An efficient parallelization of phosphorylated peptide and protein identification

Authors

  • Leheng Wang,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
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    • These authors contributed equally to this work.

  • Wenping Wang,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, P.R. China
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    • These authors contributed equally to this work.

  • Hao Chi,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, P.R. China
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  • Yanjie Wu,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, P.R. China
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  • You Li,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, P.R. China
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  • Yan Fu,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
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  • Chen Zhou,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, P.R. China
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  • Ruixiang Sun,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
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  • Haipeng Wang,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, P.R. China
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  • Chao Liu,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, P.R. China
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  • Zuofei Yuan,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, P.R. China
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  • Liyun Xiu,

    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, P.R. China
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  • Si-Min He

    Corresponding author
    1. Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China
    2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P.R. China
    • Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, P.R. China.
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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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