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Filtering Degenerate Patterns with Application to Protein Sequence Analysis

Department of Information Engineering, University of Padova, Padova 35131, Italy
Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore
Author to whom correspondence should be addressed.
Algorithms 2013, 6(2), 352-370;
Received: 29 March 2013 / Revised: 30 April 2013 / Accepted: 3 May 2013 / Published: 22 May 2013
(This article belongs to the Special Issue Algorithms for Sequence Analysis and Storage)
In biology, the notion of degenerate pattern plays a central role for describing various phenomena. For example, protein active site patterns, like those contained in the PROSITE database, e.g., [FY ]DPC[LIM][ASG]C[ASG], are, in general, represented by degenerate patterns with character classes. Researchers have developed several approaches over the years to discover degenerate patterns. Although these methods have been exhaustively and successfully tested on genomes and proteins, their outcomes often far exceed the size of the original input, making the output hard to be managed and to be interpreted by refined analysis requiring manual inspection. In this paper, we discuss a characterization of degenerate patterns with character classes, without gaps, and we introduce the concept of pattern priority for comparing and ranking different patterns. We define the class of underlying patterns for filtering any set of degenerate patterns into a new set that is linear in the size of the input sequence. We present some preliminary results on the detection of subtle signals in protein families. Results show that our approach drastically reduces the number of patterns in output for a tool for protein analysis, while retaining the representative patterns. View Full-Text
Keywords: pattern discovery and filtering; degenerate patterns; analysis of biological data pattern discovery and filtering; degenerate patterns; analysis of biological data
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Comin, M.; Verzotto, D. Filtering Degenerate Patterns with Application to Protein Sequence Analysis. Algorithms 2013, 6, 352-370.

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