SEED: efficient clustering of next-generation sequences.

ABSTRACT:

Motivation

Similarity clustering of next-generation sequences (NGS) is an important computational problem to study the population sizes of DNA/RNA molecules and to reduce the redundancies in NGS data. Currently, most sequence clustering algorithms are limited by their speed and scalability, and thus cannot handle data with tens of millions of reads.

Results

Here, we introduce SEED-an efficient algorithm for clustering very large NGS sets. It joins sequences into clusters that can differ by up to three mismatches and three overhanging residues from their virtual center. It is based on a modified spaced seed method, called block spaced seeds. Its clustering component operates on the hash tables by first identifying virtual center sequences and then finding all their neighborin

SUBMITTER: Bao E 

PROVIDER: S-EPMC3167058 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature