ArticleJournal of proteome research2011
Fast parallel tandem mass spectral library searching using GPU hardware acceleration.
Article in Journal of proteome research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- High-Performance Computing in Tandem Mass Spectrometry (MS/MS) Peptide Identification.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Communication Lower-Bounds for Distributed-Memory Computations for Mass Spectrometry based Omics Data.Journal of parallel and distributed computing · 2022Article
- Deep learning embedder method and tool for mass spectra similarity search.Journal of proteomics · 2021Article
- Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey.IEEE access : practical innovations, open solutions · 2021Article
- Extremely Fast and Accurate Open Modification Spectral Library Searching of High-Resolution Mass Spectra Using Feature Hashing and Graphics Processing Units.Journal of proteome research · 2019Article
- SPECTRUM - A MATLAB Toolbox for Proteoform Identification from Top-Down Proteomics Data.Scientific reports · 2019Article
- MCtandem: an efficient tool for large-scale peptide identification on many integrated core (MIC) architecture.BMC bioinformatics · 2019Article
- Optimization of TripleTOF spectral simulation and library searching for confident localization of phosphorylation sites.PloS one · 2019Article
- Accelerating a cross-correlation score function to search modifications using a single GPU.BMC bioinformatics · 2018Article
- Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing.Journal of proteome research · 2018Article
- GPU-DAEMON: GPU algorithm design, data management & optimization template for array based big omics data.Computers in biology and medicine · 2018Article
- Accelerating the scoring module of mass spectrometry-based peptide identification using GPUs.BMC bioinformatics · 2014Article
- Next generation distributed computing for cancer research.Cancer informatics · 2014Review
- Current algorithmic solutions for peptide-based proteomics data generation and identification.Current opinion in biotechnology · 2013Review
- Hydra: a scalable proteomic search engine which utilizes the Hadoop distributed computing framework.BMC bioinformatics · 2012Article
- Tempest: GPU-CPU computing for high-throughput database spectral matching.Journal of proteome research · 2012Article
- High speed data processing for imaging MS-based molecular histology using graphical processing units.Journal of the American Society for Mass Spectrometry · 2012Article
- Faster SEQUEST searching for peptide identification from tandem mass spectra.Journal of proteome research · 2011Article
- Communication-avoiding micro-architecture to compute Xcorr scores for peptide identification.International Conference on Field-programmable Logic and Applications : [proceedings]. International Conference on Field-Programmable Logic and ApplicationsArticle
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Authors and funding
5 authors.
Funding
Abstract
Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate-limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper, we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching), is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA, which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.