MotifGP: DNA Motif Discovery Using Multiobjective Evolution

dc.contributor.authorBelmadani, Manuel
dc.description.abstractThe motif discovery problem is becoming increasingly important for molecular biologists as new sequencing technologies are producing large amounts of data, at rates which are unprecedented. The solution space for DNA motifs is too large to search with naive methods, meaning there is a need for fast and accurate motif detection tools. We propose MotifGP, a multiobjective motif discovery tool evolving regular expressions that characterize overrepresented motifs in a given input dataset. This thesis describes and evaluates a multiobjective strongly typed genetic programming algorithm for the discovery of network expressions in DNA sequences. Using 13 realistic data sets, we compare the results of our tool, MotifGP, to that of DREME, a state-of-art program. MotifGP outperforms DREME when the motifs to be sought are long, and the specificity is distributed over the length of the motif. For shorter motifs, the performance of MotifGP compares favourably with the state-of-the-art method. Finally, we discuss the advantages of multi-objective optimization in the context of this specific motif discovery problem.
dc.publisherUniversité d'Ottawa / University of Ottawa
dc.subjectGenetic Programming
dc.subjectMultiobjective optimization
dc.subjectMotif Discovery
dc.subjectEvolutionary Computing
dc.titleMotifGP: DNA Motif Discovery Using Multiobjective Evolution
dc.faculty.departmentSchool of Electrical Engineering and Computer Science
dc.contributor.supervisorTurcotte, Marcelénie / Engineeringénie / Engineering
uottawa.departmentScience informatique et génie électrique / Electrical Engineering and Computer Science
CollectionThèses, 2011 - // Theses, 2011 -