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MPUSP/snakemake-ms-proteomics

Pipeline for automatic processing and quality control of mass spectrometry data

Overview​

Testing: GitHub Actions Workflow Status GitHub Actions Workflow Status

Last update: 2026-09-25

Latest release: v1.1.1

Topics: bioinformatics conda mass-spectrometry pipeline proteomics snakemake snakemake-workflow workflow

Authors: @m-jahn @Copilot

Configuration​

The following configuration details are extracted from the config's README file.

workflow overview​

This workflow is a best-practice workflow for the automated analysis of mass spectrometry proteomics data. It currently supports automated analysis of data-dependent acquisition (DDA) data with label-free quantification. An extension by different workflows (DIA, isotope labeling) is planned in the future.

The workflow is mainly a wrapper for the excellent tools fragpipe and MSstats, with additional modules that supply and check the required input files, and generate reports. The workflow is built using snakemake and processes MS data using the following steps:

  1. Prepare workflow file (python script)
  2. Check user-supplied sample sheet (python script)
  3. Check user-supplied database FASTA file (python script)
  4. Generate decoy proteins (DecoyPyrat)
  5. Import raw files, search protein database (fragpipe)
  6. Align feature maps using IonQuant (fragpipe)
  7. Import quantified features, infer and quantify proteins (R MSstats)
  8. Compare different biological conditions, export results (R MSstats)
  9. Generate HTML report with embedded QC plots (R markdown)
  10. Generate PDF report from HTML weasyprint
  11. Send out report by email (python script)
  12. Clean up temporary files after workflow execution (bash script)

If you want to contribute, report issues, or suggest features, please get in touch on github.

Running the workflow​

Input data​

The workflow requires the following input files:

  1. mass spectrometry data, such as Thermo *.raw or *.mzML files
  2. an (organism) database in *.fasta format. Decoys (rev_ prefix) will be added if necessary
  3. a sample sheet in tab-separated format (aka manifest file)
  4. a workflow file for fragpipe (see resources dir)

The samplesheet file has the following structure (example file: .test/config/samplesheet.tsv).

  • sample: names/paths to raw files
  • raw_file: path to raw files, can be absolute or relative to the work dir
  • condition: experimental group, treatments
  • replicate: replicate number, consecutively numbered. Repeating numbers (e.g. 1, 2, 1, 2) will be treated as paired samples!
  • method: the type of MS data, will be used to determine the workflow
  • comparison: reference condition for testing differential abundance. Optional; If you don't want to make comparisons, leave column empty.
sample_nameraw_fileconditionreplicateMS_methodcomparison
WT_exp_1data/E1_220707_22C012_sample_01.rawWT_exp1DDAWT_exp
WT_exp_2data/E1_220707_22C012_sample_02.rawWT_exp2DDAWT_exp
WT_trans_1data/E1_220707_22C012_sample_03.rawWT_trans3DDAWT_exp
WT_trans_2data/E1_220707_22C012_sample_04.rawWT_trans4DDAWT_exp

Fragpipe​

Fragpipe is not available on conda or other package archives. However, to make the workflow as user-friendly as possible, the latest fragpipe release from github (currently v22.0) is automatically installed to the respective conda environment when using the workflow the first time. After installation, the GUI (graphical user interface) will pop up and ask to you to finish the installation by downloading the missing modules MSFragger, IonQuant, and Philosopher. This step is necessary to abide to license restrictions. From then on, fragpipe will run in headless mode through command line only.

All other dependencies for the workflow are automatically pulled as conda environments by snakemake.

Missing value imputation​

Missing value imputation happens at different stages. First, the default strategy for fragpipe is to use "match between runs", i.e. non-identified features in the MS1 spectra are cross-compared with other runs of the same experiment where MS2 identification is available. This strategy is based on actual quantification data and reduces the number of missing feature quantifications.

Second, MSstats imputes two kinds of missing values where absolutely no feature quantification is available: Missing values at random positions are removed during summarization, and missing values due to low abundance are imputed at the feature level via accelerated failure time model.

Missing value treatment can be controlled through the MSstats parameters MBimpute and others. See the MSstats manual for more information.

Output​

The workflow generates the following output from its modules:

samplesheet
  • samplesheet.tsv: Samplesheet after checking file paths and options
  • log.txt: Log file for this module
workflow
  • workflow.txt: Configuration file for fragpipe, determined from samplesheet.
  • log.txt: Log file for this module
database
  • database.fasta: The downloaded or user-supplied .fasta file. In the latter case, the file is identical to the input.
  • log.txt: Log file for this module
decoypyrat
  • decoy_database.fasta: Original .fasta file supplemented with randomized protein sequences.
  • log.txt: Log file for this module
fragpipe
  • [sample_name]/: Directory containing sample specific output files for each run
  • combined_ion.tsv: Quantification of ion intensity per peptide
  • combined_modified_peptide.tsv: Quantification of peptide modifications
  • combined_peptide.tsv: Quantification of peptides/features
  • combined_protein.tsv: Quantification of proteins from petide, inferred by fragpipe
  • MSstats.csv: Qunatification of petides/features, output from fragpipe served in MSstats friendly format
  • other files such as logs, file lists, etc.
  • log.txt: Log file for this module
msstats
  • comparison_result.csv: Main table with results about the comparison between different experimental conditions
  • feature_level_data.csv: Feature-level quantification data processed by MSstats
  • model_qc.csv: Table with data about the fitted quantification models from MSstats
  • protein_level_data.csv: Protein-level quantification data processed by MSstats
  • uniprot.csv: Optionally downloaded table with protein annotation from Uniprot
  • log.txt: Log file for this module
report
  • report.html: Report with figures and tables
  • report.pdf: Report with figures and tables in PDF format. Converted from HTML
  • log.txt: Log file for this module
email
  • log.txt: Log file for this module
clean_up
  • log.txt: Log file for this module