Bioinformatics: Mining ChIP-chip data for transcription factor and

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Bioinformatics. 2005 Jun 1;21 Suppl 1:i403-i412.
Mining ChIP-chip data for transcription factor and cofactor binding sites.
Smith AD, Sumazin P, Das D, Zhang MQ.

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.