Faster sequence homology searches by clustering subsequences

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Abstract

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.

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