Faster sequence homology searches by clustering subsequences.

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, GHOS

SUBMITTER: Suzuki S 

PROVIDER: S-EPMC4393512 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature