OMSSA Parser
OMSSA Parser parses OMSSA (Open Mass Spectrometry Search Algorithm) omx result files and converts their MS/MS-derived data into Java object models to support mass spectrometry-based protein identification and downstream computational analyses.
Key Features:
- OMSSA omx parsing: Parses OMSSA omx result files including MS/MS spectra and associated search results produced from proteome studies.
- Support for gel-based and gel-free data: Handles omx files originating from both gel-based and gel-free proteome workflows.
- Java-based implementation: Implemented in Java as a programmatic library to provide platform-independent manipulation of parsed data.
- Object model conversion: Converts omx file contents into Java object models that expose protein identification and MS/MS data for programmatic access.
- Data integration support: Facilitates integration of OMSSA results with other bioinformatics workflows and databases via programmatic access to parsed data.
Scientific Applications:
- Protein Identification: Enables retrieval and analysis of OMSSA search results to support mass spectrometry-based protein identification in proteomics studies.
- Integration with bioinformatics workflows: Allows OMSSA results to be incorporated into downstream analyses and combined with other databases and tools.
- Downstream computational analyses: Provides programmatic access to parsed data for custom analysis, scoring, and result filtering within computational pipelines.
Methodology:
Reads and interprets the structured data within OMSSA omx result files and transforms that data into Java object models for programmatic access.
Topics
Collections
Details
- Tool Type:
- command-line tool, desktop application
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- Java
- Added:
- 5/17/2016
- Last Updated:
- 11/25/2024
Operations
Publications
Barsnes H, Huber S, Sickmann A, Eidhammer I, Martens L. OMSSA Parser: An open‐source library to parse and extract data from OMSSA MS/MS search results. PROTEOMICS. 2009;9(14):3772-3774. doi:10.1002/pmic.200900037. PMID:19639591.