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Proteomics Data Analysis Pipeline

A comprehensive pipeline for mass spectrometry-based proteomics data analysis including identification, quantification, and statistical analysis.

3 min read
#proteomics #mass-spectrometry #snakemake #maxquant #openms #statistical-analysis

Proteomics Data Analysis Pipeline

A comprehensive Snakemake pipeline for mass spectrometry-based proteomics data analysis, covering raw data processing, identification, quantification, and statistical analysis with support for label-based and label-free approaches.

Overview

This pipeline automates the complete proteomics analysis workflow from raw mass spectrometry data to publication-ready results. It integrates industry-standard tools like MaxQuant and OpenMS with custom analysis scripts for reproducible and scalable proteomics research.

Key Features

  • Raw Data Processing: Support for all major mass spectrometers
  • Identification: Database search with MaxQuant and Mascot
  • Quantification: Label-based (TMT, iTRAQ) and label-free quantification
  • Statistical Analysis: Differential expression with Limma and Perseus
  • Quality Control: Comprehensive QC metrics and visualizations
  • Pathway Analysis: Integration with Reactome and KEGG
  • Reproducible: Full workflow documentation and Conda environments

Pipeline Workflow

1. Data Preparation

  • Raw data conversion (proprietary formats to mzML)
  • Database preparation (FASTA files, contaminants)
  • Experimental design specification

2. Identification and Quantification

  • MaxQuant Analysis:
    • Database search with Andromeda
    • Protein and peptide identification
    • Quantification (LFQ, TMT, iTRAQ)
  • OpenMS Workflow (alternative):
    • Feature detection
    • Feature linking
    • Identification with Comet or X!Tandem

3. Post-Processing

  • False discovery rate (FDR) filtering
  • Contaminant removal
  • Protein grouping and summarization
  • Normalization (vCenter, quantile)

4. Statistical Analysis

  • Differential expression analysis with Limma
  • Multiple testing correction (Benjamini-Hochberg)
  • Volcano plots and heatmaps
  • Principal component analysis (PCA)

5. Functional Analysis

  • Gene ontology (GO) enrichment
  • KEGG pathway analysis
  • Protein-protein interaction networks
  • Motif analysis

Installation

# Clone the repository
git clone https://github.com/tamoghnadas12/proteomics-pipeline
cd proteomics-pipeline

# Install dependencies
conda env create -f environment.yml
conda activate proteomics-pipeline

# Download MaxQuant (manual step)
# wget https://maxquant.org/download/MaxQuant_2.0.3.0.zip

Configuration

The pipeline is configured through:

  • config/config.yaml: Main configuration
  • config/samples.tsv: Sample metadata
  • config/experimental_design.tsv: Experimental design

Example configuration:

# config/config.yaml
maxquant:
  version: '2.0.3.0'
  search_engine: 'andromeda'
  fdr: 0.01

quantification:
  method: 'lfq' # or "tmt", "itraq"
  normalization: 'vcenter'

analysis:
  de_method: 'limma'
  p_value_threshold: 0.05
  fc_threshold: 1.5

Usage

Label-Free Quantification

# Run LFQ pipeline
snakemake --cores 16 --use-conda --config quantification/method=lfq

# Results in results/lfq/ directory

TMT Quantification

# Run TMT pipeline
snakemake --cores 16 --use-conda --config quantification/method=tmt

# Specify TMT reporter ions in config

Output Structure

The pipeline generates organized output directories:

results/
├── maxquant/            # MaxQuant output
├── quantification/      # Processed quantification tables
├── statistics/          # Differential expression results
├── visualization/       # Plots and figures
├── functional/          # GO and pathway analysis
├── reports/             # HTML and PDF reports
└── multiqc/             # MultiQC reports

Technologies Used

  • Workflow Management: Snakemake
  • Identification: MaxQuant, Mascot, OpenMS
  • Quantification: MaxQuant, OpenMS, Skyline
  • Statistical Analysis: Limma, Perseus, R
  • Functional Analysis: ClusterProfiler, ReactomePA
  • Visualization: Matplotlib, Seaborn, Plotly
  • Environment: Conda, Singularity

Documentation

Contributing

Contributions are welcome! Please see our contributing guidelines for details on how to contribute.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Continue Learning

One Small Win

Try this quick command to get started:

git clone https://github.com/tamoghnadas12/proteomics-pipeline

Copy and paste this into your terminal to get started immediately.

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Start Your Own Project

Use our battle-tested template to jumpstart your reproducible research workflows. Pre-configured environments, standardized structure, and example workflows included.

Use This Template 
git clone https://github.com/Tamoghna12/bench2bash-starter
cd bench2bash-starter
conda env create -f env.yml
make run