详细信息
fmpRPMF: A Web Implementation for Protein Identification by Robust Peptide Mass Fingerprinting ( SCI-EXPANDED收录 EI收录)
文献类型:期刊文献
英文题名:fmpRPMF: A Web Implementation for Protein Identification by Robust Peptide Mass Fingerprinting
作者:Li, Youyuan[1];Zhuang, Yingping[1]
机构:[1]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China
年份:2018
卷号:15
期号:5
起止页码:1728
外文期刊名:IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
收录:;EI(收录号:20174304304245);WOS:【SCI-EXPANDED(收录号:WOS:000448910300036)】;
基金:This work was supported by the Fundamental Research Funds for the Central Universities (No. 222201714052). Authors acknowledge Stephen B Goldman (Department of Biology, Massachusetts Institute of Technology, USA) for the helpful maintenance of the web service.
语种:英文
外文关键词:Feature-matching pattern; peptide mass fingerprinting; protein identification; support vector machines; web service
摘要:Peptide mass fingerprinting continues to play an important role in current proteomics studies based on its good performance in sample throughput, specificity for single peptides, and insensitivity to unexpected post-translational modifications as compared with MS". We previously proposed and evaluated the use of feature-matching pattern-based support vector machines (SVMs) for robust protein identification. This approach is now facilitated with an updated web server (fmpRPMF) incorporated with several newly developed or improved modules and workflows allowing identification of proteins from MS' data. Development of the latest fmpRPMF web tool successfully provides a rapid and effective strategy for narrowing the range of candidate proteins. First, a mass-scanning procedure screens all candidate proteins matching the theoretical peptide mass at least three times, thereby reducing the number of candidate proteins from tens of thousands to thousands. Second, a crude ranking procedure screens true-positive proteins among the top six ranked times of candidates based on 17 selected features to reduce the number used for SVM prediction from thousands to tens. The improvement of forecasting efficiency met the requirements of multi-user and multi-task identification for web services. The updated fmpRPMF server is freely available at http://bioinformatics.datawisdom.net/fmp .
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