Python for Mass Spectrometry Proteomics & Multi-Omics Analysis

From Spectra to Systems Biology: A Practical Computational Proteomics Training

Mass spectrometry has become a cornerstone technology for understanding biological systems, but transforming raw spectral data into meaningful discoveries requires advanced computational approaches.

This intensive hands-on training introduces modern Python workflows for proteomics and multi-omics analysis, enabling researchers to move from raw MS files to biological interpretation.

1. Why attend this training?

✓ Process and explore mass spectrometry datasets
✓ Build reproducible proteomics analysis workflows
✓ Perform quality control and statistical analysis
✓ Identify differentially expressed proteins
✓ Generate publication-ready visualisations
✓ Perform pathway and network analysis
✓ Integrate proteomics with RNA-seq, single-cell transcriptomics, and genomic datasets
✓ Apply computational approaches to their own research data

2. Who Should Attend?

This course is designed for:

🧬 Proteomics researchers
💻 Bioinformaticians
🧠 Computational biologists
🎓 PhD students and postdoctoral researchers
🔬 Wet-lab scientists transitioning into computational analysis
🏢 Researchers working in biotechnology and biomedical sciences

Recommended background

• Basic molecular biology knowledge
• Familiarity with Python or R is helpful but not mandatory

Each student will be provided with an environment with the following software installed.

• Audience Description
• Python
• pyOpenMS
• pandas
• NumPy
• Scanpy
• Jupyter Notebook
• MaxQuant
• Perseus
• Cytoscape
• Fragpipe

3. What you will learn?

By the end of the training, participants will be able to:

✓ Process and analyse mass spectrometry datasets
✓ Work with mzML and related MS data formats
✓ Use Python libraries for proteomics analysis
✓ Perform QC, normalization, and statistical analysis
✓ Generate PCA plots, heatmaps, volcano plots, and clustering analyses
✓ Perform pathway and network analysis
✓ Integrate proteomics with transcriptomics and genomics data

4. Course Program Structure

Session 1 — Introduction to Computational Proteomics with Python and Open Source Tools

Topics

• Overview of proteomics technologies (DDA vs DIA, Label-free vs TMT/iTRAQ, Bulk vs single-cell proteomics
• Common file formats (mzML, RAW, mzXML, MGF
• Open Source Tools for Processing Mass Spectrometry Data (FragPipe, MaxQuant)
• Python ecosystem for proteomics (pyOpenMS, pymzML, pandas / numpy / scanpy)
• Setting up reproducible analysis environments
• Introduction to proteomics workflows

Session 2 — Preprocessing & Identification of Mass Spectrometry Data (Hands-on)

Topics

• Raw spectra preprocessing
• Reading and exploring raw spectra
• Inspecting chromatograms and peptide spectra
• Noise filtering
• Peak picking
• Retention time alignment
• mzML preprocessing in Python

• Running identification pipelines
• Building peptide/protein abundance matrices

Session 3 — Downstream Proteomics Data Analysis in Python (Introduction and Hands-on)

Topics

• Data normalization
• Missing value handling
• Differential protein expression analysis
• Functional enrichment analysis
• Pathway and network analysis
• Data visualization (Volcano plots, Heatmaps, PCA/UMAP, Clustering)

Hands-on

• Statistical analysis workflows
• Visualization using matplotlib/seaborn/plotly
• Biological interpretation of results

Session 4 — Multi-Omics Integration: Combining Proteomics with RNA-seq & Genomics Data (Introduction and Hands-on)

Topics

• Principles of multi-omics integration
• Matching identifiers across omics layers
• Integrating: (Proteomics + bulk RNA-seq, Proteomics + single-cell transcriptomics, Proteomics + GWAS)
• Correlation and concordance analysis
• Pathway-level integration
• Network and systems biology approaches
• Introduction to machine learning for multi-omics

Hands-on

• Integrative analysis workflows
• Joint visualization techniques
• Multi-omics biological interpretation

Session 5 — Bring Your Own Data: Guided Analysis Workshop

Participants will work with their own datasets under guided supervision.

Possible activities

• Indicative List of Software
• Data import troubleshooting
• QC assessment
• Statistical analysis guidance
• Multi-omics integration support
• Visualization and interpretation help
• Workflow optimization and reproducibility

5. Provided Certificate

Participants completing the programme will receive a Certificate of Completion:

"Python-Based Mass Spectrometry Proteomics and Multi-Omics Data Analysis"