Clustering the annotation space of proteins

Victor Kunin and Christos A Ouzounis

Computational Genomics Group, EMBL-EBI, Cambridge, CB10 1SO, UK

author email corresponding author email

BMC Bioinformatics 2005, 6:24doi:10.1186/1471-2105-6-24


Published:	9 February 2005

Abstract

Background

Current protein clustering methods rely on either sequence or functional similarities between proteins, thereby limiting inferences to one of these areas.

Results

Here we report a new approach, named CLAN, which clusters proteins according to both annotation and sequence similarity. This approach is extremely fast, clustering the complete SwissProt database within minutes. It is also accurate, recovering consistent protein families agreeing on average in more than 97% with sequence-based protein families from Pfam. Discrepancies between sequence- and annotation-based clusters were scrutinized and the reasons reported. We demonstrate examples for each of these cases, and thoroughly discuss an example of a propagated error in SwissProt: a vacuolar ATPase subunit M9.2 erroneously annotated as vacuolar ATP synthase subunit H. CLAN algorithm is available from the authors and the CLAN database is accessible at http://maine.ebi.ac.uk:8000/cgi-bin/clan/ClanSearch.pl webcite

Conclusions

CLAN creates refined function-and-sequence specific protein families that can be used for identification and annotation of unknown family members. It also allows easy identification of erroneous annotations by spotting inconsistencies between similarities on annotation and sequence levels.