {term}`TOPP tools` are accessible from a command prompt/shell or via {term}`TOPPAS`. | - - -## How to run a Tool - -In general TOPP tools are invoked via the command line, directly or indirectly (e.g. via worflow systems such as {term}`KNIME`, {term}`Nextflow`, snakemake, or {term}`TOPPAS`). - -## Adapt pipeline parameters - -The default parameters of each tool can usually be tweaked to fit the data and improve results. -Here, we describe how to work with TOPP tools on a command line (irrespective of the operating system) or using TOPPAS workflow system, which is shipped with OpenMS. -For [external workflow systems](../../run-workflows-with-openms-tools/recommended-workflow-systems.md), please refer to their documentation. - - -### Where do you change pipeline parameters? - -1. **TOPPAS**: Double-click the node of which you want to change the parameters of. A short docu for each parameter will - show up once it is selected. All parameters which would be available on the command line and in the INI - file are shown here as well. -2. **Command line**: Very basic parameters can be set on the command line, e.g. `FileFilter -rt 1000:2000 .....` -3. Passing all parameters via commandline would create a very long list, thus, use so-called `.ini` files to provide full parameter - sets to {term}`TOPP tools`. If no INI file is given, default parameters are used. To get a default `.ini` use - - ` -write_ini ` - - e.g. `FileFilter -write_ini filefilter.ini` - - Now, edit the INI file (which is a XML file) using the [INIFileEditor](../../openms-applications-and-tools/openms-applications/ini-file-editor.md), which is another GUI tool shipped with - OpenMS and similar to the one build into {term}`TOPPAS`. - -### How do I feed the INI file to a Tool? - -1. **TOPPAS**: Once you changed the parameters of a node and clicked **Ok**, the parameters are in effect. Because - they are part of the {term}`TOPPAS` workflow, they are saved together with the workflow. -2. **Command line** : Supply the INI file via the `-ini` flag, - ` -ini ` - - e.g. `FileFilter -ini filefilter.ini` - -### What parameters to set and to what value? - -The answer is complex, in general, read the tool description, change the parameters and compare the results using -{term}`TOPPView` if possible. If that does not help, [contact us](/quick-reference/contact-us.md). Please include all the necessary -details we need in order to help you. diff --git a/docs/tutorials-and-quickstart-guides/openms-user-tutorial.md b/docs/tutorials-and-quickstart-guides/openms-user-tutorial.md index d0a8fa48..65145b97 100644 --- a/docs/tutorials-and-quickstart-guides/openms-user-tutorial.md +++ b/docs/tutorials-and-quickstart-guides/openms-user-tutorial.md @@ -1,3 +1,7 @@ +--- +orphan: true +--- + # OpenMS KNIME User Tutorial ## General Remarks @@ -26,7 +30,7 @@ If you are doing this tutorial online, choose online in the following tab(s). If you are working through this tutorial at home/online, proceed with the following steps: -- Download and install OpenMS using the installation instructions for the [OpenMS tools](/openms-applications-and-tools/installation.md). +- Download and install OpenMS using the installation instructions for the [OpenMS tools](/about/installation.rst). :::{note} To install the graphical application, please use the downloadable installer for your platform, not conda, nor docker. @@ -65,7 +69,7 @@ On Linux: ```{note} You can also install OpenMS via your package manager (version availability not guaranteed) or build it on your own with our - [build instructions](/openms-applications-and-tools/installation/installation-on-gnu-linux.md#build-openms-from-source). + [build instructions](/about/installation/installation-on-gnu-linux.md#build-openms-from-source). ``` :::: @@ -94,7 +98,7 @@ To convert raw data to mzML using `ProteoWizard` you can either use MSConvertGUI graphical user interface) or `msconvert` (a simple command line tool). (Figure_1)= -|![msconvertgui](/images/openms-user-tutorial/introduction/proteowizard.png)| +|![msconvertgui](/_images/openms-user-tutorial/introduction/proteowizard.png)| |:--:| |Figure 1: `MSConvertGUI` (part of `ProteoWizard`), allows converting raw files to mzML. Select the raw files you want to convert by clicking on the browse button and then on Add. Default parameters can usually be kept as-is. To reduce the initial data size, make sure that the `peakPicking` filter (converts profile data to centroided data (see Fig. 2)) is listed, enabled (true) and applied to all MS levels (parameter ”1-”). Start the conversion process by clicking on the Start button.| @@ -118,7 +122,7 @@ msconvert raw_data_file.RAW --filter "peakPicking true 1-" in your command line. (Figure_2)= -|![profile centroided](/images/openms-user-tutorial/introduction/profilecentroided.png)| +|![profile centroided](/_images/openms-user-tutorial/introduction/profilecentroided.png)| |:--:| |Figure 2: The amount of data in a spectra is reduced by peak picking. Here a profile spectrum (blue) is converted to centroided data (green). Most algorithms from this point on will work with centroided data.| @@ -146,11 +150,11 @@ KNIME see A minimal workflow. Visualizing the data is the first step in quality control, an essential tool in understanding the data, and of course an essential step in pipeline development. OpenMS provides a convenient viewer for some of the data: TOPPView. We will guide you through some of the basic features of TOPPView. Please familiarize yourself with the key controls and visualization methods. We will make use of these later throughout the tutorial. Let’s start with a first look at one of the files of our tutorial data set. Note that conceptually, there are no differences in visualizing metabolomic or proteomic data. Here, we inspect a simple proteomic measurement: -|![TOPPView](/images/openms-user-tutorial/introduction/TOPPView.png)| +|![TOPPView](/_images/openms-user-tutorial/introduction/TOPPView.png)| |:--:| |Figure 3: TOPPView, the graphical application for viewing mass spectra and analysis results. Top window shows a small region of a peak map. In this 2D representation of the measured spectra, signals of eluting peptides are colored according to the raw peak intensities. The lower window displays an extracted spectrum (=scan) from the peak map. On the right side, the list of spectra can be browsed.| -|![TOPPView](/images/openms-user-tutorial/introduction/3dview.png)| +|![TOPPView](/_images/openms-user-tutorial/introduction/3dview.png)| |:--:| |Figure 4: 3D representation of the measured spectra, signals of eluting peptides are colored according to the raw peak intensities.| @@ -197,7 +201,7 @@ our tutorial data set. Note that conceptually, there are no differences in visua Dependent on your data MS/MS spectra can be visualized as well (see Fig.5) . You can do so, by double-click on the MS/MS spectrum shown in scan view -|![ms2 spectrum](/images/openms-user-tutorial/introduction/ms2_introduction.png)| +|![ms2 spectrum](/_images/openms-user-tutorial/introduction/ms2_introduction.png)| |:--:| |Figure 5: MS/MS spectrum| @@ -216,7 +220,7 @@ concepts will also be reviewed in this tutorial. Since version 5.0 KNIME has a new updated user interface. For the purposes of this tutorial we will continue to use the "classic user interface". Depending on your OS KNIME may have started automatically in the Modern UI, which looks like the following: -|![ms2 spectrum](/images/openms-user-tutorial/introduction/KNIME_switch_to_classic.png)| +|![ms2 spectrum](/_images/openms-user-tutorial/introduction/KNIME_switch_to_classic.png)| |:--:| |Figure 5.5: The modern KNIME UI. To switch back to the classic UI, select "Menu" and click "Switch to classic user interface"| @@ -388,7 +392,7 @@ this case, or if you want to customize the default configuration in general, you node with a double-click on the node. For all OpenMS nodes you will see a configuration dialog like the one shown in below figure. -|![Node configuration dialog of an OpenMS node](/images/openms-user-tutorial/knime-setup/knime_configure_dialog.png)| +|![Node configuration dialog of an OpenMS node](/_images/openms-user-tutorial/knime-setup/knime_configure_dialog.png)| |:--:| |Figure 6: Node configuration dialog of an OpenMS node| @@ -409,7 +413,7 @@ nodes connected to this port. #### Overview of the graphical user interface -|![The KNIME workbench](/images/openms-user-tutorial/knime-setup/knime_workbench_marked.png)| +|![The KNIME workbench](/_images/openms-user-tutorial/knime-setup/knime_workbench_marked.png)| |:--:| |Figure 7: The KNIME workbench| @@ -478,7 +482,7 @@ Those *knwf* files can again be imported by selecting: **File** > **Import KNIME ```{note} For your convenience we added all workflows discussed in this tutorial to the **Workflows** folder on the USB Stick. - Additionally, the workflow files can be found on workflow downloads. If you want to check + Additionally, the workflow files can be found on workflow downloads. If you want to check your own workflow by comparing it to the solution or got stuck, simply import the full workflow from the corresponding *knwf* file and after that double-click it in your KNIME Workflow repository to open it. ``` @@ -517,7 +521,7 @@ name and what kind of input it expects. ``` The complete workflow is shown in below image. `FileInfo` can produce two different kinds of output files. -|![A minimal workflow calling FileInfo on a single file.](/images/openms-user-tutorial/knime-setup/minimal_FileInfo.png)| +|![A minimal workflow calling FileInfo on a single file.](/_images/openms-user-tutorial/knime-setup/minimal_FileInfo.png)| |:--:| |Figure 8: A minimal workflow calling `FileInfo` on a single file.| @@ -556,7 +560,7 @@ same information on three different files and then write the output files to a f The complete workflow is shown in the top right of the figure below. -|![A minimal workflow calling the FileFilter on multiple mzML files in a loop](/images/openms-user-tutorial/knime-setup/KNIME_annotated_FileFilter.png)| +|![A minimal workflow calling the FileFilter on multiple mzML files in a loop](/_images/openms-user-tutorial/knime-setup/KNIME_annotated_FileFilter.png)| |:--:| |Figure 9: The FileFilter workflow. Showing the configure dialog for `FileFilter`, and the level selector pane. @@ -617,7 +621,7 @@ Chemical structures are often represented as SMILES (**S**implified **m**olecula identification will report SMILES as part of their result, which can then be further processed and visualized using RDKit and KNIME. -|![ Workflow to visualize a list of SMILES strings and filter them by predefined substructures](/images/openms-user-tutorial/metabo/structures_filter_workflow.png)| +|![ Workflow to visualize a list of SMILES strings and filter them by predefined substructures](/_images/openms-user-tutorial/metabo/structures_filter_workflow.png)| |:--:| |Figure 10: Workflow to visualize a list of SMILES strings and filter them by predefined substructures| @@ -633,7 +637,7 @@ list of SMILES strings and filter them by predefined substructures: column type from **string** to **smiles** and close the dialog with **Ok**. Afterwards the **SMILES** column will be visualized as chemical structures instead of text directly within all **KNIME** tables. -|![ Workflow to visualize a list of SMILES strings and filter them by predefined substructures](/images/openms-user-tutorial/metabo/KNIME_SMILES_type.png)| +|![ Workflow to visualize a list of SMILES strings and filter them by predefined substructures](/_images/openms-user-tutorial/metabo/KNIME_SMILES_type.png)| |:--:| |Figure 10.5: To change the column type for the `File Reader` select **Transformation** and then **type** for the SMILES column| @@ -650,7 +654,7 @@ list of SMILES strings and filter them by predefined substructures: The following figure shows resulting list of compounds that contains at least one aromatic carboxylic acid group. -|![Resulting list of compounds that contains at least one aromatic carboxylic acid group](/images/openms-user-tutorial/metabo/structures_filter_results.png)| +|![Resulting list of compounds that contains at least one aromatic carboxylic acid group](/_images/openms-user-tutorial/metabo/structures_filter_results.png)| |:--:| |Figure 11: Resulting list of compounds that contains at least one aromatic carboxylic acid group.| --> @@ -758,7 +762,7 @@ FDR of < 1 %. The below images shows Comet ID pipeline including FDR filtering. -|![Comet ID pipeline including FDR filtering](/images/openms-user-tutorial/labelfree/PepIdFDR.png)| +|![Comet ID pipeline including FDR filtering](/_images/openms-user-tutorial/labelfree/PepIdFDR.png)| |:--:| |Figure 12: Comet ID pipeline including FDR filtering| @@ -794,7 +798,7 @@ probabilities (PEP) and a combination of the normalized scores by considering mi In the end, the ID processing part of the workflow can be collapsed into a Metanode to keep the structure clean (see below figure which shows complete consensus identification workflow). -|![Complete consensus identification workflow](/images/openms-user-tutorial/labelfree/PepConsensusId.png)| +|![Complete consensus identification workflow](/_images/openms-user-tutorial/labelfree/PepConsensusId.png)| |:--:| |Figure 13: Complete consensus identification workflow| @@ -816,7 +820,7 @@ Now that we have successfully constructed a peptide identification pipeline, we this end, open the featureXML file in TOPPView by clicking on File Open file and add it to a new layer ( Open in New layer ). The features are now visualized on top of your raw data. If you zoom in on a small region, you should be able to see the individual boxes around features that have been detected (see Fig. 14). If you hover over the the feature centroid (small circle indicating the chromatographic apex of monoisotopic trace) additional information of the feature is displayed. - |![Visualization of detected features (boxes) in TOPPView](/images/openms-user-tutorial/labelfree/featureXML.png)| + |![Visualization of detected features (boxes) in TOPPView](/_images/openms-user-tutorial/labelfree/featureXML.png)| |:--:| |Figure 14: Visualization of detected features (boxes) in TOPPView| @@ -832,7 +836,7 @@ Now that we have successfully constructed a peptide identification pipeline, we The following image shows the final constructed workflow: -| ![Extended workflow featuring peptide identification and quantification](/images/openms-user-tutorial/labelfree/PepQuantIdNoAlign.png) | +| ![Extended workflow featuring peptide identification and quantification](/_images/openms-user-tutorial/labelfree/PepQuantIdNoAlign.png) | |:--------------------------------------------------------------------------------------------------------------------------------------:| | Figure 15: Extended workflow featuring peptide identification and feature mapping. | @@ -842,7 +846,7 @@ So far, we successfully performed peptide identification as well as feature mapp individual LC-MS runs. For differential label-free analyses, however, we need to identify and map corresponding signals in different experiments and link them together to compare their intensities. Thus, we will now run our pipeline on all three available input files and extend it a bit further, so that it is able to find and link features across several runs. -| ![Complete identification and label-free quantification workflow](/images/openms-user-tutorial/labelfree/PepQuantId.png) | +| ![Complete identification and label-free quantification workflow](/_images/openms-user-tutorial/labelfree/PepQuantId.png) | |:-----------------------------------------------------------------------------------------------------------------------------------------:| | Figure 16: Complete identification and label-free feature mapping workflow. The identification nodes are grouped together as ID metanode. | @@ -883,7 +887,7 @@ In this section we are going to use the output of the `ConsensusTextReader` for You have now constructed an entire identification and label-free feature mapping workflow including a simple data analysis using KNIME. The final workflow should like the workflow shown in the following image: -|![Simple KNIME data analysis example for LFQ](/images/openms-user-tutorial/labelfree/data_analysis.png)| +|![Simple KNIME data analysis example for LFQ](/_images/openms-user-tutorial/labelfree/data_analysis.png)| |:--:| |Figure 17: Simple KNIME data analysis example for LFQ| @@ -939,11 +943,11 @@ FDR are. Good results should show a close correspondence between these two measu The calculation is done by using a simple R script in R snippet. First, the target decoy protein FDR is computed as the proportion of decoy proteins among all significant protein IDs. Then posterior probabilistic-driven FDR is estimated by the average of the posterior error probability of all significant protein IDs. Since FDR is the property for a group of protein IDs, we can also calculate a local property for each protein: the q-value of a certain protein ID is the minimum FDR of any groups of protein IDs that contain this protein ID. We plot the protein ID results versus two different kinds of FDR estimates in R View(Table) (see Fig. 22). (Figure_21)= -|![The workflow of statistical analysis of protein inference results](/images/openms-user-tutorial/protein-inference/inference_metanode.png)| +|![The workflow of statistical analysis of protein inference results](/_images/openms-user-tutorial/protein-inference/inference_metanode.png)| |:--:| |Figure 21: The workflow of statistical analysis of protein inference results| (Figure_22)= -|![The pseudo-ROC Curve of protein IDs](/images/openms-user-tutorial/protein-inference/proteinFDR.png)| +|![The pseudo-ROC Curve of protein IDs](/_images/openms-user-tutorial/protein-inference/proteinFDR.png)| |:--:| |Figure 22: The pseudo-ROC Curve of protein IDs. The accumulated number of protein IDs is plotted on two kinds of scales: target-decoy protein FDR and Fido posterior probability estimated FDR. The largest value of posterior probability estimated FDR is already smaller than 0.04, this is because the posterior probability output from Fido is generally very high| @@ -1054,7 +1058,7 @@ quantities [fmols] #### Identification and quantification -|![KNIME data analysis of iPRG LFQ data](/images/openms-user-tutorial/labelfree/iPRG_lfq.png)| +|![KNIME data analysis of iPRG LFQ data](/_images/openms-user-tutorial/labelfree/iPRG_lfq.png)| |:--:| |Figure 18: KNIME data analysis of iPRG LFQ data.| @@ -1168,7 +1172,7 @@ Adjust the parameters in the config dialog of the converter to match the given e The downstream analysis of the peptide ions with `MSstats` is performed in several steps. These steps are reflected by several KNIME R nodes, which consume the output of `MSStatsConverter`. The outline of the workflow is shown in Figure 19. -|![MSstats analysis using KNIME](/images/openms-user-tutorial/labelfree/MSstats.png)| +|![MSstats analysis using KNIME](/_images/openms-user-tutorial/labelfree/MSstats.png)| |:--:| |Figure 19: MSstats analysis using KNIME. The individual steps (Preprocessing, Group Comparisons, Result Data Renaming, and Export) are split among several consecutive nodes.| @@ -1204,7 +1208,7 @@ In this example, we just state that missing intensity values are represented by The goal of the analysis is the determination of differentially-expressed proteins among the different conditions C1-C4. We can specify the comparisons that we want to make in a *comparison* matrix. For this, let’s consider the following example: -![comparison matrix](/images/openms-user-tutorial/labelfree/handout-clean129x.svg) +![comparison matrix](/_images/openms-user-tutorial/labelfree/handout-clean129x.svg) This matrix has the following properties: @@ -1335,7 +1339,7 @@ knime.out <- test.MSstats.cr.spikedins An excerpt of the main result of the group comparison can be seen in Figure 20. -|![Volcano plots c2_c1](/images/openms-user-tutorial/labelfree/c2_c1-.png) ![Volcano plots c3_c2](/images/openms-user-tutorial/labelfree/c3_c2-.png)| +|![Volcano plots c2_c1](/_images/openms-user-tutorial/labelfree/c2_c1-.png) ![Volcano plots c3_c2](/_images/openms-user-tutorial/labelfree/c3_c2-.png)| |:--:| |Figure 20: Volcano plots produced by the Group Comparison in MSstats The dotted line indicates an adjusted p-value threshold| @@ -1353,7 +1357,7 @@ In this section, we would like to introduce a possible workflow for the analysis Let’s have a look at the workflow (see Fig 23). (Figure_23)= -|![Workflow for the analysis of isobaric data](/images/openms-user-tutorial/isobaric/isobaric_inference_wf.png)| +|![Workflow for the analysis of isobaric data](/_images/openms-user-tutorial/isobaric/isobaric_inference_wf.png)| |:--:| |Figure 23: Workflow for the analysis of isobaric data| @@ -1379,7 +1383,7 @@ There is also an [online lecture](https://youtu.be/3CDnrQxGLbA) and tutorial for We are using the MSV000084264 ground truth dataset, which consists of TMT10plex controlled mixes of different concentrated UPS1 peptides spiked into SILAC HeLa peptides measured in a dilution series https://www.omicsdi.org/dataset/massive/MSV000084264. Figure 24 shows the experimental design. In this experiment, 5 different TMT10plex mixtures – different labeling strategies – were analysed. These were measured in triplicates represented by the 15 MS runs (3 runs each). The example data, database and experimental design to run the workflow can be found [here](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Data/isobaric_MSV000084264/). (Figure_24)= -|![Experimental Design](/images/openms-user-tutorial/isobaric/isobaric_experimental_design.jpeg)| +|![Experimental Design](/_images/openms-user-tutorial/isobaric/isobaric_experimental_design.jpeg)| |:--:| |Figure 24: Experimental Design| @@ -1480,7 +1484,7 @@ After running the worklfow, the `MSStatsConverter` will convert the OpenMS outpu Here, we depict the analysis by `MSstatsTMT` using a segment of the isobaric analysis workflow (Fig. 25). The segment is available as {path}`Workflows,MSstatsTMT.knwf`. (Figure_25)= -|![MSstatsTMT workflow segment](/images/openms-user-tutorial/isobaric/isobaric_msstatstmt_wf.png)| +|![MSstatsTMT workflow segment](/_images/openms-user-tutorial/isobaric/isobaric_msstatstmt_wf.png)| |:--:| |Figure 25: MSstatsTMT workflow segment| @@ -1569,7 +1573,7 @@ groupComparisonPlots(data=data.res.mod, type="VolcanoPlot", address=F, which.Com Here, we have a example output of the **R View**, which depicts the significant regulated UPS proteins in the comparison of 125 to 05 (Fig. 26). (Figure_26)= -|![Volcanoplot of the group comparison regarding 0125 to 05](/images/openms-user-tutorial/isobaric/isobaric_img_output_knime.png)| +|![Volcanoplot of the group comparison regarding 0125 to 05](/_images/openms-user-tutorial/isobaric/isobaric_img_output_knime.png)| |:--:| |Figure 26: Volcanoplot of the group comparison regarding 0125 to 05| @@ -1600,7 +1604,7 @@ have very different isotopic distributions. To group small molecule mass traces - Connect a `Output Folder` to the output of the `FeatureFinderMetabo` (see Fig. 27). (Figure_27)= -|![FeatureFinderMetabo workflow](/images/openms-user-tutorial/metabo/minimal_FFM_wf.png)| +|![FeatureFinderMetabo workflow](/_images/openms-user-tutorial/metabo/minimal_FFM_wf.png)| |:--:| |Figure 27: FeatureFinderMetabo workflow| @@ -1630,22 +1634,22 @@ The output file .featureXML can be visualized with TOPPView on top of the used ` First start TOPPView and open the example `.mzML` file (see Fig. 28). Afterwards open the `.featureXML` output as new layer (see Fig. 29). The overlay is depicted in Figure 30. The zoom of the `.mzML` - `.featureXML` overlay shows the individual mass traces and the assembly of those in a feature (see Fig. 31). (Figure_28)= -|![Opened .mzML in TOPPView](/images/openms-user-tutorial/metabo/ToppView_1.png)| +|![Opened .mzML in TOPPView](/_images/openms-user-tutorial/metabo/ToppView_1.png)| |:--:| |Figure 28: Opened .mzML in TOPPView| (Figure_29)= -|![Add new layer in TOPPView](/images/openms-user-tutorial/metabo/ToppView_2.png)| +|![Add new layer in TOPPView](/_images/openms-user-tutorial/metabo/ToppView_2.png)| |:--:| |Figure 29: Add new layer in TOPPView| (Figure_30)= -|![Overlay of the .mzML layer with the .featureXML layer](/images/openms-user-tutorial/metabo/ToppView_3.png)| +|![Overlay of the .mzML layer with the .featureXML layer](/_images/openms-user-tutorial/metabo/ToppView_3.png)| |:--:| |Figure 30: Overlay of the .mzML layer with the .featureXML layer| (Figure_31)= -|![Zoom of the overlay of the .mzML with the .featureXML layer](/images/openms-user-tutorial/metabo/ToppView_4.png)| +|![Zoom of the overlay of the .mzML with the .featureXML layer](/_images/openms-user-tutorial/metabo/ToppView_4.png)| |:--:| |Figure 31: Zoom of the overlay of the .mzML with the .featureXML layer. Here the individual isotope traces (blue lines) are assembled into a feature here shown as convex hull (rectangular box).| @@ -1654,7 +1658,7 @@ The workflow can be extended for multi-file analysis, here an `Input Files` node To facilitate the collection of features corresponding to the same compound ion across different samples, an alignment of the samples’ feature maps along retention time is often helpful. In addition to local, small-scale elution differences, one can often see constant retention time shifts across large sections between samples. We can use linear transformations to correct for these large scale retention differences. This brings the majority of corresponding compound ions close to each other. Finding the correct corresponding ions is then faster and easier, as we don’t have to search as far around individual features. (Figure_32)= -|![map alignment example](/images/openms-user-tutorial/metabo/align.png)| +|![map alignment example](/_images/openms-user-tutorial/metabo/align.png)| |:--:| |Figure 32: The first feature map is used as a reference to which other maps are aligned. The calculated transformation brings corresponding features into close retention time proximity. Linking of these features form a so-called consensus features of a consensus map.| @@ -1682,7 +1686,7 @@ The parameters change the behavior of `MapAlignerPoseClustering` as follows: The next step after retention time correction is the grouping of corresponding features in multiple samples. In contrast to the previous alignment, we assume no linear relations of features across samples. The used method is tolerant against local swaps in elution order. (Figure_33)= -|![feature linking example](/images/openms-user-tutorial/metabo/link.png)| +|![feature linking example](/_images/openms-user-tutorial/metabo/link.png)| |:--:| |Figure 33: Features A and B correspond to the same analyte. The linking of features between runs (indicated by an arrow) allows comparing feature intensities.| @@ -1705,7 +1709,7 @@ The next step after retention time correction is the grouping of corresponding f - Run the pipeline and inspect the output. (Figure_34)= -|![Label-free quantification workflow for metabolites](/images/openms-user-tutorial/metabo/metabo_part1_with_labels.png)| +|![Label-free quantification workflow for metabolites](/_images/openms-user-tutorial/metabo/metabo_part1_with_labels.png)| |:--:| |Figure 34: Label-free quantification workflow for metabolites.| @@ -1714,7 +1718,7 @@ You should find a single, tab-separated file containing the information on where The `FeatureLinkerUnlabeledQT` output can be visualized in TOPPView on top of the input and output of the `FeatureFinderMetabo` (see Fig 35). (Figure_35)= -|![Label-free quantification workflow for metabolites](/images/openms-user-tutorial/metabo/ToppView_5.png)| +|![Label-free quantification workflow for metabolites](/_images/openms-user-tutorial/metabo/ToppView_5.png)| |:--:| |Figure 35: Visualization of .consensusXML output over the .mzML and .featureXML ’layer’.| @@ -1740,7 +1744,7 @@ At the current state we found several metabolites in the individual maps but so The result of the **AccurateMassSearch** node is in the mzTab format[^17] so you can easily open it in a text editor or import it into Excel or KNIME, which we will do in the next section. The complete workflow from this section is shown in Figure 36. (Figure_36)= -|![Label-free quantification and identification workflow for metabolites](/images/openms-user-tutorial/metabo/metabo_part2.png)| +|![Label-free quantification and identification workflow for metabolites](/_images/openms-user-tutorial/metabo/metabo_part2.png)| |:--:| |Figure 36: Label-free quantification and identification workflow for metabolites.| @@ -1750,7 +1754,7 @@ The result from the TextExporter node as well as the result from the **AccurateM When executed, both nodes will import the OpenMS files and provide access to the data as KNIME tables. The retention time values are exported as a list using the **MzTabReader** based on the current PSI-Standard. This has to be parsed using the **SplitCollectionColumn**, which outputs a ”Split Value 1” based on the first entry in the rention time list, which has to be renamed to retention time using the **ColumnRename**. You can now combine both tables using the **Joiner** node (**Manipulation** > **Column** > **Split & Combine**) and configure it to match the m/z and retention time values of the respective tables. The full workflow is shown in Figure 37. (Figure_37)= -|![Label-free quantification and identification workflow for metabolites that loads the results into KNIME and joins the tables](/images/openms-user-tutorial/metabo/metabo_part3.png)| +|![Label-free quantification and identification workflow for metabolites that loads the results into KNIME and joins the tables](/_images/openms-user-tutorial/metabo/metabo_part3.png)| |:--:| |Figure 37: Label-free quantification and identification workflow for metabolites that loads the results into KNIME and joins the tables.| @@ -1771,7 +1775,7 @@ Options to respect or replace ion charges or adducts allow for example: - More specific feature linking (**FeatureLinkerUnlabeledQT → algorithm → ignore_adduct** set to “false”) (Figure_38)= -|![Metabolite Adduct Decharger adduct grouping workflow](/images/openms-user-tutorial/metabo/mad.png)| +|![Metabolite Adduct Decharger adduct grouping workflow](/_images/openms-user-tutorial/metabo/mad.png)| |:--:| |Figure 38: Metabolite Adduct Decharger adduct grouping workflow. | @@ -1811,7 +1815,7 @@ Construct the workflow as shown in Fig. 39. Use the fil
    (Figure_39)= -|![Spectral library identification workflow](/images/openms-user-tutorial/metabo/speclib.png)| +|![Spectral library identification workflow](/_images/openms-user-tutorial/metabo/speclib.png)| |:--:| |Figure 39: Spectral library identification workflow.| @@ -1833,7 +1837,7 @@ Check in the .xlsx output from the Excel writer (XLS) if you can find glutathion
    (Figure_40)= -|![Tandem spectrum of glutathione. Visualized in TOPPView.](/images/openms-user-tutorial/metabo/glutathioneTV.png)| +|![Tandem spectrum of glutathione. Visualized in TOPPView.](/_images/openms-user-tutorial/metabo/glutathioneTV.png)| |:--:| |Figure 40: Tandem spectrum of glutathione. Visualized in TOPPView.| @@ -1843,7 +1847,7 @@ On the METLIN homepage search for **Name** Glutathione using the **Advanced Sear (Figure_41)= -|![Tandem spectrum of glutathione. Visualized in Metlin. Note that several fragment spectra from varying collision energies are available.](/images/openms-user-tutorial/metabo/glutathioneMetlin.png)| +|![Tandem spectrum of glutathione. Visualized in Metlin. Note that several fragment spectra from varying collision energies are available.](/_images/openms-user-tutorial/metabo/glutathioneMetlin.png)| |:--:| |Figure 41: Tandem spectrum of glutathione. Visualized in Metlin. Note that several fragment spectra from varying collision energies are available.| @@ -1871,7 +1875,7 @@ Use the [file](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/E Below we show an example workflow for de novo identification (Fig. 42). Here, the node `FeatureFinderMetabo` is used for feature detection to annotate analytes in mz, rt, intensity and charge. This is followed by adduct grouping, trying to asses possible adducts based on the feature space using the **MetaboliteAdductDecharger**. In addition, the **HighResPrecursorMassCorrector** can use the newly generated feature information to map MS2 spectra, which were measured on one of the isotope traces to the monoisotopic precursor. This helps with feature mapping and analyte identification in the **SiriusAdapter** due to the usage of additional MS2 spectra that belong to a specific feature. (Figure_42)= -|![De novo identification workflow](/images/openms-user-tutorial/metabo/denovoid.png)| +|![De novo identification workflow](/_images/openms-user-tutorial/metabo/denovoid.png)| |:--:| |Figure 42: *De novo* identification workflow| @@ -1961,7 +1965,7 @@ Even though the basic capabilities for (interactive) plots in KNIME are valuable Following the identification, quantification and statistical analysis our data is merged and formatted for reporting. First we want to discard our normalized and logarithmized intensity values in favor of the original ones. To this end we first remove the intensity columns (`Column Filter`) and add the original intensities back (**Joiner**). For that, we use an Inner Join 2 with the **Joiner** node. In the dialog of the node, we add two entries for the Joining Columns and for the first column we pick `retention_time` from the top input (i.e. the **AccurateMassSearch** output) and `rt_cf` (the retention time of the consensus features) for the bottom input (the result from the quantification). For the second column you should choose `exp_mass_to_charge` and `mz_cf` respectively to make the joining unique. Note that the workflow needs to be executed up to the previous nodes for the possible selections of columns to appear. (Figure_43)= -|![Data preparation for reporting](/images/openms-user-tutorial/metabo/reporting.png)| +|![Data preparation for reporting](/_images/openms-user-tutorial/metabo/reporting.png)| |:--:| |Figure 43: Data preparation for reporting| @@ -2139,7 +2143,7 @@ The main output will be called: {path}`OpenSWATH,result,mProphetxallxpeakgroups. Please note that due to the semi-supervised machine learning approach of mProphet the results differ slightly when mProphet is executed several times. (Figure_44)= -|![OpenSWATH KNIME Workflow.](/images/openms-user-tutorial/openswath/OpenSWATHWF.png)| +|![OpenSWATH KNIME Workflow.](/_images/openms-user-tutorial/openswath/OpenSWATHWF.png)| |:--:| |Figure 44: OpenSWATH KNIME Workflow.| @@ -2161,17 +2165,17 @@ We would like to present an automated DIA/SWATH analysis workflow for metabolomi The workflow follows multiple steps (see Fig. 45). (Figure_45)= -|![DIAMetAlyzer - pipeline for assay library generation and targeted analysis with statistical validation](/images/openms-user-tutorial/openswath/pipeline_overview.png)| +|![DIAMetAlyzer - pipeline for assay library generation and targeted analysis with statistical validation](/_images/openms-user-tutorial/openswath/pipeline_overview.png)| |:--:| |Figure 45: DIAMetAlyzer - pipeline for assay library generation and targeted analysis with statistical validation. DDA data is used for candidate identification containing feature detection, adduct grouping and accurate mass search. Library construction uses fragment annotation via compositional fragmentation trees and decoy generation using a fragmentation tree re-rooting method to create a target-decoy assay library. This library is used in a second step to analyse metabolomics DIA data performing targeted extraction, scoring and statistical validation (FDR estimation).| (Figure_46)= -|![Assay library generation](/images/openms-user-tutorial/openswath/assay_library_generation.png)| +|![Assay library generation](/_images/openms-user-tutorial/openswath/assay_library_generation.png)| |:--:| |Figure 46: Assay library generation. The results of the compound identification (feature, molecular formula, adduct), with the corresponding fragment spectra for the feature, are used to perform fragment annotation via SIRIUS, using the compositional fragmentation trees. Then, the n highest intensity transitions are extracted and stored in the assay library.| (Figure_47)= -|![Decoy generation](/images/openms-user-tutorial/openswath/decoy_generation.png)| +|![Decoy generation](/_images/openms-user-tutorial/openswath/decoy_generation.png)| |:--:| |Figure 47: Decoy generation. The compositional fragmentations trees from the step above are used to run the fragmentation tree re-rooting method from Passatutto, generating a compound specific decoy MS2 spectrum. Here, the n highest intensity decoy transitions are extracted and stored in the target-decoy assay library.| @@ -2260,7 +2264,7 @@ The example data can be found [here](https://abibuilder.cs.uni-tuebingen.de/arch Example workflow for the usage of the DIAMetAlyzer Pipeline in KNIME (see Fig. 48). Inputs are the SWATH-MS data in profile mode (.mzML), a path for saving the new target-decoy assay library, the SIRIUS 4.9.0 executable, the DDA data (.mzML), custom libraries and adducts for **AccurateMassSearch**, the min/max fragment mass-to-charge to be able to restrict the mass of the transitions and the path to the PyProphet executable. The DDA is used for feature detection, adduct grouping, accurate mass search and forwarded to the **AssayGeneratorMetabo**. Here, feature mapping is performed to collect MS2 spectra that belong to a feature. All information collected before (feautre, adduct, putative identification, MS2 spectra) are then internally forwarded to SIRIUS. SIRIUS is used for fragment annotation and decoy generation based on the fragmentation tree re-rooting approach. This information is then used to filter spectra/decoys based on their explained intensity (min. 85%). Afterwards internal feature linking is performed which is most important for untargeted experiments using a lot of DDA data to construct the library. The constructed target-decoy assay library is processed with the SWATH-MS data in OpenSWATH. The results are used by PyProphet for scoring and output a list of metabolites with their respective q-value and quantitative information. (Figure_48)= -|![Example workflow for the usage of the DIAMetAlyzer Pipeline in KNIME](/images/openms-user-tutorial/openswath/oswm_example_wf.png)| +|![Example workflow for the usage of the DIAMetAlyzer Pipeline in KNIME](/_images/openms-user-tutorial/openswath/oswm_example_wf.png)| |:--:| |Figure 48: Example workflow for the usage of the DIAMetAlyzer Pipeline in KNIME.| @@ -2372,7 +2376,7 @@ It is also possible to use identified and in addition unknown (non-identified) f The universal workflow for untargeted metabolomics always consists of feature detection in the individual MS sample files and their linkage to consensus features with common m/z and retention time values. In addition, there are optional steps such as adduct detection and annotation of features with associated MS2 spectra. This workflow prepares all the file necessary to do formula and structural annotations via `SiriusAdapter`. Furthermore it prepares all required files to run `GNPSExport`, which generates all files necessary to directly run [GNPS](https://gnps.ucsd.edu) Feature Based Molecular Networking (FBMN) and Ion Identity Molecular Networking (IIMN). (Figure_49)= -|![metabolomics preprocessing](/images/tutorials/metabolomics-preprocessing.png)| +|![metabolomics preprocessing](/_images/tutorials/metabolomics-preprocessing.png)| |:--:| |Figure 49: Metabolomics preprocessing steps| @@ -2421,7 +2425,7 @@ TextExporter -in Preprocessed.consensusXML -out Features.tsv -consensus:add_meta You can recreate this workflow in KNIME. [Download the KNIME workflow here](/workflows/UntargetedMetabolomicsPreProcessing.knwf). The workflow should look like this: (Figure_50)= -|![metabolomics preprocessing workflow in KNIME](/images/tutorials/metabolomics-preprocessing-knime-workflow.png)| +|![metabolomics preprocessing workflow in KNIME](/_images/tutorials/metabolomics-preprocessing-knime-workflow.png)| |:--:| |Figure 50: Metabolomics preprocessing workflow in KNIME| @@ -2862,7 +2866,7 @@ install.packages("scales") Press **Eval script** to execute the script. (Figure_51)= -|![Basic QC setup within a LFQ workflow.](/images/openms-user-tutorial/quality-control/qc_basic.png)| +|![Basic QC setup within a LFQ workflow.](/_images/openms-user-tutorial/quality-control/qc_basic.png)| |:--:| |Figure 51: Basic QC setup within a LFQ workflow.| @@ -2902,7 +2906,7 @@ ggplot(knime.in, aes(x=peptide_charge)) + - Now we redirect the **QCEmbedders** output to the `Output Folder` from before and can have a look at how our qcML is coming along after restarting the workflow. (Figure_52)= -|![QC with new metric](/images/openms-user-tutorial/quality-control/qc_extra.png)| +|![QC with new metric](/_images/openms-user-tutorial/quality-control/qc_extra.png)| |:--:| |Figure 52: QC with new metric.| @@ -2919,13 +2923,13 @@ For this, we will first collect all created qcML files, merge them together and When inspecting the set-qcML file in a browser, we will be presented another overview. After the set content listing, the basic QC parameters (like number of identifications) are each displayed in a graph. Each set member (or run) has its own section on the x-axis and each run is connected with that graph via a link in the mouseover on one of the QC parameter values. (Figure_53)= -|![QC set creation from ZipLoop](/images/openms-user-tutorial/quality-control/qc_set.png)| +|![QC set creation from ZipLoop](/_images/openms-user-tutorial/quality-control/qc_set.png)| |:--:| |Figure 53: QC set creation from ZipLoop.|

    **Task**

    -For ideas on new QC metrics and parameters, as you add them in your qcML files as generic parameters, feel free to [contact us](/quick-reference/contact-us.md), so we can include them in the CV. +For ideas on new QC metrics and parameters, as you add them in your qcML files as generic parameters, feel free to [contact us](/about/communication.md), so we can include them in the CV.
    ## Advanced topic: R integration @@ -2987,7 +2991,7 @@ regularly needed if column names contain spaces, tabs or other special character ## Troubleshooting guide -This section will show you where you can turn to when you encounter any problems with this tutorial or with our nodes in general. Please see the [FAQ](/quick-reference/contributor-faq.md) first. If your problem is not listed or the proposed solution does not work, feel free to leave us a message at the means of support that you see most fit. If that is the case, please provide us with as much information as you can. In an ideal case, that would be: +This section will show you where you can turn to when you encounter any problems with this tutorial or with our nodes in general. Please see the [FAQ](/manual/contribute.md) first. If your problem is not listed or the proposed solution does not work, feel free to leave us a message at the means of support that you see most fit. If that is the case, please provide us with as much information as you can. In an ideal case, that would be: - Your operating system and its version (e.g. Windows 8, Ubuntu 14.04). - Your KNIME version (e.g. KNIME 3.1.2 full, KNIME 3.1.1 core). diff --git a/docs/tutorials-and-quickstart-guides/tutorials.md b/docs/tutorials-and-quickstart-guides/tutorials.md deleted file mode 100644 index cf93f973..00000000 --- a/docs/tutorials-and-quickstart-guides/tutorials.md +++ /dev/null @@ -1,200 +0,0 @@ -Worked Examples: Different OpenMS Methods to Achieve the Same Outcome -====================================================================== - -The following tutorials provide the opportunity to complete a real-world example while also seeing the different methods OpenMS makes available to complete the same task. - -There are four ways to use OpenMS to complete a task. For example, say you want to read a file and store the information in an output file. You can do this by: - -- **Using TOPP shell** to run a shell script or execute a command directly. -- **Using pyOpenMS** and creating and running a python script. -- **Using TOPPView**, a graphical user interface provided by OpenMS. -- **Constructing a workflow in KNIME**, which can be saved and executed on multiple input files. -- **Running a script** using Nextflow, a language based on the Groovy programming language. - -The following sections explain how to read a file and store the information in an output file using these five different methods. - -## Using TOPP shell - -As explained in the command-line quick start guide, TOPP shell is available for those who are comfortable with executing command line tools and writing scripts. - -To read the information of a file, just type the following in the command line and press Enter. - -```bash -FileInfo -in -out -``` - -You can also copy and paste this into a shell script (file with an .sh extension), and then type bash in the command line to execute the script. - -## Using pyOpenMS - -You can replicate the functionality of the `FileInfo` TOPP tool in pyOpenMS, using one of the following examples, depending on the type of file you want to get information about. - -- Example for `mzML` file - - ```python - from pyopenms import * - - exp = MSExperiment() - MzMLFile().load("sample.mzML", exp) - exp.updateRanges() - - ms_levels = exp.getMSLevels() - num_spectra = {level: 0 for level in ms_levels} - - for spec in exp: - num_spectra[spec.getMSLevel()] += 1 - - print("Instrument:") - for analyzer in exp.getInstrument().getMassAnalyzers(): - print(f"\tMass Analyzer: {analyzer.getType()} (resolution: {analyzer.getResolution()})") - - print("\nMS levels: "+", ".join([str(level) for level in ms_levels])) - print(f"Total number of peaks: {sum([spec.size() for spec in exp])}") - print(f"Total number of spectra: {exp.size()}") - - print("\nRanges:") - print(f"\tretention time: {exp.getMinRT()} .. {exp.getMaxRT()} ({round((exp.getMaxRT()-exp.getMinRT())/60, 2)} min)") - print(f"\tmass-to-charge: {exp.getMinMZ()} .. {exp.getMaxMZ()}") - print(f"\tintensity: {exp.getMinIntensity()} .. {exp.getMaxIntensity()}") - - print("\nNumber of spectra per MS level:") - for level, number in num_spectra.items(): - print(f"\tlevel {level}: {number}") - ``` - - Example for `featureXML` file - - ```python - feature_map = FeatureMap() - FeatureXMLFile().load("sample.featureXML", feature_map) - - charges = {} - number_of_ids = {} - tic = 0 - - for feature in feature_map: - charge = feature.getCharge() - if charge in charges.keys(): - charges[charge] += 1 - else: - charges[charge] = 1 - num_ids = len(feature.getPeptideIdentifications()) - if num_ids in number_of_ids.keys(): - number_of_ids[num_ids] += 1 - else: - number_of_ids[num_ids] = 1 - tic += feature.getIntensity() - - print(f"Number of features: {feature_map.size()}") - - print("\nRanges:") - print(f"\tretention time: {feature_map.getMinRT()} .. {feature_map.getMaxRT()} ({round((feature_map.getMaxRT()-feature_map.getMinRT())/60, 2)} min)") - print(f"\tmass-to-charge: {feature_map.getMinMZ()} .. {feature_map.getMaxMZ()}") - print(f"\tintensity: {feature_map.getMinIntensity()} .. {feature_map.getMaxIntensity()}") - - print(f"\nTotal ion current in features: {int(tic)}") - - print("\nCharge distribution:") - for charge, occurence in charges.items(): - print(f"\tcharge {charge}: {occurence}x") - - print("\nDistribution of peptide identifications (IDs) per feature:") - for num_ids, occurence in number_of_ids.items(): - print(f"\t{num_ids} IDs: {occurence}") - - print(f"\nUnassigned peptide identifications: {len(feature_map.getUnassignedPeptideIdentifications())}") - ``` - -## Using TOPPView - -If you want a graphical user interface to interact with, then use TOPPView. Follow these steps to read the file information using TOPPView: - -1. Go to **File** > **Open file** and open a file by following the prompts. -2. Go to **Tools** > **Apply TOPP tool (whole layer)**. -3. Set **TOPP tool** to `FileInfo` and **output argument** to **out**. -4. Load an existing INI file by clicking **Load** and selecting an INI file from the file importer. If you don’t have an INI file, click **Store**, enter a file name and click **OK** to generate an INI file, then click **Load** and load the file. - - ![apply topp tool to layer](/images/tutorials/toppview/apply-topp-tool-to-layer.png) -5. Click **OK**. -6. Open the **Log** panel at the bottom of the screen to view the resulting file information. - -## Constructing a workflow in KNIME - -KNIME is available for those who want a graphical user interface to create and use workflows. Here is an example of how to report file information on an input file to an output file using KNIME. - -**1. Install OpenMS plugin**. -
      -
    1. Go to File > Install KNIME Extensions.
    2. -
    3. Search for OpenMS.
    4. -
    5. Select the checkbox next to OpenMS and click Next.
    6. -
    7. Click Next.
    8. -
    9. Accept the terms of conditions.
    10. -
    11. Restart KNIME
    12. -
    - -**2. Open a new file** by going to **File** > **New file**. - -**3. Construct workflow by adding nodes.** -
      -
    1. Go to the Node repository
    2. -
    3. In the search field, type Input File. You should see an Input File node in the filtered list.
    4. -
    5. Drag and drop the Input File node from the Node repository into the workspace.
    6. - add node to workspace -
    7. Repeat steps b and c for the FileInfo node and the Output File node.
    8. -
    9. Connect the Input File node to the FileInfo node. Connect the FileInfo node to the Output File node. Your workflow should look like this:
    10. - connected nodes - Once you configure your nodes in the next step, the warnings should disappear. -
    - -**4. Configure the nodes in workflow.** -
      -
    1. Right-click the Input File node and select Configure.
    2. -
    3. Click Browse and choose the appropriate file from the file importer.
    4. -
    5. Click OK.
    6. -
    7. Right-click the Output File node and select Configure.
    8. -
    9. Either choose an existing file or enter the file name of the new file. If you choose an existing file, select the Overwrite file(s) if it/they exist checkbox.
    10. -
    11. Click OK.
    12. -
    13. Your workflow should be cleared of warnings and look like this:
    14. - connected-nodes-no-warnings -
    - -**5. Play the workflow.** -
      -
    1. Click the Execute all executable nodes button in the toolbar at the top of the screen.
    2. -
    3. You should produce an output file at the specified location. Here is an example of what can be produced:
    4. - -
    - -## Running a Nextflow script - -Nextflow is a scripting language based on the Groovy programming language. - -The following is a Nextflow script that executes the `FileInfo` TOPP tool on an input file. - -```groovy -params.input_file = 'path/to/input_file' - -process runFileInfo { - output: - stdout - - """ - FileInfo -in ${params.input_file} - """ -} - -workflow { - runFileInfo | view { it } -} -``` - -The above script can be executed from the command line by entering: - -```bash -nextflow run file_info.nf -``` - -You can also specify a file path from the command line by using: - -```bash -nextflow run file_info.nf --input_file path/to/file -``` diff --git a/docs/tutorials/KNIME/KNIME-tutorial.md b/docs/tutorials/KNIME/KNIME-tutorial.md deleted file mode 100644 index 2addc494..00000000 --- a/docs/tutorials/KNIME/KNIME-tutorial.md +++ /dev/null @@ -1,66 +0,0 @@ ---- -orphan: true ---- -KNIME Tutorial -============== - -The below image shows an example workflow in {term}`KNIME`. - -![](/images/tutorials/knime/KNIME_screenshot.png) - - -## Installing OpenMS in KNIME - -Installation of OpenMS in {term}`KNIME` is platform-independent across Windows, MacOSX, and Linux. - -1. Download the latest {term}`KNIME` release from the [KNIME website](https://www.knime.com/). -2. In the full install of {term}`KNIME` skip the following installation routine since all required plugins should be - installed by default. For the standard (core) installation, follow the instructions here or in the extended [user-tutorial](../../tutorials-and-quickstart-guides/openms-user-tutorial.md). - - ![](/images/tutorials/knime/KNIME_Install.png) - -3. In KNIME click on **Help** > **Install new Software**. -4. Install the required KNIME File Handling nodes from the official KNIME Update Site (a standard entry in the update - sites). Choose the update site from the **Work with:** dropdown menu. - - **Name**: KNIME Analytics Platform {{ knime_version }} Update Site. - - **Location**: {{ 'http://update.knime.org/analytics-platform/{0}'.format(knime_version) }} - -5. Filter the results for **File handling** and select the {term}`KNIME` File Handling Nodes. Click **Next** and install. - - ![](/images/tutorials/knime/KNIME_update_site.jpeg) - -6. Now, install the actual OpenMS plugin. Next to the **Work with:** dropdown menu, click on **Add...**. In the opening - dialog fill in at least one of the following additional Update Sites (if not already present): - - **Recommended**: - - **Name**: KNIME Community Contributions (Stable) - - **Location**: {{ 'http://update.knime.org/community-contributions/trusted/{0}'.format(knime_version) }} - - **Unstable**: - - **Name**: KNIME Nightly Community Contributions (trunk) - - **Location**: http://update.knime.org/community-contributions/trunk - -7. Use the search or navigate to **KNIME Community Contributions – Bioinformatics & NGS** and select **OpenMS**. Then - click **Next** and follow the installation instructions. A restart of KNIME might be necessary afterward. On Windows, - if prompted to install additional requirements like the Microsoft Visual Studio Redistributable for the conversion - software ProteoWizard that is packaged with our plugin. -8. After a restart of KNIME the OpenMS nodes will be available in your Node Repository (panel on the lower left) under - **Community Nodes**. - -## Creating workflows with KNIME - -Download Introduction to OpenMS in KNIME [user tutorial](../../tutorials-and-quickstart-guides/openms-user-tutorial.md) containing hands-on training material covering also basic -usage of KNIME. See the official [KNIME Getting Started Guide](https://www.knime.com/getting-started-guide) for a more in-depth view of -the KNIME functionality besides OpenMS. - -If you face any issues, please [contact us](/quick-reference/contact-us.md) and specifically for the usage of OpenMS in KNIME, the KNIME community contribution [forum](https://forum.knime.com/tag/openms). - -## Creating your own Generic KNIME Nodes - -To create your own generic KNIME nodes, refer to their [documentation](https://docs.knime.com/latest/pure_python_node_extensions_guide/index.html#introduction). diff --git a/docs/tutorials/knime-user-tutorial.md b/docs/tutorials/knime-user-tutorial.md new file mode 100644 index 00000000..78ca0d9d --- /dev/null +++ b/docs/tutorials/knime-user-tutorial.md @@ -0,0 +1,262 @@ +KNIME +===== + +Using OpenMS in combination with KNIME, you can create, edit, open, save, and run workflows that combine TOPP tools with +the powerful data analysis capabilities of KNIME. Workflows can be created conveniently in a graphical user interface. +The parameters of all involved tools can be edited within the application and are also saved as part of the workflow. +Furthermore, KNIME interactively performs validity checks during the workflow editing process, to make it more +difficult to create an invalid workflow. Throughout most parts of this tutorial, you will use KNIME to create and execute +workflows. The first step is to become familiar with KNIME. Additional information on the basic usage of KNIME can be +found on the KNIME [Getting Started page](https://www.knime.com/getting-started-guide). However, the most important +concepts will also be reviewed in this tutorial. + +## General Remarks + +- This handout will guide you through an introductory tutorial for the OpenMS/TOPP software package[^1]. + +- OpenMS[^2],[^3] is a versatile open-source library for mass spectrometry data analysis. Based on this library, we offer a collection of command-line tools ready to be used by end users. These so-called TOPP tools (short for ”The OpenMS Pipeline”)[^4] can be understood as small building blocks of arbitrarily complex data analysis workflows. + +- In order to facilitate workflow construction, OpenMS was integrated into KNIME[^5], the Konstanz Information Miner, an open-source integration platform providing a powerful and flexible workflow system combined with advanced data analytics, visualization, and report capabilities. Raw MS data as well as the results of data processing using TOPP can be visualized using TOPPView[^6]. + +- This tutorial was designed for use in a hands-on tutorial session but can also be worked through at home using the online resources. You will become familiar with some of the basic functionalities of OpenMS/TOPP, TOPPView, as well as KNIME and learn how to use a selection of TOPP tools used in the tutorial workflows. + +- If you are attending the tutorial and received a USB stick, all sample data referenced in this tutorial can be found in the {path}`C:,Example_Data` folder, on the USB stick, or released online on our [Archive](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/tutorial.zip). + +## Getting Started + +### Installation + +Before we get started, we will install OpenMS with its viewer TOPPView, KNIME and the OpenMS KNIME plugin. If you take part in a live training session you will have likely received an USB stick from us that contains the required data and software. If we provide laptops with the software you may of course skip the installation process and continue reading the next section. +If you are doing this tutorial online, choose online in the following tab(s). + +:::::{tab-set} + +::::{tab-item} Online +:sync: online + +If you are working through this tutorial at home/online, proceed with the following steps: + +- Download and install OpenMS using the installation instructions for the [OpenMS tools](/about/installation.rst). + :::{note} + To install the graphical application, please use the downloadable installer for your platform, + not conda, nor docker. + ::: + +- Download and install [KNIME](https://www.knime.org/downloads/overview) + +:::: + +::::{tab-item} USB Stick +:sync: usb + +Please choose the directory that matches your operating system and execute the installer. + +For Windows, you run: + +```{note} +The OpenMS installer for windows now supports installing only for a single user. If you choose this option the location of the tools will be different than {{ '{path}'+'`C:,Program Files,OpenMS-{0}`'.format(version) }} specified in this document. In most cases they will install to {{ '{path}'+'`C:,Users,$YOUR_USER,AppData,Local,OpenMS-{0}`'.format(version) }} where $YOUR_USER is replaced with your username. +``` +- The OpenMS installer: {{ '{path}'+'`Windows,OpenMS-{0}-Win64.exe`'.format(version) }} +- The KNIME installer: {{ '{path}'+'`Windows,KNIME-{0}-Installer-64bit.exe`'.format(knime_version) }} + +On macOS(x86), you run: + +- The OpenMS installer: {{ '{path}'+'`Mac,OpenMS-{0}-macOS.dmg`'.format(version) }} +- The KNIME installer: {{ '{path}'+'`Mac,knime_{0}.app.macosx.cocoa.x86_64.dmg`'.format(knime_version) }} + +On macOS(arm), you run: + +- The OpenMS installer: {{ '{path}'+'`Mac,OpenMS-{0}-macOS.dmg`'.format(version) }} +- The KNIME installer: {{ '{path}'+'`Mac,knime_{0}.app.macosx.cocoa.aarch64.dmg`'.format(knime_version) }} + +On Linux: +- The OpenMS package: {{ '{path}'+'`Linux,OpenMS-{0}-Debian-Linux-x86_64.deb`'.format(version) }} can be installed with your package manager +- The KNIME package can be extracted to a folder of your choice from {{ '{path}'+'`knime_{0}.linux.gtk.x86_64.tar`'.format(knime_version) }} + + ```{note} + You can also install OpenMS via your package manager (version availability not guaranteed) or build it on your own with our + [build instructions](/about/installation/installation-on-gnu-linux.md#build-openms-from-source). + ``` + +:::: +::::: + +### KNIME Modern and Classic UI #### + +Since version 5.0 KNIME has a new updated user interface. For the purposes of this tutorial we will continue to use the "classic user interface". +Depending on your OS KNIME may have started automatically in the Modern UI, which looks like the following: +|![ms2 spectrum](/_images/openms-user-tutorial/introduction/KNIME_switch_to_classic.png)| +|:--:| +|Figure 5.5: The modern KNIME UI. To switch back to the classic UI, select "Menu" and click "Switch to classic user interface"| + +### Plugin and dependency + +Before we can start with the tutorial, we need to install all the required extensions for KNIME. +Since KNIME 3.2.1, the program automatically +detects missing plugins when you open a workflow but to make sure that the right source for the +OpenMS plugin is chosen, please follow the instructions here. + +#### Required KNIME plugins + +First, we install some additional extensions that are +required by our OpenMS nodes or used in the Tutorials for downstream processing, visualization or reporting. + +1. In KNIME, click on **Help** > **Install New Software**. +2. From the '**Work with**:' drop-down list, select the _update site_ 'KNIME 5.2 - https://update.knime.com/analytics-platform/5.2' +3. Now select the following KNIME core plugins from the KNIME & Extensions category +- KNIME Base Chemistry Types & Nodes +- KNIME Chemistry Add-Ons +- KNIME Interactive R Statistics Integration +- KNIME Report Designer +4. Click on **Next** and follow the instructions (it's not necessary to restart KNIME now). +5. Click again on **Help** > **Install New Software** +6. From the '**Work with**:' drop-down list, select the _update site_ 'KNIME Community Extensions (Trusted) - https://update.knime.com/community-contributions/trusted/5.2' +7. From the "KNIME Community Contributions - Cheminformatics" category select +- RDKit Nodes Feature +8. From the "KNIME Community Extensions - Other" category select +- Generic Worfkflow Nodes for KNIME +9. Click on **Next** and follow the instructions and after a restart of KNIME the dependencies will be installed. + +#### R programming language and its KNIME integration + +In addition, we need to install `R` for the statistical downstream analysis. Choose the directory that matches your +operating system, double-click the `R` installer and follow the instructions. We recommend to use the default settings +whenever possible. On macOS you also need to install `XQuartz` from the same directory. + +Afterwards open your `R` installation. If you use Windows, you will find an ”R x64 4.3.2” icon on your desktop. If you use +macOS, you will find R in your Applications folder. In `R`, type the following lines (you might also copy them from the file +{path}`R,install_R_packages.R` on the USB stick): + +```r +install.packages('Rserve',,"http://rforge.net/",type="source") +install.packages("Cairo") + +install.packages("devtools") +install.packages("ggplot2") +install.packages("ggfortify") + +if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager") + +BiocManager::install() +BiocManager::install(c("MSstats")) +``` +In KNIME, click on **File** > **Preferences**, select the category **KNIME** > **R** and set the ”Path to R Home” to +your installation path. You can use the following settings, if you installed R as described above: + +- Windows: `C:\Program Files\R\R-4.3.2'` +- macOS: `/Library/Frameworks/R.framework/Versions/4.3/Resources` + +#### KNIME OpenMS plugin + +You are now ready to install the OpenMS nodes. +- In KNIME, click on **Help** > **Install New Software** + +You now have to choose an _update site_ to install the OpenMS plugin from. Which _update site_ to choose depends on if you received an USB stick +in a hands-on Tutorial or if you are doing this Tutorial online. + +::::{tab-set} + +:::{tab-item} Online +:sync: online + +To install the OpenMS KNIME plugin from the internet, do the following: + +1. From the '**Work with**:' drop-down list, select the _update site_ 'KNIME Community Extensions (Trusted) - https://update.knime.com/community-contributions/trusted/5.2' +2. Now select the following plugin from the "KNIME Community Contributions - Bioinformatics & NGS" category +- OpenMS +- OpenMSThirdParty +3. Click on **Next** and follow the instructions and after a restart of KNIME the OpenMS nodes will be available in the Node repository under + "Community Nodes". + +```{note} +If this does not work for you, report it and while waiting for a reply/fix, try to use an _update site_ of an older KNIME version by editing the KNIME +version number in the URL or by using our inofficial _update site_ at https://abibuilder.cs.uni-tuebingen.de/archive/openms/knime-plugin/updateSite/release/latest +``` + +::: + +:::{tab-item} USB +:sync: usb + +We included a custom KNIME update site to install the OpenMS KNIME plugins from the USB stick. If you do not have a stick available, please see below. + +- In the now open dialog choose **Add** (in the upper right corner of the dialog) to define a new update site. In the + opening dialog enter the following details. + + **Name:** OpenMS {{ version }} UpdateSite + + **Location:** {{ '`file:/KNIMEUpdateSite/{0}/`'.format(version) }} +- After pressing **OK** KNIME will show you all the contents of the added Update Site. + +```{note} +From now on, you can use this repository for plugins in the **Work with**: drop-down list. +``` + +- Select the OpenMS nodes in the ”Uncategorized” category and click **Next**. +- Follow the instructions and after a restart of KNIME the OpenMS nodes will be available in the Node repository under + "Community Nodes”. + +::: + +:::{tab-item} Online experimental +:sync: onlineexp + +To install the nightly/experimental version of the OpenMS KNIME plugin from the internet, do the following: + +- In the now open dialog, choose **Add** (in the upper right corner of the dialog) to define a new _update site_. In the opening dialog enter the following details. + + **Name:** OpenMS {{ version }} UpdateSite + + **Location:** https://abibuilder.cs.uni-tuebingen.de/archive/openms/knime-plugin/updateSite/nightly/ +- After pressing **OK** KNIME will show you all the contents of the added Update Site. + +```{note} +From now on, you can use this repository for plugins in the **Work with:** drop-drown list. +``` +- Select the OpenMS nodes in the "Uncategorized" category and click **Next**. +- Follow the instructions and after a restart of KNIME the OpenMS nodes will be available in the Node repository under + "Community Nodes". + +::: +:::: + + +```{toctree} +--- +maxdepth: 1 +titlesonly: true +--- + +knime-user-tutorial/file-conversion.md +knime-user-tutorial/knime-gui.md +knime-user-tutorial/minimal-workflow.md +knime-user-tutorial/lfq-peptide-protein.md +knime-user-tutorial/msstats.md +knime-user-tutorial/lfq-metabolites.md +knime-user-tutorial/openswath.md +knime-user-tutorial/openswath-metabolomics.md +knime-user-tutorial/quality-control.md + +``` + +## References + +[^1]: OpenMS, OpenMS home page [online]. + +[^2]: M. Sturm, A. Bertsch, C. Gröpl, A. Hildebrandt, R. Hussong, E. Lange, N. Pfeifer, +O. Schulz-Trieglaff, A. Zerck, K. Reinert, and O. Kohlbacher, OpenMS - an opensource software framework for mass spectrometry., BMC bioinformatics 9(1) +(2008), doi:10.1186/1471-2105-9-163. 7, 83 + +[^3]: H. L. Röst, T. Sachsenberg, S. Aiche, C. Bielow, H. Weisser, F. Aicheler, S. Andreotti, +H.-C. Ehrlich, P. Gutenbrunner, E. Kenar, et al., OpenMS: a flexible open-source +software platform for mass spectrometry data analysis, Nature Methods 13(9), +741–748 (2016). 7 + +[^4]: O. Kohlbacher, K. Reinert, C. Gröpl, E. Lange, N. Pfeifer, O. Schulz-Trieglaff, and +M. Sturm, TOPP–the OpenMS proteomics pipeline., Bioinformatics 23(2) (Jan. +2007). 7, 83 + +[^5]: M. R. Berthold, N. Cebron, F. Dill, T. R. Gabriel, T. Kötter, T. Meinl, P. Ohl, C. Sieb, K. Thiel, and B. Wiswedel, KNIME: The Konstanz Information Miner, in Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007), Springer, 2007. + +[^6]: M. Sturm and O. Kohlbacher, TOPPView: An Open-Source Viewer for Mass Spectrometry Data, Journal of proteome research 8(7), 3760–3763 (July 2009), doi:10.1021/pr900171m. 7 diff --git a/docs/tutorials/knime-user-tutorial/file-conversion.md b/docs/tutorials/knime-user-tutorial/file-conversion.md new file mode 100644 index 00000000..37236cd3 --- /dev/null +++ b/docs/tutorials/knime-user-tutorial/file-conversion.md @@ -0,0 +1,69 @@ +Data conversion +=============== + +Each MS instrument vendor has one or more formats for storing the acquired data. +Converting these data into an open format (preferably mzML) is the very first step +when you want to work with open-source mass spectrometry software. A freely available conversion tool is MSConvert, which is part of a `ProteoWizard` installation. All files +used in this tutorial have already been converted to mzML by us, so you do not need +to perform the data conversion yourself. However, we provide a small raw file so you +can try the important step of raw data conversion for yourself. + +```{note} +The OpenMS installation package for Windows automatically installs +ProteoWizard, so you do not need to download and install it separately. Due +to restrictions from the instrument vendors, file format conversion for most +formats is only possible on Windows systems. In practice, performing the +conversion to mzML on the acquisition PC connected to the instrument is +usually the most convenient option. +``` + +To convert raw data to mzML using `ProteoWizard` you can either use MSConvertGUI (a +graphical user interface) or `msconvert` (a simple command line tool). + +(Figure_1)= +|![msconvertgui](/_images/openms-user-tutorial/introduction/proteowizard.png)| +|:--:| +|Figure 1: `MSConvertGUI` (part of `ProteoWizard`), allows converting raw files to mzML. Select the raw files you want to convert by clicking on the browse button and then on Add. Default parameters can usually be kept as-is. To reduce the initial data size, make sure that the `peakPicking` filter (converts profile data to centroided data (see Fig. 2)) is listed, enabled (true) and applied to all MS levels (parameter ”1-”). Start the conversion process by clicking on the Start button.| + +Both tools are available in: {{ '{path}'+'`C:,Program Files,OpenMS-{0},share,OpenMS,THIRDPARTY,pwiz-bin`'.format(version) }}. + +You can find a small RAW file on the USB stick {path}`Example_Data,Introduction,datasets,raw`. + +## MSConvertGUI + +`MSConvertGUI` (see Fig. 1) exposes the main parameters for data conversion in a convenient graphical user interface. + +## msconvert + +The `msconvert` command line tool has no graphical user interface but offers more options than the application `MSConvertGUI`. Additionally, since it can be used within a batch script, it allows converting large numbers of files and can be much more easily automatized. +To convert and pick the file `raw_data_file.RAW` you may write: + +```bash +msconvert raw_data_file.RAW --filter "peakPicking true 1-" +``` + +in your command line. + +(Figure_2)= +|![profile centroided](/_images/openms-user-tutorial/introduction/profilecentroided.png)| +|:--:| +|Figure 2: The amount of data in a spectra is reduced by peak picking. Here a profile spectrum (blue) is converted to centroided data (green). Most algorithms from this point on will work with centroided data.| + +To convert all RAW files in a folder may write: + +```bash +msconvert *.RAW -o my_output_dir +``` + +```{note} +To display all options you may type `msconvert --help` . Additional information is available on the `ProteoWizard` web page. +``` + +## ThermoRawFileParser + +Recently the open-source platform independent ThermoRawFileParser tool has been developed. While Proteowizard and MSConvert are only available for Windows systems this new tool allows to also convert raw data on Mac or Linux. + +```{note} +To learn more about the `ThermoRawFileParser` and how to use it in +KNIME see Minimal Workflow. +``` \ No newline at end of file diff --git a/docs/tutorials/knime-user-tutorial/knime-gui.md b/docs/tutorials/knime-user-tutorial/knime-gui.md new file mode 100644 index 00000000..f2db4409 --- /dev/null +++ b/docs/tutorials/knime-user-tutorial/knime-gui.md @@ -0,0 +1,92 @@ +Overview of the graphical user interface +======================================== + +|![The KNIME workbench](/_images/openms-user-tutorial/knime-setup/knime_workbench_marked.png)| +|:--:| +|Figure 7: The KNIME workbench| + +The graphical user interface (GUI) of KNIME consists of different components or so-called panels that are shown in +above image. We will briefly introduce the individual panels and their purposes below. + +## Workflow Editor + +The workflow editor is the central part of the KNIME GUI. Here you assemble the workflow by adding nodes from the Node +Repository via ”drag & drop”. For quick creation of a workflow, note that double-clicking on a node in the repository +automatically connects it to the selected node in the workbench (connecting all the inputs with as many fitting outputs +of the last node). Manually, nodes can be connected by clicking on the output port of one node and dragging the edge +until releasing the mouse at the desired input port of the next node. Deletions are possible by selecting nodes and/or +edges and pressing DEL or Fn + Backspace depending on your OS and settings. Multiselection +happens via dragging rectangles with the mouse or adding elements to the selection by clicking them while holding down +Ctrl. + +## KNIME Explorer + +Shows a list of available workflows (also called workflow projects). You can open a workflow by double-clicking it. A +new workflow can be created with a right-click in the Workflow Explorer followed by choosing **New KNIME Workflow** +from the appearing context menu. Remember to save your workflow often with the Ctrl + S shortcut. + +## Workflow Coach + +Shows a list of suggested following nodes, based on the last added/clicked nodes. When you are not sure which node to +choose next, you have a reasonable suggestion based on other users behavior there. Connect them to the last node with a +double-click. + +## Node Repository + +Shows all nodes that are available in your KNIME installation. Every plugin you install will provide new nodes that can +be found here. The OpenMS nodes can be found in **Community Node** > **OpenMS** Nodes to hook up to external search engines +and the RawFileConverter are found under **Community Node** > **OpenMSThirdParty** Nodes for managing files (e.g., Input +Files or Output Folders) can be found in **Community Nodes** > **GenericKnimeNode**. You can search the node repository +by typing the node name into the small text box in the upper part of the node repository. + +## Outline + +The Outline panel contains a small overview of the complete workflow. While of limited use when working on a small +workflow, this feature is very helpful as soon as the workflows get bigger. You can adjust the zoom level of the explorer +by adjusting the percentage in the toolbar at the top of KNIME. + +## Console + +In the console panel, warning and error messages are shown. This panel will provide helpful information if one of the +nodes failed or shows a warning sign. + +## Node Description + +As soon as a node is selected, the Node Description window will show the documentation of the node including +documentation for all its parameters and especially their in- and outputs, such that you know what types of data nodes +may produce or expect. For OpenMS nodes you will also find a link to the tool page of the online documentation. + +## Creating workflows + +Workflows can easily be created by a right click in the Workflow Explorer followed by clicking on **New KNIME workflow**. + +## Sharing workflows + +To be able to share a workflow with others, KNIME supports the import and export of complete workflows. To export a +workflow, select it in the Workflow Explorer and select **File** > **Export KNIME Workflow**. KNIME will export +workflows as a _knwf_ file containing all the information on nodes, their connections, and their parameter configuration. + +Those *knwf* files can again be imported by selecting: **File** > **Import KNIME Workflow** + +```{note} +For your convenience we added all workflows discussed in this tutorial to the **Workflows** folder on the USB Stick. + Additionally, the workflow files can be found on workflow downloads. If you want to check + your own workflow by comparing it to the solution or got stuck, simply import the full workflow from the corresponding + *knwf* file and after that double-click it in your KNIME Workflow repository to open it. +``` + +## Duplicating workflows + +In this tutorial, a lot of the workflows will be created based on the workflow from a previous task. To keep the +intermediate workflows, we suggest you create copies of your workflows so you can see the progress. To create a copy of +your workflow, save it, close it and follow the next steps. + +- Right click on the workflow you want to create a copy of in the Workflow Explorer and select **Copy**. +- Right click again somewhere on the workflow explorer and select **Paste**. +- This will create a workflow with same name as the one you copied with a (2) appended. +- To distinguish them later on you can easily rename the workflows in the Workflow Explorer by right clicking on the + workflow and selecting **Rename**. + +```{note} +To rename a workflow it has to be closed. +``` \ No newline at end of file diff --git a/docs/tutorials/knime-user-tutorial/lfq-metabolites.md b/docs/tutorials/knime-user-tutorial/lfq-metabolites.md new file mode 100644 index 00000000..55d584c6 --- /dev/null +++ b/docs/tutorials/knime-user-tutorial/lfq-metabolites.md @@ -0,0 +1,421 @@ +Label-free quantification of metabolites +======================================== + +## Introduction + +Quantification and identification of chemical compounds are basic tasks in metabolomic studies. In this tutorial session we construct a UPLC-MS based, label-free quantification and identification workflow. Following quantification and identification we then perform statistical downstream analysis to detect quantification values that differ significantly between two conditions. This approach can, for example, be used to detect biomarkers. Here, we use two spike-in conditions of a dilution series (0.5 mg/l and 10.0 mg/l, male blood background, measured in triplicates) comprising seven isotopically labeled compounds. The goal of this tutorial is to detect and quantify these differential spike-in compounds against the complex background. + +## Basics of non-targeted metabolomics data analysis + +For the metabolite quantification we choose an approach similar to the one used for peptides, but this time based on the OpenMS `FeatureFinderMetabo` method. This feature finder again collects peak picked data into individual mass traces. The reason +why we need a different feature finder for metabolites lies in the step after trace detection: the aggregation of isotopic traces belonging to the same compound ion into the same feature. Compared to peptides with their averagine model, small molecules +have very different isotopic distributions. To group small molecule mass traces correctly, an aggregation model tailored to small molecules is thus needed. + +- Create a new workflow called for instance ”Metabolomics”. +- Add an `File Importer` node and configure it with one mzML file [from the](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Example_Data/Metabolomics/datasets/) {path}`Example_Data,Metabolomics,datasets`. +- Add a `FeatureFinderMetabo` node (from **Community Nodes** > **OpenMS** > **Quantitation**) and + connect the first output port of the `File Importer` to the `FeatureFinderMetabo`. +- For an optimal result adjust the following settings. Please note that some of these are advanced parameters. +- Connect a `Output Folder` to the output of the `FeatureFinderMetabo` (see Fig. 27). + +(Figure_27)= +|![FeatureFinderMetabo workflow](/_images/openms-user-tutorial/metabo/minimal_FFM_wf.png)| +|:--:| +|Figure 27: FeatureFinderMetabo workflow| + +In the following advanced parameters will be highlighted. These parameter can be altered if the `Show advanced parameter` field in the specific tool is activated. + +|**parameter**|**value**| +|:------------|:--------| +|*algorithm*→*common*→*chrom_fwhm*|8.0| +|*algorithm*→*mtd*→*trace_termination_criterion*|sample_rate| +|*algorithm*→*mtd*→*min_trace_length*|3.0| +|*algorithm*→*mtd*→*max_trace_length*|600.0| +|*algorithm*→*epd*→*width_filtering*|off| +|*algorithm*→*ffm*→*report_convex_hulls*|true| + +The parameters change the behavior of `FeatureFinderMetabo` as follows: + +- **chrom_fwhm**: The expected chromatographic peak width in seconds. +- **trace_termination_criterion**: In the first stage `FeatureFinderMetabo` assembles + mass traces with a pre-defined mass accuracy. If this parameter is set to ’outlier’, the extension of a mass trace is stopped after a predefined number of consecutive outliers is found. If this parameter is set to ’sample_rate’, the extension of a mass trace is stopped once the ratio of collected peaks versus visited spectra falls below the ratio given by `min_sample_rate`. +- **min_trace_length**: Minimal length of a mass trace in seconds. Choose a small value, if you want to identify low-intensity compounds. +- **max_trace_length**: Maximal length of a mass trace in seconds. Set this parameter to -1 to disable the filtering by maximal length. +- **width_filtering**: `FeatureFinderMetabo` can remove features with unlikely peak widths from the results. If activated it will use the interval provided by the parameters `min_fwhm` and `max_fwhm`. +- **report_convex_hulls**: If set to true, convex hulls including mass traces will be reported for all identified features. This increases the output size considerably. + +The output file .featureXML can be visualized with TOPPView on top of the used `.mzML` file - in a so called layer - to look at the identified features. + +First start TOPPView and open the example `.mzML` file (see Fig. 28). Afterwards open the `.featureXML` output as new layer (see Fig. 29). The overlay is depicted in Figure 30. The zoom of the `.mzML` - `.featureXML` overlay shows the individual mass traces and the assembly of those in a feature (see Fig. 31). + +(Figure_28)= +|![Opened .mzML in TOPPView](/_images/openms-user-tutorial/metabo/ToppView_1.png)| +|:--:| +|Figure 28: Opened .mzML in TOPPView| + +(Figure_29)= +|![Add new layer in TOPPView](/_images/openms-user-tutorial/metabo/ToppView_2.png)| +|:--:| +|Figure 29: Add new layer in TOPPView| + +(Figure_30)= +|![Overlay of the .mzML layer with the .featureXML layer](/_images/openms-user-tutorial/metabo/ToppView_3.png)| +|:--:| +|Figure 30: Overlay of the .mzML layer with the .featureXML layer| + +(Figure_31)= +|![Zoom of the overlay of the .mzML with the .featureXML layer](/_images/openms-user-tutorial/metabo/ToppView_4.png)| +|:--:| +|Figure 31: Zoom of the overlay of the .mzML with the .featureXML layer. Here the individual isotope traces (blue lines) are assembled into a feature here shown as convex hull (rectangular box).| + +The workflow can be extended for multi-file analysis, here an `Input Files` node is to be used instead of the `File Importer` node. In front of the `FeatureFinderMetabo`, a `ZipLoopStart` and behind `ZipLoopEnd` has to be used, since `FeatureFinderMetabo` will analysis on file to file bases. + +To facilitate the collection of features corresponding to the same compound ion across different samples, an alignment of the samples’ feature maps along retention time is often helpful. In addition to local, small-scale elution differences, one can often see constant retention time shifts across large sections between samples. We can use linear transformations to correct for these large scale retention differences. This brings the majority of corresponding compound ions close to each other. Finding the correct corresponding ions is then faster and easier, as we don’t have to search as far around individual features. + +(Figure_32)= +|![map alignment example](/_images/openms-user-tutorial/metabo/align.png)| +|:--:| +|Figure 32: The first feature map is used as a reference to which other maps are aligned. The calculated transformation brings corresponding features into close retention time proximity. Linking of these features form a so-called consensus features of a consensus map.| + +- After the `ZipLoopEnd` node, add a `MapAlignerPoseClustering` node (**Community Nodes**>**OpenMS**>**Map Alignment**), set its Output Type to featureXML, and adjust the following settings: + +|**parameter**| **value**| +|:------------|:---------| +|*algorithm* → *max_num_peaks_considered*| −1| +|*algorithm* → *superimposer* → *mz_pair_max_distance*| 0.005| +|*algorithm* → *superimposer* → *num_used_points*| 10000| +|*algorithm* → *pairfinder* → *distance_RT* → *max_difference*| 20.0| +|*algorithm* → *pairfinder* → *distance_MZ* → *max_difference*| 20.0| +|*algorithm* → *pairfinder* → *distance_MZ* → *unit*| ppm| + +`MapAlignerPoseClustering` provides an algorithm to align the retention time scales of multiple input files, correcting shifts and distortions between them. Retention time adjustment may be necessary to correct for chromatography differences e.g. before data from multiple LC-MS runs can be combined (feature linking). The alignment algorithm implemented here is the pose clustering algorithm. + +The parameters change the behavior of `MapAlignerPoseClustering` as follows: +- **max_num_peaks_considered**: The maximal number of peaks/features to be considered per map. To use all, set this parameter to -1. +- **mz_pair_max_distance**: Maximum of m/z deviation of corresponding elements in different maps. This condition applies to the pairs considered in hashing. +- **num_used_points**: Maximum number of elements considered in each map (selected by intensity). Use a smaller number to reduce the running time and to disregard weak signals during alignment. +- **distance_RT → max_difference**: Features that have a larger RT difference will never be paired. +- **distance_MZ →max_difference**: Features that have a larger m/z difference will never be paired. +- **distance_MZ →unit**: Unit used for the parameter distance_MZ max_difference, either Da or ppm. + +The next step after retention time correction is the grouping of corresponding features in multiple samples. In contrast to the previous alignment, we assume no linear relations of features across samples. The used method is tolerant against local swaps in elution order. + +(Figure_33)= +|![feature linking example](/_images/openms-user-tutorial/metabo/link.png)| +|:--:| +|Figure 33: Features A and B correspond to the same analyte. The linking of features between runs (indicated by an arrow) allows comparing feature intensities.| + +- After the `MapAlignerPoseClustering` node, add a `FeatureLinkerUnlabeledQT` node (**Community Nodes** > **OpenMS**>**Map Alignment**) and adjust the following settings: + + |**parameter**|**value**| + |:------------|:--------| + |*algorithm* → *distance_RT* → *max_difference*|40| + |*algorithm* → *distance_MZ* → *max_difference*|20| + |*algorithm* → *distance_MZ* → *unit*|ppm| + + The parameters change the behavior of `FeatureLinkerUnlabeledQT` as follows (similar to the parameters we adjusted for `MapAlignerPoseClustering`): + + - **distance_RT → max_difference**: Features that have a larger RT difference will never be paired. + - **distance_MZ → max_difference**: Features that have a larger m/z difference will never be paired. + - **distance_MZ → unit**: Unit used for the parameter distance_MZ max_difference, either Da or ppm. + +- After the `FeatureLinkerUnlabeledQT` node, add a **TextExporter** node (**Community Nodes** > **OpenMS** > **File Handling**). +- Add an `Output Folder` node and configure it with an output directory where you want to store the resulting files. +- Run the pipeline and inspect the output. + +(Figure_34)= +|![Label-free quantification workflow for metabolites](/_images/openms-user-tutorial/metabo/metabo_part1_with_labels.png)| +|:--:| +|Figure 34: Label-free quantification workflow for metabolites.| + +You should find a single, tab-separated file containing the information on where metabolites were found and with which intensities. You can also add `Output Folder` nodes at different stages of the workflow and inspect the intermediate results (e.g., identified metabolite features for each input map). The complete workflow can be seen in Figure 34. In the following section we will try to identify those metabolites. + +The `FeatureLinkerUnlabeledQT` output can be visualized in TOPPView on top of the input and output of the `FeatureFinderMetabo` (see Fig 35). + +(Figure_35)= +|![Label-free quantification workflow for metabolites](/_images/openms-user-tutorial/metabo/ToppView_5.png)| +|:--:| +|Figure 35: Visualization of .consensusXML output over the .mzML and .featureXML ’layer’.| + +## Basic metabolite identification +At the current state we found several metabolites in the individual maps but so far don’t know what they are. To identify metabolites, OpenMS provides multiple tools, including search by mass: the AccurateMassSearch node searches observed masses against the Human Metabolome Database (HMDB)[^1], [^2], [^3]. We start with the workflow from the previous section (see Figure 34). + +- Add a **FileConverter** node (**Community Nodes** > **OpenMS** > **File Handling**) and connect the output of the FeatureLinkerUnlabeledQT to the incoming port. +- Open the Configure dialog of the **FileConverter** node and select the tab **OutputTypes**. In the drop down list for FileConverter.1.out select **featureXML**. +- Add an **AccurateMassSearch** node (**Community Nodes** > **OpenMS** > **Utilities**) and connect the output of the **FileConverter** node to the first port of the **AccurateMassSearch** node. +- Add four `File Importer` nodes and configure them with the following [files](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Example_Data/Metabolomics/databases/): + - {path}`Example_Data,Metabolomics,databases,PositiveAdducts.tsv` + This file specifies the list of adducts that are considered in the positive mode. Each line contains the formula and charge of an adduct separated by a semicolon (e.g. M+H;1+). The mass of the adduct is calculated automatically. + - {path}`Example_Data,Metabolomics,databases,NegativeAdducts.tsv` + This file specifies the list of adducts that are considered in the negative mode analogous to the positive mode. + - {path}`Example_Data,Metabolomics,databases,HMDBMappingFile.tsv` + This file contains information from a metabolite database in this case from HMDB. It has three (or more) tab-separated columns: mass, formula, and identifier(s). This allows for an efficient search by mass. + - {path}`Example_Data,Metabolomics,databases,HMDB2StructMapping.tsv` + This file contains additional information about the identifiers in the mapping file. It has four tab-separated columns that contain the identifier, name, SMILES, and INCHI. These will be included in the result file. The identifiers in this file must match the identifiers in the `HMDBMappingFile.tsv`. +- In the same order as they are given above connect them to the remaining input ports of the **AccurateMassSearch** node. +- Add an `Output Folder` node and connect the first output port of the + **AccurateMassSearch** node to the `Output Folder` node. + +The result of the **AccurateMassSearch** node is in the mzTab format[^4] so you can easily open it in a text editor or import it into Excel or KNIME, which we will do in the next section. The complete workflow from this section is shown in Figure 36. + +(Figure_36)= +|![Label-free quantification and identification workflow for metabolites](/_images/openms-user-tutorial/metabo/metabo_part2.png)| +|:--:| +|Figure 36: Label-free quantification and identification workflow for metabolites.| + +### Convert your data into a KNIME table + +The result from the TextExporter node as well as the result from the **AccurateMassSearch** node are files while standard KNIME nodes display and process only KNIME tables. To convert these files into KNIME tables we need two different nodes. For the **AccurateMassSearch** results, we use the **MzTabReader** node (**Community Nodes** > **OpenMS** > **Conversion** > **mzTab**) and its **Small Molecule Section** port. For the result of the **TextExporter**, we use the `ConsensusTextReader` (**Community Nodes** > **OpenMS** > **Conversion**). +When executed, both nodes will import the OpenMS files and provide access to the data as KNIME tables. The retention time values are exported as a list using the **MzTabReader** based on the current PSI-Standard. This has to be parsed using the **SplitCollectionColumn**, which outputs a ”Split Value 1” based on the first entry in the rention time list, which has to be renamed to retention time using the **ColumnRename**. You can now combine both tables using the **Joiner** node (**Manipulation** > **Column** > **Split & Combine**) and configure it to match the m/z and retention time values of the respective tables. The full workflow is shown in Figure 37. + +(Figure_37)= +|![Label-free quantification and identification workflow for metabolites that loads the results into KNIME and joins the tables](/_images/openms-user-tutorial/metabo/metabo_part3.png)| +|:--:| +|Figure 37: Label-free quantification and identification workflow for metabolites that loads the results into KNIME and joins the tables.| + +### Adduct grouping + +Metabolites commonly co-elute as ions with different adducts (e.g., glutathione+H, glutathione+Na) or with charge-neutral modifications (e.g., water loss). Grouping such related ions allows to leverage information across features. For example, a low intensity, single trace feature could still be assigned a charge and adduct due to a matching high-quality feature. Several OpenMS tools, such as **AccurateMassSearch**, can use this information to, for example, narrow down candidates for identification. + +For this grouping task, we provide the **MetaboliteAdductDecharger** node. Its method explores the combinatorial space of all adduct combinations in a charge range for optimal explanations. Using defined adduct probabilities, it assigns co-eluting features having suitable mass shifts and charges those adduct combinations which maximize overall ion probabilities. + +The tool works natively with featureXML data, allowing the use of reported convex hulls. On such a single-sample level, co-elution settings can be chosen more stringently, as ionization-based adducts should not influence the elution time: Instead, elution differences of related ions should be due to slightly differently estimated times for their feature centroids. + +Alternatively, consensusXML data from feature linking can be converted for use, though with less chromatographic information. Here, the elution time averaging for features linked across samples, motivates wider co-elution tolerances. + +The two main tool outputs are a consensusXML file with compound groups of related input ions, and a featureXML containing the input file but annotated with inferred adduct information and charges. + +Options to respect or replace ion charges or adducts allow for example: +- Heuristic but faster, iterative adduct grouping(**MetaboliteAdductDecharger → MetaboliteFeatureDeconvolution → q_try** set to “feature”) by chaining multiple **MetaboliteAdductDecharger** nodes with growing adduct sets, charge ranges or otherwise relaxed tolerances. +- More specific feature linking (**FeatureLinkerUnlabeledQT → algorithm → ignore_adduct** set to “false”) + +(Figure_38)= +|![Metabolite Adduct Decharger adduct grouping workflow](/_images/openms-user-tutorial/metabo/mad.png)| +|:--:| +|Figure 38: Metabolite Adduct Decharger adduct grouping workflow. | + +
    +

    **Task**

    +A modified metabolomics workflow with exemplary MetaboliteAdductDecharger use and parameters is provided in {path}`Workflows,MetaboliteAdductGrouping.knwf`. Run the workflow, inspect tool outputs and compare **AccurateMassSearch** results with and without adduct grouping. +
    + +### Visualizing data + +Now that you have your data in KNIME you should try to get a feeling for the capabilities of KNIME. + +
    +

    **Task**

    +Check out the **Molecule Type Cast** node (**Chemistry** > **Translators**) together with subsequent cheminformatics nodes (e.g. **RDKit From Molecule**(**Community Nodes** > **RDKit** > **Converters**)) to render the structural formula contained in the result table. +
    + +
    +

    **Task**

    +Have a look at the `Column Filter` node to reduce the table to the interesting columns, e.g., only the Ids, chemical formula, and intensities. +
    + +
    +

    **Task**

    +Try to compute and visualize the m/z and retention time error of the different feature elements (from the input maps) of each consensus feature. Hint: A nicely configured **Math Formula (Multi Column)** node should suffice. +
    + +### Spectral library search + +Identifying metabolites using only the accurate mass may lead to ambiguous results. In practice, additional information (e.g. the retention time) is used to further narrow down potential candidates. Apart from MS1-based features, tandem mass spectra (MS2) of metabolites provide additional information. In this part of the tutorial, we take a look on how metabolite spectra can be identified using a library of previously identified spectra. + +Because these libraries tend to be large we don’t distribute them with OpenMS. + +
    +

    **Task**

    +Construct the workflow as shown in Fig. 39. Use the file {path}`ExampleData,Metabolomics,datasets,MetaboliteIDSpectraDBpositive.mzML` as input for your workflow. You can use the spectral library from {path}`ExampleData,Metabolomics,databases,MetaboliteSpectralDB.mzML` as second input. The first input file contains tandem spectra that are identified by the **MetaboliteSpectralMatcher**. The resulting mzTab file is read back into a KNIME table The retention time values are exported as a list based on the current PSI-Standard. This has to be parsed using the **SplitCollectionColumn**, which outputs a ”Split Value 1” based on the first entry in the rention time list, which has to be renamed to retention time using the **ColumnRename** before it is stored in an Excel table. Make sure that you connect the **MzTabReader** port corresponding to the Small Molecule Section to the **Excel writer (XLS)**. Please select the ”add column headers” option in the **Excel writer (XLS)**). +
    + +(Figure_39)= +|![Spectral library identification workflow](/_images/openms-user-tutorial/metabo/speclib.png)| +|:--:| +|Figure 39: Spectral library identification workflow.| + +Run the workflow and inspect the output. + +### Manual validation + +In metabolomics, matches between tandem spectra and spectral libraries are manually validated. Several commercial and free online resources exist which help in that task. Some examples are: + +- mzCloud contains only spectra from Thermo Orbitrap instruments. The webpage requires Microsoft Silverlight which currently does not work in modern browsers (see the following [link](https://www.mzcloud.org/DataViewer). +- MassBank North America (MoNA) has spectra from different instruments but falls short in number of spectra (compared to Metlin and mzCloud). See the following [link](http://mona.fiehnlab.ucdavis.edu/spectra/display/KNA00122). +- METLIN includes 961,829 molecules ranging from lipids, steroids, metabolites, small peptides, carbohydrates, exogenous drugs and toxicants. In total over 14,000 metabolites. + +Here, we will use METLIN to manually validate metabolites. + +
    +

    **Task**

    +Check in the .xlsx output from the Excel writer (XLS) if you can find glutathione. Use the retention time column to find the spectrum in the mzML file. Here open the [file](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Example_Data/Metabolomics/datasets/Metabolite_ID_SpectraDB_positive.mzML) in the {path}`Example_Data,Metabolomics,datasets,MetaboliteIDSpectraDBpositive.mzML` in TOPPView. The MSMS spectrum with the retention time of 67.6 s is used as example. The spectrum can be selected based on the retention time in the scan view window. Therefore the MS1 spectrum with the retention time of 66.9 s has to be double clicked and the MSMS spectra recorded in this time frame will show up. Select the tandem spectrum of Glutathione, but do not close TOPPView, yet. +
    + +(Figure_40)= +|![Tandem spectrum of glutathione. Visualized in TOPPView.](/_images/openms-user-tutorial/metabo/glutathioneTV.png)| +|:--:| +|Figure 40: Tandem spectrum of glutathione. Visualized in TOPPView.| + +
    +

    **Task**

    +On the METLIN homepage search for **Name** Glutathione using the **Advanced Search**. See the [link](https://metlin.scripps.edu/landing_page.php?pgcontent=advanced_search). Note that free registration is required. Which collision energy (and polarity) gives the best (visual) match to your experimental spectrum in TOPPView? Here you can compare the fragmentation patterns in both spectra shown by the Intensity or relative Intensity, the m/z of a peak and the distance between peaks. Each distance between two peaks corresponds to a fragment of elemental composition (e.g., NH2 with the charge of one would have mass of two peaks of 16.023 Th). +
    + +(Figure_41)= +|![Tandem spectrum of glutathione. Visualized in Metlin. Note that several fragment spectra from varying collision energies are available.](/_images/openms-user-tutorial/metabo/glutathioneMetlin.png)| +|:--:| +|Figure 41: Tandem spectrum of glutathione. Visualized in Metlin. Note that several fragment spectra from varying collision energies are available.| + +### De novo identification + +Another method for MS2 spectra-based metabolite identification is de novo identification. This approach can be used in addition to the other methods (accurate mass search, spectral library search) or individually if no spectral library is available. In this part of the tutorial, we discuss how metabolite spectra can be identified using de +novo tools. To this end, the tools SIRIUS and CSI:FingerID ([^5], [^6], [^7]) were integrated in the OpenMS Framework as SiriusAdapter. SIRIUS uses isotope pattern analysis to detect the molecular formula and further analyses the fragmentation pattern +of a compound using fragmentation trees. CSI:FingerID is a method for searching a +fingerprint of a small molecule (metabolite) in a molecular structure database. +The node **SiriusAdapter** is able to work in different modes depending on the provided input. + +- Input: mzML - SiriusAdapter will search all MS2 spectra in a map. +- Input: mzML, featureXML (FeatureFinderMetabo) - SiriusAdapter can use the provided feature information to reduce the search space to valid features with MS2 spectra. Additionally it can use the isotopic trace information. +- Input: mzML, featureXML (FeatureFinderMetabo / MetaboliteAdductDecharger / AccurateMassSearch) - SiriusAdapter can use the feature information as mentioned above together with feature adduct information from adduct grouping or previous identification. + +By using a mzML and featureXML, SIRIUS gains a lot of additional information by using the OpenMS tools for preprocessing. + +
    +

    **Task**

    +Construct the workflow as shown in Fig. 42. +{path}`Example_Data,Metabolomics,datasets` +Use the [file](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Example_Data/Metabolomics/datasets/Metabolite_DeNovoID.mzML) `MetaboliteDeNovoID.mzML` as input for your workflow. +
    + +Below we show an example workflow for de novo identification (Fig. 42). Here, the node `FeatureFinderMetabo` is used for feature detection to annotate analytes in mz, rt, intensity and charge. This is followed by adduct grouping, trying to asses possible adducts based on the feature space using the **MetaboliteAdductDecharger**. In addition, the **HighResPrecursorMassCorrector** can use the newly generated feature information to map MS2 spectra, which were measured on one of the isotope traces to the monoisotopic precursor. This helps with feature mapping and analyte identification in the **SiriusAdapter** due to the usage of additional MS2 spectra that belong to a specific feature. + +(Figure_42)= +|![De novo identification workflow](/_images/openms-user-tutorial/metabo/denovoid.png)| +|:--:| +|Figure 42: *De novo* identification workflow| + +Run the workflow and inspect the output. + +The output consists of two mzTab files and an internal .ms file. One mzTab for SIRIUS and the other for the CSI:FingerID. These provide information about the chemical formula, adduct and the possible compound structure. The information is referenced to the spectrum used in the analysis. Additional information can be extracted from the **SiriusAdapter** by setting an ”out_workspace_directory”. Here the SIRIUS workspace will be provided after the calculation has finished. This workspace contains information about annotated fragments for each successfully explained compound. + +## Downstream data analysis and reporting + +In this part of the metabolomics session we take a look at more advanced downstream analysis and the use of the statistical programming language R. As laid out in the introduction we try to detect a set of spike-in compounds against a complex blood background. As there are many ways to perform this type of analysis we provide a complete workflow. + +
    +

    **Task**

    +Import the workflow from WorkflowsmetaboliteID.knwf in KNIME: +**File** > **Import KNIME Workflow...** +
    + +The section below will guide you in your understanding of the different parts of the workflow. Once you understood the workflow you should play around and be creative. Maybe create a novel visualization in KNIME or R? Do some more elaborate statistical analysis? Note that some basic R knowledge is required to fully understand the processing in **R Snippet** nodes. + +### Signal processing and data preparation for identification + +The following part is analogous to what you did for the simple metabolomics pipeline. + +### Data preparation for quantification + +The first part is identical to what you did for the simple metabolomics pipeline. Additionally, we convert zero intensities into NA values and remove all rows that contain at least one NA value from the analysis. We do this using a very simple **R Snippet** and subsequent **Missing Value filter** node. + +
    +

    **Task**

    +Inspect the **R Snippet** by double-clicking on it. The KNIME table that is passed to an **R Snippet** node is available in R as a data.frame named knime.in. The result of this node will be read from the data.frame knime.out after the script finishes. Try to understand and evaluate parts of the script (Eval Selection). In this dialog you can also print intermediary results using for example the R command head(knime.in) or cat(knime.in) to the Console pane. +
    + +### Statistical analysis + +After we linked features across all maps, we want to identify features that are significantly deregulated between the two conditions. We will first scale and normalize the data, then perform a t-test, and finally correct the obtained p-values for multiple testing using Benjamini-Hochberg. All of these steps will be carried out in individual **R Snippet** nodes. +- Double-click on the first **R Snippet** node labeled ”log scaling” to open the **R Snippet** dialog. In the middle you will see a short R script that performs the log scaling. To perform the log scaling we use a so-called regular expression (grepl) to select all columns containing the intensities in the six maps and take the log2 logarithm. +- The output of the log scaling node is also used to draw a boxplot that can be used to examine the structure of the data. Since we only want to plot the intensities in the different maps (and not m/z or rt) we first use a `Column Filter` node to keep only the columns that contain the intensities. We connect the resulting table to a **Box Plot** node which draws one box for every column in the input table. Right-click and select **View: Box Plot** +- The median normalization is performed in a similar way to the log scaling. First we calculate the median intensity for each intensity column, then we subtract the median from every intensity. +- Open the **Box Plot** connected to the normalization node and compare it to the box plot connected to the log scaling node to examine the effect of the median normalization. +- To perform the t-test we defined the two groups we want to compare. Finally we save the p-values and fold-changes in two new columns named p-value and FC. +- The **Numeric Row Splitter** is used to filter less interesting parts of the data. In this case we only keep columns where the fold-change is ≥ 2. +- We adjust the p-values for multiple testing using Benjamini-Hochberg and keep all consensus features with a q-value ≤ 0.01 (i.e. we target a false-discovery rate of 1%). + +### Interactive visualization + +KNIME supports multiple nodes for interactive visualization with interrelated output. The nodes used in this part of the workflow exemplify this concept. They further demonstrate how figures with data dependent customization can be easily realized using basic KNIME nodes. Several simple operations are concatenated in order to enable an interactive volcano plot. + +- We first log-transform fold changes and p-values in the **R Snippet** node. We then append columns noting interesting features (concerning fold change and p-value). +- With this information, we can use various Manager nodes (**Views** > **Property**) to emphasize interesting data points. The configuration dialogs allow us to select columns to change color, shape or size of data points dependent on the column values. +- The **Scatter Plot** node (from the **Views** repository) enables interactive visualization of the logarithmized values as a volcano plot: the log-transformed values can be chosen in the ‘Column Selection’ tab of the plot view. Data points can be selected in the plot and highlighted via the menu option. The highlighting transfers to all other interactive nodes connected to the same data table. In our case, selection and the highlighting will also occur in the **Interactive Table** node (from the **Views** repository). +- Output of the interactive table can then be filtered via the ”HiLite” menu tab. For example, we could restrict shown rows to points highlighted in the volcano plot. + +
    +

    **Task**

    +Inspect the nodes of this section. Customize your visualization and possibly try to visualize other aspects of your data. +
    + +### Advanced visualization + +R Dependencies: This section requires that the R packages ggplot2 and ggfortify are both installed. ggplot2 is part of the KNIME R Statistics Integration (Windows Binaries) which should already be installed via the full KNIME installer, ggfortify however is not. In case that you use an R installation where one or both of them are not yet installed, add an **R Snippet** node and double-click to configure. In the R Script text editor, enter the following code: + +```r +#Include the next line if you also have to install ggplot2: +install.packages("ggplot2") + +#Include the following lines to install ggfortify: +install.packages("ggfortify") + +library(ggplot2) +library(ggfortify) +``` + +You can remove the: + +```r +install.packages +``` + +commands once it was successfully installed. + +Even though the basic capabilities for (interactive) plots in KNIME are valuable for initial data exploration, professional looking depiction of analysis results often relies on dedicated plotting libraries. The statistics language R supports the addition of a large variety of packages, including packages providing extensive plotting capabilities. This part of the workflow shows how to use R nodes in KNIME to visualize more advanced figures. Specifically, we make use of different plotting packages to realize heatmaps. +- The used `RView (Table)` nodes combine the possibility to write R snippet code with visualization capabilities inside KNIME. Resulting images can be looked at in the output RView, or saved via the **Image Writer (Port)** node. +- The heatmap nodes make use of the `gplots` libary, which is by default part of the R Windows binaries (for full KNIME version 3.1.1 or higher). We again use regular expressions to extract all measured intensity columns for plotting. For clarity, feature names are only shown in the heatmap after filtering by fold changes. + +### Data preparation for reporting + +Following the identification, quantification and statistical analysis our data is merged and formatted for reporting. First we want to discard our normalized and logarithmized intensity values in favor of the original ones. To this end we first remove the intensity columns (`Column Filter`) and add the original intensities back (**Joiner**). For that, we use an Inner Join 2 with the **Joiner** node. In the dialog of the node, we add two entries for the Joining Columns and for the first column we pick `retention_time` from the top input (i.e. the **AccurateMassSearch** output) and `rt_cf` (the retention time of the consensus features) for the bottom input (the result from the quantification). For the second column you should choose `exp_mass_to_charge` and `mz_cf` respectively to make the joining unique. Note that the workflow needs to be executed up to the previous nodes for the possible selections of columns to appear. + +(Figure_43)= +|![Data preparation for reporting](/_images/openms-user-tutorial/metabo/reporting.png)| +|:--:| +|Figure 43: Data preparation for reporting| + +
    +

    **Question**

    +What happens if we use a *Left Outer Join*, *Right Outer Join* or *Full Outer Join* instead of the *Inner Join*? +
    + +
    +

    **Task**

    +Inspect the output of the join operation after the Molecule Type Cast and RDKit molecular structure generation. +
    + +While all relevant information is now contained in our table the presentation could be improved. Currently, we have several rows corresponding to a single consensus feature (=linked feature) but with different, alternative identifications. It would be more convenient to have only one row for each consensus feature with all accurate mass identifications added as additional columns. To this end, we use the **Column to Grid** node that flattens several rows with the same consensus number into a single one. Note that we have to specify the maximum number of columns in the grid so we set this to a large value (e.g. 100). We finally export the data to an Excel file (**XLS Writer**). + +## References + +[^1]: D. S. Wishart, D. Tzur, C. Knox, et al., HMDB: the Human Metabolome Database, +Nucleic Acids Res 35(Database issue), D521–6 (Jan 2007), doi:10.1093/nar/gkl923. 69 + +[^2]: D. S. Wishart, C. Knox, A. C. Guo, et al., HMDB: a knowledgebase for the human +metabolome, Nucleic Acids Res 37(Database issue), D603–10 (Jan 2009), doi: 10.1093/nar/gkn810. 69 + +[^3]: D. S. Wishart, T. Jewison, A. C. Guo, M. Wilson, C. Knox, et al., HMDB 3.0–The +Human Metabolome Database in 2013, Nucleic Acids Res 41(Database issue),D801–7 (Jan 2013), doi:10.1093/nar/gks1065. 69 + +[^4]: J. Griss, A. R. Jones, T. Sachsenberg, M. Walzer, L. Gatto, J. Hartler, G. G. +Thallinger, R. M. Salek, C. Steinbeck, N. Neuhauser, J. Cox, S. Neumann, J. Fan, +F. Reisinger, Q.-W. Xu, N. Del Toro, Y. Perez-Riverol, F. Ghali, N. Bandeira, I. Xenarios, O. Kohlbacher, J. A. Vizcaino, and H. Hermjakob, The mzTab Data Exchange Format: communicating MS-based proteomics and metabolomics experimental results to a wider audience, Mol Cell Proteomics (Jun 2014), doi:10.1074/mcp.O113.036681. 69 + +[^5]: S. Böcker, M. C. Letzel, Z. Lipták, and A. Pervukhin, SIRIUS: Decomposing isotope +patterns for metabolite identification, Bioinformatics 25(2), 218–224 (2009), doi:10.1093/bioinformatics/btn603. 75 + +[^6]: S. Böcker and K. Dührkop, Fragmentation trees reloaded, J. Cheminform. 8(1), +1–26 (2016), doi:10.1186/s13321-016-0116-8. 75 + +[^7]: K. Dührkop, H. Shen, M. Meusel, J. Rousu, and S. Böcker, Searching molecular structure databases with tandem mass spectra using CSI:FingerID, Proc. Natl. +Acad. Sci. 112(41), 12580–12585 (oct 2015), doi:10.1073/pnas.1509788112. 75 diff --git a/docs/tutorials/knime-user-tutorial/lfq-peptide-protein.md b/docs/tutorials/knime-user-tutorial/lfq-peptide-protein.md new file mode 100644 index 00000000..c4306ac2 --- /dev/null +++ b/docs/tutorials/knime-user-tutorial/lfq-peptide-protein.md @@ -0,0 +1,295 @@ +# Label-free quantification of peptides and proteins + +## Introduction + +In the following chapter, we will build a workflow with OpenMS / KNIME to quantify a label-free experiment. Label-free +quantification is a method aiming to compare the relative amounts of proteins or peptides in two or more samples. We will +start from the minimal workflow of the last chapter and, step-by-step, build a label-free quantification workflow. + +The complete workflow can be downloaded [here](https://hub.knime.com/openms-team/spaces/Tutorial%20Workflows%20OpenMS%203.0/Proteomics_LFQ~MvMoVSrTZKLI6H3B/current-state) as well. + +## Peptide identification + +As a start, we will extend the minimal workflow so that it performs a peptide identification using the Comet search +engine. Comet is included in the OpenMS installation, so you do not need to download and +install it yourself. + +Let’s start by replacing the input files in our `Input Files` node by the three mzML files in +**Example Data** > **Labelfree** > **datasets** > **lfqxspikeinxdilutionx1-3.mzML**. This is a reduced toy dataset where +each of the three runs contains a constant background of `S. pyogenes` peptides as well as human spike-in peptides in +different concentrations. [^1] + +- Instead of `FileFilter`, we want to perform Comet identification, so we simply replace the `FileFilter` node with the + `CometAdapter` node **Community Nodes** > **OpenMSThirdParty** > **Identification**, and we are almost done. Just make sure you + have connected the `ZipLoopStart` node with the `in` (top) port of the `CometAdapter` node. +- Comet, like most mass spectrometry identification engines, relies on searching the input spectra against sequence + databases. Thus, we need to introduce a search database input. As we want to use the same search database for all of + our input files, we can just add a single `File Importer` node to the workflow and connect it directly with the + `CometAdapter database` (middle) port. KNIME will automatically reuse this Input node each time a new ZipLoop iteration is + started. In order to specify the database, [select](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Example_Data/Labelfree/databases/s_pyo_sf370_potato_human_target_decoy_with_contaminants.fasta) {path}`Example_Data,Labelfree,databases,/break,s_pyo_sf370_potato_human_target_decoy_with_contaminants.fasta` +- Connect the **out** port of the `CometAdapter` to `ZipLoopEnd` and we have a very basic peptide identification workflow. + + ```{note} + You might also want to save your new identification workflow under a different name. Have a look at duplicating workflows + for information on how to create copies of workflows. + ``` +- The result of a single Comet run is basically a number of peptide-spectrum-matches (PSM) with a score each, and these + will be stored in an idXML file. Now we can run the pipeline and after execution is finished, we can have a first look + at the the results: just open the output folder with a file browser and from there open one of the three input mzML's in **TOPPView**. +- Here, annotate this spectrum data file with the peptide identification results. Choose **Tools** > **Annonate with identification** + from the menu and select the idXML file that `CometAdapter` generated (it is located within the output directory that + you specified when starting the pipeline). +- On the right, select the tab **Identification view**. All identified peptides can be seen using this view. User can also + browse the corresponding MS2 spectra. + + ```{note} + Opening the output file of `CometAdapter` (the idXML file) directly is also possible, but unless you REALLY like XML, reading + idXML files is less useful. + ``` +- The search results stored in the idXML file can also be read back into a KNIME table for inspection and subsequent + analyses: Add a `TextExporter` node from **Community Nodes** > **OpenMS** > **File Handling** to your workflow and + connect the output port of your `CometAdapter` (the same port `ZipLoopEnd` is connected to) to its input port. This + tool will convert the idXML file to a more human-readable text file which can also be read into a KNIME table using + the `IDTextReader` node. Add an `IDTextReader` node(**Community Nodes** > **OpenMS** > **Conversion**) after + `TextExporter` and execute it. Now you can right click `IDTextReader` and select **ID Table** to browse your peptide + identifications. +- From here, you can use all the tools KNIME offers for analyzing the data in this table. As a simple example, add a + `Histogram` node (from category **Views**) node after `IDTextReader`, double-click it, select + `peptide_charge` as Dimension, click **Save and Execute** to generate a plot showing the charge state distribution + of your identifications. + +In the next step, we will tweak the parameters of Comet to better reflect the instrument’s accuracy. Also, we will +extend our pipeline with a false discovery rate (FDR) filter to retain only those identifications that will yield an +FDR of < 1 %. + +- Open the configuration dialog of `CometAdapter`. The dataset was recorded using an LTQ Orbitrap XL mass spectrometer, + set the `precursor_mass_tolerance` to 5 and `precursor_error_units` to `ppm`. + + ```{note} + Whenever you change the configuration of a node, the node as well as all its successors will be reset to the Configured + state (all node results are discarded and need to be recalculated by executing the nodes again). + ``` + +- Make sure that `Carbamidomethyl (C)` is set as fixed modification and `Oxidation(M)` as variable modification. + + ```{note} + To add a modification click on the empty value field in the configuration dialog to open the list editor dialog. In the + new dialog click **Add**. Then select the newly added modification to open the drop down list where you can select the + the correct modification. + ``` +- A common step in analysis is to search not only against a regular protein database, but to also search against a decoy + database for FDR estimation. The fasta file we used before already contains such a decoy database. For OpenMS to know + which Comet PSM came from which part of the file (i.e. target versus decoy), we have to index the results. To this end, + extend the workflow with a `PeptideIndexer` node **Community Nodes** > **OpenMS** > **ID Processing**. This node needs + the idXML as input as well as the database file (see below figure). + + ```{tip} + You can direct the files of an `File Importer` node to more than just one destination port. + ``` +- The decoys in the database are prefixed with “DECOY_”, so we have to set `decoy_string` to `DECOY_` and `decoy_string_position` + to `prefix` in the configuration dialog of `PeptideIndexer`. +- Now we can go for the FDR estimation, which the `FalseDiscoveryRate` node will calculate for us (you will find it in + **Community Nodes** > **OpenMS** > **Identification Processing**). `FalseDiscoveryRate` is meant to be run on data with protein inferencences + (more on that later), in order to just use it for peptides, open the configure window, select "show advanced parameter" and toggle "force" to true. +- In order to set the FDR level to 1%, we need an `IDFilter` node from **Community Nodes** > **OpenMS** > **Identification Processing**. + Configuring its parameter `score→pep` to 0.01 will do the trick. The FDR calculations (embedded in the idXML) from + the `FalseDiscoveryRate` node will go into the *in* port of the `IDFilter` node. +- Execute your workflow and inspect the results using `IDTextReader` like you did before. How many peptides did you + identify at this FDR threshold? + + +The below images shows Comet ID pipeline including FDR filtering. + +|![Comet ID pipeline including FDR filtering](/_images/openms-user-tutorial/labelfree/PepIdFDR.png)| + |:--:| +|Figure 12: Comet ID pipeline including FDR filtering| + + +### Bonus task: Identification using several search engines + +```{note} +If you are ahead of the tutorial or later on, you can further improve your FDR identification workflow by a so-called +consensus identification using several search engines. Otherwise, just continue with quantification. +``` + +It has become widely accepted that the parallel usage of different search engines can increase peptide identification +rates in shotgun proteomics experiments. The ConsensusID algorithm is based on the calculation of posterior error +probabilities (PEP) and a combination of the normalized scores by considering missing peptide sequences. + +- Next to the `CometAdapter` add a `XTandemAdapter` **Community Nodes** > **OpenMSThirdParty** > **Identification of Proteins** > **Peptides(SearchEngines)** node and set + its parameters and ports analogously to the `CometAdapter`. In XTandem, to get more evenly distributed scores, we + decrease the number of candidates a bit by setting the precursor mass tolerance to 5 ppm and the fragment mass + tolerance to 0.1 Da. +- To calculate the PEP, introduce a `IDPosteriorErrorProbability` **Community Nodes** > **OpenMS** > **Identification Processing** + node to the output of each ID engine adapter node. This will calculate the PEP to each hit and output an updated idXML. +- To create a consensus, we must first merge these two files with a `FileMerger` node **Community Nodes** > + **GenericKnimeNode** > **Flow** so we can then merge the corresponding IDs with a `IDMerger` **Community Nodes** > + **OpenMS** > **File Handling**. +- Now we can create a consensus identification with the `ConsensusID` **Community Nodes** > **OpenMS** > **Identification Processing** + node. We can connect this to the `PeptideIndexer` and go along with our existing FDR filtering. + + ```{note} + By default, X!Tandem takes additional enzyme cutting rules into consideration (besides the specified tryptic digest). + Thus for the tutorial files, you have to set PeptideIndexer’s `enzyme→specificity` parameter to `none` to accept + X!Tandem's non-tryptic identifications as well. + ``` + +In the end, the ID processing part of the workflow can be collapsed into a Metanode to keep the structure clean (see below figure which shows complete consensus identification workflow). + +|![Complete consensus identification workflow](/_images/openms-user-tutorial/labelfree/PepConsensusId.png)| +|:--:| +|Figure 13: Complete consensus identification workflow| + +## Feature Mapping + +Now that we have successfully constructed a peptide identification pipeline, we can assign this information to the corresponding feature signals. + +- Add a `FeatureFinderCentroided` node from **Community Nodes** > **OpenMS** > **Quantitation** + which gets input from the first output port of the `ZipLoopStart` node. Also, add + an `IDMapper` node (from **Community Nodes** > **OpenMS** > **Identification Processing** ) which receives + input from the `FeatureFinderCentroided` node (Port 1) and the `IDFilter` node (Port 0). The output of the `IDMapper` node is then connected to an in port of the `ZipLoopEnd` node. +- `FeatureFinderCentroided` finds and quantifies peptide ion signals contained in + the MS1 data. It reduces the entire signal, i.e., all peaks explained by one and + the same peptide ion signal, to a single peak at the maximum of the chromatographic elution profile of the monoisotopic mass trace of this peptide ion and + assigns an overall intensity. +- `FeatureFinderCentroided` produces a featureXML file as output, containing only quantitative information of so-far unidentified peptide signals. In order to annotate these with the corresponding ID information, we need the `IDMapper` node. +- Run your pipeline and inspect the results of the `IDMapper` node in TOPPView. Open the mzML file of your data to display the raw peak intensities. +- To assess how well the feature finding worked, you can project the features contained in the featureXML file on the raw data contained in the mzML file. To + this end, open the featureXML file in TOPPView by clicking on File Open file and add it to a new layer ( Open in New layer ). The features are now visualized on top of your raw data. If you zoom in on a small region, you should be able to see the individual boxes around features that have been detected (see Fig. 14). If you + hover over the the feature centroid (small circle indicating the chromatographic apex of monoisotopic trace) additional information of the feature is displayed. + + |![Visualization of detected features (boxes) in TOPPView](/_images/openms-user-tutorial/labelfree/featureXML.png)| + |:--:| + |Figure 14: Visualization of detected features (boxes) in TOPPView| + + ```{note} + The chromatographic RT range of a feature is about 30-60 s and + its m/z range around 2.5 m/z in this dataset. If you have trouble zooming in on a feature, select the full RT range and zoom only into the + m/z dimension by holding down Ctrl ( cmd ⌘ on macOS) and repeatedly + dragging a narrow box from the very left to the very right + ``` +- You can see which features were annotated with a peptide identification by first + selecting the featureXML file in the **Layers** window on the upper right side and then clicking on the icon with the letters A, B and C on the upper icon bar. Now, + click on the small triangle next to that icon and select **Peptide identification**. + +The following image shows the final constructed workflow: + +| ![Extended workflow featuring peptide identification and quantification](/_images/openms-user-tutorial/labelfree/PepQuantIdNoAlign.png) | +|:--------------------------------------------------------------------------------------------------------------------------------------:| +| Figure 15: Extended workflow featuring peptide identification and feature mapping. | + +### Combining features across several label-free experiments + +So far, we successfully performed peptide identification as well as feature mapping on +individual LC-MS runs. For differential label-free analyses, however, we need to identify and map corresponding signals in different experiments and link them together to compare their intensities. Thus, we will now run our pipeline on all three +available input files and extend it a bit further, so that it is able to find and link features across several runs. + +| ![Complete identification and label-free quantification workflow](/_images/openms-user-tutorial/labelfree/PepQuantId.png) | +|:-----------------------------------------------------------------------------------------------------------------------------------------:| +| Figure 16: Complete identification and label-free feature mapping workflow. The identification nodes are grouped together as ID metanode. | + +- To link features across several maps, we first have to align them to correct for retention time shifts between the different label-free measurements. With the `MapAlignerPoseClustering` node in **Community Nodes** > **OpenMS** > **Map Alignment**, we can align corresponding peptide signals to each other as closely as possible by applying a transformation in the RT dimension. + + ```{note} + `MapAlignerPoseClustering` consumes several featureXML files and its output should still be several featureXML files containing the same features, but with the transformed RT values. In its configuration dialog, make sure that **OutputTypes** is set to **featureXML**. + ``` +- With the `FeatureLinkerUnlabeledQT` node in **Community Nodes** > **OpenMS** > **Map Alignment**, we can then perform the actual linking of corresponding features. Its output is a consensusXML file containing linked groups of corresponding features across the different experiments. +- Since the overall intensities can vary a lot between different measurements (for example, because the amount of injected analytes was different), we apply the **ConsensusMapNormalizer** node in **Community Node** > **OpenMS** > **Map Alignment** as a last processing step. Configure its parameters with setting `algorithm_type` to `median`. It will then normalize the maps in such a way that the median intensity of all input maps is equal. +- Export the resulting normalized consensusXML file to a csv format using the **TextExporter** node. +- Use the `ConsensusTextReader` node in **Community Nodes** > **OpenMS** > **Conversion** to convert the output into a KNIME table. After running the node you can view the KNIME table by right-clicking on the `ConsensusTextReader` node and selecting `Consensus Table`. Every row in this table corresponds to a so-called consensus feature, i.e., a peptide signal quantified across several runs. The first couple of columns describe the consensus feature as a whole (average RT and m/z across the maps, charge, etc.). The remaining columns describe the exact positions and intensities of the quantified features separately for all input samples (e.g., intensity_0 is the intensity of the feature in the first input file). The last 11 columns contain information on peptide identification. + + ```{note} + You can specify the desired column separation character in the parameter settings (by default, it is set to “ ” (a space)). The output file of `TextExporter` can also be opened with external tools, e.g., Microsoft Excel, for downstream statistical analyses. + ``` + +## Basic data analysis in KNIME + +In this section we are going to use the output of the `ConsensusTextReader` for downstream analysis of the quantification results: + +- Let’s say we want to plot the log intensity distributions of the human spike-in peptides for all input files. In addition, we will plot the intensity distributions of the background peptides. +- As shown in Fig. 17, add a `Row Splitter` node (**Data Manipulation** > **Row** > **Filter**) after the `ConsensusTextReader` node. Double-click it to configure. The human spike-in peptides have accessions starting with “hum”. Thus, set the column to apply the test to `accessions`, select pattern matching as matching criterion, enter `hum*` into the corresponding text field, and check the contains wild cards box. Press **OK** and execute the node. +- `Row Splitter` produces two output tables: the first one contains all rows from the input table matching the filter criterion, and the second table contains all other rows. You can inspect the tables by right-clicking and selecting **Filtered** and **Filtered Out**. The former table should now only contain peptides with a human accession, whereas the latter should contain all remaining peptides (including unidentified ones). +- Now, since we only want to plot intensities, we can add a `Column Filter` node by going to **Data Manipulation** > `Column Filter`. Connect its input port to the **Filtered output** port of the **Row Filter** node, and open its configuration dialog. We could either manually select the columns we want to keep, or, more elegantly, select **Wildcard/Regex Selection** and enter `intensity_?` as the pattern. KNIME will interactively show you which columns your pattern applies to while you’re typing. +- Since we want to plot log intensities, we will now compute the log of all intensity values in our table. The easiest way to do this in KNIME is a small piece of R code. Add an **R Snippet** node `R` after `Column Filter` and double-click to configure. In the R Script text editor, enter the following code: + + ```r + x <- knime.in # store copy of input table in x + + x[x == 0] <- NA # replace all zeros by NA (= missing value) + + x <- log10(x) # compute log of all values + knime.out <- x # write result to output table + ``` +- Now we are ready to plot! Add a `Box Plot (JavaScript)` node `Views -JavaScript` after the **R Snippet** node, execute it, and open its view. If everything went well, you should see a significant fold change of your human peptide intensities across the three runs. +- To verify that the concentration of background peptides is constant in all three runs, copy and paste the three nodes after `Row Splitter` and connect the duplicated `Column Filter` to the second output port (Filtered Out) of `Row Splitter`, as shown in Fig. 17. Execute and open the view of your second **Box Plot**. + +You have now constructed an entire identification and label-free feature mapping workflow including a simple data analysis using KNIME. The final workflow should like the workflow shown in the following image: + +|![Simple KNIME data analysis example for LFQ](/_images/openms-user-tutorial/labelfree/data_analysis.png)| +|:--:| +|Figure 17: Simple KNIME data analysis example for LFQ| + + +## Extending the LFQ workflow by protein inference and quantification + +We have made the following changes compared to the original label-free quantification workflow from the last chapter: + +- First, we have added a **ProteinQuantifier** node and connected its input port to the output port of the **ConsensusMapNormalizer** node. +- This already enables protein quantification. **ProteinQuantifier** quantifies peptides by summarizing over all observed charge states and proteins by summarizing over their quantified peptides. It stores two output files, one for the quantified peptides and one for the proteins. +- In this example, we consider only the protein quantification output file, which is written to the first output port of **ProteinQuantifier**. +- Because there is no dedicated node in KNIME to read back the **ProteinQuantifier** output file format into a KNIME table, we have to use a workaround. Here, we have added an additional URI Port to Variable node which converts the name of the output file to a so-called “flow variable” in KNIME. This variable is passed on to the next node **CSV Reader**, where it is used to specify the name of the input file to be read. If you double-click on **CSV Reader**, you will see that the text field, where you usually enter the location of the CSV file to be read, is greyed out. Instead, the flow variable is used to specify the location, as indicated by the small green button with the “v=?” label on the right. +- The table containing the **ProteinQuantifier** results is filtered one more time in order to remove decoy proteins. You can have a look at the final list of quantified protein groups by right-clicking the **Row Filter** and selecting **Filtered**. +- By default, i.e., when the second input port `protein_groups` is not used, **ProteinQuantifier** quantifies proteins using only the unique peptides, which usually results in rather low numbers of quantified proteins. +- In this example, however, we have performed protein inference using Fido and + used the resulting protein grouping information to also quantify indistinguishable proteins. In fact, we also used a greedy method in **FidoAdapter** (parameter `greedy_group_resolution`) to uniquely assign the peptides of a group to the most probable protein(s) in the respective group. This boosts the number of quantifications but slightly raises the chances to yield distorted protein quantities. +- As a prerequisite for using **FidoAdapter**, we have added an **IDPosteriorErrorProbability** node within the ID meta node, between the **XTandemAdapter** (note the replacement of OMSSA because of ill-calibrated scores) and **PeptideIndexer**. We have + set its parameter `prob_correct` to `true`, so it computes posterior probabilities instead of posterior error probabilities (1 - PEP). These are stored in the resulting idXML file and later on used by the Fido algorithm. Also note that we excluded FDR filtering from the standard meta node. Harsh filtering before inference impacts the calibration of the results. Since we filter peptides before quantification though, no potentially random peptides will be included in the results anyway. +- Next, we have added a third outgoing connection to our ID meta node and connected it to the second input port of `ZipLoopEnd`. Thus, KNIME will wait until all input files have been processed by the loop and then pass on the resulting list of idXML files to the subsequent IDMerger node, which merges all identifications from all idXML files into a single idXML file. This is done to get a unique assignment of peptides to proteins over all samples. +- Instead of the meta node **Protein inference** with **FidoAdapter**, we could have just used a **FidoAdapter** node ( **Community Nodes** > **OpenMS** > **Identification Processing**). However, the meta node contains an additional subworkflow which, besides calling **FidoAdapter**, performs a statistical validation (e.g. (pseudo) receiver operating curves; ROCs) of the protein inference results using some of the more advanced KNIME and R nodes. The meta node also shows how to use **MzTabExporter** and **MzTabReader**. + +## Statistical validation of protein inference results + +In the following section, we will explain the subworkflow contained in the **Protein inference with FidoAdapter** meta node. + +### Data preparation + +For downstream analysis on the protein ID level in KNIME, it is again necessary to convert the idXML-file-format result generated from **FidoAdapter** into a KNIME table. + +- We use the **MzTabExporter** to convert the inference results from **FidoAdapter** to a human readable, tab-separated mzTab file. mzTab contains multiple sections, + that are all exported by default, if applicable. This file, with its different sections can again be read by the **MzTabReader** that produces one output in KNIME table + format (triangle ports) for each section. Some ports might be empty if a section did not exist. Of course, we continue by connecting the downstream nodes with the protein section output (second port). +- Since the protein section contains single proteins as well as protein groups, we filter them for single proteins with the standard **Row Filter**. + +### ROC curve of protein ID + +ROC Curves (Receiver Operating Characteristic curves) are graphical plots that visualize sensitivity (true-positive rate) against fall-out (false positive rate). They are often used to judge the quality of a discrimination method like e.g., peptide or protein identification engines. ROC Curve already provides the functionality of drawing ROC curves for binary classification problems. When configuring this node, select the `opt_global_target_decoy` column as the class (i.e. target outcome) column. We want to find out, how good our inferred protein probability discriminates between them, +therefore add `best_search_engine_score[1]` (the inference engine score is treated like a peptide search engine score) to the list of *”Columns containing positive class probabilities”*. View the plot by right-clicking and selecting **View: ROC Curves**. A perfect classifier has +an area under the curve (AUC) of 1.0 and its curve touches the upper left of the plot. However, in protein or peptide identification, the ground-truth (i.e., which target +identifications are true, which are false) is usually not known. Instead, so called pseudoROC Curves are regularly used to plot the number of target proteins against the false +discovery rate (FDR) or its protein-centric counterpart, the q-value. The FDR is approximated by using the target-decoy estimate in order to distinguish true IDs from +false IDs by separating target IDs from decoy IDs. + +### Posterior probability and FDR of protein IDs + +ROC curves illustrate the discriminative capability of the scores of IDs. In the case of protein identifications, Fido produces the posterior probability of each protein as +the output score. However, a perfect score should not only be highly discriminative (distinguishing true from false IDs), it should also be “calibrated” (for probability indicating that all IDs with reported posterior probability scores of 95% should roughly have a 5% probability of being false. This implies that the estimated number of false +positives can be computed as the sum of posterior error probabilities ( = 1 - posterior probability) in a set, divided by the number of proteins in the set. Thereby a +posterior-probability-estimated FDR is computed which can be compared to the actual target-decoy FDR. We can plot calibration curves to help us visualize the quality of +the score (when the score is interpreted as a probability as Fido does), by comparing how similar the target-decoy estimated FDR and the posterior probability estimated +FDR are. Good results should show a close correspondence between these two measurements, although a non-correspondence does not necessarily indicate wrong results. + +The calculation is done by using a simple R script in R snippet. First, the target decoy protein FDR is computed as the proportion of decoy proteins among all significant protein IDs. Then posterior probabilistic-driven FDR is estimated by the average of the posterior error probability of all significant protein IDs. Since FDR is the property for a group of protein IDs, we can also calculate a local property for each protein: the q-value of a certain protein ID is the minimum FDR of any groups of protein IDs +that contain this protein ID. We plot the protein ID results versus two different kinds of FDR estimates in R View(Table) (see Fig. 22). +(Figure_21)= +|![The workflow of statistical analysis of protein inference results](/_images/openms-user-tutorial/protein-inference/inference_metanode.png)| +|:--:| +|Figure 21: The workflow of statistical analysis of protein inference results| +(Figure_22)= +|![The pseudo-ROC Curve of protein IDs](/_images/openms-user-tutorial/protein-inference/proteinFDR.png)| +|:--:| +|Figure 22: The pseudo-ROC Curve of protein IDs. The accumulated number of protein IDs is plotted on two kinds of scales: target-decoy protein FDR and Fido posterior probability estimated FDR. The largest value of posterior probability estimated FDR is already smaller than 0.04, this is because the posterior probability output from Fido is generally very high| + +## References + +[^1]: A. Chawade, M. Sandin, J. Teleman, J. Malmström, and F. Levander, Data Processing Has Major Impact on the Outcome of Quantitative Label-Free LC-MS Analysis, Journal of Proteome Research 14(2), 676–687 (2015), PMID: 25407311, +arXiv:http://dx.doi.org/10.1021/pr500665j, doi:10.1021/pr500665j. 30 \ No newline at end of file diff --git a/docs/tutorials/knime-user-tutorial/minimal-workflow.md b/docs/tutorials/knime-user-tutorial/minimal-workflow.md new file mode 100644 index 00000000..feb60f73 --- /dev/null +++ b/docs/tutorials/knime-user-tutorial/minimal-workflow.md @@ -0,0 +1,108 @@ +Minimal Workflow +================ + +Let us start with the creation of a simple workflow. As a first step, we will gather some basic +information about the data set before starting the actual development of a data analysis workflow. This minimal workflow +can also be used to check if all requirements are met and that your system is compatible. + +- Create a new workflow. +- Add an `File Importer` node and an `Output Folder` node (found in **Community Nodes** > **GenericKnimeNodes** > **IO**) + and a `FileInfo` node (to be found in the category **Community Node** > **OpenMS** > **File Handling**) to the workflow. +- Connect the File Importer node to the FileInfo node, and the first output port of the FileInfo node to the Output Folder + node. + +```{tip} +In case you are unsure about which node port to use, hovering the cursor over the port in question will display the port +name and what kind of input it expects. +``` +The complete workflow is shown in below image. `FileInfo` can produce two different kinds of output files. + +|![A minimal workflow calling FileInfo on a single file.](/_images/openms-user-tutorial/knime-setup/minimal_FileInfo.png)| +|:--:| +|Figure 8: A minimal workflow calling `FileInfo` on a single file.| + +- All nodes are still marked red, since we are missing an actual input file. Double-click the File Importer node and select + **Browse**. In the file system browser [select](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Example_Data/Introduction/datasets/tiny/velos005614.mzML) {path}`Example_Data,Introduction,datasets,tiny,velos005614.mzML` + and click **Open**. Afterwards close the dialog by clicking **Ok**. + +- The `File Importer` node and the `FileInfo` node should now have switched to yellow, but the `Output Folder` node is still red. + Double-click on the `Output Folder` node and click on **Browse** to select an output directory for the generated data. +- Great! Your first workflow is now ready to be run. Press + F7 (shift key + F7; or the + button with multiple green triangles in the KNIME Toolbar) to execute the complete workflow. You can also right click + on any node of your workflow and select Execute from the context menu. +- The traffic lights tell you about the current status of all nodes in your workflow. Currently running tools show either + a progress in percent or a moving blue bar, nodes waiting for data show the small word “queued”, and successfully + executed ones become green. If something goes wrong (e.g., a tool crashes), the light will become red. +- In order to inspect the results, you can just right-click the Output Folder node and select **View: Open the output folder** + You can then open the text file and inspect its contents. You will find some basic information of the data contained + in the mzML file, e.g., the total number of spectra and peaks, the RT and m/z range, and how many MS1 and MS2 spectra + the file contains. + +Workflows are typically constructed to process a large number of files automatically. As a simple example, consider you +would like to filter multiple mzML files to only include MS1 spectra. We will now modify the workflow to compute the +same information on three different files and then write the output files to a folder. + +- We start from the previous workflow. +- First we need to replace our single input file with multiple files. Therefore we add the `Input Files` node from the + category **Community Nodes** > **GenericKnimeNodes** > **IO**. +- To select the files we double-click on the `Input Files` node and click on **Add**. In the filesystem browser we select + all three files from the [directory](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Example_Data/Introduction/datasets/tiny) **Example_Data** > **Introduction** > **datasets** > **tiny**. And close the dialog + with **Ok**. +- We now add two more nodes: the `ZipLoopStart` and the `ZipLoopEnd` node from the category + **Community Nodes** > **GenericKnimeNodes* > **Flow** and replace the `FileInfo` node with `FileFilter` from **Community Nodes** > **OpenMS** > **File Handling**. +- Afterwards we connect the `Input Files` node to the first port of the `ZipLoopStart` node, the first port of the `ZipLoopStart` + node to the **FileConverter** node, the first output port of the **FileConverter** node to the first input port of the + `ZipLoopEnd` node, and the first output port of the `ZipLoopEnd` node to the `Output Folder` node. + +The complete workflow is shown in the top right of the figure below. + +|![A minimal workflow calling the FileFilter on multiple mzML files in a loop](/_images/openms-user-tutorial/knime-setup/KNIME_annotated_FileFilter.png)| +|:--:| +|Figure 9: The FileFilter workflow. Showing the configure dialog for `FileFilter`, and the level selector pane. + +Now we need to configure the `FileFilter` to only store MS1 data. To do this we double click on the `FileFilter` node to open the configuration dialog (see left pane above), double click "level", select 2 +from the sub-pane (see bottom right panel above), and click delete. Repeat the process for 3. Select OK to exit the sub-pane, and then OK again in the configuration dialog. + +Execute the workflow and inspect the output as before. + +Now, if you open the resulting files in TOPPView, you can see that only the MS1 spectra remain. + +In case you had trouble to understand what `ZipLoopStart` and `ZipLoopEnd` do, here is a brief explanation: + +- The `Input Files` node passes a list of files to the `ZipLoopStart` node. +- The `ZipLoopStart` node takes the files as input, but passes the single files sequentially (that is: one after the other) + to the next node. +- The `ZipLoopEnd` collects the single files that arrive at its input port. After all files have been processed, the collected + files are passed again as file list to the next node that follows. + +## Advanced topic: Metanodes + +Workflows can get rather complex and may contain dozens or even hundreds of nodes. KNIME provides a simple way to +improve handling and clarity of large workflows: + +**Metanodes** allow to bundle several nodes into a single **Metanode**. + +
    +

    **Task**

    + Select multiple nodes (e.g. all nodes of the **ZipLoop** including the start and end node). To select a set of nodes, draw a rectangle around them with the left mouse button or hold Ctrl to add/remove single nodes from the selection. +
    +

    **Tip**

    + There is a **Select Scope** option when you right-click a node in a loop, that does exactly that for you. Then, open the + context menu (right-click on a node in the selection) and select **Create Metanode**. Enter a caption for the **Metanode**. + The previously selected nodes are now contained in the **Metanode**. Double-clicking on the **Metanode** will display + the contained nodes in a new tab window. +
    +
    + +
    +

    **Task**

    +Create the Metanode to let it behave like an encapsulated single node. First select the **Metanode**, open the context +menu (right-click) and select **Metanode** > **Convert to Component**. The differences between Metanodes and components +are marginal (Metanodes allow exposing user inputs, workflow variables and contained nodes). Therefore, we suggest to use standard +Metanodes to clean up your workflow and cluster common subparts until you actually notice their limits. +
    + +
    +

    **Task**

    +Undo the packaging. First select the **Metanode/Component**, open the context menu (right-click) and select **Metanode/Component** > **Expand**. +
    \ No newline at end of file diff --git a/docs/tutorials/knime-user-tutorial/msstats.md b/docs/tutorials/knime-user-tutorial/msstats.md new file mode 100644 index 00000000..c54d3a8b --- /dev/null +++ b/docs/tutorials/knime-user-tutorial/msstats.md @@ -0,0 +1,644 @@ +MSStats +======= + +## R integration + +KNIME provides a large number of nodes for a wide range of statistical analysis, machine learning, data processing, and +visualization. Still, more recent statistical analysis methods, specialized visualizations or cutting edge algorithms +may not be covered in KNIME. In order to expand its capabilities beyond the readily available nodes, external scripting +languages can be integrated. In this tutorial, we primarily use scripts of the powerful statistical computing language R. +Note that this part is considered advanced and might be difficult to follow if you are not familiar with R. In this case +you might skip this part. + +**R View (Table)** allows to seamlessly include R scripts into KNIME. We will +demonstrate on a minimal example how such a script is integrated. + +
    +

    **Task**

    +

    +First we need some example data in KNIME, which we will generate using the **Data Generator** node (**IO** > **Other** > **Data Generator**). +You can keep the default settings and execute the node. The table contains four columns, each containing random coordinates and one column +containing a cluster number (Cluster_0 to Cluster_3). Now place a **R View (Table)** node into the workflow and connect +the upper output port of the **Data Generator** node to the input of the **R View (Table)** node. Right-click and +configure the node. If you get an error message like `Execute failed: R_HOME does not contain a folder with name ’bin’.` +or `Execution failed: R Home is invalid.`: please change the R settings in the preferences. To do so open **File** > +**Preferences** > **KNIME** > **R** and enter the path to your R installation (the folder that contains the bin +directory. e.g., {path}`C:,Program Files,R,R-3.4.3`). +

    +

    +If you get an error message like: ”Execute failed: Could not find Rserve package. Please install it in your R +installation by running ”install.packages(’Rserve’)”.” You may need to run your R binary as administrator (In windows +explorer: right-click ”Run as administrator”) and enter install.packages(’Rserve’) to install the package. +

    +

    +If R is correctly recognized we can start writing an R script. Consider that we are interested in plotting the first and +second coordinates and color them according to their cluster number. In R this can be done in a single line. In the +**R view (Table)** text editor, enter the following code: +```r +plot(x=knime.in$Universe_0_0, y=knime.in$Universe_0_1, main="Plotting column Universe_0_0 vs. Universe_0_1", col=knime.in$"Cluster Membership") +``` +

    +

    +**Explanation:** The table provided as input to the **R View (Table)** node is available as R **data.frame** with name +`knime.in`. Columns (also listed on the left side of the R View window) can be accessed in the usual R way by first +specifying the `data.frame` name and then the column name (e.g., `knime.in$Universe_0_0`). `plot` is the plotting function +we use to generate the image. We tell it to use the data in column `Universe_0_0` of the dataframe object **knime.in** +(denoted as `knime.in$Universe_0_0`) as x-coordinate and the other column `knime.in$Universe_0_1` as y-coordinate in the +plot. `main` is simply the main title of the plot and `col` the column that is used to determine the color (in this case +it is the `Cluster Membership` column). +

    +

    +Now press the Eval script and Show plot buttons. +

    +
    + +```{note} +Note that we needed to put some extra quotes around `Cluster Membership`. If we omit those, R would interpret the column +name only up to the first space `(knime.in$Cluster)` which is not present in the table and leads to an error. Quotes are +regularly needed if column names contain spaces, tabs or other special characters like $ itself. +``` + +## Using MSstats in a KNIME workflow + +The R package `MSstats` can be used for statistical relative quantification of proteins and peptides in mass spectrometry-based proteomics. Supported are label-free as well as labeled experiments in combination with data-dependent, targeted and data independent acquisition. Inputs can be identified and quantified entities (peptides or proteins) and the output is a list of differentially abundant entities, or summaries of their relative abundance. It depends on accurate feature detection, identification +and quantification which can be performed e.g. by an OpenMS workflow. MSstats can be used for data processing & visualization, as well as statistical modeling & inference. Please see [^1] and the [MSstats](http://msstats.org) website for further +information. + +### Identification and quantification of the iPRG2015 data with subsequent MSstats analysis + +Here, we describe how to use OpenMS and MSstats for the analysis of the ABRF iPRG2015 dataset[^2]. + +```{note} +Reanalysing the full dataset from scratch would take too long. In the following tutorial, we will focus on just the conversion process and the downstream analysis. +``` + + +#### Dataset + +The iPRG (Proteome Informatics Research Group) dataset from the study in 2015, as +described in [^2], aims at evaluating the effect of statistical analysis software on the +accuracy of results on a proteomics label-free quantification experiment. The data is +based on four artificial samples with known composition (background: 200 ng *S. cerevisiae*). These were spiked with different quantities of individual digested proteins, +whose identifiers were masked for the competition as yeast proteins in the provided +database (see Table 1). + +
    + +

    + + + +
    Table 1: Samples (background: 200 ng S. cerevisiae) with spiked-in proteins
    in different +quantities [fmols]
    +
    + + + + + + + + +
    Samples
    +
    Name
    Origin
    Molecular Weight
    1
    2 3 4
    A Ovalbumin Egg White 45 KD 65 55 15 2
    B Myoglobin Equine Heart 17 KD 55 15 2 65
    C Phosphorylase b Rabbit Muscle 97 KD 15 2 65 55
    D Beta-Glactosidase Escherichia Coli 116 KD 2 65 55 15
    E Bovine Serum Albumin Bovine Serum 66 KD 11 0.6 10 500
    F Carbonic Anhydrase Bovine Erythrocytes 29 KD 10 500 11 0.6
    + +
    +
    + +#### Identification and quantification + +|![KNIME data analysis of iPRG LFQ data](/_images/openms-user-tutorial/labelfree/iPRG_lfq.png)| +|:--:| +|Figure 18: KNIME data analysis of iPRG LFQ data.| + +The iPRG LFQ workflow (Fig. 18) consists of an identification and a quantification part. The identification is achieved by searching the computationally calculated MS2 spectra from a sequence database (`File Importer` node, here with the given database from iPRG: +{path}`ExampleData,iPRG2015,database,iPRG2015targetdecoynocontaminants.fasta` +against the MS2 from the original data (`Input Files` node with all mzMLs following {path}`ExampleData,iPRG2015,datasets,JD06232014sample*.mzML` using the `CometAdapter`. + +```{note} +If you want to reproduce the results at home, you have to download the iPRG data in mzML format and perform peak picking on it or convert and pick the raw data with `msconvert`. +``` + +Afterwards, the results are scored using the **FalseDiscoveryRate** node and filtered to obtain only unique peptides (`IDFilter`) since `MSstats` does not support shared peptides, yet. The quantification is achieved by using the `FeatureFinderCentroided` node, which performs the feature detection on the samples (maps). In the end the quantification results are combined with the filtered identification results (`IDMapper`). In addition, a linear retention time alignment is performed (`MapAlignerPoseClustering`), followed by the feature linking process (**FeatureLinkerUnlabledQT**). The **ConsensusMapNormalizer**s is used to normalize the intensities via robust regression over a set of maps and the `IDConflictResolver` assures that only one identification (best score) is associated with a feature. The output of this workflow is a consensusXML file, which can now be converted using the `MSStatsConverter` (see Conversion and downstream analysis section). + +#### Experimental design + +As mentioned before, the downstream analysis can be performed using `MSstats`. In this case, an experimental design has to be specified for the OpenMS workflow. The structure of the experimental design used in OpenMS in case of the iPRG dataset is specified in Table 2. + +
    + + +

    + + + + +
    Table 2: OpenMS Experimental design for the iPRG2015 dataset.
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Fraction_Group Fraction Spectra_Filepath Label Sample
    1 1 Sample1-A 1 1
    2 1 Sample1-B 1 2
    3 1 Sample1-C 1 3
    4 1 Sample2-A 1 4
    5 1 Sample2-B 1 5
    6 1 Sample2-C 1 6
    7 1 Sample3-A 1 7
    8 1 Sample3-B 1 8
    9 1 Sample3-C 1 9
    10 1 Sample4-A 1 10
    11 1 Sample4-B 1 11
    12 1 Sample4-C 1 12
    Sample MSstats_Condition MSstats_BioReplicate
    1 1 1
    2 1 2
    3 1 3
    4 2 4
    5 2 5
    6 2 6
    7 3 7
    8 3 8
    9 3 9
    10 4 10
    11 4 11
    12 4 12
    + + +
    +
    + +An explanation of the variables can be found in Table 3. + +
    + + +

    + + + + +
    Table 3: Explanation of the column of the experimental design table
    + + + + + + + + + + + +
    variables

    value

    Fraction_Group

    Index used to group fractions and source files.

    Fraction

    1st, 2nd, .., fraction. Note: All runs must have the same number of + fractions.

    Spectra_Filepath

    Path to mzML files

    Label

    label-free: always 1

    TMT6Plex: 1...6

    SILAC with light and heavy: 1..2

    Sample

    Index of sample measured in the specified label X, in fraction Y of + fraction group Z.

    Conditions

    Further specification of different conditions (e.g. MSstats_Condition; + MSstats_BioReplicate)

    + + +
    +
    + +The conditions are highly dependent on the type of experiment and on which kind of analysis you want to perform. For the `MSstats` analysis the information which sample belongs to which condition and if there are biological replicates are mandatory. This can be specified in further condition columns as explained in Table 3. For a detailed description of the MSstats-specific terminology, see their documentation e.g. in the R vignette. + +#### Conversion and downstream analysis + +Conversion of the OpenMS-internal consensusXML format (which is an aggregation of quantified and possibly identified features across several MS-maps) to a table (in MSstats-conformant CSV format) is very easy. First, create a new KNIME workflow. Then, run the `MSStatsConverter` node with a consensusXML and the manually created (e.g. in Excel) experimental design as inputs (loaded via `File Importer` nodes). The first input can be found in: + +{path}`ExampleData,iPRG2015,openmsLFQResults,iPRGlfq.consensusXML` + +This file was generated by using the {path}`Workflows,openmsLFQiPRG2015.knwf` workflow (seen in Fig. 18). The second input is specified in: + +{path}`ExampleData,iPRG2015,experimentaldesign.tsv` + +Adjust the parameters in the config dialog of the converter to match the given experimental design file and to use a simple summing for peptides that elute in multiple features (with the same charge state, i.e. m/z value). + +|**parameter**|**value**| +|------------:|:--------| +|*msstats_bioreplicate*|MSstats_Bioreplicate| +|*msstats_condition*|MSstats_Condition| +|*labeled_reference_peptides*|false| +|*retention_time_summarization_method (advanced)*|sum| + +The downstream analysis of the peptide ions with `MSstats` is performed in several steps. These steps are reflected by several KNIME R nodes, which consume the output of `MSStatsConverter`. The outline of the workflow is shown in Figure 19. + +|![MSstats analysis using KNIME](/_images/openms-user-tutorial/labelfree/MSstats.png)| +|:--:| +|Figure 19: MSstats analysis using KNIME. The individual steps (Preprocessing, Group Comparisons, Result Data Renaming, and Export) are split among several consecutive nodes.| + +We load the file resulting from `MSStatsConverter` either by saving it with an `Output File` node and reloading it with the `File Reader`. Alternatively, for advanced users, you can use a URI Port to Variable node and use the variable in the File Reader config dialog (**V** button - located on the right of the **Browse** button) to read from the temporary file. + +##### Preprocessing + +The first node (`Table to R`) loads `MSstats` as well as the data from the previous KNIME node and performs a preprocessing step on the input data. The following inline R script ( needs to be pasted into the config dialog of the node): + +```r +library(MSstats) +data <- knime.in +quant <- OpenMStoMSstatsFormat(data, removeProtein_with1Feature = FALSE) +``` + +The inline R script allows further preparation of the data produced by `MSStatsConverter` before the actual analysis is performed. In this example, the lines with proteins, which were identified with only one feature, were retained. Alternatively they could be removed. +In the same node, most importantly, the following line transforms the data into a format that is understood by `MSstats`: + +```r +processed.quant <- dataProcess(quant, censoredInt = 'NA') +``` +Here, `dataProcess` is one of the most important functions that the R package provides. The function performs the following steps: + +1. Logarithm transformation of the intensities +2. Normalization +3. Feature selection +4. Missing value imputation +5. Run-level summarization + +In this example, we just state that missing intensity values are represented by the `NA` string. + +##### Group Comparison + +The goal of the analysis is the determination of differentially-expressed proteins among the different conditions C1-C4. We can specify the comparisons that we want to make in a *comparison* matrix. For this, let’s consider the following example: + +![comparison matrix](/_images/openms-user-tutorial/labelfree/handout-clean129x.svg) + +This matrix has the following properties: + +- The number of rows equals the number of comparisons that we want to perform, the number of columns equals the number of conditions (here, column 1 refers to C1, column 2 to C2 and so forth). +- The entries of each row consist of exactly one 1 and one -1, the others must be 0. +- The condition with the entry `1` constitutes the enumerator of the log2 fold-change. The one with entry `-1` denotes the denominator. Hence, the first row states that we want calculate: +```{math} + \begin{equation} \log_2 \frac{C_{2}}{C_{1}} \end{equation} +``` +We can generate such a matrix in R using the following code snippet in (for example) a new **R to R** node that takes over the R workspace from the previous node with all its variables: + +```r +comparison1<-matrix(c(-1,1,0,0),nrow=1) +comparison2<-matrix(c(-1,0,1,0),nrow=1) + +comparison3<-matrix(c(-1,0,0,1),nrow=1) +comparison4<-matrix(c(0,-1,1,0),nrow=1) + +comparison5<-matrix(c(0,-1,0,1),nrow=1) +comparison6<-matrix(c(0,0,-1,1),nrow=1) + +comparison <- rbind(comparison1, comparison2, comparison3, comparison4, comparison5, comparison6) +row.names(comparison)<-c("C2-C1","C3-C1","C4-C1","C3-C2","C4-C2","C4-C3") +``` + +Here, we assemble each row in turn, concatenate them at the end, and provide row names for labeling the rows with the respective condition. +In MSstats, the group comparison is then performed with the following line: + +```r +test.MSstats <- groupComparison(contrast.matrix=comparison, data=processed.quant) +``` +No more parameters need to be set for performing the comparison. + +##### Result processing + +In a next R to R node, the results are being processed. The following code snippet will rename the spiked-in proteins to A,B,C,D,E, and F and remove the names of other proteins, which will be beneficial for the subsequent visualization, as for example performed in Figure 20: + +```r + test.MSstats.cr <- test.MSstats$ComparisonResult + + + # Rename spiked ins to A,B,C.... + pnames <- c("A", "B", "C", "D", "E", "F") + + names(pnames) <- c( + "sp|P44015|VAC2_YEAST", + "sp|P55752|ISCB_YEAST", + + "sp|P44374|SFG2_YEAST", + "sp|P44983|UTR6_YEAST", + "sp|P44683|PGA4_YEAST", + + "sp|P55249|ZRT4_YEAST" + ) + + test.MSstats.cr.spikedins <- bind_rows( + + test.MSstats.cr[grep("P44015", test.MSstats.cr$Protein),], + + test.MSstats.cr[grep("P55752", test.MSstats.cr$Protein),], + + test.MSstats.cr[grep("P44374", test.MSstats.cr$Protein),], + + test.MSstats.cr[grep("P44683", test.MSstats.cr$Protein),], + + test.MSstats.cr[grep("P44983", test.MSstats.cr$Protein),], + + test.MSstats.cr[grep("P55249", test.MSstats.cr$Protein),] + ) + # Rename Proteins + + test.MSstats.cr.spikedins$Protein <- sapply(test.MSstats.cr.spikedins$Protein, function(x) {pnames[as.character(x)]}) + + + + test.MSstats.cr$Protein <- sapply(test.MSstats.cr$Protein, function(x) { + + + x <- as.character(x) + + if (x %in% names(pnames)) { + + + return(pnames[as.character(x)]) + } else { + return("") + + } + }) + +``` + +##### Export + +The last four nodes, each connected and making use of the same workspace from the last node, will export the results to a textual representation and volcano plots for further inspection. Firstly, quality control can be performed with the following snippet: + +```r +qcplot <- dataProcessPlots(processed.quant, type="QCplot", + ylimDown=0, + +which.Protein = 'allonly', +width=7, height=7, address=F) +``` + +The code for this snippet is embedded in the first output node of the workflow. The resulting boxplots show the log2 intensity distribution across the MS runs. +The second node is an **R View (Workspace)** node that returns a Volcano plot which displays differentially expressed proteins between conditions C2 vs. C1. The plot is described in more detail in the following Result section. This is how you generate it: + +```r +groupComparisonPlots(data=test.MSstats.cr, type="VolcanoPlot", + + width=12, height=12,dot.size = 2,ylimUp = 7, + + which.Comparison = "C2-C1", + address=F) +``` +The last two nodes export the `MSstats` results as a KNIME table for potential further analysis or for writing it to a (e.g. csv) file. Note that you could also write output inside the Rscript if you are familiar with it. Use the following for an **R to Table** node exporting all results: + +```r +knime.out <- test.MSstats.cr +``` +And this for an **R to Table** node exporting only results for the spike-ins: + +```r +knime.out <- test.MSstats.cr.spikedins +``` + +#### Result + +An excerpt of the main result of the group comparison can be seen in Figure 20. + +|![Volcano plots c2_c1](/_images/openms-user-tutorial/labelfree/c2_c1-.png) ![Volcano plots c3_c2](/_images/openms-user-tutorial/labelfree/c3_c2-.png)| +|:--:| +|Figure 20: Volcano plots produced by the Group Comparison in MSstats The dotted line indicates an adjusted p-value threshold| + +The Volcano plots show differently expressed spiked-in proteins. In the left plot, which shows the fold-change C2-C1, we can see the proteins D and F (`sp|P44983|UTR6_YEAST` and `sp|P55249|ZRT4_YEAST`) are significantly over-expressed in C2, while the proteins B,C, and E (`sp|P55752|ISCB_YEAST`, `sp|P55752|ISCB_YEAST`, and `sp|P44683|PGA4_YEAST`) are under-expressed. In the right plot, which shows the fold-change ratio of C3 vs. C2, we can see the proteins E and C (`sp|P44683|PGA4_YEAST` and `sp|P44374|SFG2_YEAST`) over-expressed and the proteins A and F (`sp|P44015|VAC2_YEAST` and `sp|P55249|ZRT4_YEAST`) under-expressed. The plots also show further differentially-expressed proteins, which do not belong to the spiked-in proteins. + +The full analysis workflow can be found under: +{path}`Workflows,MSstatsstatPostProcessingiPRG2015.knwf` + + +### Isobaric analysis workflow + +In the last chapters, we identified and quantified peptides in a label-free experiment. + +In this section, we would like to introduce a possible workflow for the analysis of isobaric data. +Let’s have a look at the workflow (see Fig 23). + +(Figure_23)= +|![Workflow for the analysis of isobaric data](/_images/openms-user-tutorial/isobaric/isobaric_inference_wf.png)| +|:--:| +|Figure 23: Workflow for the analysis of isobaric data| + +The full analysis workflow can be found here: +{path}`Workflows,IdentificationquantificationisobaricinferenceepifanyMSstatsTMT` + +The workflow has four input nodes. The first for the experimental design to allow for MSstatsTMT compatible export (`MSStatsConverter`). The second for the `.mzML` files with the centroided spectra from the isobaric labeling experiment and the third one for the `.fasta` database used for identification. The last one allows to specify an output path for the plots generated by the R View, which runs MSstatsTMT (I). The quantification (A) is performed using the **IsobaricAnalzyer**. The tool is able to extract and normalize quantitative information from TMT and iTRAQ data. The values can be assessed from centroided MS2 or MS3 spectra (if available). Isotope correction is performed based on the specified correction matrix (as provided by the manufacturer). The identification (C) is applied as known from the previous chapters by using database search and a target-decoy database. + +To reduce the complexity of the data for later inference the q-value estimation and FDR filtering is performed on PSM level for each file individually (B). Afterwards the identification (PSM) and quantiative information is combined using the `IDMapper`. After the processing of all available files, the intermediate results are aggregated (**FileMerger** - D). All PSM results are used for score estimation and protein inference (**Epifany**) (E). For detailed information about protein inference please see Chaper 4. Then, decoys are removed and the inference results are filtered via a protein group FDR. Peptide level results can be exported via **MzTabExporter** (F), protein level results can be obtained via the **ProteinQuantifier** (G) or the results can exported (`MSStatsConverter` - H) and further processed with the following R pipeline to allow for downstream processing using `MSstatsTMT`. + +Please import the workflow from {path}`Workflows,IdentificationquantificationisobaricinferenceepifanyMSstatsTMT` into KNIME via the menu entry **File** > **Import KNIME workflow** > **Select file** and double-click the imported workflow in order to open it. Before you can execute the workflow, you have to correct the locations of the files in the `Input Files` nodes (don’t forget the one for the FASTA database inside the “ID” meta node). Try and run your workflow by executing all nodes at once. + +#### Excursion MSstatsTMT + +The R package `MSstatsTMT` can be used for protein significance analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. `MSstatsTMT` provides functionality for two types of analysis & their visualization: Protein summarization based on peptide quantification and Model-based group comparison to detect significant changes in abundance. It depends on accurate feature detection, identification and quantification which can be performed e.g. by an OpenMS workflow. + +In general, `MSstatsTMT` can be used for data processing & visualization, as well as statistical modeling. Please see [^3] and the [MSstats](http://msstats.org/msstatstmt/) website for further information. + +There is also an [online lecture](https://youtu.be/3CDnrQxGLbA) and tutorial for `MSstatsTMT` from the May Institute Workshop 2020. + +#### Dataset and experimental design + +We are using the MSV000084264 ground truth dataset, which consists of TMT10plex controlled mixes of different concentrated UPS1 peptides spiked into SILAC HeLa peptides measured in a dilution series https://www.omicsdi.org/dataset/massive/MSV000084264. Figure 24 shows the experimental design. In this experiment, 5 different TMT10plex mixtures – different labeling strategies – were analysed. These were measured in triplicates represented by the 15 MS runs (3 runs each). The example data, database and experimental design to run the workflow can be found [here](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Data/isobaric_MSV000084264/). + +(Figure_24)= +|![Experimental Design](/_images/openms-user-tutorial/isobaric/isobaric_experimental_design.jpeg)| +|:--:| +|Figure 24: Experimental Design| + +The experimental design in table format allows for `MSstatsTMT` compatible export. The design is represented by two tables. The first one 4 represents the overall structure of the experiment in terms of samples, fractions, labels and fraction groups. The second one 5 adds to the first by specifying specific conditions, biological replicates as well as mixtures and label for each channel. For additional information about the experimental design please see Table 3. + +
    + + +

    + + + + +
    Table 4: Experimental Design 1 +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Spectra_Filepath Fraction Label Fraction_Group Sample
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_01.mzML 1 1 1 1
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_01.mzML 1 2 1 2
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_01.mzML 1 3 1 3
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_01.mzML 1 4 1 4
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_01.mzML 1 5 1 5
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_01.mzML 1 6 1 6
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_01.mzML 1 7 1 7
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_01.mzML 1 8 1 8
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_01.mzML 1 9 1 9
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_01.mzML 1 10 1 10
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_02.mzML 1 1 2 11
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_02.mzML 1 2 2 12
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_02.mzML 1 3 2 13
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_02.mzML 1 4 2 14
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_02.mzML 1 5 2 15
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_02.mzML 1 6 2 16
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_02.mzML 1 7 2 17
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_02.mzML 1 8 2 18
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_02.mzML 1 9 2 19
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_02.mzML 1 10 2 20
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_03.mzML 1 1 3 21
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_03.mzML 1 2 3 22
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_03.mzML 1 3 3 23
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_03.mzML 1 4 3 24
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_03.mzML 1 5 3 25
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_03.mzML 1 6 3 26
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_03.mzML 1 7 3 27
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_03.mzML 1 8 3 28
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_03.mzML 1 9 3 29
    161117_SILAC_HeLa_UPS1_TMT10_SPS_MS3_Mixture1_03.mzML 1 10 3 30
    +
    +
    + + +
    + + +

    + + + + +
    Table 5: Experimental Design 2
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Sample MSstats_Condition MSstats_BioReplicate MSstats_Mixture LabelName
    1 Norm Norm 1 126
    2 0.667 0.667 1 127N
    3 0.125 0.125 1 127C
    4 0.5 0.5 1 128N
    5 1 1 1 128C
    6 0.125 0.125 1 129N
    7 0.5 0.5 1 129C
    8 1 1 1 130N
    9 0.667 0.667 1 130C
    10 Norm Norm 1 131
    11 Norm Norm 1 126
    12 0.667 0.667 1 127N
    13 0.125 0.125 1 127C
    14 0.5 0.5 1 128N
    15 1 1 1 128C
    16 0.125 0.125 1 129N
    17 0.5 0.5 1 129C
    18 1 1 1 130N
    19 0.667 0.667 1 130C
    20 Norm Norm 1 131
    21 Norm Norm 1 126
    22 0.667 0.667 1 127N
    23 0.125 0.125 1 127C
    24 0.5 0.5 1 128N
    25 1 1 1 128C
    26 0.125 0.125 1 129N
    27 0.5 0.5 1 129C
    28 1 1 1 130N
    29 0.667 0.667 1 130C
    30 Norm Norm 1 131
    +
    +
    + +After running the worklfow, the `MSStatsConverter` will convert the OpenMS output in addition with the experimental design to a file (.csv) which can be processed by using `MSstatsTMT`. + +#### MSstatsTMT analysis + +Here, we depict the analysis by `MSstatsTMT` using a segment of the isobaric analysis workflow (Fig. 25). The segment is available as {path}`Workflows,MSstatsTMT.knwf`. + +(Figure_25)= +|![MSstatsTMT workflow segment](/_images/openms-user-tutorial/isobaric/isobaric_msstatstmt_wf.png)| +|:--:| +|Figure 25: MSstatsTMT workflow segment| + +There are two input nodes, the first one takes the result (.csv) from the `MSStatsConverter` and the second a path to the directory where the plots generated by `MSstatsTMT` should be saved. The **R source** node loads the required packages, such as `dplyr` for data wrangling, `MSstatsTMT` for analysis and `MSstats` for plotting. The inputs are further processed in the **R View** node. + +Here, the data of the `File Importer` is loaded into **R** using the flow variable [”URI-0”]: + +```r +file <- substr(knime.flow.in[["URI-0"]], 6, nchar(knime.flow.in[["URI-0"]])) + +MSstatsConverter_OpenMS_out <- read.csv(file) +data <- MSstatsConverter_OpenMS_out +``` + +The `OpenMStoMSstatsTMTFormat` function preprocesses the OpenMS report and converts it into the required input format for `MSstatsTMT`, by filtering based on unique peptides and measurements in each MS run. + +```r +processed.data <- OpenMStoMSstatsTMTFormat(data) +``` + +Afterwards different normalization steps are performed (global, protein, runs) as well as data imputation by using the msstats method. In addition peptide level data is summarized to protein level data. + +```r +quant.data <- proteinSummarization(processed.data, + method="msstats", + + global_norm=TRUE, + reference_norm=TRUE, + + MBimpute = TRUE, + maxQuantileforCensored = NULL, + + remove_norm_channel = TRUE, + remove_empty_channel = TRUE) +``` + +There a lot of different possibilities to configure this method please have a look at the MSstatsTMT package for [additional detailed information](http://bioconductor.org/packages/release/bioc/html/MSstatsTMT.html). + +The next step is the comparions of the different conditions, here either a pairwise comparision can be performed or a confusion matrix can be created. The goal is to detect and compare the UPS peptides spiked in at different concentrations. + +```r +# prepare contrast matrix +unique(quant.data$Condition) + +comparison<-matrix(c(-1,0,0,1, + + 0,-1,0,1, + 0,0,-1,1, + + 0,1,-1,0, + 1,-1,0,0), nrow=5, byrow = T) + + +# Set the names of each row +row.names(comparison)<- contrasts <- c("1-0125", + + "1-05", + "1-0667", + + "05-0667", + "0125-05") + +# Set the column names +colnames(comparison)<- c("0.125", "0.5", "0.667", "1") +``` + +The constructed confusion matrix is used in the `groupComparisonTMT` function to test for significant changes in protein abundance across conditions based on a family of linear mixed-effects models in TMT experiments. + +```r +data.res <- groupComparisonTMT(data = quant.data, + contrast.matrix = comparison, + + moderated = TRUE, # do moderated t test + + adj.method = "BH") # multiple comparison adjustment +data.res <- data.res %>% filter(!is.na(Protein)) +``` + +In the next step the comparison can be plotted using the `groupComparisonPlots` function by `MSstats`. + +```r +library(MSstats) +groupComparisonPlots(data=data.res.mod, type="VolcanoPlot", address=F, which.Comparison = "0125-05", sig = 0.05) +``` + +Here, we have a example output of the **R View**, which depicts the significant regulated UPS proteins in the comparison of 125 to 05 (Fig. 26). + +(Figure_26)= +|![Volcanoplot of the group comparison regarding 0125 to 05](/_images/openms-user-tutorial/isobaric/isobaric_img_output_knime.png)| +|:--:| +|Figure 26: Volcanoplot of the group comparison regarding 0125 to 05| + +All plots are saved to the in the beginning specified output directory in addition. + +#### Note + +The isobaric analysis does not always has to be performed on protein level, for example for phosphoproteomics studies one is usually interested on the peptide level - in addition inference on peptides with post-translational modification is not straight forward. Here, we present and additonal workflow on peptide level, which can potentially be adapted and used for such cases. Please see {path}`Workflows,IdentificationquantificationisobaricMSstatsTMT` + +## References + +[^1]: A. Chawade, M. Sandin, J. Teleman, J. Malmström, and F. Levander, Data Processing Has Major Impact on the Outcome of Quantitative Label-Free LC-MS Analysis, Journal of Proteome Research 14(2), 676–687 (2015), PMID: 25407311, arXiv:http://dx.doi.org/10.1021/pr500665j, doi:10.1021/pr500665j. 30 + +[^2]: M. Choi, Z. F. Eren-Dogu, C. Colangelo, J. Cottrell, M. R. Hoopmann, E. A. Kapp, +S. Kim, H. Lam, T. A. Neubert, M. Palmblad, B. S. Phinney, S. T. Weintraub, B. MacLean, and O. Vitek, ABRF Proteome Informatics Research Group (iPRG) +2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LC-MS/MS Experiments, J. Proteome Res. 16(2), 945–957 (2017), doi: 10.1021/acs.jproteome.6b00881. 40 + +[^3]: T. Huang, M. Choi, S. Hao, and O. Vitek, MSstatsTMT: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem +mass tag (TMT) labeling., (2020), doi:10.18129/B9.bioc.MSstatsTMT. 55 diff --git a/docs/tutorials/knime-user-tutorial/openswath-metabolomics.md b/docs/tutorials/knime-user-tutorial/openswath-metabolomics.md new file mode 100644 index 00000000..abc82031 --- /dev/null +++ b/docs/tutorials/knime-user-tutorial/openswath-metabolomics.md @@ -0,0 +1,240 @@ +OpenSWATH for Metabolomics +========================== + +## Introduction + +We would like to present an automated DIA/SWATH analysis workflow for metabolomics, which takes advantage of experiment specific target-decoy assay library generation. This allows for targeted extraction, scoring and statistical validation of metabolomics DIA data[^1], [^2]. + +## Workflow + +The workflow follows multiple steps (see Fig. 45). + +(Figure_45)= +|![DIAMetAlyzer - pipeline for assay library generation and targeted analysis with statistical validation](/_images/openms-user-tutorial/openswath/pipeline_overview.png)| +|:--:| +|Figure 45: DIAMetAlyzer - pipeline for assay library generation and targeted analysis with statistical validation. DDA data is used for candidate identification containing feature detection, adduct grouping and accurate mass search. Library construction uses fragment annotation via compositional fragmentation trees and decoy generation using a fragmentation tree re-rooting method to create a target-decoy assay library. This library is used in a second step to analyse metabolomics DIA data performing targeted extraction, scoring and statistical validation (FDR estimation).| + +(Figure_46)= +|![Assay library generation](/_images/openms-user-tutorial/openswath/assay_library_generation.png)| +|:--:| +|Figure 46: Assay library generation. The results of the compound identification (feature, molecular formula, adduct), with the corresponding fragment spectra for the feature, are used to perform fragment annotation via SIRIUS, using the compositional fragmentation trees. Then, the n highest intensity transitions are extracted and stored in the assay library.| + +(Figure_47)= +|![Decoy generation](/_images/openms-user-tutorial/openswath/decoy_generation.png)| +|:--:| +|Figure 47: Decoy generation. The compositional fragmentations trees from the step above are used to run the fragmentation tree re-rooting method from Passatutto, generating a compound specific decoy MS2 spectrum. Here, the n highest intensity decoy transitions are extracted and stored in the target-decoy assay library.| + +- **Candidate identification** + Feature detection, adduct grouping and accurate mass search are applied on DDA data. +- **Library construction** + The knowledge determined from the DDA data, about compound identification, its potential adduct and the corresponding fragment spectra are used to perform fragment annotation via compositional fragmentation trees sugin SIRIUS 4[^3]. Afterwards transitions, which are the reference of a precursor to its fragment ions are stored in a so-called assay library (Fig. 46). Assay libraries usually contain additional metadata (i.e. retention time, peak intensities). FDR estimation is based on the target-decoy approach[^4]. For the generation of the MS2 decoys, the fragmentation tree-based rerooting method by Passatutto ensure the consistency of decoy spectra (Fig.47)[^5]. The target-decoy assay library is then used to analyse the SWATH data. +- **Targeted extraction** + Chromatogram extraction and peak-group scoring. This step is performed using an algorithm based on OpenSWATH[^1] for metabolomics data. +- **Statistical validation** + FDR estimation uses the PyProphet algorithm[^2]. To prevent overfitting we chose the simpler linear model (LDA) for target-decoy discrimination in PyProphet, using MS1 and MS2 scoring with low correlated scores. + +## Prerequisites +Apart from the usual KNIME nodes, the workflow uses python scripting nodes. One basic requirement for the installation of python packages, in particular pyOpenMS, is a package manager for python. Using conda as an environment manger allows to specify a specific environment in the KNIME settings (**File**>**Preferences**>**KNIME**>**Python**). + +### Windows + +We suggest do use a virtual environment for the Python 3 installation on windows. Here you can install miniconda and follow the further instructions. + +1. Create new `conda` python environment. + ```bash + conda create -n py39 python=3.9 + ``` +2. Activate `py39` environment. + ```bash + conda activate py39 + ``` +3. Install pip (see above). +4. On the command line: + ```bash + python -m pip install -U pip + python -m pip install -U numpy + python -m pip install -U pandas + + python -m pip install -U pyprophet + python -m pip install -U pyopenms + ``` + +### macOS + +We suggest do use a virtual environment for the Python 3 installation on Mac. Here you can install miniconda and follow the further instructions. + +1. Create new `conda` python environment. + ```bash + conda create -n py39 python=3.9 + ``` +2. Activate py39 environment. + ```bash + conda activate py39 + ``` +3. On the Terminal: + ```bash + python -m pip install -U pip + python -m pip install -U numpy + python -m pip install -U pandas + + python -m pip install -U pyprophet + python -m pip install -U pyopenms + ``` + +### Linux + +Use your package manager apt-get or yum, where possible. + +1. Install Pytohn 3.9 (Debian: python-dev, RedHat: python-devel) +2. Install NumPy (Debian/RedHat: python-numpy). +3. Install setuptools (Debian/RedHat: python-setuptools). +4. On the Terminal: + ```bash + python -m pip install -U pip + python -m pip install -U numpy + python -m pip install -U pandas + + python -m pip install -U pyprophet + python -m pip install -U pyopenms + ``` + +## Benchmark data + +For the assay library construction pesticide mixes (Agilent Technologies, Waldbronn, Germany) were measured individually in solvent (DDA). Benchmark DIA samples were prepared by spiking different commercially available pesticide mixes into human plasma metabolite extracts in a 1:4 dilution series, which covers 5 orders of magnitude. + +The example data can be found [here](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Data/DIAMetAlyzer/). + +## Example workflow + +Example workflow for the usage of the DIAMetAlyzer Pipeline in KNIME (see Fig. 48). Inputs are the SWATH-MS data in profile mode (.mzML), a path for saving the new target-decoy assay library, the SIRIUS 4.9.0 executable, the DDA data (.mzML), custom libraries and adducts for **AccurateMassSearch**, the min/max fragment mass-to-charge to be able to restrict the mass of the transitions and the path to the PyProphet executable. The DDA is used for feature detection, adduct grouping, accurate mass search and forwarded to the **AssayGeneratorMetabo**. Here, feature mapping is performed to collect MS2 spectra that belong to a feature. All information collected before (feautre, adduct, putative identification, MS2 spectra) are then internally forwarded to SIRIUS. SIRIUS is used for fragment annotation and decoy generation based on the fragmentation tree re-rooting approach. This information is then used to filter spectra/decoys based on their explained intensity (min. 85%). Afterwards internal feature linking is performed which is most important for untargeted experiments using a lot of DDA data to construct the library. The constructed target-decoy assay library is processed with the SWATH-MS data in OpenSWATH. The results are used by PyProphet for scoring and output a list of metabolites with their respective q-value and quantitative information. + +(Figure_48)= +|![Example workflow for the usage of the DIAMetAlyzer Pipeline in KNIME](/_images/openms-user-tutorial/openswath/oswm_example_wf.png)| +|:--:| +|Figure 48: Example workflow for the usage of the DIAMetAlyzer Pipeline in KNIME.| + +## Run the workflow + +These steps need to be followed to run the workflow successfully: +- Add DDA Input Files (.mzML). +- Specify SIRIUS 4.9.0 executable. +- Specify library files (mapping, struct) for **AccurateMassSearch**. +- Add positive/negative adducts lists for **AccurateMassSearch**. +- Supply an output path for the SIRIUS workspace in the **AssayGeneratorMetabo**. +- Specify additional paths and variables, such as an output path for the target-decoy assay library and a path to the pyprophet installation as well as decoy fragment mz filter (min/max). +- Input DIA/SWATH files (.mzML). +- Specify output path in the output folders. + +You can now run the workflow. + +## Important parameters + +Please have a look at the most important parameters, which should be tweaked to fit your data. In general, OpenMS has a lot of room for parameter optimization to best fit your chromatography and instrumental settings. + +### FeatureFinderMetabo + +|**parameter**|**explanation**| +|:------------|:--------------| +|*noise_threshold_int*|Intensity threshold below which peaks are regarded as noise.| +|*chrom_fwhm*|Expected chromatographic peak width (in seconds).| +|*mass_error_ppm*|Allowed mass deviation (in ppm).| + +### MetaboliteAdductDecharger + +|**parameter**|**explanation**| +|:------------|:--------------| +|*mass_max_diff*|Maximum allowed mass tolerance per feature..| +|*potential_adducts*|Adducts used to explain mass differences - These should fit to the adduct list specified for AccurateMassSearch.| + +### AccurateMassSearch + +|**parameter**|**explanation**| +|:------------|:--------------| +|*mass_error_value*|Tolerance allowed for accurate mass search.| +|*ionization_mode*|Positive or negative ionization mode.| + +### AssayGeneratorMetabo + +|**parameter**|**explanation**| +|:------------|:--------------| +|*min_transitions*|Minimal number of transitions (3).| +|*max_transitions*|Maximal number of transitions (3).| +|**min_fragment_mz**|Minimal m/z of a fragment ion choosen as a transition| +|**max_fragment_mz**|Maximal m/z of a fragment ion choosen as a transition| +|*transitions_threshold*|Further transitions need at least x% of the maximum intensity.| +|**fragment_annotation_score_threshold**|Filters annotations based on the explained intensity of the peaks in a spectrum (0.8).| +|SIRIUS (internal):| +|*out_workspace_directory*|Output directory for SIRIUS workspace (Fragmentation Trees).| +|*filter_by_num_masstraces*|Features have to have at least x MassTraces. To use this parameter feature_only is neccessary.| +|*precursor_mass_tolerance*|Tolerance window for precursor selection (Feature selection in regard to the precursor).| +|*precursor_rt_tolerance*|Tolerance allowed for matching MS2 spectra depending on the feature size (should be around the FWHM of the chromatograms).| +|*profile*|Specify the used analysis profile (e.g. qtof).| +|*elements*|Allowed elements for assessing the putative sumformula (e.g. CHNOP[5]S[8]Cl[1]). Elements found in the isotopic pattern are added automatically, but can be specified nonetheless.| +|Feature linking (internal):| +|**ambiguity_resolution mz_tolerance**|M/z tolerance for the resolution of identification ambiguity over multiple files - Feature linking m/z tolerance.| +|**ambiguity_resolution rt_tolerance**|RT tolerance in seconds for the resolution of identification ambiguity over multiple files - Feature linking m/z tolerance.| +|**total_occurrence_filter**|Filter compound based on total occurrence in analysed samples.| + +In case of the **total_occurrence_filter** the value to chose depends on the analysis strategy used. In the instance you are using only identified compounds (**use_known_unknowns**= false) - it will filter based on identified features. This means that even if the feature +was detected in e.g. 50% of all samples it might be only identified correctly by accurate mass search in 20% of all samples. Using a **total_occurrence_filter** this specific feature would still be filtered out due to less identifications. + +### OpenSWATH + +|**parameter**|**explanation**| +|:------------|:--------------| +|*rt_extraction_window*|Extract x seconds around this value.| +|*rt_normalization_factor*|Please use the range of your gradient e.g. 950 seconds.| + +If you are analysing a lot of big DIA mzML files ≈ 3-20GB per File, it makes sense to change how OpenSWATH processes the spectra. + +|**parameter**|**explanation**| +|:------------|:--------------| +|*readOptions*|Set cacheWorkingInMemory - will cache the files to disk +and read SWATH-by-SWATH into memory| +|*tempDirectory*|Set a directory, where cached mzMLs are stored (be +aware that his directory can be quite huge depending on the data).| + +In the workflow pyprophet is called after OpenSWATH, it merges the result files, which allows to get enough data for the model training. + +```bash +pyprophet merge --template path_to_target-decoy_assay_library.pqp --out merged.osw,→ ./*.osw +``` +Afterwards, the results are scored using the MS1 and MS2 levels and filter for metabolomics scores, which have a low correlation. + +```bash +pyprophet score --in merged.osw --out scored.osw --level ms1ms2 --ss_main_score,→ ”var_isotope_correlation_score” --ss_score_filter metabolomics +``` +Export the non filtered results: + +```bash +pyprophet export-compound --in scored.osw --out scored + ”_pyprophet_nofilter_ms1ms2.tsv” --max_rs_peakgroup_qvalue 1000.0 +``` + +Please see the workflow for actual parameter values used for the benchmarking dataset. + +The workflow can be used without any identification (remove AccurateMassSearch). +Here, all features (**known_unknowns**) are processed. The assay library is constructed based on the chemical composition elucidated via the fragment annotation (SIRIUS 4). +It is also possible to use identified and in addition unknown (non-identified) features, by using **AccurateMassSearch** in combination with the use_known_unknowns in the **AssayGeneratorMetabo**. + + +## References + +[^1]: H. L. Röst, G. Rosenberger, P. Navarro, L. Gillet, S. M. Miladinović, O. T. Schubert, W. Wolski, B. C. Collins, J. Malmström, L. Malmström, and R. Aebersold, +OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data., Nat. Biotechnol. 32(3), 219–23 (2014), doi:10.1038/nbt.2841. +89, 90 + +[^2]: J. Teleman, H. L. Röst, G. Rosenberger, U. Schmitt, L. Malmström, J. Malmström, and F. Levander, DIANA-algorithmic improvements for analysis of dataindependent acquisition MS data, Bioinformatics 31(4), 555–562 (2015), arXiv: +9808008, doi:10.1093/bioinformatics/btu686. 89, 90 + +[^3]: K. Dührkop, M. Fleischauer, M. Ludwig, A. A. Aksenov, A. V. Melnik, M. Meusel, +P. C. Dorrestein, J. Rousu, and S. Böcker, SIRIUS 4: a rapid tool for turning tandem +mass spectra into metabolite structure information, Nat. Methods 16(4), 299– +302 (apr 2019), doi:10.1038/s41592-019-0344-8. 89 + +[^4]: J. E. Elias and S. P. Gygi, Target-decoy search strategy for increased confidence +in large-scale protein identifications by mass spectrometry, Nat. Methods 4(3), +207–214 (Mar. 2007). 89 + +[^5]: K. Scheubert, F. Hufsky, D. Petras, M. Wang, L. F. Nothias, K. Dührkop, N. Bandeira, P. C. Dorrestein, and S. Böcker, Significance estimation for large scale +metabolomics annotations by spectral matching, Nat. Commun. 8(1) (2017), +doi:10.1038/s41467-017-01318-5. 89 \ No newline at end of file diff --git a/docs/tutorials/knime-user-tutorial/openswath.md b/docs/tutorials/knime-user-tutorial/openswath.md new file mode 100644 index 00000000..3c7ac86d --- /dev/null +++ b/docs/tutorials/knime-user-tutorial/openswath.md @@ -0,0 +1,213 @@ +OpenSWATH +========= + +## Introduction + +OpenSWATH [^3] allows the analysis of LC-MS/MS DIA (data independent acquisition) data using the approach described by Gillet *et al*. [^4]. The DIA approach described there uses 32 cycles to iterate through precursor ion windows from 400-426 Da to 1175-1201 Da and at each step acquires a complete, multiplexed fragment ion spectrum of all precursors present in that window. After 32 fragmentations (or 3.2 seconds), the cycle is restarted and the first window (400-426 Da) is fragmented again, thus delivering complete “snapshots” of all fragments of a specific window every 3.2 seconds. +The analysis approach described by Gillet et al. extracts ion traces of specific fragment ions from all MS2 spectra that have the same precursor isolation window, thus generating data that is very similar to SRM traces. + +## Installation of OpenSWATH + +OpenSWATH has been fully integrated since OpenMS 1.10 [^2], [^1], [^5], [^6], [^7]. + +## Installation of mProphet + +mProphet[^8] is available as standalone script in {path}`External_Tools,mProphet`. [R](http://www.r-project.org/) and the package [MASS](http://cran.r-project.org/web/packages/MASS/) are further required to execute mProphet. Please obtain a version for either Windows, Mac or Linux directly from CRAN. +PyProphet, a much faster reimplementation of the mProphet algorithm is available from [PyPI](https://pypi.python.org/pypi/pyprophet/). The usage of pyprophet instead of mProphet is suggested for large-scale applications. + +mProphet will be used in this tutorial. + +## Generating the Assay Library + +### Generating TraML from transition lists + +OpenSWATH requires an assay library to be supplied in the TraML format[^9]. To enable manual editing of transition lists, the TOPP tool **TargetedFileConverter** is available, which uses tab separated files as input. Example datasets are provided in {path}`ExampleData,OpenSWATH,assay`. Please note that the transition lists need to be named `.tsv`. + +The header of the transition list contains the following variables (with example values in brackets): + +**Required Columns**: +`PrecursorMz` + +The mass-to-charge (m/z) of the precursor ion. (924.539) + +`ProductMz` + +The mass-to-charge (m/z) of the product or fragment ion. (728.99) + +`LibraryIntensity` + +The relative intensity of the transition. (0.74) + +`NormalizedRetentionTime` + +The normalized retention time (or iRT)[^10] of the peptide. (26.5) + +**Targeted Proteomics Columns** +`ProteinId` + +A unique identifier for the protein. (AQUA4SWATH_HMLangeA) + +`PeptideSequence` + +The unmodified peptide sequence. (ADSTGTLVITDPTR) + +`ModifiedPeptideSequence` + +The peptide sequence with UniMod modifications. (ADSTGTLVITDPTR(UniMod:267)) + +`PrecursorCharge` + +The precursor ion charge. (2) + +`ProductCharge` + +The product ion charge. (2) + +**Grouping Columns**: +`TransitionGroupId` + +A unique identifier for the transition group. +(AQUA4SWATH_HMLangeA_ADSTGTLVITDPTR(UniMod:267)/2) + +`TransitionId` + +A unique identifier for the transition. +(AQUA4SWATH_HMLangeA_ADSTGTLVITDPTR(UniMod:267)/2_y8) + +`Decoy` + +A binary value whether the transition is target or decoy. (target: 0, decoy: 1) + +`PeptideGroupLabel` + +Which label group the peptide belongs to. + +`DetectingTransition` + +Use transition for peak group detection. (1) + +`IdentifyingTransition` + +Use transition for peptidoform inference using IPF. (0) + +`QuantifyingTransition` + +Use transition to quantify peak group. (1) + +For further instructions about generic transition list and assay library generation please see the following [link](http://openswath.org/en/latest/docs/generic.html). +To convert transitions lists to TraML, use the TargetedFileConverter: Please use the absolute path to your OpenMS installation. + +**Linux or Mac** + +On the Terminal: + +```bash + TargetedFileConverter -in OpenSWATH_SGS_AssayLibrary_woDecoy.tsv -out OpenSWATH_SGS_AssayLibrary_woDecoy.TraML +``` + +**Windows** + +On the TOPP command: + +```bash + TargetedFileConverter.exe -in OpenSWATH_SGS_AssayLibrary_woDecoy.tsv -out OpenSWATH_SGS_AssayLibrary_woDecoy.TraML +``` + +### Appending decoys to a TraML file + +In addition to the target assays, OpenSWATH requires decoy assays in the library which are later used for classification and error rate estimation. For the decoy generation it is crucial that the decoys represent the targets in a realistic but unnatural manner without interfering with the targets. The methods for decoy generation implemented in OpenSWATH include ’shuffle’, ’pseudo-reverse’, ’reverse’ and ’shift’. To append decoys to a TraML, the TOPP tool **OpenSwathDecoyGenerator** can be used: Please use the absolute path to your OpenMS installation. + +**Linux or Mac** + +On the Terminal: + +```bash +OpenSwathDecoyGenerator -in OpenSWATH_SGS_AssayLibrary_woDecoy.TraML -out OpenSWATH_SGS_AssayLibrary.TraML -method shuffle -switchKR false +``` + +**Windows** + +On the TOPP command: + +```bash +OpenSwathDecoyGenerator.exe -in OpenSWATH_SGS_AssayLibrary_woDecoy.TraML -out OpenSWATH_SGS_AssayLibrary.TraML -method shuffle -switchKR false +``` + +## OpenSWATH KNIME + +An example KNIME workflow for OpenSWATH is supplied in `Workflows` (Fig. 44). The example dataset can be used for this workflow (filenames in brackets): + +1. Open {path}`Workflows,OpenSWATH.knwf` in KNIME: **File** > **Import KNIME Workflow...** +2. Select the normalized retention time (iRT) assay library in TraML format by double-clicking on node `File Importer` > **iRT Assay Library**. ({path}`ExampleData,OpenSWATH,assay,OpenSWATHiRTAssayLibrary.TraML`). +3. Select the SWATH MS data in mzML format as input by double-clicking on node **Input File** > **SWATH-MS files**. ({path}`ExampleData,OpenSWATH,data,splitnapedroL120420x010SW-*.nf.pp.mzML`). +4. Select the target peptide assay library in TraML format as input by double-clicking on node `Input Files` > **Assay Library**. ({path}`ExampleData,OpenSWATH,assay,OpenSWATHSGSAssayLibrary.TraML`). +5. Set the output destination by double-clicking on node `Output File`. +6. Run the workflow. + +The resulting output can be found at your selected path, which will be used as input for mProphet. Execute the script on the Terminal (Linux or Mac) or cmd.exe (Windows) in {path}`ExampleData,OpenSWATH,result`. Please use the absolute path to your R installation and the result file: + +```r +R --slave --args bin_dir=../../../External_Tools/mProphet/ mquest=OpenSWATH_quant.tsv workflow=LABEL_FREE num_xval=5 run_log=FALSE write_classifier=1 write_all_pg=1 < ../../../External_Tools/mProphet/mProphet.R +``` + +or for Windows: + +```r +"C:\Program Files\R\R-3.5.1\bin\x86\R.exe" --slave --args bin_dir=../../../External_Tools/mProphet/ mquest=OpenSWATH_quant.tsv workflow=LABEL_FREE num_xval=5 run_log=FALSE write_classifier=1 write_all_pg=1 < ../../../External_Tools/mProphet/mProphet.R +``` + +The main output will be called: {path}`OpenSWATH,result,mProphetxallxpeakgroups.xls` with statistical information available in {path}`OpenSWATH,result,mProphet.pdf`. + +Please note that due to the semi-supervised machine learning approach of mProphet the results differ slightly when mProphet is executed several times. + +(Figure_44)= +|![OpenSWATH KNIME Workflow.](/_images/openms-user-tutorial/openswath/OpenSWATHWF.png)| +|:--:| +|Figure 44: OpenSWATH KNIME Workflow.| + +Additionally, the chromatogram output (.mzML) can be visualized for inspection with TOPPView. +For additional instructions on how to use pyProphet instead of mProphet please have a look at the [PyProphet Legacy Workflow](http://openswath.org/en/latest/docs/pyprophet_legacy.html). If you want to use the SQLite-based workflow in your lab in the future, please have a look [here](http://openswath.org/en/latest/docs/pyprophet.html). The SQLite-based workflow will not be part of the tutorial. + +## From the example dataset to real-life applications + +The sample dataset used in this tutorial is part of the larger SWATH MS Gold Standard (SGS) dataset which is described in the publication of Roest *et al.*[^3]. It contains one of 90 SWATH-MS runs with significant data reduction (peak picking of the raw, profile data) to make file transfer and working with it easier. Usually SWATH-MS datasets are huge with several gigabyte per run. Especially when complex samples in combination with large assay libraries are analyzed, the TOPP tool based workflow requires a lot of computational resources. Additional information and instruction can be found at the following [link](http://openswath.org/en/latest/). + + +## References + +[^1]: M. Sturm, A. Bertsch, C. Gröpl, A. Hildebrandt, R. Hussong, E. Lange, N. Pfeifer, +O. Schulz-Trieglaff, A. Zerck, K. Reinert, and O. Kohlbacher, OpenMS - an opensource software framework for mass spectrometry., BMC bioinformatics 9(1) +(2008), doi:10.1186/1471-2105-9-163. 7, 83 + +[^2]: O. Kohlbacher, K. Reinert, C. Gröpl, E. Lange, N. Pfeifer, O. Schulz-Trieglaff, and +M. Sturm, TOPP–the OpenMS proteomics pipeline., Bioinformatics 23(2) (Jan. +2007). 7, 83 + +[^3]: H. L. Röst, G. Rosenberger, P. Navarro, L. Gillet, S. M. Miladinovic, O. T. Schubert, W. Wolski, B. C. Collins, J. Malmstrom, L. Malmström, and R. Aebersold, +OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data, Nature Biotechnology 32(3), 219–223 (Mar. 2014). 83, 87 + +[^4]: L. C. Gillet, P. Navarro, S. Tate, H. Röst, N. Selevsek, L. Reiter, R. Bonner, and +R. Aebersold, Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis, Molecular & Cellular Proteomics 11(6) (June 2012), doi:10.1074/mcp.O111.016717, 83 + +[^5]: A. Bertsch, C. Gröpl, K. Reinert, and O. Kohlbacher, OpenMS and TOPP: open source software for LC-MS data analysis., Methods in molecular biology (Clifton, +N.J.) 696, 353–367 (2011), doi:10.1007/978-1-60761-987-1_23. 83 + +[^6]: H. L. Röst, T. Sachsenberg, S. Aiche, C. Bielow, H. Weisser, F. Aicheler, S. Andreotti, H.-c. Ehrlich, P. Gutenbrunner, E. Kenar, X. Liang, S. Nahnsen, L. Nilse, +J. Pfeuffer, G. Rosenberger, M. Rurik, U. Schmitt, J. Veit, M. Walzer, D. Wojnar, +W. E. Wolski, O. Schilling, J. S. Choudhary, L. Malmström, R. Aebersold, K. Reinert, and O. Kohlbacher, OpenMS: a flexible open-source software platform +for mass spectrometry data analysis, Nat. Methods 13(9), 741–748 (sep 2016),doi:10.1038/nmeth.3959. 83 + +[^7]: J. Pfeuffer, T. Sachsenberg, O. Alka, M. Walzer, A. Fillbrunn, L. Nilse, O. Schilling, +K. Reinert, and O. Kohlbacher, OpenMS - A platform for reproducible analysis +of mass spectrometry data, J. Biotechnol. 261(February), 142–148 (2017), doi:10.1016/j.jbiotec.2017.05.016. 83 + +[^8]: L. Reiter, O. Rinner, P. Picotti, R. Huttenhain, M. Beck, M.-Y. Brusniak, M. O. Hengartner, and R. Aebersold, mProphet: automated data processing and statistical validation for large-scale SRM experiments, Nature Methods 8(5), 430–435 (May +2011), doi:10.1038/nmeth.1584. 83 + +[^9]: E. W. Deutsch, M. Chambers, S. Neumann, F. Levander, P.-A. Binz, J. Shofstahl, +D. S. Campbell, L. Mendoza, D. Ovelleiro, K. Helsens, L. Martens, R. Aebersold, +R. L. Moritz, and M.-Y. Brusniak, TraML—A Standard Format for Exchange of Selected Reaction Monitoring Transition Lists, Molecular & Cellular Proteomics +11(4) (Apr. 2012), doi:10.1074/mcp.R111.015040, 84 + +[^10]: C. Escher, L. Reiter, B. MacLean, R. Ossola, F. Herzog, J. Chilton, M. J. MacCoss, and O. Rinner, Using iRT, a normalized retention time for more targeted measurement of peptides., Proteomics 12(8), 1111–1121 (Apr. 2012), +doi:10.1002/pmic.201100463. 84 diff --git a/docs/tutorials/knime-user-tutorial/quality-control.md b/docs/tutorials/knime-user-tutorial/quality-control.md new file mode 100644 index 00000000..a33b44e7 --- /dev/null +++ b/docs/tutorials/knime-user-tutorial/quality-control.md @@ -0,0 +1,119 @@ +Quality control +=============== + +## Introduction + +In this chapter, we will build on an existing workflow with OpenMS / KNIME to add some quality control (QC). We will utilize the qcML tools in OpenMS to create a file with which we can collect different measures of quality to the mass spectrometry runs themselves and the applied analysis. The file also serves the means of visually reporting on the collected quality measures and later storage along the other analysis result files. We will, step-by-step, extend the label-free quantitation workflow from section 3 with QC functions and thereby enrich each time the report given by the qcML file. But first, to make sure you get the most of this tutorial section, a little primer on how we handle QC on the technical level. + +### QC metrics and qcML + +To assert the quality of a measurement or analysis we use quality metrics. Metrics are describing a certain aspect of the measurement or analysis and can be anything from a single value, over a range of values to an image plot or other summary. Thus, qcML metric representation is divided into QC parameters (QP) and QC attachments (QA) to be able to represent all sorts of metrics on a technical level. +A QP may (or may not) have a value which would equal a metric describable with a single value. If the metric is more complex and needs more than just a single value, the QP does not require the single value but rather depends on an attachment of values (QA) for full meaning. Such a QA holds the plot or the range of values in a table-like form. Like this, we can describe any metric by a QP and an optional QA. +To assure a consensual meaning of the quality parameters and attachments, we created a controlled vocabulary (CV). Each entry in the CV describes a metric or part/extension thereof. We embed each parameter or attachment with one of these and by doing so, connect a meaning to the QP/QA. Like this, we later know exactly what we collected and the programs can find and connect the right dots for rendering the report or calculating new metrics automatically. You can find the constantly growing controlled vocabulary [here](https://github.com/qcML/qcML-development/blob/master/cv/qc-cv.obo). +Finally, in a qcml file, we split the metrics on a per mass-spectrometry-run base or a set of mass-spectrometry-runs respectively. Each run or set will contain its QP/QA we calculate for it, describing their quality. + +## Building a qcML file per run + +As a start, we will build a basic qcML file for each mzML file in the label-free analysis. We are already creating the two necessary analysis files to build a basic qcML file upon each mzML file, a feature file and an identification file. We use the **QCCalculator** node from **Community** > **OpenMS** > **Utilities** where also all other QC* nodes will be found. The **QCCalculator** will create a very basic qcML file in which it will store collected and calculated quality data. + +- Copy your label-fee quantitation workflow into a new lfq-qc workflow and open it. +- Place the **QCCalculator** node after the `IDMapper` node. Being inside the **ZipLoop**, it will execute for each of the three mzML files the **Input** node. +- Connect the first **QCCalculator** port to the first `ZipLoopStart` outlet port, which will carry the individual mzML files. +- Connect the last’s ID outlet port (`IDFilter` or the ID metanode) to the second **QCCalculator** port for the identification file. +- Finally, connect the `IDMapper` outlet to the third **QCCalculator** port for the feature file. + +The created qcML files will not have much to show for, basic as they are. So we will extend them with some basic plots. + +- First, we will add an 2D overview image of the given mass spectrometry run as you may know it from TOPPView. Add the **ImageCreator** node from **Community Nodes** > **OpenMS** > **Utilities**. Change the width and heigth parameters to 640x640 as we don’t want it to be too big. Connect it to the first `ZipLoopStart` outlet port, so it will create an image file of the mzML’s contained run. +- Now we have to embed this file into the qcML file, and attach it to the right **QualityParameter**. For this, place a **QCEmbedder** node behind the **ImageCreator** and connect that to its third inlet port. Connect its first inlet port to the outlet of the **QCCalculator** node to pass on the qcML file. Now change the parameter cv_acc to QC:0000055 which designates the attached image to be of type QC:0000055 - MS experiment heatmap. Finally, change the parameter qp_att_acc to QC:0000004, to attach the image to the QualityParameter QC:0000004 - MS acquisition result details. +- For a reference of which CVs are already defined for qcML, have a look at the following [link](https://github.com/qcML/qcML-development/blob/master/cv/qc-cv.obo). + +There are two other basic plots which we almost always might want to look at before judging the quality of a mass spectrometry run and its identifications: the **total ion current** (TIC) and the **PSM mass error** (Mass accuracy), which we have available as pre-packaged QC metanodes. + +
    +

    **Task**

    +Import the workflow from {path}`Workflows,Quality Control,QC Metanodes.zip` by navigating to **File** > **Import KNIME Workflow...** +
    + +- Copy the **Mass accuracy** metanode into the workflow behind the **QCEmbedder** node and connect it. The qcML will be passed on and the Mass accuracy plots added. The information needed was already collected by the **QCCalculator**. +- Do the same with the **TIC** metanode so that your qcML file will get passed on and enriched on each step. + +R Dependencies: This section requires that the R packages `ggplot2` and scales are both installed. This is the same procedure as in this section. In case that you use an R installation where one or both of them are not yet installed, open the **R Snippet** nodes inside the metanodes you just used (double-click). Edit the script in the *R Script* text editor from: + +```r +#install.packages("ggplot2") +#install.packages("scales") +``` +to +```r +install.packages("ggplot2") +install.packages("scales") +``` +Press **Eval script** to execute the script. + +(Figure_51)= +|![Basic QC setup within a LFQ workflow.](/_images/openms-user-tutorial/quality-control/qc_basic.png)| +|:--:| +|Figure 51: Basic QC setup within a LFQ workflow.| + +```{note} +To have a peek into what our qcML now looks like for one of the **ZipLoop** iterations, we can add an `Output Folder` node from **Community Nodes** > **GenericKnimeNodes** > **IO** and set its destination parameter to somewhere we want to find our intermediate qcML files in, for example **tmp** > **qcxlfq**. If we now connect the last metanode with the Output Folder and restart the workflow, we can start inspecting the qcML files. +``` + +
    +

    **Task**

    +Find your first created qcML file and open it with the browser (not IE), and the contained QC parameters will be rendered for you. +
    + +## Adding brand new QC metrics + +We can also add brand new QC metrics to our qcML files. Remember the **Histogram** you added inside the **ZipLoop** during the label-free quantitation section? Let’s imagine for a moment this was a brand new and utterly important metric and plot for the assessment of your analyses quality. There is an easy way to integrate such new metrics into your qcMLs. Though the **Histogram** node cannot pass its plot to an image, we can do so with a **R View (table)**. + +- Add an **R View (table)** next to the **IDTextReader** node and connect them. +- Edit the **R View (table)** by adding the *R Script* according to this: + +```r + #install.packages("ggplot2") +library("ggplot2") +ggplot(knime.in, aes(x=peptide_charge)) + + + geom_histogram(binwidth=1, origin =-0.5) + + scale_x_discrete() + + + ggtitle("Identified peptides charge histogram") + + ylab("Count") +``` + +- This will create a plot like the **Histogram** node on *peptide_charge* and pass it on as an *image*. +- Now add and connect a **Image2FilePort** node from **Community Nodes** > **GenericKnimeNodes** > **Flow** to the **R View (table)**. +- We can now use a **QCEmbetter** node like before to add our new metric plot into the qcML. +- After looking for an appropriate target from the following [link](https://github.com/qcML/qcML-development/blob/master/cv/qc-cv.obo), we found that we can attach our plot to the MS *identification result details* by setting the parameter `qp_att_acc` to `QC:0000025`, as we are plotting the charge histogram of our identified peptides. +- To have the plot later displayed properly, we assign it the parameter `cv_acc` of `QC:0000051`, a generic plot. Also we made sure in the *R Script*, that our plot carries a caption so that we know which is which, if we had more than one new plot. +- Now we redirect the **QCEmbedders** output to the `Output Folder` from before and can have a look at how our qcML is coming along after restarting the workflow. + +(Figure_52)= +|![QC with new metric](/_images/openms-user-tutorial/quality-control/qc_extra.png)| +|:--:| +|Figure 52: QC with new metric.| + +## Set QC metrics + +Besides monitoring the quality of each individual mass spectrometry run analysis, another capability of QC with OpenMS and qcML is to monitor the complete set. The easiest control is to compare mass spectrometry runs which should be similar, e.g. technical replicates, to spot any aberrations in the set. +For this, we will first collect all created qcML files, merge them together and use the qcML onboard set QC properties to detect any outliers. + +- Connect the **QCEmbedders** output from last section to the **ZipLoopEnds** second input port. +- The corresponding output port will collect all qcML files from each **ZipLoop** iteration and pass them on as a list of files. +- Now we add a **QCMerger** node after the `ZipLoopEnd` and feed it that list of qcML files. In addition, we set its parameter `setname` to give our newly created set a name - say `spikein_replicates`. +- To inspect all the QCs next to each other in that created qcML file, we have to add a new `Output Folder` to which we can connect the **QCMerger** output. + +When inspecting the set-qcML file in a browser, we will be presented another overview. After the set content listing, the basic QC parameters (like number of identifications) are each displayed in a graph. Each set member (or run) has its own section on the x-axis and each run is connected with that graph via a link in the mouseover on one of the QC parameter values. + +(Figure_53)= +|![QC set creation from ZipLoop](/_images/openms-user-tutorial/quality-control/qc_set.png)| +|:--:| +|Figure 53: QC set creation from ZipLoop.| + +
    +

    **Task**

    +For ideas on new QC metrics and parameters, as you add them in your qcML files as generic parameters, feel free to [contact us](/about/communication.md), so we can include them in the CV. +
    diff --git a/docs/tutorials/toppview-user-tutorial.md b/docs/tutorials/toppview-user-tutorial.md new file mode 100644 index 00000000..2d7940f7 --- /dev/null +++ b/docs/tutorials/toppview-user-tutorial.md @@ -0,0 +1,69 @@ +TOPPView +======== + +Visualizing the data is the first step in quality control, an essential tool in understanding the data, and of course an essential step in pipeline development. OpenMS provides a convenient viewer for some of the data: TOPPView. We will guide you through some of the basic features of TOPPView. Please familiarize yourself with the key controls and visualization methods. We will make use of these later throughout the tutorial. Let’s start with a first look at one of the files of +our tutorial data set. Note that conceptually, there are no differences in visualizing metabolomic or proteomic data. Here, we inspect a simple proteomic measurement: + +|![TOPPView](/_images/openms-user-tutorial/introduction/TOPPView.png)| +|:--:| +|Figure 3: TOPPView, the graphical application for viewing mass spectra and analysis results. Top window shows a small region of a peak map. In this 2D representation of the measured spectra, signals of eluting peptides are colored according to the raw peak intensities. The lower window displays an extracted spectrum (=scan) from the peak map. On the right side, the list of spectra can be browsed.| + +|![TOPPView](/_images/openms-user-tutorial/introduction/3dview.png)| +|:--:| +|Figure 4: 3D representation of the measured spectra, signals of eluting peptides are colored according to the raw peak intensities.| + +- Start TOPPView (see Windows' Start-Menu or {{ '{path}'+'`Applications,OpenMS-{0}`'.format(version) }} on macOS) + +- Go to **File** > **Open File**, navigate to the directory where you copied the contents + of the USB stick to, and [select](https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Example_Data/Introduction/datasets/small/velos005614.mzML) {path}`Example_Data,Introduction,datasets,small,velos005614.mzML`. This file contains only a reduced LC-MS map of a label-free proteomic platelet measurement recorded on an Orbitrap velos. The other two mzML files contain technical replicates of this experiment. First, we want to + obtain a global view on the whole LC-MS map - the default option Map view 2D + is the correct one and we can click the Ok button. + +- Play around. + +- Three basic modes allow you to interact with the displayed data: scrolling, zooming and measuring: + - **Scroll mode** + - Is activated by default (though each loaded spectra file is displayed zoomed out first, so you do not need to scroll). + - Allows you to browse your data by moving around in RT and m/z range. + - When zoomed in, you can scroll through the spectra. Click-drag on the current view. + - Arrow keys can be used to scroll the view as well. + - **Zoom mode** + - Zooming into the data; either mark an area in the current view with + your mouse while holding the left mouse button plus the Ctrl key to + zoom to this area or use your mouse wheel to zoom in and out. + - All previous zoom levels are stored in a zoom history. The zoom history + can be traversed using Ctrl + + or Ctrl + - or the mouse wheel (scroll up and down). + - Pressing backspace zooms out to show the full LC-MS map (and + also resets the zoom history). + - **Measure mode** + - It is activated using the ⇧ Shift key. + - Press the left mouse button down while a peak is selected and drag + the mouse to another peak to measure the distance between peaks. + - This mode is implemented in the 1D and 2D mode only. +- Right click on your 2D map and select **Switch to 3D mode** and examine your data in 3D mode (see Fig. 4). +- Visualize your data in different intensity normalization modes, use linear, percentage (set intensity axis scale to percentage), snap and log-view (icons on + the upper left tool bar). You can hover over the icons for additional information. + + ```{note} + On macOS, due to a bug in one of the external libraries used by + OpenMS, you will see a small window of the 3D mode when switching + to 2D. Close the 3D tab in order to get rid of it. + ``` +- In TOPPView you can also execute TOPP tools. Go to **Tools** > **Apply tool (whole layer)** + and choose a TOPP tool (e.g., `FileInfo`) and inspect the results. + +Dependent on your data MS/MS spectra can be visualized as well (see Fig.5) . You can +do so, by double-click on the MS/MS spectrum shown in scan view + +|![ms2 spectrum](/_images/openms-user-tutorial/introduction/ms2_introduction.png)| +|:--:| +|Figure 5: MS/MS spectrum| + +```{toctree} +:maxdepth: 1 + +toppview-user-tutorial/smoothing-raw-data.md +toppview-user-tutorial/subtracting-a-baseline-from-a-spectrum.md +toppview-user-tutorial/profile-data-processing.md + +``` \ No newline at end of file diff --git a/docs/topp-command-line-tools/profile-data-processing.md b/docs/tutorials/toppview-user-tutorial/profile-data-processing.md similarity index 98% rename from docs/topp-command-line-tools/profile-data-processing.md rename to docs/tutorials/toppview-user-tutorial/profile-data-processing.md index 9243d96a..e546457a 100644 --- a/docs/topp-command-line-tools/profile-data-processing.md +++ b/docs/tutorials/toppview-user-tutorial/profile-data-processing.md @@ -12,7 +12,7 @@ To find all peaks in the profile data: 2. The now smoothed profile data can be further processed by subtracting the baseline with the **BaselineFilter**. 3. Then use one of the **PeakPickers** to find all peaks in the baseline-reduced profile data. -![TOPP raw data](/images/tutorials/topp/TOPP_raw_data.png) +![TOPP raw data](/_images/tutorials/topp/TOPP_raw_data.png) There are two different smoothing filters: NoiseFilterGaussian and NoiseFilterSGolay. To use the Savitzky Golay filter, or the **BaselineFilter** with non equally spaced profile data, e.g. TOF data, you have to generate equally spaced data diff --git a/docs/topp-command-line-tools/smoothing-raw-data.md b/docs/tutorials/toppview-user-tutorial/smoothing-raw-data.md similarity index 85% rename from docs/topp-command-line-tools/smoothing-raw-data.md rename to docs/tutorials/toppview-user-tutorial/smoothing-raw-data.md index 255794f4..f33020c1 100644 --- a/docs/topp-command-line-tools/smoothing-raw-data.md +++ b/docs/tutorials/toppview-user-tutorial/smoothing-raw-data.md @@ -8,8 +8,8 @@ To smooth raw data, call one of the available NoiseFilters via the Tools-menu, ( For the Gaussian filter, the gaussian width and the ppm tolerance for a flexible gaussian width depending on the `m/z` value can be adapted. Press **Ok** to run the selected `NoiseFilter`. -![](/images/tutorials/topp/TOPPView_tools_noisefilter.png) +![](/_images/tutorials/topp/TOPPView_tools_noisefilter.png) The following image shows a part of the spectrum after smoothing as red line with the un-smoothed data in green. -![](/images/tutorials/topp/TOPPView_tools_noisefilter_filtered.png) +![](/_images/tutorials/topp/TOPPView_tools_noisefilter_filtered.png) diff --git a/docs/topp-command-line-tools/subtracting-a-baseline-from-a-spectrum.md b/docs/tutorials/toppview-user-tutorial/subtracting-a-baseline-from-a-spectrum.md similarity index 82% rename from docs/topp-command-line-tools/subtracting-a-baseline-from-a-spectrum.md rename to docs/tutorials/toppview-user-tutorial/subtracting-a-baseline-from-a-spectrum.md index 20083fc4..a8e78f13 100644 --- a/docs/topp-command-line-tools/subtracting-a-baseline-from-a-spectrum.md +++ b/docs/tutorials/toppview-user-tutorial/subtracting-a-baseline-from-a-spectrum.md @@ -11,10 +11,10 @@ between different types of filters (green rectangle), the one mainly used is Top the length of the structuring element (blue rectangle). The default value is `3` Thomson. Press **Ok** to start the baseline subtraction. -![TOPPView Tools Baseline](/images/tutorials/topp/TOPPView_tools_baseline.png) +![TOPPView Tools Baseline](/_images/tutorials/topp/TOPPView_tools_baseline.png) The following image shows: - A part of the spectrum after baseline filtering as a green line. - The original raw data as a blue line. -![TOPPView Tools Baseline Filtered](/images/tutorials/topp/TOPPView_tools_baseline_filtered.png) +![TOPPView Tools Baseline Filtered](/_images/tutorials/topp/TOPPView_tools_baseline_filtered.png)

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zZp&LL$7rQsORoTcE-ODveLzq614e*&u9U!Uo0_&&9&K@9)+_qI(9=9~2k}tKy_BsB zr8b!mmkqwzu^*9-54=ixdhXU?-2FyDHe{9IcSr9c2I8<)@7ZQ3In<$qdb&nHR%?!_98nn8YzECrkJ@xyM#4~Q2BnQU?q_>(GK^6|%SeOK)&M3~ z8hbPX>Njuu{P3sPf`h<~apG+Q+3=p;(1P~4+dX8?YVix zsSUJV=Jw^qGojP<3fF$YBmNEVfS3>o{xGUvZW zc*kq-;=ryVY(D^8KXVb<2x_7rJDu^b#ky1nH3NSTUlg1K7ToGjYyb80((V7zPukxM z-vRXUYy6`6u+xSkon($u>Vg`sn0_1~_J+XhTmS1#8QP#Xb;LH5%(dYmAt49VarBCQ zF&`xQ?s(Gu^=16Oo6p|*4a`{>u!Tdq=0Px=;v130vB^k`o_A`(&0ng44+XU~|Bmv6 z3M*=HJ^>F$Ru=s(;7-e(D4FaqcfmNDAuwrgHpod&=iuk(M{T*=G^Gm4K-k6f#N^zM5*cln0FfS@Em z!u2%&y3-O-Mk5lpfR{!7moS6mt@pE+qbF7tpjKy%%~)gzTjr|^X&ztSQ9$v6_&s<-*VLnJGd4o*3BNg?L`c}aVue82Ly6`0KpSQ z-}dX|4xn~m&%z#q%Q}gO!YWV65@ZWxsEtDiscord + +Discord allows the users to communicate in different channels, publicly as well as privately + +*** + +### GitHub + +The following repositories can be used: +- [OpenMS](https://github.com/OpenMS/OpenMS) (Bug Reports, Feature requests) +- [OpenMS-docs](https://github.com/OpenMS/OpenMS-docs) (Documentation-related issues) +- [OpenMS.de](https://github.com/OpenMS/openms.de) (Any issue related to website) +- [pyopenms-docs](https://github.com/OpenMS/pyopenms-docs) (Any issue related to pyopenms) + +In case if you are confused, please ask your query [here](https://openms.de/help-request/) + +*** + +### GitHub issue tracker + +The issue tracker can be used for: + +OpenMS +- [Bug reports](https://github.com/OpenMS/OpenMS/labels/bug) (e.g. "Filter doesn't work for 1D data") +- [Feature requests](https://github.com/OpenMS/OpenMS/labels/enhancement) +- [Tools](https://github.com/OpenMS/OpenMS/issues?q=is%3Aopen+is%3Aissue+label%3ATOPP) (**T**he **O**penMS **P**i**p**eline - TOPP) +- [TOPPView](https://github.com/OpenMS/OpenMS/labels/TOPPView) + +Documentation +- [Bug reports](https://github.com/OpenMS/OpenMS-docs/labels/bug) +- [Feature requests](https://github.com/OpenMS/OpenMS-docs/labels/enhancement) + +*** + +### Gitter + +A real-time chat room to ask questions about OpenMS. + +*** + +### OpenMS mailing list + + +These lists are the main form of receiving OpenMS-related updates, like new features, changes to the roadmap, and all kinds of project-wide decision making. + +There are two mailing list currently: +- [open-ms-announcements](https://sourceforge.net/p/open-ms/mailman/open-ms-announcements/) - Announcements about OpenMS, such as for releases, developer meetings, sprints or conference talks are made on this list. +- [open-ms-general](https://sourceforge.net/p/open-ms/mailman/open-ms-general/) - For addressing any queries or suggestion from the users. + +*** + +### Twitter + +Contact us or just follow the latest OpenMS news on Twitter. + +*** + +Join the OpenMS Community +------------------------- + +To thrive, the OpenMS project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to OpenMS. + +If you are interested in becoming a OpenMS contributor (yay!) we recommend checking out our [Contribute](/manual/contribute.md) page. diff --git a/docs/about/installation.rst b/docs/about/installation.rst new file mode 100644 index 00000000..4d5425c4 --- /dev/null +++ b/docs/about/installation.rst @@ -0,0 +1,92 @@ +Installation +============ + +.. toctree:: + :hidden: + + installation/installation-on-gnu-linux.md + installation/installation-on-macos.md + installation/installation-on-windows.md + +Workflow Editor +--------------- + +You can run OpenMS TOPP tools from the command line using your custom scripts, or use powerful +workflow systems designed to make workflow creation and maintenance more fun, find out more in :ref:`workflows`. + +.. list-table:: + :widths: 25 25 25 25 + :class: borderless only-light + :align: center + + * - .. image:: /_images/introduction/KNIMELogoTM.svg + :target: ../getting-started/knime-get-started.html + :width: 90% + + - .. image:: /_images/introduction/nextflow2014_no-bg.png + :target: ../getting-started/nextflow-get-started.html + :width: 90% + + - .. image:: /_images/introduction/galaxy_project_logo.png + :target: ../getting-started/galaxy-get-started.html + :width: 90% + + - .. image:: /_images/introduction/TOPPAS_logo_white.png + :target: ../getting-started/toppas-get-started.html + :width: 90% + + +.. list-table:: + :widths: 25 25 25 25 + :class: borderless only-dark + :align: center + + * - .. image:: /_images/introduction/KNIMELogoTM_white.svg + :target: ../getting-started/knime-get-started.html + :width: 90% + + - .. image:: /_images/introduction/nextflow2014_no-bg-bright.png + :target: ../getting-started/nextflow-get-started.html + :width: 90% + + - .. image:: /_images/introduction/galaxy_project_logo_white.png + :target: ../getting-started/galaxy-get-started.html + :width: 90% + + - .. image:: /_images/introduction/TOPPAS_logo_dark.png + :target: ../getting-started/toppas-get-started.html + :width: 90% + + +Graphical and Command-Line Tools +-------------------------------- +For instructions on how to install the OpenMS graphical and command-line tools, choose your operating system +from the items below. + + +.. grid:: + :gutter: 4 + + .. grid-item-card:: GNU/Linux + :img-top: /_images/installations/linux_logo.svg + :link: installation/installation-on-gnu-linux + :link-type: doc + :columns: 4 + + Installation on Linux + + .. grid-item-card:: macOS + :img-top: /_images/installations/macos_logo.svg + :link: installation/installation-on-macos + :link-type: doc + :columns: 4 + + Installation on macOS + + .. grid-item-card:: Windows + :img-top: /_images/installations/Windows_logo.svg + :link: installation/installation-on-windows + :link-type: doc + :columns: 4 + + Installation on Windows diff --git a/docs/openms-applications-and-tools/installation/installation-on-gnu-linux.md b/docs/about/installation/installation-on-gnu-linux.md similarity index 97% rename from docs/openms-applications-and-tools/installation/installation-on-gnu-linux.md rename to docs/about/installation/installation-on-gnu-linux.md index dcc1cc9f..ca4f31d6 100644 --- a/docs/openms-applications-and-tools/installation/installation-on-gnu-linux.md +++ b/docs/about/installation/installation-on-gnu-linux.md @@ -1,7 +1,9 @@ GNU/Linux ========================= + ```{include} installation-with-conda.md +:start-after: "% start-after" ``` ## Install via package managers @@ -65,6 +67,7 @@ If you encounter errors with unavailable packages, troubleshoot using the follow Add the folders in your `PATH` for a convenient use of the adapters. ```{include} run-in-container.md +:start-after: "% start-after" ``` ## Build OpenMS from source diff --git a/docs/openms-applications-and-tools/installation/installation-on-macos.md b/docs/about/installation/installation-on-macos.md similarity index 85% rename from docs/openms-applications-and-tools/installation/installation-on-macos.md rename to docs/about/installation/installation-on-macos.md index 19ea1b28..e761e3c0 100644 --- a/docs/openms-applications-and-tools/installation/installation-on-macos.md +++ b/docs/about/installation/installation-on-macos.md @@ -2,6 +2,7 @@ macOS ==================== ```{include} installation-with-conda.md +:start-after: "% start-after" ``` ## Install via macOS installer @@ -11,40 +12,40 @@ To install OpenMS on macOS, run the following steps: 1. Download and install the macOS drag-and-drop installer from the [archive](https://abibuilder.cs.uni-tuebingen.de/archive/openms/OpenMSInstaller/release/latest/). 2. Double click on the downloaded file. It will start to open the `OpenMS--macOS.dmg` disk image file. -```{image} /images/installations/macos/opening-openms2-8-macos.png +```{image} /_images/installations/macos/opening-openms2-8-macos.png :alt: Opening OpenMS--macOS.dmg :width: 500px ``` 3. Verify the download. -```{image} /images/installations/macos/verifying-openms2-8-macos.png +```{image} /_images/installations/macos/verifying-openms2-8-macos.png :alt: Verifying OpenMS--macOS.dmg :width: 500px ``` 4. Agree to the license agreements. -```{image} /images/installations/macos/license-agreements.png +```{image} /_images/installations/macos/license-agreements.png :alt: License agreement :width: 500px ``` 5. Drag OpenMS to the Applications folder. -```{image} /images/installations/macos/move-openms-to-applications.png +```{image} /_images/installations/macos/move-openms-to-applications.png :alt: Move to Applications :width: 500px ``` 6. It will start copying to applications. -```{image} /images/installations/macos/preparing-to-copy-to-applications.png +```{image} /_images/installations/macos/preparing-to-copy-to-applications.png :alt: Preparing to copy to Applications :width: 500px ``` -```{image} /images/installations/macos/copying-to-applications.png +```{image} /_images/installations/macos/copying-to-applications.png :alt: Copying to Applications :width: 500px ``` @@ -85,6 +86,7 @@ To use {term}`TOPP` as regular app in the shell, add the following lines to the ::: ```{include} run-in-container.md +:start-after: "% start-after" ``` ## Build OpenMS from source diff --git a/docs/openms-applications-and-tools/installation/installation-on-windows.md b/docs/about/installation/installation-on-windows.md similarity index 98% rename from docs/openms-applications-and-tools/installation/installation-on-windows.md rename to docs/about/installation/installation-on-windows.md index c919e89e..21aef1e8 100644 --- a/docs/openms-applications-and-tools/installation/installation-on-windows.md +++ b/docs/about/installation/installation-on-windows.md @@ -12,7 +12,7 @@ To Install the binary package of OpenMS & {term}`TOPP`: Click on "More Info", and then "Run anyways". - ![](/images/installations/win/smartscreen.gif) + ![](/_images/installations/win/smartscreen.gif) When asked for an admin authentication, please enter the credentials (it is not advised to directly invoke the installer using an admin account). diff --git a/docs/openms-applications-and-tools/installation/installation-with-conda.md b/docs/about/installation/installation-with-conda.md similarity index 99% rename from docs/openms-applications-and-tools/installation/installation-with-conda.md rename to docs/about/installation/installation-with-conda.md index 2d86be0f..5c5b837a 100644 --- a/docs/openms-applications-and-tools/installation/installation-with-conda.md +++ b/docs/about/installation/installation-with-conda.md @@ -1,3 +1,6 @@ +# Conda + +% start-after ## Install via Conda ```{warning} diff --git a/docs/openms-applications-and-tools/installation/run-in-container.md b/docs/about/installation/run-in-container.md similarity index 99% rename from docs/openms-applications-and-tools/installation/run-in-container.md rename to docs/about/installation/run-in-container.md index 45075bb9..7b88d5a4 100644 --- a/docs/openms-applications-and-tools/installation/run-in-container.md +++ b/docs/about/installation/run-in-container.md @@ -1,3 +1,6 @@ +# Container + +% start-after ## Run OpenMS inside a (Bio)Container 1. Install a containerization software (e.g., [Docker](https://docs.docker.com/engine/install/) or [Singularity](https://sylabs.io/guides/3.0/user-guide/quick_start.html#quick-installation-steps)) diff --git a/docs/about/learning/background.md b/docs/about/learning/background.md new file mode 100644 index 00000000..2ec6dc77 --- /dev/null +++ b/docs/about/learning/background.md @@ -0,0 +1,28 @@ +Learning +======== + +Proteomics and metabolomics focus on complex interactions within biological systems; the former is centered on proteins while the latter is based on metabolites. To understand these interactions, we need to accurately identify the different biological components involved. + +{term}`Liquid chromatography` (LC) and {term}`mass spectrometry` (MS) are the analytical techniques used to isolate and identify biological components in proteomics and metabolomics. LC-MS data can be difficult to analyze manually given its amount and complexity. Therefore, we need specialized software that can analyze high-throughput LC-MS data quickly and accurately. + +**Why use OpenMS** + +OpenMS is an open-source, C++ framework for analyzing large volumes of mass spectrometry data. It has been specially designed for analyzing high performance LC-MS data but over recent times, has been extended to analyze data generated by other techniques. + +```{note} +OpenMS in recent times has been expanded to support a wide variety of mass spectrometry experiments. To design your analysis solution, [contact the OpenMS team](https://openms.github.io/community/) today. +``` + +To use OpenMS effectively, an understanding of chromatography and mass spectrometry is required as many of the algorithms are based on these techniques. +This section provides a detailed explanation on LC and MS, and how they are combined to identify and quantify substances. + + +```{toctree} +--- +maxdepth: 1 +--- + +lc-chromatography.md +mass-spectrometry.md +id-and-quant.md +``` diff --git a/docs/about/learning/id-and-quant.md b/docs/about/learning/id-and-quant.md new file mode 100644 index 00000000..3fde1644 --- /dev/null +++ b/docs/about/learning/id-and-quant.md @@ -0,0 +1,89 @@ +Identification and Quantification +================================= + +While the combination of liquid chromatography and mass spectrometry can ease the process of characterising molecules of interest, further techniques are required to easily identify and quantify these molecules. This section discusses both labeled and label-free quantification techniques. + +## Labeling + +Relative quantification is one strategy where one sample is chemically treated and compared to another sample without treatment. This section discusses a particular relative quanitification technique called **labeling** or **stable isotope labeling** which involves the addition of isotopes to one sample. An isotope of an element behaves the same chemically but has a different mass. Stable isotope labeling is used in mass spectrometry so that scientists can easily identify proteins and metabolites. + +Two types of stable isotope labeling exist: chemical labeling and metabolic labeling. + +### Chemical labeling + +During chemical labeling, the label is attached at specific functional groups in a molecule like the N-terminus of a peptide or specific side chains. + +Chemical labeling occurs late in the process, therefore experiments that incorporate this technique are not highly reproducible. + +#### Isobaric labeling + +Isobaric labeling, is a technique where peptides and proteins are labeled with chemical groups that have an identical mass, but vary in terms of of distribution of heavy isotopes in their structure. + +