MPUSP/snakemake-ms-proteomics

Pipeline for automatic processing and quality control of mass spectrometry data

Overview

Topics: bioinformatics conda mass-spectrometry pipeline proteomics snakemake

Latest release: v1.0.0, Last update: 2025-01-30

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Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.

When using Mamba, run

mamba create -c conda-forge -c bioconda --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

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 v1.0.0

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 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

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.

snakemake-ms-proteomics

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 wokflows (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)
Fetch protein database from NCBI or use user-supplied fasta file (python, NCBI Datasets)
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.

Installation

Snakemake

Step 1: Install snakemake with conda, mamba, micromamba (or any another conda flavor). This step generates a new conda environment called snakemake-ms-proteomics, which will be used for all further installations.

conda create -c conda-forge -c bioconda -n snakemake-ms-proteomics snakemake

Step 2: Activate conda environment with snakemake

source /path/to/conda/bin/activate
conda activate snakemake-ms-proteomics

Alternatively, install snakemake using pip:

pip install snakemake

Or install snakemake globally from linux archives:

sudo apt install snakemake

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.

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 OR a NCBI Refseq ID. 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 with four mandatory columns and no header (example file: test/input/samplesheet/samplesheet.tsv).

sample: names/paths to raw files
condition: experimental group, treatments
replicate: replicate number, consecutively numbered. Repeating numbers (e.g. 1,2,1,2) will be treated as paired samples!
type: the type of MS data, will be used to determine the workflow
control: reference condition for testing differential abudandance

sample	condition	replicate	type	control
sample_1	condition_1	1	DDA	condition_1
sample_2	condition_1	2	DDA	condition_1
sample_3	condition_2	3	DDA	condition_1
sample_4	condition_2	4	DDA	condition_1

Execution

To run the workflow from command line, change the working directory.

cd /path/to/snakemake-ms-proteomics

Adjust options in the default config file config/config.yml. Before running the entire workflow, you can perform a dry run using:

snakemake --dry-run

To run the complete workflow with test files using conda, execute the following command. The definition of the number of compute cores is mandatory.

snakemake --cores 10 --sdm conda --directory .test

To supply options that override the defaults, run the workflow like this:

snakemake --cores 10 --sdm conda --directory .test \
  --configfile 'config/config.yml' \
  --config \
  samplesheet='my/sample_sheet.tsv'

Parameters

This table lists all global parameters to the workflow.

parameter	type	details	example
samplesheet	`*.tsv`	tab-separated file	`test/input/config/samplesheet.tsv`
database	`*.fasta` OR refseq ID	plain text	`test/input/database/database.fasta`, `GCF_000009045.1`
workflow	`*.workflow` OR string	a fragpipe workflow	`workflows/LFQ-MBR.workflow`, `from_samplesheet`

This table lists all module-specific parameters and their default values, as included in the config.yml file.

module	parameter	default	details
decoypyrat	`cleavage_sites`	`KR`	amino acids residues used for decoy peptide generation
	`decoy_prefix`	`rev`	decoy prefix appended to proteins names
fragpipe	`target_dir`	`share`	default path in conda env to store fragpipe
	`executable`	`fragpipe/bin/fragpipe`	path to fragpipe executable
	`download`	FragPipe-22.0 (see config)	downlowd link to Fragpipe Github repo
msstats	`logTrans`	`2`	base for log fold change transformation
	`normalization`	`equalizeMedians`	normalization strategy for feature intensity, see MSstats manual
	`featureSubset`	`all`	which features to use for quantification
	`summaryMethod`	`TMP`	how to calculate protein from feature intensity
	`MBimpute`	`True`	Imputes missing values with Accelerated failure time model
report	`html`	`True`	Generate HTLM report
	`pdf`	`True`	Generate PDF report
email	`send`	`False`	whether reports should send out by email
	`port`	`0`	default port for email server
	`smtp_server`	`smtp.example.com`	smtp server address
	`smtp_user`	`user`	smtp server user name
	`smtp_pw`	`password`	smtp server user password
	`from`	`sender@email.com`	sender's email address
	`to`	`["receiver@email.com"]`	receiver's email address(es), a list
	`subject`	`"Results MS proteomics workflow"`	subject line for email

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