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Add 'metaQuantome function' workflow #628

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21 changes: 21 additions & 0 deletions workflows/proteomics/metaquantome-function/.dockstore.yml
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version: 1.2
workflows:
- name: main
subclass: Galaxy
publish: true
primaryDescriptorPath: /metaquantome_function_wf.ga
testParameterFiles:
- /metaquantome_function_wf-tests.yml
authors:
- name: Subina Mehta
orcid: 0000-0001-9818-0537
- name: Timothy J. Griffin
orcid: 0000-0001-6801-2559
- name: Pratik Jagtap
orcid: 0000-0003-0984-0973
- name: Emma Leith
alternateName: emmaleith
- name: Marie Crane
alternateName: mariecrane
- name: Praveen Kumar
alternateName: pravs3683
4 changes: 4 additions & 0 deletions workflows/proteomics/metaquantome-function/CHANGELOG.md
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# Changelog

## [0.1] 2024-12-10
First release.
9 changes: 9 additions & 0 deletions workflows/proteomics/metaquantome-function/README.md
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# Creating plots using metaQuantome: Functional enrichment analysis and clustering

metaQuantome software suite (Easterly et al. 2019) was developed by the Team for quantitative and statistical analysis of metaproteomics data. For taxonomic and functional expression analysis within the microbial community, metaQuantome leverages peptide-level quantitative information to generate visual outputs for data interpretation. It also generates outputs that help in understanding the taxonomic contribution to a selected function as well as functions expressed by selected taxonomic group.

This workflow uses a function file, an intensity file and the metaQuantome databases (NCBI Taxonomy, GO term and ENZYME database) as inputs. Four modules of the metaQuantome suite are used: **expand**,**filter**, **stat** and **visualize**. After the experimental conditions and samples are manually specified, the **expand** module analyzes differentially expressed functions in the samples using the metaQuantome databases to correlate the information in the input files with GO terms, taxa and enzyme classifications. The output file and the manually specified samples are then used in the **filter** module where the expanded terms are filtered to those that are representative of the data according to the user-specified sample parameters. Finally, the **stat** module performs a functional analysis of the filtered data on multiple conditions. The tabular output file is then used as input in the **visualize** module to generate a bar plot and a volcano plot. Users can also generate a PCA plot and a heatmap for cluster analysis.

***Note:*** The **expand** module of the metaQuantome suite is run in "Functional analysis" mode in this workflow.

More background on this workflow can be found in the [metaQuantome 2: Function tutorial](https://training.galaxyproject.org/training-material/topics/proteomics/tutorials/metaquantome-function/tutorial.html)
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- doc: Test outline for metaquantome_function_wf.ga
job:
Functional file:
class: File
path: https://zenodo.org/records/4110725/files/Function-File.tabular
filetype: tabular
Intensity file:
class: File
path: https://zenodo.org/records/4110725/files/Intensity-File.tabular
filetype: tabular
outputs:
Samples metaQuantome file:
asserts:
has_text:
text: "colnames"
metaQuantome expand file:
asserts:
has_text:
text: "mitochondrial"
metaQuantome filtered file:
asserts:
has_text:
text: "ribosomal"
metaQuantome stats file:
asserts:
has_text:
text: "GO:0000015"
Bar plot data for sample T2:
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What's sample T2 / T4 / T7 ?

asserts:
has_text:
text: "binding"
Bar plot data for sample T4:
asserts:
has_text:
text: "catalytic activity"
Bar plot data for sample T7:
asserts:
has_text:
text: "binding"
Volcano plot data for T7 and T2:
asserts:
has_text:
text: "ion binding"
Volcano plot data for T4 and T2:
asserts:
has_text:
text: "ribosomal"
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