A generalized sequence pattern matching algorithm using complementary dual-seeding

Abstract

In this work, we define generalized (sequence) patterns, which is based on several real Biological problems, including transcription factors (TFs) binding to transcription factor binding sites (TFBSs), cis-regulatory modules, protein domain analysis, and alternative splicing etc. Simply speaking, a generalized pattern is composed of several substrings with gaps in-between two substrings. We propose a generalized pattern matching algorithm that uses a complementary dual-seeding strategy, which is sensitive to errors (both mismatches and indels). We also develop a generalized pattern matching tool 1, which is to our knowledge the first ever developed specially for generalized pattern matching. Rather than replacing the existing general purpose matching tools, such as BLAST, BLAT, and PatternHunter etc, our tool provides an alternative and helps users to solve real problems, especially those that can be modeled as generalized patterns. We use data randomly sampled from reference sequences of human genome (NCBI build v18) in experiments, and hit 98.74% generalized patterns on average. The tool runs on both LINUX and Windows platforms, and the memory peak goes to a little bit larger than 1GB only. ©2010 IEEE.