Database peptide search algorithms deduce peptides from mass spectrometry data. There has been substantial effort in improving their computational efficiency to achieve larger and more complex systems biology studies. However, modern serial and high-performance computing (HPC) algorithms exhibit suboptimal performance mainly due to their ineffective parallel designs (low resource utilization) and high overhead costs. We present an HPC framework, called HiCOPS, for efficient acceleration of the database peptide search algorithms on distributed-memory supercomputers. HiCOPS provides, on average, more than tenfold improvement in speed and superior parallel performance over several existing HPC database search software. We also formulate a mathematical model for performance analysis and optimization, and report near-optimal results for several key metrics including strong-scale efficiency, hardware utilization, load-balance, inter-process communication and I/O overheads. The core parallel design, techniques and optimizations presented in HiCOPS are search-algorithm-independent and can be extended to efficiently accelerate the existing and future algorithms and software.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Scientific Reports Open Access 31 October 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
All of the datasets used in this study are publicly available from PXD and can be accessed via https://www.ebi.ac.uk/pride/archive/projects/<AccessionNum>, where AccessionNum is the accession number for each dataset mentioned in the text (for example, to access S1 PXD009072, use https://www.ebi.ac.uk/pride/archive/projects/PXD009072). The Homo sapiens protein sequence database can be downloaded from UniProtKB via https://www.uniprot.org/proteomes/UP000005640. The UniProt SwissProt (reviewed) database can be downloaded via https://www.uniprot.org/uniprot/?query=reviewed:yes. Source data are provided with this paper.
The HiCOPS software has been implemented using object-oriented C++17, MPI, OpenMP, Python, Bash and CMake. Instrumentation interface is implemented via Timemory42 for performance analysis. Command-line tools for MPI task mapping (Supplementary Section 7), database processing, file format conversion and result post-processing are also distributed with the software. HiCOPS is under active development and all documentation updates, source code releases and so on will be updated on the same web page. The source code is available open-source at https://doi.org/10.5281/zenodo.5094072 (ref. 50) and https://github.com/hicops/hicops. Please refer to https://hicops.github.io for detailed documentation, licensing and future software updates.
Nesvizhskii, A. I. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J. Proteomics 73, 2092–2123 (2010).
Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSfragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513 (2017).
McIlwain, S. et al. Crux: rapid open source protein tandem mass spectrometry analysis. J. Proteome Res. 13, 4488–4491 (2014).
Yuan, Z.-Fe et al. pParse: a method for accurate determination of monoisotopic peaks in high-resolution mass spectra. Proteomics 12, 226–235 (2012).
Deng, Y. et al. pClean: an algorithm to preprocess high-resolution tandem mass spectra for database searching. J. Proteome Res. 18, 3235–3244 (2019).
Degroeve, S. & Martens, L. Ms2pip: a tool for ms/ms peak intensity prediction. Bioinformatics 29, 3199–3203 (2013).
Zhou, X.-X. et al. pDeep: predicting MS/MS spectra of peptides with deep learning. Anal. Chem. 89, 12690–12697 (2017).
Zhang, J. et al. PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification. Mol. Cell. Proteomics 11, M111–010587 (2012).
Devabhaktuni, A. et al. TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets. Nat. Biotechnol. 1, 469–479 (2019).
Chi, H. et al. Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nat. Biotechnol. 36, 1059–1061 (2018).
Bern, M., Cai, Y. & Goldberg, D. Lookup peaks: a hybrid of de novo sequencing and database search for protein identification by tandem mass spectrometry. Anal. Chem. 79, 1393–1400 (2007).
Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spec. 5, 976–989 (1994).
Craig, R. & Beavis, R. C. A method for reducing the time required to match protein sequences with tandem mass spectra. Rapid Commun. Mass Spec. 17, 2310–2316 (2003).
Diament, B. J. & Noble, W. S. Faster sequest searching for peptide identification from tandem mass spectra. J. Proteome Res. 10, 3871–3879 (2011).
Eng, J. K., Fischer, B., Grossmann, J. & MacCoss, M. J. A fast sequest cross correlation algorithm. J. Proteome Res. 7, 4598–4602 (2008).
Park, C. Y., Klammer, A. A., Kall, L., MacCoss, M. J. & Noble, W. S. Rapid and accurate peptide identification from tandem mass spectra. J. Proteome Res. 7, 3022–3027 (2008).
Geer, L. Y. et al. Open mass spectrometry search algorithm. J. Proteome Res. 3, 958–964 (2004).
Hebert, A. S. et al. The one hour yeast proteome. Mol. Cell. Proteomics 13, 339–347 (2014).
Nesvizhskii, A. I. et al. Dynamic spectrum quality assessment and iterative computational analysis of shotgun proteomic data toward more efficient identification of post-translational modifications, sequence polymorphisms, and novel peptides. Mol. Cell. Proteomics 5, 652–670 (2006).
Eng, J. K., Searle, B. C., Clauser, K. R. & Tabb, D. L. A face in the crowd: recognizing peptides through database search. Mol. Cell. Proteomics 10, R111.009522 (2011).
Haseeb, M. & Saeed, F. Efficient shared peak counting in database peptide search using compact data structure for fragment-ion index. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 275–278 (IEEE, 2019).
Williams, S., Waterman, A. & Patterson, D. Roofline: an insightful visual performance model for multicore architectures. Commun. ACM 52, 65–76 (2009).
Chi, H. et al. pFIND–Alioth: a novel unrestricted database search algorithm to improve the interpretation of high-resolution MS/MS data. J. Proteomics 125, 89–97 (2015).
Marx, V. The big challenges of big data. Nature 498, 255–260 (2013).
Duncan, D. T., Craig, R. & Link, A. J. Parallel tandem: a program for parallel processing of tandem mass spectra using PVM or MPI and X! tandem. J. Proteome Res. 4, 1842–1847 (2005).
Bjornson, R. D. et al. X!!Tandem, an improved method for running X!Tandem in parallel on collections of commodity computers. J. Proteome Res. 7, 293–299 (2007).
Pratt, B., Howbert, J. J., Tasman, N. I. & Nilsson, E. J. MR-tandem: parallel X! Tandem using Hadoop MapReduce on Amazon Web Services. Bioinformatics 28, 136–137 (2011).
Li, C., Li, K., Li, K. & Lin, F. MCtandem: an efficient tool for large-scale peptide identification on many integrated core (MIC) architecture. BMC Bioinformatics 20, 397 (2019).
Li, C., Li, K., Chen, T., Zhu, Y. & He, Q. SW-Tandem: a highly efficient tool for large-scale peptide sequencing with parallel spectrum dot product on Sunway TaihuLight. Bioinformatics 35, 3861–3863 (2019).
Chen, L. et al. MS-PyCloud: an open-source, cloud computing-based pipeline for LC-MS/MS data analysis. Preprint at https://www.biorxiv.org/content/10.1101/320887v1 (2018).
Prakash, A., Ahmad, S., Majumder, S., Jenkins, C. & Orsburn, B. Bolt: a new age peptide search engine for comprehensive MS/MS sequencing through vast protein databases in minutes. J. Am. Soc. Mass Spec. 30, 2408–2418 (2019).
Kaiser, P. et al. High-resolution community analysis of deep-sea copepods using maldi-tof protein fingerprinting. Deep Sea Res. I 138, 122–130 (2018).
Rossel, S. & Arbizu, P. M. Revealing higher than expected diversity of Harpacticoida (Crustacea: Copepoda) in the North Sea using MALDI-TOF MS and molecular barcoding. Sci. Rep. 9, 1–14 (2019).
Yates III, J. R. Proteomics of communities: metaproteomics. J. Proteome Res. 18, 2359 (2019).
Saeed, F., Haseeb, M. & Lyengar, S. S. Communication lower-bounds for distributed-memory computations for mass spectrometry based omics data. Preprint at https://arxiv.org/abs/2009.14123v2 (2021).
Beyter, D., Lin, M. S., Yu, Y., Pieper, R. & Bafna, V. Proteostorm: an ultrafast metaproteomics database search framework. Cell Syst. 7, 463–467 (2018).
Valiant, L. G. A bridging model for parallel computation. Commun. ACM 33, 103–111 (1990).
Tiskin, A. BSP (Bulk Synchronous Parallelism) 192–199 (Springer, 2011); https://doi.org/10.1007/978-0-387-09766-4_311
Towns, J. et al. XSEDE: accelerating scientific discovery. Comput. Sci. Eng. 16, 62–74 (2014).
Eng, J. K., Jahan, T. A. & Hoopmann, M. R. Comet: an open-source MS/MS sequence database search tool. Proteomics 13, 22–24 (2013).
Craig, R. & Beavis, R. C. Tandem: matching proteins with tandem mass spectra. Bioinformatics 20, 1466–1467 (2004).
Madsen, J. R. et al. Timemory: modular performance analysis for HPC. In International Conference on High Performance Computing 434–452 (Springer, 2020).
Stevens, R., Ramprakash, J., Messina, P., Papka, M. & Riley, K. Aurora: Argonne’s Next-Generation Exascale Supercomputer Technical Report (Argonne National Laboratory, 2019).
Liu, K., Li, S., Wang, L., Ye, Y. & Tang, H. Full-spectrum prediction of peptides tandem mass spectra using deep neural network. Analytical chemistry 92, 4275–4283 (2020).
Lin, Y.-M., Chen, C.-T. & Chang, J.-M. MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks. BMC Genomics 20, 1–10 (2019).
Haseeb, M., Afzali, F. & Saeed, F. LBE: a computational load balancing algorithm for speeding up parallel peptide search in mass-spectrometry based proteomics. In 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) 191–198 (IEEE, 2019).
Ding, J., Shi, J., Poirier, G. G. & Wu, F.-X. A novel approach to denoising ion trap tandem mass spectra. Proteome Sci. 7, 9 (2009).
Fenyö, D. & Beavis, R. C. A method for assessing the statistical significance of mass spectrometry-based protein identifications using general scoring schemes. Anal. Chem. 75, 768–774 (2003).
LaViola, J. J. Double exponential smoothing: an alternative to kalman filter-based predictive tracking. In Proc. Workshop on Virtual Environments 2003 199–206 (The Eurographics Association, 2003).
Haseeb, M. & Saeed, F. hicops/hicops: HiCOPS v1.0.0—1st Public Release (Zenodo, 2021); https://doi.org/10.5281/zenodo.5094072
Haseeb, M. & Saeed, F. Source Data: High Performance Computing Framework for Tera-Scale Database Search of Mass Spectrometry Data (Zenodo, 2021); https://doi.org/10.5281/zenodo.5076575
This work used the National Science Foundation (NSF) XSEDE supercomputers through allocations TG-CCR150017 and TG-ASC200004 (F.S.). This research was supported by the NIGMS of the National Institutes of Health (NIH) under award number: R01GM134384 (F.S.). The authors were further supported by the NSF under award number: NSF CAREER OAC-1925960 (F.S.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH and/or the NSF.
The authors declare no competing interests.
Reviewer recognition statement Nature Computational Science thanks Robert Bjornson, Benjamin Neely, Yasset Perez-Riverol and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Editor recognition statement Handling editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.
HiCOPS hyperscores and expectscores across serial and parallel runs and common peptide identifications for MSFragger and HiCOPS.
Runtime profiles for several tools for speed comparison and other insights.
Raw code instrumentation results for performance evaluation.
Raw code instrumentation results for overhead evaluation.
About this article
Cite this article
Haseeb, M., Saeed, F. High performance computing framework for tera-scale database search of mass spectrometry data. Nat Comput Sci 1, 550–561 (2021). https://doi.org/10.1038/s43588-021-00113-z
This article is cited by
Scientific Reports (2023)