MPUSP/snakemake-ms-proteomics

Pipeline for automatic processing and quality control of mass spectrometry data

Overview

Latest release: v1.1.0, Last update: 2026-08-14

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=MPUSP/snakemake-ms-proteomics

Quality control: linting: passed formatting: passed

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

Workflow Rule Graph

This visualization of the workflow’s rule graph was automatically generated using Snakevision

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/MPUSP/snakemake-ms-proteomics . --tag v1.1.0

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

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:

Prepare workflow file (python script)
Check user-supplied sample sheet (python script)
Check user-supplied database FASTA file (python script)
Generate decoy proteins (DecoyPyrat)
Import raw files, search protein database (fragpipe)
Align feature maps using IonQuant (fragpipe)
Import quantified features, infer and quantify proteins (R MSstats)
Compare different biological conditions, export results (R MSstats)
Generate HTML report with embedded QC plots (R markdown)
Generate PDF report from HTML weasyprint
Send out report by email (python script)
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:

mass spectrometry data, such as Thermo *.raw or *.mzML files
an (organism) database in *.fasta format. Decoys (rev_ prefix) will be added if necessary
a sample sheet in tab-separated format (aka manifest file)
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_name	raw_file	condition	replicate	MS_method	comparison
WT_exp_1	data/E1_220707_22C012_sample_01.raw	WT_exp	1	DDA	WT_exp
WT_exp_2	data/E1_220707_22C012_sample_02.raw	WT_exp	2	DDA	WT_exp
WT_trans_1	data/E1_220707_22C012_sample_03.raw	WT_trans	3	DDA	WT_exp
WT_trans_2	data/E1_220707_22C012_sample_04.raw	WT_trans	4	DDA	WT_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

Workflow parameters

The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.

Parameter	Type	Description	Required	Default
samplesheet	string	path to the sample sheet in tsv format	yes	.test/samplesheet/samplesheet.tsv
database	string	path to protein database in fasta format or a RefSeq identifier	yes	.test/database/database.fasta
workflow	string	path to a FragPipe workflow file or from_samplesheet	yes	from_samplesheet
decoypyrat		options for decoy sequence generation	yes
. cleavage_sites	string	amino acid residues used for decoy peptide generation	yes	KR
. decoy_prefix	string	prefix appended to generated decoy protein IDs	yes	rev
fragpipe		options for FragPipe setup and execution	yes
. executable	string	path to FragPipe executable	yes	fragpipe/bin/fragpipe
. download	string	URL to FragPipe release archive	yes	https://github.com/Nesvilab/FragPipe/releases/download/24.0/FragPipe-24.0-linux.zip
. extra	string	extra command line options for FragPipe execution	yes
msstats		options for MSstats quantification and statistics	yes
. logTrans	integer	base for log fold-change transformation	yes	2
. normalization	string	normalization method used by MSstats	yes	equalizeMedians
. featureSubset	string	feature subset used for quantification	yes	all
. summaryMethod	string	method to summarize feature intensities to proteins	yes	TMP
. MBimpute	boolean	whether to impute missing values at low abundance	yes	true
report		report generation settings	yes
. html	boolean	generate HTML report	yes	true
. pdf	boolean	generate PDF report	yes	true
email		optional email notification settings	yes
. send	boolean	whether to send reports by email	yes	false
. port	integer	SMTP server port	yes	0
. smtp_server	string	SMTP server hostname	yes	smtp.example.com
. smtp_user	string	SMTP user name	yes	user
. smtp_pw	string	SMTP user password	yes	password
. from	string	sender email address	yes	sender@email.com
. to	array	recipient email addresses	yes	[‘receiver@email.com’]
. subject	string	email subject line	yes	Results MS proteomics pipeline