Automate case study creation | starting with rmd report

[jananiravi]

• Start w/ @the-mayer's rmd report
• Identify proteins to run through MolEvolvR
• Create case studies
• Which additional summarization/visualizations would help with these case studies?

[Cateline]

Cateline
Kindly assign this task to me

[jananiravi]

@the-mayer could you pass along your rmd doc to Cateline? @Cateline, you can get started with MolEvolvR web submissions in the meantime to help you understand what the different functions are doing (which summarizations and visualizations they result in).

[the-mayer]

I'm attaching the report template and some sample output, for reference.
example_report.zip

Note, this report is parameterized, so figures can be supplied as parameters when rendering. As an example, the MolEvolvR web app renders this report by calling:

## List of graphics to include in report
        params <- list(
                    ## Results Summary
                    ### Domain Architecture
                    rs_interproscan_visualization = rs_IprGenes_rx(),
                    ### Proximity Network
                    proximity_network = rval_rs_network_layout_rx(), 
                    ## Phylogeny
                    ### Sunburst
                    sunburst = data()@df,
                    ### Data
                    data = rs_data_table_rx(),
                    ## Query Data
                    ### Data Table
                    queryDataTable = queryDataTable_rx(),
                    ### FASTA
                    fastaDataText = fastaDataText_rx(),
                    ### Query Heatmap
                    heatmap = query_heatmap_rx(),
                    ### Domain Architecture
                    query_data = query_data(),
                    query_domarch_cols = query_domarch_cols(),
                    query_iprDatabases = input$query_iprDatabases,
                    query_iprVisType = input$query_iprVisType,
                    ## Homolog Data
                    mainTable = mainTable_rx(),
                    ## Domain Architecture
                    ### Table
                    DALinTable = DALinTable_rx(),
                    ### Heatmap
                    DALinPlot = DALinPlot_rx(),
                    ### Network
                    DANetwork = DANetwork_rx(),
                    DA_Prot = DA_Prot(),
                    domarch_cols = domarch_cols(),
                    DA_Col = input$DA_Col,
                    DACutoff = DACutoff(),
                    ### Interproscan Viz
                    da_interproscan_visualization = da_IprGenes_rx(),
                    ### Upset Plot
                    # uses existing params
                    ## Phylogeny
                    ### Sunburst
                    phylo_sunburst_levels = input$levels,
                    phylo_sunburst = phylogeny_prot(),
                    ### Tree
                    tree_msa_tool = input$tree_msa_tool,
                    ### MSA
                    rep_accnums = rep_accnums(),
                    msa_rep_num = input$msa_rep_num, 
                    app_data = app_data(),
                    PhyloSelect = input$PhyloSelect, 
                    acc_to_name = acc_to_name(),
                    rval_phylo = rval_phylo(),
                    query_pin = query_pin(),
                    msa_reduce_by = input$msa_reduce_by
                  )
        ## Render RMarkdown report, with included graphics
        rmarkdown::render(tempReport, output_file = file, params = params, envir = new.env(parent = globalenv()))

As you become more familiar with the way reports are generated in the Web App, we can work together to supply the correct parameters to this report in an automated fashion. Let me know if you have any questions in the meantime!

[Cateline]

Thank you for sharing.

…

On Thu, 3 Oct 2024, 23:55 D Mayer, ***@***.***> wrote: I'm attaching the report template and some sample output, for reference. example_report.zip <https://github.com/user-attachments/files/17249962/example_report.zip> Note, this report is parameterized, so figures can be supplied as parameters when rendering. As an example, the MolEvolvR web app renders this report by calling: ## List of graphics to include in report params <- list( ## Results Summary ### Domain Architecture rs_interproscan_visualization = rs_IprGenes_rx(), ### Proximity Network proximity_network = rval_rs_network_layout_rx(), ## Phylogeny ### Sunburst sunburst = ***@***.***, ### Data data = rs_data_table_rx(), ## Query Data ### Data Table queryDataTable = queryDataTable_rx(), ### FASTA fastaDataText = fastaDataText_rx(), ### Query Heatmap heatmap = query_heatmap_rx(), ### Domain Architecture query_data = query_data(), query_domarch_cols = query_domarch_cols(), query_iprDatabases = input$query_iprDatabases, query_iprVisType = input$query_iprVisType, ## Homolog Data mainTable = mainTable_rx(), ## Domain Architecture ### Table DALinTable = DALinTable_rx(), ### Heatmap DALinPlot = DALinPlot_rx(), ### Network DANetwork = DANetwork_rx(), DA_Prot = DA_Prot(), domarch_cols = domarch_cols(), DA_Col = input$DA_Col, DACutoff = DACutoff(), ### Interproscan Viz da_interproscan_visualization = da_IprGenes_rx(), ### Upset Plot # uses existing params ## Phylogeny ### Sunburst phylo_sunburst_levels = input$levels, phylo_sunburst = phylogeny_prot(), ### Tree tree_msa_tool = input$tree_msa_tool, ### MSA rep_accnums = rep_accnums(), msa_rep_num = input$msa_rep_num, app_data = app_data(), PhyloSelect = input$PhyloSelect, acc_to_name = acc_to_name(), rval_phylo = rval_phylo(), query_pin = query_pin(), msa_reduce_by = input$msa_reduce_by ) ## Render RMarkdown report, with included graphics rmarkdown::render(tempReport, output_file = file, params = params, envir = new.env(parent = globalenv())) As you become more familiar with the way reports are generated in the Web App, we can work together to supply the correct parameters to this report in an automated fashion. Let me know if you have any questions in the meantime! — Reply to this email directly, view it on GitHub <#27 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/BIQBLEIJL52JH43BOY6Q7Q3ZZWVLZAVCNFSM6AAAAABPH7U2RGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDGOJSGMYTQMBXHE> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[MyleeeA]

Hi @jananiravi
Is this a good first issue to start with?

I’ll like to be assigned to this, to get started

[jananiravi]

Yes! @Cateline @MyleeeA which proteins are you both starting with, or do you want us to assign? If that's the case, give me a day to add them. Thanks!

[Cateline]

Hi @jananiravi , please assign me some proteins I can work with. You can add them today

[jananiravi]

Each of you can start with one of the 6 ESKAPE species in the CARD antibiotic resistance genes database: https://card.mcmaster.ca/download

1. Enterobacter spp
2. Staphylococcus aureus
3. Klebsiella pneumoniae
4. Acinetobacter baumannii
5. Pseudomonas aeruginosa
6. Enterococcus faecalis

• Start with one drug/drug class at a time before moving into 1 drug across species or 1 species across drugs
• generate generalizable functions to download, filter by species/drugs
• input datasets into molevolvr to generate case study reports
• download these analyses data systematically --> towards populating knowledgebases.

(Reach out via slack to those interested in bio/bioinfo to take other species -- those interested in bioinfo/r-pkg to work on well-annotated functions.)

Hope this helps!

[jananiravi]

@AbhirupaGhosh @charmvang @wolfeet1 @klterwelp if you card data workflows or top genes readily available, please share those as starting points as well.

[jananiravi]

@KewalinSamart if you have top TB disease/drug genes, create a new spinoff issue for TB gene case study with the same tags as this one. We can request assignees for that, too.

[MyleeeA]

Thank you so much @jananiravi

[AbhirupaGhosh]

Title: Process CARD Data, Map Short Names, and Run MolEvolveR

• Download CARD Data: Retrieve the latest CARD dataset. (DOWNLOAD)
• Open ARO_index.tsv: Parse the file (in R).
• Map CARD Short Name: Map the CARD Short Name column to shortname_antibiotics.tsv and shortname_pathogens.tsv. The CARD Short Name values follow the format pathogen_gene or pathogen_gene_drug.
• Sort and Group the data by pathogens and antibiotics.
• Filter Favorite Bug-Drug or Bug for further analysis.
• Download FASTA Sequences for the list of protein accessions filtered. (use Entrez)
• Run MolEvolvR: Run the protein sequences through the MolEvolvR tool for evolutionary analysis.

I hope this helps @Cateline

[Cateline]

Yes, this is now clear. Does it mean that I need to cancel the pull request I had already made?

[AbhirupaGhosh]

Yes that would be better.

[Cateline]

Great. Thanks for the help

…

On Wed, Oct 9, 2024 at 12:40 PM Abhirupa Ghosh ***@***.***> wrote: Yes that would be better. — Reply to this email directly, view it on GitHub <#27 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/BIQBLEJCVD6ZVG3GJPMOZ7DZ2WBC7AVCNFSM6AAAAABPH7U2RGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDIMBTGI4TIMZWGA> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[MyleeeA]

@Cateline

I see you have a better understanding now
Would you be so kind as to guide me abit
Thank you 🙏🏾

[Cateline]

Yeah of course Where are you stuck on?

…

On Thu, 10 Oct 2024, 07:10 MyleeeA, ***@***.***> wrote: @Cateline <https://github.com/Cateline> I see you have a better understanding now Would you be so kind as to guide me abit Thank you 🙏🏾 — Reply to this email directly, view it on GitHub <#27 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/BIQBLEPRKSA73GXHIV7D7V3Z2X42LAVCNFSM6AAAAABPH7U2RGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDIMBTHEZDMNJWGU> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[MyleeeA]

First of all, I'm struggling with getting the right version for R and R studio so I clone the MolEvolvR repo to enable me use it's functions in R
I understand this are the steps I need to follow.
I found the information but can't seem to locate it, I'll appreciate a link to that @Cateline

CC: @jananiravi

[Cateline]

https://posit.co/download/rstudio-desktop/
Try downloading from this site
I also found this resource useful: https://happygitwithr.com/rstudio-git-github#rstudio-git-github

[MyleeeA]

Thank you for helping out everytime I need your help @Cateline
I'll check it out

[jananiravi]

• Phase 1: The first PR can be for the set of functions going from CARD SNPs to MolEvolvR input protein sequences (fasta). This commit will add a file to R/ with docstring/roxygen2 documentation as with other R functions in that folder.
• Phase 2: Run these proteins through the MolEvolvR web-app and submit the reports for starters.
• Phase 3: Create a qmd/rmd markdown (R/Quarto) report file that does everything the web-app report generator does but does so locally with the R-package functions (fully in-house within the MolEvolvR repo).

cc: @falquaddoomi @the-mayer @epbrenner