Introduction

PyProteinInference is a Python package for running various protein inference algorithms on tandem mass spectrometry search results and generating protein to peptide mappings with protein level false discovery rates.

For a quick start guide please click here.

Key Features

  • Protein Inference and Scoring:
    • Maps peptides to proteins.
    • Generates protein scores from provided PSMs.
    • Calculates set-based protein-level false discovery rates for MS data filtering.
  • Supported Input Formats:

  • Output:

    • User-friendly CSV file containing Proteins, Peptides, q-values, and Protein Scores.
    • Details on output formats: supplementary.

  • Supported Inference Procedures:

Please see the Inference Types section for more information on Inference Types.

In Addition to these inference types Py Protein Inference can also score proteins with a variety of methods:

  1. Best Peptide Per Protein
    • Takes the best scoring PSM as the protein score.
  2. Multiplicative Log
    • Multiplies each PSM score per protein and takes -log10 of the combined score (smaller psm scores must be better i.e. Pep or Q values).
  3. Top Two Combined
    • Takes the top two best scoring PSMs and multiplies or adds them together based on if the selected psm score is Pep/Q value style or Mascot Ion Score style.
  4. Additive
    • Adds each PSM score per protein (larger psm scores must be better i.e. Xcorr, Mascot Ion Score, Percolator Score).
  5. Down Weighted Multiplicative Log
    • Multiplicative Log but with a normalization against the number of PSMs.
  6. Iterative Down Weighted Log
    • Multiplicative Log but with an iterative normalization against the number of PSMs.
  7. Geometric Mean
    • Geometric Mean algorithm scoring.

Please see the Protein Score Types section for more information on scoring algorithms.

Using Py Protein Inference

  1. Yaml Parameter File
  2. Input File Examples (idXML, mzIdentML, pepXML, Tab Delimited)
  3. Fasta Database
  4. Running Py Protein Inference