ASA³P is an automatic and highly scalable assembly, annotation and higher-level analyses pipeline for closely related bacterial isolates.
At its core it's a command line tool creating standard bioinformatics file formats as well as sophisticated HTML5 documents. Its main purpose is the automatic processing of NGS data of multiple closely related isolates, thus transforming raw reads into assembled and annotated genomes and finally getting as much information on every single bacterial genome as possible. Per-isolate analyses are finally complemented by first comparative insights. Therefore, the pipeline incorporates many best-in-class open source bioinformatics tools and thus minimizes the burden of ever repeating tasks. Envisaged as an upfront tool it provides comprehensive insights as well as a general overview and comparison of analysed genomes along with all necessary result files for subsequent deeper analyses. All results are presented via modern HTML5 documents comprising interactive visualizations.
- quality/adapter clipping
- assembly (Illumnia, PacBio & ONT)
- scaffolding
- annotation
- taxonomic classification (Kmer, 16S and ANI)
- multi locus sequence typing (MLST)
- antibiotic resistance detection
- virulence factor detection
- reference mapping
- SNP detection
- calculation of core/pan genome and singleton genes
- phylogenetic tree creation
Targeting different project sizes, i.e. number of genomes which should be analysed together, we offer ASA³P in two versions:
- Docker: linux container for small to medium projects
- OpenStack: highly scalable cloud version for (very) large projects
For small to medium projects (up to ~200 isolates) but also for the sake of simplicity, reproducibility and easy distribution, we offer ASA³P as a Docker image hosted at: Docker Hub (https://hub.docker.com/r/oschwengers/asap/).
Setup:
$ sudo docker pull oschwengers/asap
$ wget https://s3.computational.bio.uni-giessen.de/swift/v1/asap/latest/asap.tar.gz
$ tar -xzf asap.tar.gz
$ rm asap.tar.gzRunning an ASA³P Container using the asap-docker.sh shell wrapper script:
$ sudo asap/asap-docker.sh <PROJECT_DIR> [<SCRATCH_DIR>]Parameters:
<PROJECT_DIR>: path to the actual project directory (containingconfig.xlsanddatadirectory)<SCRATCH_DIR>: optionally path to a distinct scratch/tmp dir
Note
Make sure to always leave the asap-docker.sh script within the ASA³P directory
as by this the right internal paths will be auto-detected and forwarded.
Complete example: (user name: ubuntu)
$ sudo docker pull oschwengers/asap
$ wget https://s3.computational.bio.uni-giessen.de/swift/v1/asap/latest/asap.tar.gz
$ tar -xzf asap.tar.gz
$ rm asap.tar.gz
$ wget https://s3.computational.bio.uni-giessen.de/swift/v1/asap/example-lmonocytogenes.tar.gz
$ tar -xzf example-lmonocytogenes.tar.gz
$ rm example-lmonocytogenes.tar.gz
$ sudo asap/asap-docker.sh asap/ example-lmonocytogenes/For further information have a look at the Docker readme (DOCKER.md ).
ASA³P's OpenStack based cloud version targets the analysis of hundreds to even thousands of bacterial isolates. Therefore, it offers automatic creation, setup and orchestration of an SGE based compute cluster and its entire underlying infrastructure. The OpenStack cloud version internally takes advantage of the BiBiGrid (https://github.com/BiBiServ/bibigrid) framework. Hence, analysis of thousands of genomes can be achieved in a highly parallel manner and adequate amount of time. ASA³P takes care of all setup and orchestration aspects and thus hides away as much technical complexity as possible. For further information please have a look at our user manual (https://s3.computational.bio.uni-giessen.de/swift/v1/asap/latest/manual.pdf)
ASA³P tarball containing software and databases (for Docker): https://s3.computational.bio.uni-giessen.de/swift/v1/asap/latest/asap.tar.gz
Additional files:
- comprehensive manual: https://s3.computational.bio.uni-giessen.de/swift/v1/asap/latest/manual.pdf
- configuration template: https://s3.computational.bio.uni-giessen.de/swift/v1/asap/latest/config.xls
- example projects:
- 4 public L. monocytogenes genomes: https://s3.computational.bio.uni-giessen.de/swift/v1/asap/example-lmonocytogenes.tar.gz
- artificial E. coli project merely showing support of different input types: https://s3.computational.bio.uni-giessen.de/swift/v1/asap/example-input.tar.gz
ASA³P expects all input files and information regarding a single batch run (i.e. a "project") within a dedicated directory. All necessary information (meta information, reference genomes, isolate/sample names and files) are provided via an Excel config file named config.xls. A corresponding template can be downloaded here. For further details on how to fill out a proper configuration file, please have a look at the manual and the exemplary projects listed above. All input files referenced in a configuration spreadsheet need to be placed in a subdirectory called data.
Example:
project-dir
├── config.xls
├── data
│ ├── reference-genome-1.gbk
│ ├── reference-genome-2.fasta
│ ├── isolate-1-1.fastq.gz
│ ├── isolate-1-2.fastq.gz
│ ├── isolate-2-1.fastq.gz
│ ├── isolate-2-2.fastq.gz
│ ├── isolate-3.1.bax.h5
│ ├── isolate-3.2.bax.h5
│ ├── isolate-3.3.bax.h5
│ ├── ...
tl; dr Just open your browser and open the index.html file located at:
project-dir
├── reports (HTML5 reports)
│ ├── index.html
ASA³P stores all output files within the specified project directory leaving input files untouched:
- empty status file indicating ASA³P current status, one of:
- status.running
- status.finished
- status.failed
- log file (asap.log)
- internal configuration file (config.json)
- report directory containing HTML5 report pages (reports)
Furthermore, for each analysis ASA³P creates a corresponding subdirectory containing result files such as:
- empty status file indicating an analysis' status, one of:
- status.running
- status.finished
- status.failed
- JSON file (info.json) comprising collected and aggregated information
- binary result files in standard file formats (.fasta, .gbk, .gff, .bam, .vcf.gz, etc...)
Where necessary ASA³P creates subdirectories for each isolate within an analysis directory.
Example:
project-dir
├── [state.running | state.finished | state.failed]
├── asap.log (global logging file)
├── config.xls (config spreadsheet)
├── config.json (internal config)
├── reports (HTML5 reports)
│ ├── index.html
│ ├── ...
├── reads_qc (quality clipped read files)
│ ├── <sample-name>
│ ├── ├── [state.finished | state.failed]
│ ├── ├── isolate-1-1.fastq.gz
│ ├── ├── isolate-1-2.fastq.gz
│ ├── ├── info.json
│ ├── ...
├── assembly (assemblies)
│ ├── <sample-name>
│ ├── ├── [state.finished | state.failed]
│ ├── ├── <sample-name>.fasta
│ ├── ├── <sample-name>-discarded.fasta
│ ├── ├── info.json
│ ├── ...
├── scaffolds (scaffolded contigs)
│ ├── <sample-name>
│ ├── ├── [state.finished | state.failed]
│ ├── ├── <sample-name>.fasta (scaffolds)
│ ├── ├── <sample-name>-pseudo.fasta (pseudo genome)
│ ├── ├── info.json
│ ├── ...
├── annotations
│ ├── <sample-name>
│ ├── ├── [state.finished | state.failed]
│ ├── ├── <sample-name>.gbk (Genbank)
│ ├── ├── <sample-name>.gff (GFF3)
│ ├── ├── <sample-name>.ffn (gene sequences)
│ ├── ├── <sample-name>.faa (protein sequences)
│ ├── ├── info.json
│ ├── ...
├── taxonomy (taxonomic classfication results)
│ ├── [<sample-name>.finished | <sample-name>.failed]
│ ├── <sample-name>.json
│ ├── ...
├── mlst (multi-locus sequence typing results)
│ ├── [<sample-name>.finished | <sample-name>.failed]
│ ├── <sample-name>.json
│ ├── ...
├── abr (antibiotic resistance genes detection)
│ ├── [<sample-name>.finished | <sample-name>.failed]
│ ├── <sample-name>.json
│ ├── ...
├── vf (virulence factor detection results)
│ ├── [<sample-name>.finished | <sample-name>.failed]
│ ├── <sample-name>.json
│ ├── ...
├── mappings (reference mappings)
│ ├── [<sample-name>.finished | <sample-name>.failed]
│ ├── <sample-name>.json
│ ├── <sample-name>.bam
│ ├── <sample-name>.bam.bai
│ ├── ...
├── snps (called single nucleotide polymorphisms)
│ ├── [<sample-name>.finished | <sample-name>.failed]
│ ├── <sample-name>.json
│ ├── <sample-name>.consensus.fasta (mpileup consensus file)
│ ├── <sample-name>.vcf.gz (SNPs in variant calling format file)
│ ├── <sample-name>.vcf.gz.tbi
│ ├── <sample-name>.chk (bcftools stats)
│ ├── <sample-name>.csv (SNPeff per gene statisics)
│ ├── ...
├── corepan
│ ├── [state.finished | state.failed]
│ ├── info.json
│ ├── core.fasta (core genome sequences)
│ ├── pan.fasta (pan genome sequences)
│ ├── pan-matrix.tsv (pan genome matrix)
│ ├── <sample-name>.json
│ ├── ...
├── phylogeny
│ ├── [state.finished | state.failed]
│ ├── info.json
│ ├── tree.nwk (phylogenetic tree in newick file)
│ ├── consensus.fasta (global consensus file)
├── data
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Is there a public example project? Just download this exemplary project containing a set of 4 public Listeria monocytogenes genomes from SRA: https://s3.computational.bio.uni-giessen.de/swift/v1/asap/example-lmonocytogenes.tar.gz
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How to cite ASA³P? A manuscript is currently in preparation. Stay tuned!
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Can I install ASA³P by myself? Yes you can! Nevertheless, we highly encourage to use either the Docker container or the OpenStack images. As there are too many combinations of linux distributions and tool/database versions we cannot give any support for this.
ASA³P itself is published and distributed under GPL3 license. In contradiction, some of its dependencies bundled within the ASA³P tarball (asap.tar.gz file) are published under different licenses, e.g. GPL2, BSD, MIT, LGPL, etc. A file (README.md) within the ASA³P directory contains a list of all dependencies and related licenses.
NOTE Please, notice that some bundled dependecies are published under a free-for-academic or free-for-non-commercial usage license model. To our best knowledge this is true for at least the following databases:
- CARD: free for academic usage
- PubMLST: proprietary but free to use
If you face any problems using the pipeline please have a look at the manual. If the problems remain, please send us an email: [email protected]
For technical issues or feature requests please submit an issue: https://github.com/oschwengers/asap/issues