Position-Dependent Motif Characterization Using Non-Negative Matrix Factorization
Hutchins, Lucie N.
Murphy, Sean M.
Graber, Joel H.
MetadataShow full item record
CitationHutchins, Lucie N., Sean M. Murphy, Priyam Singh, Joel H. Graber. "Position-dependent motif characterization using non-negative matrix factorization" Bioinformatics 24(23): 2684-2690. (2008)
Motivation: Cis-acting regulatory elements are frequently constrained by both sequence content and positioning relative to a functional site, such as a splice or polyadenylation site. We describe an approach to regulatory motif analysis based on non-negative matrix factorization (NMF). Whereas existing pattern recognition algorithms commonly focus primarily on sequence content, our method simultaneously characterizes both positioning and sequence content of putative motifs. Results: Tests on artificially generated sequences show that NMF can faithfully reproduce both positioning and content of test motifs. We show how the variation of the residual sum of squares can be used to give a robust estimate of the number of motifs or patterns in a sequence set. Our analysis distinguishes multiple motifs with significant overlap in sequence content and/or positioning. Finally, we demonstrate the use of the NMF approach through characterization of biologically interesting datasets. Specifically, an analysis of mRNA 3′-processing (cleavage and polyadenylation) sites from a broad range of higher eukaryotes reveals a conserved core pattern of three elements. Contact: firstname.lastname@example.org Supplementary information: Supplementary data are available at Bioinformatics online.