ArticleBMC bioinformatics2012
Hydra: a scalable proteomic search engine which utilizes the Hadoop distributed computing framework.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Cloud Computing Enabled Big Multi-Omics Data Analytics.Bioinformatics and biology insights · 2021Review
- Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson's Correlation Coefficients for Time Series Data-fMRI Study.High-throughput · 2018Article
- Deep Learning-based MSMS Spectra Reduction in Support of Running Multiple Protein Search Engines on Cloud.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2017Article
- Single-cell Transcriptome Study as Big Data.Genomics, proteomics & bioinformatics · 2016Review
- Big Data Application in Biomedical Research and Health Care: A Literature Review.Biomedical informatics insights · 2016Review
- Processing shotgun proteomics data on the Amazon cloud with the trans-proteomic pipeline.Molecular & cellular proteomics : MCP · 2015Article
- Cloud4Psi: cloud computing for 3D protein structure similarity searching.Bioinformatics (Oxford, England) · 2014Article
- Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.BMC systems biology · 2014Article
- Next generation distributed computing for cancer research.Cancer informatics · 2014Review
- Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends.BioData mining · 2014Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundFor shotgun mass spectrometry based proteomics the most computationally expensive step is in matching the spectra against an increasingly large database of sequences and their post-translational modifications with known masses. Each mass spectrometer can generate data at an astonishingly high rate, and the scope of what is searched for is continually increasing. Therefore solutions for improving our ability to perform these searches are needed.
resultsWe present a sequence database search engine that is specifically designed to run efficiently on the Hadoop MapReduce distributed computing framework. The search engine implements the K-score algorithm, generating comparable output for the same input files as the original implementation. The scalability of the system is shown, and the architecture required for the development of such distributed processing is discussed.
conclusionThe software is scalable in its ability to handle a large peptide database, numerous modifications and large numbers of spectra. Performance scales with the number of processors in the cluster, allowing throughput to expand with the available resources.
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Registered trials
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.