Computational biology: toward deciphering gene regulatory information in mammalian genomes.
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.
Ji
Hongkai
H
Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, Massachusetts 02138, USA. ji@fas.harvard.edu
Wong
Wing Hung
WH
D000818:Animals; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D005786:Gene Expression Regulation; D005809:Genes, Regulator; D015894:Genome, Human; D023281:Genomics / Q000706:statistics & numerical data*; D006801:Humans; D008322:Mammals / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D016415:Sequence Alignment / Q000706:statistics & numerical data; D013045:Species Specificity; D014157:Transcription Factors / Q000378:metabolism
D014157:Transcription Factors; D004247:DNA