Journal Article

Benchmarking homology detection procedures with low complexity filters

Kristoffer Forslund and Erik LL Sonnhammer

in Bioinformatics

Volume 25, issue 19, pages 2500-2505
Published in print October 2009 | ISSN: 1367-4803
Published online July 2009 | e-ISSN: 1460-2059 | DOI:
Benchmarking homology detection procedures with low complexity filters

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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


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  4951 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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