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An improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple CPUs

BACKGROUND: A real-time peptide-spectrum matching (RT-PSM) algorithm is a database search method to interpret tandem mass spectra (MS/MS) with strict time constraints. Restricted by the hardware and architecture of individual workstation, previous RT-PSM algorithms either are not fast enough to sati...

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Detalles Bibliográficos
Autores principales: Sun, Jian, Chen, Bolin, Wu, Fang-Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4021225/
https://www.ncbi.nlm.nih.gov/pubmed/24721686
http://dx.doi.org/10.1186/1477-5956-12-18
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author Sun, Jian
Chen, Bolin
Wu, Fang-Xiang
author_facet Sun, Jian
Chen, Bolin
Wu, Fang-Xiang
author_sort Sun, Jian
collection PubMed
description BACKGROUND: A real-time peptide-spectrum matching (RT-PSM) algorithm is a database search method to interpret tandem mass spectra (MS/MS) with strict time constraints. Restricted by the hardware and architecture of individual workstation, previous RT-PSM algorithms either are not fast enough to satisfy all real-time system requirements or need to sacrifice the level of inference accuracy to provide the required processing speed. RESULTS: We develop two parallelized algorithms for MS/MS data analysis: a multi-core RT-PSM (MC RT-PSM) algorithm which works on individual workstations and a distributed computing RT-PSM (DC RT-PSM) algorithm which works on a computer cluster. Two data sets are employed to evaulate the performance of our proposed algorithms. The simulation results show that our proposed algorithms can reach approximately 216.9-fold speedup on a sub-task process (similarity scoring module) and 84.78-fold speedup on the overall process compared with a single-thread process of the RT-PSM algorithm when 240 logical cores are employed. CONCLUSIONS: The improved RT-PSM algorithms can achieve the processing speed requirement without sacrificing the level of inference accuracy. With some configuration adjustments, the proposed algorithm can support many peptide identification programs, such as X!Tandem, CUDA version RT-PSM, etc.
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spelling pubmed-40212252014-05-28 An improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple CPUs Sun, Jian Chen, Bolin Wu, Fang-Xiang Proteome Sci Methodology BACKGROUND: A real-time peptide-spectrum matching (RT-PSM) algorithm is a database search method to interpret tandem mass spectra (MS/MS) with strict time constraints. Restricted by the hardware and architecture of individual workstation, previous RT-PSM algorithms either are not fast enough to satisfy all real-time system requirements or need to sacrifice the level of inference accuracy to provide the required processing speed. RESULTS: We develop two parallelized algorithms for MS/MS data analysis: a multi-core RT-PSM (MC RT-PSM) algorithm which works on individual workstations and a distributed computing RT-PSM (DC RT-PSM) algorithm which works on a computer cluster. Two data sets are employed to evaulate the performance of our proposed algorithms. The simulation results show that our proposed algorithms can reach approximately 216.9-fold speedup on a sub-task process (similarity scoring module) and 84.78-fold speedup on the overall process compared with a single-thread process of the RT-PSM algorithm when 240 logical cores are employed. CONCLUSIONS: The improved RT-PSM algorithms can achieve the processing speed requirement without sacrificing the level of inference accuracy. With some configuration adjustments, the proposed algorithm can support many peptide identification programs, such as X!Tandem, CUDA version RT-PSM, etc. BioMed Central 2014-04-11 /pmc/articles/PMC4021225/ /pubmed/24721686 http://dx.doi.org/10.1186/1477-5956-12-18 Text en Copyright © 2014 Sun et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Sun, Jian
Chen, Bolin
Wu, Fang-Xiang
An improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple CPUs
title An improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple CPUs
title_full An improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple CPUs
title_fullStr An improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple CPUs
title_full_unstemmed An improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple CPUs
title_short An improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple CPUs
title_sort improved peptide-spectral matching algorithm through distributed search over multiple cores and multiple cpus
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4021225/
https://www.ncbi.nlm.nih.gov/pubmed/24721686
http://dx.doi.org/10.1186/1477-5956-12-18
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