Regulatory motif discovery: from decoding to meta-analysis

Zhou, Q. and Gupta, M. (2009) Regulatory motif discovery: from decoding to meta-analysis. In: Fan, J., Lin, X. and Liu, J.S. (eds.) New Developments in Biostatistics and Bioinformatics. Series: Frontiers of Statistics (1). World Scientific, pp. 179-208. ISBN 9789812837431 (doi: 10.1142/9789812837448_0008)

Abstract

Gene transcription is regulated by interactions between transcription factors and their target binding sites in the genome. A motif is the sequence pattern recognized by a transcription factor to mediate such interactions. With the availability of high-throughput genomic data, computational identification of transcription factor binding motifs has become a major research problem in computational biology and bioinformatics. In this chapter, we present a series of Bayesian approaches to motif discovery. We start from a basic statistical framework for motif finding, extend it to the identification of cis-regulatory modules, and then discuss methods that combine motif finding with phylogenetic footprinting, gene expression or ChIP-chip data, and nucleosome positioning information. Simulation studies and applications to biological data sets are presented to illustrate the utility of these methods.

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Gupta, Professor Mayetri
• Authors: Zhou, Q., and Gupta, M.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Publisher: World Scientific
• ISBN: 9789812837431