Abstract
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.
Original language | English (US) |
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Pages (from-to) | 645-663 |
Number of pages | 19 |
Journal | Biometrics |
Volume | 62 |
Issue number | 3 |
DOIs | |
State | Published - Sep 2006 |
Externally published | Yes |
Keywords
- ChIP-chip
- Cis-regulatory module
- Comparative genomics
- Gene expression
- Microarray
- Motif discovery
- Transcription factor
ASJC Scopus subject areas
- Statistics and Probability
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology
- General Agricultural and Biological Sciences
- Applied Mathematics