OptiMHC

An optimized rescoring pipeline for immunopeptidomics data that significantly enhances peptide identification performance.

OptiMHC integrates multiple rescoring features with machine learning-based rescoring to maximize the number of confidently identified peptides from mass spectrometry experiments.

How It Works

Input files (PepXML / PIN)
  → Parsing & feature extraction
    → PsmContainer (central data structure)
      → Feature generation (Basic, Spectral, RT, MHC binding, PWM, Overlap, …)
        → Machine learning rescoring (Percolator / XGBoost / RandomForest)
          → Visualization & output

1. Parse search engine results from PepXML or PIN format into a unified PsmContainer.
2. Generate features using a configurable set of features — each adds new scoring dimensions to the PSM data.
3. Rescore PSMs with machine learning models (Percolator SVM, XGBoost, or RandomForest) trained via mokapot to separate targets from decoys at a controlled FDR.
4. Visualize results with q-value curves, feature importance plots, and target/decoy distributions.

Getting Started

• Installation — set up OptiMHC on your system
• Quick Start — run your first rescoring pipeline in minutes

Learn More

• Tutorial — examples, pipeline walkthrough, and feature explanations
• API Reference — detailed module and class documentation
• Development — set up a development environment