KClust: fast and sensitive clustering of large protein sequence databases.

ABSTRACT:

Background

Fueled by rapid progress in high-throughput sequencing, the size of public sequence databases doubles every two years. Searching the ever larger and more redundant databases is getting increasingly inefficient. Clustering can help to organize sequences into homologous and functionally similar groups and can improve the speed, sensitivity, and readability of homology searches. However, because the clustering time is quadratic in the number of sequences, standard sequence search methods are becoming impracticable.

Results

Here we present a method to cluster large protein sequence databases such as UniProt within days down to 20%-30% maximum pairwise sequence identity. kClust owes its speed and sensitivity to an alignment-free prefilter that calculates the cumulative

SUBMITTER: Hauser M 

PROVIDER: S-EPMC3843501 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature