Bioinformatics Advance Access published online on October 13, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn526
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Position-Dependent Motif Characterization Using Nonnegative Matrix Factorization
1Center for Genome Dynamics, The Jackson Laboratory, Bar Harbor ME 04609 USA
2Bioinformatics Program, Boston University, Boston MA 02215 USA
*To whom correspondence should be addressed. Dr. Joel H. Graber, E-mail: joel.graber{at}jax.org
| Abstract |
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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 nonnegative 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 data sets. 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: joel.graber{at}jax.org
Associate Editor: Dr. Limsoon Wong
Received on May 16, 2008; revised on September 12, 2008; accepted on October 6, 2008