Discrete profile comparison using information bottleneck

Sequence homologs are an important source of information about proteins. Amino acid profiles, representing the position-specific mutation probabilities found in profiles, are a richer encoding of biological sequences than the individual sequences themselves. However, profile comparisons are an order of magnitude slower than sequence comparisons, making profiles impractical for large datasets. Also, because they are such a rich representation, profiles are difficult to visualize. To address these problems, we describe a method to map probabilistic profiles to a discrete alphabet while preserving most of the information in the profiles. We find an informationally optimal discretization using the Information Bottleneck approach (IB). We observe that an 80-character IB alphabet captures nearly 90% of the amino acid occurrence information found in profiles, compared to the consensus sequence's 78%. Distant homolog search with IB sequences is 88% as sensitive as with profiles compared ...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Sean O'Rourke, Gal Chechik, Robin Friedman, Eleazar Eskin
• Keywords: BMCBI 2006, Profile, Sequence Comparison, Sequences