
doi: 10.1093/bib/4.2.168
pmid: 12846397
Genome studies have become central to a wide range of biological areas. This paper is intended to discuss the state of art and potentiality of using statistics in genome comparison. The problem of dependence and dimensionality in genome data is addressed first. The focus is on using phylogenetic methods in genome comparison, on combining sequence and gene expression data to find transcription sites, and on multiple hypothesis testing in gene expression data. The paper concludes with speculations on the future course of statistical and bioinformatics analyses.
Base Composition, Likelihood Functions, Genome, Models, Genetic, Computational Biology, Genomics, Chromatin, Evolution, Molecular, Gene Expression Regulation, Animals, Humans, Transcription Initiation Site, Phylogeny
Base Composition, Likelihood Functions, Genome, Models, Genetic, Computational Biology, Genomics, Chromatin, Evolution, Molecular, Gene Expression Regulation, Animals, Humans, Transcription Initiation Site, Phylogeny
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