A methodology for motif discovery employing iterated cluster re-assignment.
Motif discovery is a crucial part of regulatory network identification, and therefore widely studied in the literature. Motif discovery programs search for statistically significant, well-conserved and over-represented patterns in given promoter sequences. When gene expression data is available, there are mainly three paradigms for motif discovery; cluster-first, regression, and joint probabilistic. The success of motif discovery depends highly on the homogeneity of input sequences, regardless of paradigm employed. In this work, we propose a methodology for getting homogeneous subsets from input sequences for increased motif discovery performance. It is a unification of cluster-first and regression paradigms based on iterative cluster re-assignment. The experimental results show the effectiveness of the methodology.
Abul
Osman
O
Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway. osman.abul@ntnu.no
Drabløs
Finn
F
Sandve
Geir Kjetil
GK
D020816:Amino Acid Motifs; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D005810:Multigene Family; D011336:Probability; D011487:Protein Conformation; D017434:Protein Structure, Tertiary; D040901:Proteomics / Q000379:methods; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D014157:Transcription Factors / Q000737:chemistry
D014157:Transcription Factors