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An efficient parallelization of phosphorylated peptide and protein identification
Author(s) -
Wang Leheng,
Wang Wenping,
Chi Hao,
Wu Yanjie,
Li You,
Fu Yan,
Zhou Chen,
Sun Ruixiang,
Wang Haipeng,
Liu Chao,
Yuan Zuofei,
Xiu Liyun,
He SiMin
Publication year - 2010
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.4578
Subject(s) - speedup , chemistry , parallel computing , tandem mass spectrometry , identification (biology) , scheduling (production processes) , computer science , search engine , database search engine , task (project management) , tandem , mass spectrometry , chromatography , information retrieval , botany , management , economics , composite material , biology , operations management , materials science
Protein sequence database search based on tandem mass spectrometry is an essential method for protein identification. As the computational demand increases, parallel computing has become an important technique for accelerating proteomics data analysis. In this paper, we discuss several factors which could affect the runtime of the pFind search engine and build an estimation model. Based on this model, effective on‐line and off‐line scheduling methods were developed. An experiment on the public dataset from PhosphoPep consisting of 100 RAW files of phosphopeptides shows that the speedup on 100 processors is 83.7. The parallel version can complete the identification task within 9 min, while a stand‐alone process on a single PC takes more than 10 h. On another larger dataset consisting of 1 366 471 spectra, the speedup on 320 processors is 258.9 and the efficiency is 80.9%. Our approach can be applied to other similar search engines. Copyright © 2010 John Wiley & Sons, Ltd.