Biostatistics Advance Access published online on May 28, 2007
Biostatistics, doi:10.1093/biostatistics/kxm014
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Integrating quantitative information from ChIP-chip experiments into motif finding
Department of Statistics, 1300 University Avenue, University of Wisconsin, Madison, WI 53705, USA
*
Department of Statistics and Department of Biostatistics and Medical Informatics, 1300 University Avenue, University of Wisconsin, Madison, WI 53705, USA keles{at}stat.wisc.edu
* To whom correspondence should be addressed.
Identifying binding locations of transcription factors (TFs) within long segments of noncoding DNA is a challenging task. Recent chromatin immunoprecipitation on microarray (ChIP-chip) experiments utilizing tiling arrays are especially promising for this task since they provide high-resolution genome-wide maps of the interactions between the TFs and the DNA. Data from these experiments are invaluable for characterizing DNA recognition profiles (regulatory motifs) of TFs. A 2-step paradigm is commonly used for performing motif searches based on ChIP-chip data. First, candidate bound sequences that are in the order of 500–1000 bp are identified from ChIP-chip data. Then, motif searches are performed among these sequences. These 2 steps are typically carried out in a disconnected fashion in the sense that the quantitative nature of the ChIP-chip information is ignored in the second step. More specifically, all bound regions are assumed to equally likely have the motif(s), and the motifs are assumed to reside at any position of the bound regions with equal probability. We develop a conditional two-component mixture (CTCM) model that relaxes both these common assumptions by adaptively incorporating ChIP-chip information. The performances of the new and existing methods are compared using simulated data and ChIP-chip data from recently available ENCODE studies (Consortium, 2004). These studies indicate that CTCM efficiently utilizes the information available in the ChIP-chip experiments and has superior sensitivity and specificity especially when the motif of interest has low abundance among the ChIP-chip bound regions and/or low information content.
Keywords: ChIP-chip data; Conditional mixture models; Piecewise constant linear regression; Prior models; Regulatory motif finding; Tiling microarrays
Received December 11, 2006; accepted for publication March 8, 2007.