Research Article

An efficient parallelization of phosphorylated peptide and protein identification

  1. Leheng Wang1,2,†,
  2. Wenping Wang1,2,3,†,
  3. Hao Chi1,2,3,
  4. Yanjie Wu1,2,3,
  5. You Li1,2,3,
  6. Yan Fu1,2,
  7. Chen Zhou1,2,3,
  8. Ruixiang Sun1,2,
  9. Haipeng Wang1,2,3,
  10. Chao Liu1,2,3,
  11. Zuofei Yuan1,2,3,
  12. Liyun Xiu1,2,3,
  13. Si-Min He1,2,*

Article first published online: 17 MAY 2010

DOI: 10.1002/rcm.4578

Issue

Rapid Communications in Mass Spectrometry

Rapid Communications in Mass Spectrometry

Volume 24, Issue 12, pages 1791–1798, 30 June 2010

Additional Information

How to Cite

Wang, L., Wang, W., Chi, H., Wu, Y., Li, Y., Fu, Y., Zhou, C., Sun, R., Wang, H., Liu, C., Yuan, Z., Xiu, L. and He, S.-M. (2010), An efficient parallelization of phosphorylated peptide and protein identification. Rapid Communications in Mass Spectrometry, 24: 1791–1798. doi: 10.1002/rcm.4578

Author Information

  1. 1

    Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China

  2. 2

    Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China

  3. 3

    Graduate University of Chinese Academy of Sciences, Beijing 100049, P.R. China

  1. †

    These authors contributed equally to this work.

*Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China.

Publication History

  1. Issue published online: 19 MAY 2010
  2. Article first published online: 17 MAY 2010
  3. Manuscript Accepted: 12 APR 2010
  4. Manuscript Revised: 19 MAR 2010
  5. Manuscript Received: 8 NOV 2009

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Abstract

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.

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