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Computational Biology: Toward Deciphering Gene Regulatory Information in Mammalian Genomes
Author(s) -
Ji Hongkai,
Wong Wing Hung
Publication year - 2006
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00625.x
Subject(s) - computational biology , biology , genome , gene , systems biology , cis regulatory module , gene regulatory network , selection (genetic algorithm) , regulation of gene expression , transcription factor , gene selection , computer science , data science , genetics , microarray analysis techniques , gene expression , machine learning , enhancer
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.