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An Effective Data Analysis System for Improving Throughput of Shotgun Proteomic Data based on Machine Learning  

Na, Seung-Jin (서울시립대학교 기계정보공학과)
Paek, Eun-Ok (서울시립대학교 기계정보공학과)
Publication Information
Journal of KIISE:Software and Applications / v.34, no.10, 2007 , pp. 889-899 More about this Journal
Abstract
In proteomics, recent advancements In mass spectrometry technology and in protein extraction and separation technology made high-throughput analysis possible. This leads to thousands to hundreds of thousands of MS/MS spectra per single LC-MS/MS experiment. Such a large amount of data creates significant computational challenges and therefore effective data analysis methods that make efficient use of computational resources and, at the same time, provide more peptide identifications are in great need. Here, SIFTER system is designed to avoid inefficient processing of shotgun proteomic data. SIFTER provides software tools that can improve throughput of mass spectrometry-based peptide identification by filtering out poor-quality tandem mass spectra and estimating a Peptide charge state prior to applying analysis algorithms. SIFTER tools characterize and assess spectral features and thus significantly reduce the computation time and false positive rates by localizing spectra that lead to wrong identification prior to full-blown analysis. SIFTER enables fast and in-depth interpretation of tandem mass spectra.
Keywords
Proteomics; tandem mass spectra; peptide identification; spectral qualify; charge state determination;
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