ArticleBioinformatics (Oxford, England)2015
Faster sequence homology searches by clustering subsequences.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- Comparative genomic analyses illuminate the distinct evolution of megabats within Chiroptera.DNA research : an international journal for rapid publication of reports on genes and genomes · 2020Article
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- A Novel Eukaryotic Denitrification Pathway in Foraminifera.Current biology : CB · 2018Article
- The genome of Rhizophagus clarus HR1 reveals a common genetic basis for auxotrophy among arbuscular mycorrhizal fungi.BMC genomics · 2018Article
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- Fast batch searching for protein homology based on compression and clustering.BMC bioinformatics · 2017Article
- Survey of (Meta)genomic Approaches for Understanding Microbial Community Dynamics.Indian journal of microbiology · 2017Review
- Evolution of Chemosensory Gene Families in Arthropods: Insight from the First Inclusive Comparative Transcriptome Analysis across Spider Appendages.Genome biology and evolution · 2017Article
- ALP & FALP: C++ libraries for pairwise local alignment E-values.Bioinformatics (Oxford, England) · 2016Article
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4 authors.
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Abstract
motivationSequence homology searches are used in various fields. New sequencing technologies produce huge amounts of sequence data, which continuously increase the size of sequence databases. As a result, homology searches require large amounts of computational time, especially for metagenomic analysis.
resultsWe developed a fast homology search method based on database subsequence clustering, and implemented it as GHOSTZ. This method clusters similar subsequences from a database to perform an efficient seed search and ungapped extension by reducing alignment candidates based on triangle inequality. The database subsequence clustering technique achieved an ∼2-fold increase in speed without a large decrease in search sensitivity. When we measured with metagenomic data, GHOSTZ is ∼2.2-2.8 times faster than RAPSearch and is ∼185-261 times faster than BLASTX. AVAILABILITY AND IMPLEMENTATION: The source code is freely available for download at http://www.bi.cs.titech.ac.jp/ghostz/ CONTACT: akiyama@cs.titech.ac.jp SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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