Analysis of Cis-Regulatory Motifs in Cassette Exons by Incorporating Exon Skipping Rates
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
Cis regulatory module discovery in immune cell development
ISB '10 Proceedings of the International Symposium on Biocomputing
An interaction-dependent model for transcription factor binding
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Challenges rising from learning motif evaluation functions using genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Discriminative Motif Finding for Predicting Protein Subcellular Localization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Efficient algorithm for mining correlated Protein-DNA binding cores
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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Motivation: Identification of single motifs and motif pairs that can be used to predict transcription factor localization in ChIP-chip data, and gene expression in tissue-specific microarray data. Results: We describe methodology to identify de novo individual and interacting pairs of binding site motifs from ChIP-chip data, using an algorithm that integrates localization data directly into the motif discovery process. We combine matrix-enumeration based motif discovery with multivariate regression to evaluate candidate motifs and identify motif interactions. When applied to the HNF localization data in liver and pancreatic islets, our methods produce motifs that are either novel or improved known motifs. All motif pairs identified to predict localization are further evaluated according to how well they predict expression in liver and islets and according to how conserved are the relative positions of their occurrences. We find that interaction models of HNF1 and CDP motifs provide excellent prediction of both HNF1 localization and gene expression in liver. Our results demonstrate that ChIP-chip data can be used to identify interacting binding site motifs. Availability: Motif discovery programs and analysis tools are available on request from the authors. Contact: asmith@cshl.edu