ArticleBMC bioinformatics2019
MCtandem: an efficient tool for large-scale peptide identification on many integrated core (MIC) architecture.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data.BMC bioinformatics · 2022Article
- Communication Lower-Bounds for Distributed-Memory Computations for Mass Spectrometry based Omics Data.Journal of parallel and distributed computing · 2022Article
- High Performance Computing Framework for Tera-Scale Database Search of Mass Spectrometry Data.Nature computational science · 2021Article
- Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey.IEEE access : practical innovations, open solutions · 2021Article
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4 authors.
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Abstract
backgroundTandem mass spectrometry (MS/MS)-based database searching is a widely acknowledged and widely used method for peptide identification in shotgun proteomics. However, due to the rapid growth of spectra data produced by advanced mass spectrometry and the greatly increased number of modified and digested peptides identified in recent years, the current methods for peptide database searching cannot rapidly and thoroughly process large MS/MS spectra datasets. A breakthrough in efficient database search algorithms is crucial for peptide identification in computational proteomics.
resultsThis paper presents MCtandem, an efficient tool for large-scale peptide identification on Intel Many Integrated Core (MIC) architecture. To support big data processing capability, a novel parallel match scoring algorithm, named MIC-SDP (spectrum dot product), and its two-level parallelization are presented in MCtandem's design. In addition, a series of optimization strategies on both the host CPU side and the MIC side, which includes pre-fetching, optimized communication overlapping scheme, multithreading and hyper-threading, are exploited to improve the execution performance.
conclusionsFor fair comparisons, we first set up experiments and verified the 28 fold times speedup on a single MIC against the original CPU-based implementation. We then execute the MCtandem for a very large dataset on an MIC cluster (a component of the Tianhe-2 supercomputer) and achieved much higher scalability than in a benchmark MapReduce-based programs, MR-Tandem. MCtandem is an open-source software tool implemented in C++. The source code and the parameter settings are available at https://github.com/LogicZY/MCtandem .
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