Publication Date:
2015-04-12
Description:
Motivation: Sequence 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. Results: We 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.
Print ISSN:
1367-4803
Electronic ISSN:
1460-2059
Topics:
Biology
,
Computer Science
,
Medicine
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