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. 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

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