Benchmarking homology detection procedures with low complexity filters | Zendy

Sofia K. Forslund | Zendy; Erik L. L. Sonnhammer | Zendy

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Benchmarking homology detection procedures with low complexity filters

Author(s) -

Sofia K. Forslund,

Erik L. L. Sonnhammer

Publication year - 2009

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btp446

Subject(s) - benchmarking , benchmark (surveying) , perl , computer science , data mining , false positive paradox , software , filter (signal processing) , sequence alignment , range (aeronautics) , smith–waterman algorithm , algorithm , computational biology , artificial intelligence , biology , genetics , peptide sequence , materials science , geodesy , marketing , world wide web , business , composite material , computer vision , gene , programming language , geography

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.

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