PMC:1570465 / 567-1342 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1570465","sourcedb":"PMC","sourceid":"1570465","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1570465","text":"Results\nWe 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.","divisions":[{"label":"title","span":{"begin":0,"end":7}}],"tracks":[]}