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.