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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. 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. The source code is freely available for download at Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.


Shuji Suzuki, Masanori Kakuta, Takashi Ishida, Yutaka Akiyama. Faster sequence homology searches by clustering subsequences. Bioinformatics (Oxford, England). 2015 Apr 15;31(8):1183-90

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PMID: 25432166

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