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.
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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.
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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.
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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