Bioinformatics, Vol 14, 423-429, Copyright © 1998 by Oxford University Press
L Holm and C Sander
MOTIVATION: To maximize the chances of biological discovery, homology
searching must use an up-to-date collection of sequences. However, the
available sequence databases are growing rapidly and are partially
redundant in content. This leads to increasing strain on CPU resources and
decreasing density of first-hand annotation. RESULTS: These problems are
addressed by clustering closely similar sequences to yield a covering of
sequence space by a representative subset of sequences. No pair of
sequences in the representative set has >90% mutual sequence identity.
The representative set is derived by an exhaustive search for close
similarities in the sequence database in which the need for explicit
sequence alignment is significantly reduced by applying deca- and
pentapeptide composition filters. The algorithm was applied to the union of
the Swissprot, Swissnew, Trembl, Tremblnew, Genbank, PIR, Wormpep and PDB
databases. The all-against-all comparison required to generate a
representative set at 90% sequence identity was accomplished in 2 days CPU
time, and the removal of fragments and close similarities yielded a size
reduction of 46%, from 260 000 unique sequences to 140 000 representative
sequences. The practical implications are (i) faster homology searches
using, for example, Fasta or Blast, and (ii) unified annotation for all
sequences clustered around a representative. As tens of thousands of
sequence searches are performed daily world-wide, appropriate use of the
non-redundant database can lead to major savings in computer resources,
without loss of efficacy. AVAILABILITY: A regularly updated non-redundant
protein sequence database (nrdb90), a server for homology searches against
nrdb90, and a Perl script (nrdb90.pl) implementing the algorithm are
available for academic use from http://www.embl-ebi.ac. uk/~holm/nrdb90.
CONTACT: holm@embl- ebi.ac.uk
ARTICLES
Removing near-neighbour redundancy from large protein sequence collections
EMBL-EBI, Cambridge CB10 1SD, UK.
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