Benchmarking homology detection procedures with low complexity filters
2009 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1460-2059, Vol. 25, no 19, 2500-2505 p.Article in journal (Refereed) Published
BACKGROUND: Low-complexity sequence regions present a common problem in finding true homologs to a protein query sequence. Several solutions to this have been suggested, but a detailed comparison between these on challenging data has so far been lacking. A common benchmark for homology detection procedures is to use SCOP/ASTRAL domain sequences belonging to the same or different superfamilies, but these contain almost no low complexity sequences.
RESULTS: We here introduce an alternative benchmarking strategy based around Pfam domains and clans on whole-proteome data sets. This gives a realistic level of low complexity sequences. We used it to evaluate all six built-in BLAST low complexity filter settings as well as a range of settings in the MSPcrunch post-processing filter. The effect on alignment length was also assessed.
CONCLUSION: Score matrix adjustment methods provide a low false positive rate at a relatively small loss in sensitivity relative to no filtering, across the range of test conditions we apply. MSPcrunch achieved even less loss in sensitivity, but at a higher false positive rate. A drawback of the score matrix adjustment methods is however that the alignments often become truncated.
AVAILABILITY: Perl scripts for MSPcrunch BLAST filtering and for generating the benchmark dataset are available at http://sonnhammer.sbc.su.se/download/software/MSPcrunch+Blixem/benchmark.tar.gz
Place, publisher, year, edition, pages
2009. Vol. 25, no 19, 2500-2505 p.
IdentifiersURN: urn:nbn:se:su:diva-33341DOI: 10.1093/bioinformatics/btp446ISI: 000270446400007PubMedID: 19620098OAI: oai:DiVA.org:su-33341DiVA: diva2:283023