Genomic "Bump Finding" from ChIP-chip Data and DNA Sequence"

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Bioinformatics and Computational Genomics seminar

Hao Wu, PhD
Assistant Professor
Department of Biostatistics and Bioinformatics
Center for Comprehensive Informatics
Emory University

Exploring genomic landscapes of biological endpoints is an important approach for understanding biological processes and disease etiologies. Such endpoints can be found in studies of genomic sequence composition, DNA methylation, histone modifications, and binding sites for transcription factors. With completion of the human genome project and new advances in high-throughput technologies, tightly spaced measurements have been collected from linear chromosomes to create unbiased maps at the whole-genome scale. Detecting regions of interests from these data can be categorized as a general "bump finding" problem, where a bump is defined as a genomic location for which data behaves differently from the majority of the genome. In the talk I will present two examples of genomic bump finding. In the first example we construct a hierarchical model to detect transcription factor binding sites by jointly analyzing multiple ChIP-chip datasets. This model captures locational correlations among datasets, which provides basis for sharing information across experiments. Tests on simulated and real data sets illustrate the advantage of the joint model over strategies that analyze individual datasets separately. In the second example we introduce Hidden Markov Model based algorithm for finding CpG islands (CGI) in genomic sequences. The main advantage of this approach is its ability to summarize the evidence for a CGI presence as a probability score. This method provides flexibility in CGI definition and facilitates generation of CGI maps for many species.


  • Workflow Status: Published
  • Created By: Colly Mitchell
  • Created: 12/02/2010
  • Modified By: Fletcher Moore
  • Modified: 10/07/2016

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