Abstract
BackgroundDiscovering approximately repeated patterns, or motifs, in biological sequences is an important and widely-studied problem in computational molecular biology. Most frequently, motif finding applications arise when identifying shared regulatory signals within DNA sequences or shared functional and structural elements within protein sequences. Due to the diversity of contexts in which motif finding is applied, several variations of the problem are commonly studied.ResultsWe introduce a versatile combinatorial optimization framework for motif finding that couples graph pruning techniques with a novel integer linear programming formulation. Our approach is flexible and robust enough to model several variants of the motif finding problem, including those incorporating substitution matrices and phylogenetic distances. Additionally, we give an approach for determining statistical significance of uncovered motifs. In testing on numerous DNA and protein datasets, we demonstrate that our approach typically identifies statistically significant motifs corresponding to either known motifs or other motifs of high conservation. Moreover, in most cases, our approach finds provably optimal solutions to the underlying optimization problem.ConclusionOur results demonstrate that a combined graph theoretic and mathematical programming approach can be the basis for effective and powerful techniques for diverse motif finding applications.
Highlights
Discovering approximately repeated patterns, or motifs, in biological sequences is an important and widely-studied problem in computational molecular biology
We evaluate the overlap between motif predictions made by our approach and the known motifs using the nucleotide level performance coefficient [1,17]
We show sSn and nucleotide level performance coefficient (nPC) values while ignoring significance for each of the three other methods compared to linear programs (LPs)/dead-end elimination (DEE) in Figure 1, only displaying transcription factor datasets for which a difference in performance is observed
Summary
Discovering approximately repeated patterns, or motifs, in biological sequences is an important and widely-studied problem in computational molecular biology. Motif finding applications arise when identifying shared regulatory signals within DNA sequences or shared functional and structural elements within protein sequences. Motif discovery is the problem of finding approximately repeated patterns in unaligned sequence data. It is important in uncovering transcriptional networks, as short common subsequences in genomic data may correspond to a regulatory protein's binding sites, and in protein function identification, where short blocks of conserved amino acids code for important structural or functional elements. For DNA sequences, motif finding is often applied to sets of sequences from a single genome that have been identified as possessing a common motif, either through DNA microarray studies [3], ChIP-chip experiments [4] or protein binding microarrays [5].
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