Benchmarking homology detection procedures with low complexity filters.
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.


CONCLUSIONS
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.


BACKGROUND
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
Forslund
Kristoffer
K
Stockholm Bioinformatics Center, Stockholm University, SE-10691 Stockholm, Sweden. kristoffer.forslund@sbc.su.se
Sonnhammer
Erik L L
EL
D000595:Amino Acid Sequence; D019985:Benchmarking / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D011506:Proteins