Computational Biology: Toward Deciphering Gene Regulatory Information in Mammalian Genomes

Authors

  • Hongkai Ji,

    Corresponding author
    1. Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, Massachusetts 02138, U.S.A.
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  • Wing Hung Wong

    Corresponding author
    1. Departments of Statistics and Health Research and Policy, Stanford University, 390 Serra Mall, Stanford, California 94305, U.S.A.
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email:ji@fas.harvard.edu

email:whwong@stanford.edu

Abstract

Summary Computational biology is a rapidly evolving area where methodologies from computer science, mathematics, and statistics are applied to address fundamental problems in biology. The study of gene regulatory information is a central problem in current computational biology. This article reviews recent development of statistical methods related to this field. Starting from microarray gene selection, we examine methods for finding transcription factor binding motifs and cis-regulatory modules in coregulated genes, and methods for utilizing information from cross-species comparisons and ChIP-chip experiments. The ultimate understanding of cis-regulatory logic in mammalian genomes may require the integration of information collected from all these steps.

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