Overview
Transform raw mass spectrometry data into biological insights using modern Python workflows for proteomics and integrative omics analysis.
From Spectra to Systems:
Python for Mass Spectrometry Proteomics & Multi-Omics Analysis
Transform raw mass spectrometry data into biological insights using modern Python workflows for proteomics and integrative omics analysis.
This hands-on webinar series introduces computational proteomics analysis workflows, from preprocessing raw MS data to downstream statistical analysis, visualization, and integration with transcriptomics and genomics datasets.
Suggested Session Structure
Session 1 — Introduction to Computational Proteomics with Python and Open Source Tools
Topics
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- 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
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- 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
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- Data import troubleshooting
- QC assessment
- Statistical analysis guidance
- Multi-omics integration support
- Visualization and interpretation help
- Workflow optimization and reproducibility
Indicative List of Software
Each student will be provided with a Virtual Machine environment with the following software installed.
- Python
- pyOpenMS
- pandas
- NumPy
- Scanpy
- Jupyter Notebook
- MaxQuant
- Perseus
- Cytoscape
- Fragpipe
Audience Description
This webinar is suitable for:
- Bioinformaticians
- Proteomics researchers
- Computational biologists
- PhD students and postdocs
- Wet-lab scientists transitioning into data analysis
- Researchers interested in multi-omics integration
Recommended background
- Basic molecular biology knowledge
- Familiarity with Python or R is helpful but not mandatory