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This article is part of the supplement: NIPS workshop on New Problems and Methods in Computational Biology

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Discrete profile comparison using information bottleneck

Sean O'Rourke1*, Gal Chechik2, Robin Friedman1 and Eleazar Eskin1

Author Affiliations

1 Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Dr., San Diego, CA 92093

2 Department of Computer Science, Stanford University, 353 Serra Mall, Stanford University, Stanford CA 94305

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BMC Bioinformatics 2006, 7(Suppl 1):S8  doi:10.1186/1471-2105-7-S1-S8

Published: 20 March 2006


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 to 61% with consensus sequences (AUC scores 0.73, 0.83, and 0.51, respectively), but like simple sequence comparison, is 30 times faster. Discrete IB encoding can therefore expand the range of sequence problems to which profile information can be applied to include batch queries over large databases like SwissProt, which were previously computationally infeasible.