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Exploiting thread-level and instruction-level parallelism to cluster mass spectrometry data using multicore architectures

  • Fahad SaeedEmail author
  • Jason D. Hoffert
  • Trairak Pisitkun
  • Mark A. Knepper
Original Article
  • 293 Downloads

Abstract

Modern mass spectrometers can produce large numbers of peptide spectra from complex biological samples in a short time. A substantial amount of redundancy is observed in these data sets from peptides that may get selected multiple times in liquid chromatography tandem mass spectrometry experiments. A large number of spectra do not get mapped to specific peptide sequences due to low signal-to-noise ratio of the spectra from these machines. Clustering is one way to mitigate the problems of these complex mass spectrometry data sets. Recently, we presented a graph theoretic framework, known as CAMS, for clustering of large-scale mass spectrometry data. CAMS utilized a novel metric to exploit the spatial patterns in the mass spectrometry peaks which allowed highly accurate clustering results. However, comparison of each spectrum with every other spectrum makes the clustering problem computationally inefficient. In this paper, we present a parallel algorithm, called P-CAMS, that uses thread-level and instruction-level parallelism on multicore architectures to substantially decrease running times. P-CAMS relies on intelligent matrix completion to reduce the number of comparisons, threads to run on each core and single instruction multiple data (SIMD) paradigm inside each thread to exploit massive parallelism on multicore architectures. A carefully crafted load-balanced scheme that uses spatial locations of the mass spectrometry peaks mapped to nearest level cache and core allows super-linear speedups. We study the scalability of the algorithm with a wide variety of mass spectrometry data and variation in architecture specific parameters. The results show that SIMD style data parallelism combined with thread-level parallelism for multicore architectures is a powerful combination that allows substantial reduction in run-times even for all-to-all comparison algorithms. The quality assessment is performed using real-world data set and is shown to be consistent with the serial version of the same algorithm.

Keywords

Mass Spectrometry Data Single Instruction Multiple Data Multicore Architecture Global Array Hardware Thread 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was funded by the operating budget of Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health (NIH), Project ZO1-HL001285 and National Science Foundation (NSF) under grant CNS-1250264. All the Mass Spectrometry data was produced at Proteomics Core at System Biology Center (SBC), NHLBI, NIH.

References

  1. Beausoleil A, Jedrychowski M, Schwartz D, Elias E, Villen J, Li J, Cohn A, Cantley C, Gygi P (2004) Large-scale characterization of hela cell nuclear phosphoproteins. Proc Natl Acad Sci USA 101:12130CrossRefGoogle Scholar
  2. Beer I, Barnea E, Ziv T, Admon A (2004) Improving large-scale proteomics by clustering of mass spectrometry data. Proteomics 4(4):950–960CrossRefGoogle Scholar
  3. Catalyurek UV, Feo J, Gebremedhin AH, Halappanavar M, Pothen A (2012) Graph coloring algorithms for multi-core and massively multithreaded architectures. Parallel Comput 38(1011):576–594CrossRefMathSciNetGoogle Scholar
  4. Cantin T, Venable D, Cociorva D, Yates R (2006) Iii quantitative phosphoproteomic analysis of the tumor necrosis factor pathway. J. Proteome Res. 5:127CrossRefGoogle Scholar
  5. Dutta D, Chen T (2007) Speeding up tandem mass spectrometry database search: metric embeddings and fast near neighbor search. Bioinformatics 23(5):612–618CrossRefGoogle Scholar
  6. Du X, Yang F, Manes NP, Stenoien DL, Monroe ME, Adkins JN, States DJ, Purvine SO, Camp DG II, Smith RD (2008) Linear discriminant analysis-based estimation of the false discovery rate for phosphopeptide identifications. J Proteome Res 7(6):2195–2203CrossRefGoogle Scholar
  7. Frank AM, Bandeira N, Shen Z, Tanner S, Briggs SP, Smith RD, Pevzner PA (2008) Clustering Millions of tandem mass spectra. J Proteome Res 7:113–122CrossRefGoogle Scholar
  8. Gruhler A, Olsen JV, Mohammed S, Mortensen P, FÃrgeman NJ, Mann M, Jensen ON (2005) Quantitative Phosphoproteomics Applied to the Yeast Pheromone Signaling Pathway. Mol Cell Proteomics 4:310–327CrossRefGoogle Scholar
  9. Hoffert J, Pisitkun T, Wang G, Shen F, Knepper M (2006) Quantitative phosphoproteomics of vasopressin-sensitive renal cells: regulation of aquaporin-2 phosphorylation at two sites. Proc Natl Acad Sci USA 103(18):7159–7164CrossRefGoogle Scholar
  10. Jiang X, Ye M, Han G, Dong X, Zou H (2010) Classification filtering strategy to improve the coverage and sensitivity of phosphoproteome analysis. Anal Chem 82(14):6168–6175CrossRefGoogle Scholar
  11. Li X, Gerber SA, Rudner AD, Beausoleil SA, Haas W, Elias JE, Gygi SP (2007) Large-scale phosphorylation analysis of alpha-factor-arrested saccharomyces cerevisiae. J Proteome Res 6(3):1190–1197CrossRefGoogle Scholar
  12. Liu Y, Schmidt B, Maskell D (2011) Parallelized short read assembly of large genomes using de bruijn graphs. BMC Bioinform 12(1):354CrossRefGoogle Scholar
  13. Majumder T, Borgens M, Pande P, Kalyanaraman A (2012) On-chip network-enabled multicore platforms targeting maximum likelihood phylogeny reconstruction, Computer-Aided Design of Integrated Circuits and Systems. IEEE Transactions on 31:1061–1073Google Scholar
  14. Ozyer T, Alhajj R (2009) Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer. Appl Intell 31(3):318–331CrossRefGoogle Scholar
  15. Ramakrishnan SR, Mao R, Nakorchevskiy AA, Prince JT, Willard WS, Xu W, Marcotte EM, Miranker DP (2006) A fast coarse filtering method for peptide identification by mass spectrometry. Bioinformatics 22(12):1524–1531CrossRefGoogle Scholar
  16. Riedy J, Meyerhenke H, Bader D, Ediger D, Mattson T (2012) Analysis of streaming social networks and graphs on multicore architectures. In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, 5337–5340 IEEEGoogle Scholar
  17. Ruttenberg BE, Pisitkun T, Knepper MA, Hoffert JD (2008) PhosphoScore: an open-source phosphorylation site assignment tool for MSn data. J Proteome Res 7:3054–3059CrossRefGoogle Scholar
  18. Saeed F, Khokhar A (2009) A domain decomposition strategy for alignment of multiple biological sequences on multiprocessor platforms. J Parallel Distrib Comput 69(7):666–677CrossRefGoogle Scholar
  19. Saeed F, Pisitkun T, Knepper MA, Hoffert JD (2012) An efficient algorithm for clustering of large-scale mass spectrometry data. In: Bioinformatics and biomedicine (BIBM), 2012 IEEE International Conference on 1–4 IEEEGoogle Scholar
  20. Saeed F, Pisitkun T, Hoffert JD, Wang G, Gucek M, Knepper MA (2012) An efficient dynamic programming algorithm for phosphorylation site assignment of large-scale mass spectrometry data. In: Bioinformatics and biomedicine Workshops (BIBMW), 2012 IEEE International Conference on, pp 618–625, IEEEGoogle Scholar
  21. Saeed F, Hoffert JD, Knepper MA (2013) A high performance algorithm for clustering of large-scale protein mass spectrometry data using multi-core architectures. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM'13). ACM, New York, pp 923–930Google Scholar
  22. Saeed F, Hoffert JD, Knepper MA (2014) Cams-rs: clustering algorithm for large-scale mass spectrometry data using restricted search space and intelligent random sampling. IEEE/ACM Trans Comput Biol Bioinform (in press)Google Scholar
  23. Sarje A, Zola J, Aluru S (2011) Accelerating pairwise computations on cell processors. Parallel and Distributed Systems, IEEE Transactions on 22:69–77Google Scholar
  24. Tabb DL, MacCoss MJ, Wu CC, Anderson SD, Yates JR (2003) Similarity among tandem mass spectra from proteomic experiments, detection, significance, and utility. Anal Chem 75(10):2470–2477CrossRefGoogle Scholar
  25. Tabb DL, Thompson MR, Khalsa-Moyers G, VerBerkmoes NC, McDonald WH (2005) Ms2grouper: Group assessment and synthetic replacement of duplicate proteomic tandem mass spectra. J Am Soc Mass Spectrom16(8):1250–1261CrossRefGoogle Scholar
  26. Whitelegge JP (2003) Hplc and mass spectrometry of intrinsic membrane proteins, 251Google Scholar

Copyright information

© Springer Verlag Wien (outside the USA) 2014

Authors and Affiliations

  • Fahad Saeed
    • 1
    • 2
    • 4
    Email author
  • Jason D. Hoffert
    • 2
  • Trairak Pisitkun
    • 2
    • 3
  • Mark A. Knepper
    • 2
  1. 1.Department of Computer ScienceWestern Michigan UniversityKalamazooUSA
  2. 2.Epithelial Systems Biology Laboratory National Heart Lung and Blood Institute (NHLBI), National Institutes of Health (NIH)BethesdaUSA
  3. 3.Faculty of MedicineChulalongkorn UniversityBangkokThailand
  4. 4.Department of Electrical and Computer EngineeringWestern Michigan UniversityKalamazooUSA

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