ProteoformClassifier

ProteoformClassifier classifies proteoform identifications using a five-level system to delineate ambiguity in proteoform assignments for mass spectrometry–based proteomics.


Key Features:

  • Five-Level Classification System: Implements the five-level classification framework that categorizes proteoform identifications by their ambiguity.
  • Software-Independent Implementation: Evaluates proteoform identifications from arbitrary search programs provided their outputs include the information required for ambiguity assessment.
  • Feedback Mechanism for Developers: Reports when program outputs lack sufficient information for ambiguity assessment to inform developers which data are missing.
  • Compatibility with MetaMorpheus: Integrates with MetaMorpheus and supports classification of identifications from outputs that include bottom-up and top-down proteomic identifications.
  • Validation of Transparency in Results: Produces classification labels that make the ambiguity of proteoform identifications explicit for downstream analysis.

Scientific Applications:

  • Comparative Proteomics: Enables standardized comparison of proteoform identification ambiguity across different experimental setups and software platforms.
  • Proteome Diversity Studies: Supports analysis of proteome diversity within samples by clarifying which proteoform identifications are unambiguous versus ambiguous.
  • Software Development and Improvement: Guides developers in modifying search program outputs to include the data required for proteoform ambiguity classification.

Methodology:

Evaluates outputs from search programs to determine whether they contain sufficient information for ambiguity assessment and uses a small test file and database to perform this evaluation.

Topics

Details

License:
MIT
Tool Type:
command-line tool, desktop application
Programming Languages:
C#
Added:
11/29/2021
Last Updated:
11/29/2021

Operations

Publications

Rolfs Z, Smith LM. Automated Assignment of Proteoform Classification Levels. Unknown Journal. 2021. doi:10.1101/2021.05.18.444659.

Documentation

Links