Statistical inference of sequence-dependent mutation rates.
Several lines of research are now converging towards an integrated understanding of mutational mechanisms and their evolutionary implications. Experimentally, crystal structures reveal the effect of sequence context on polymerase fidelity; large-scale sequencing projects generate vast amounts of sequence polymorphism data; and locus-specific databases are being constructed. Computationally, software and analytical tools have been developed to analyze mutational data, to identify mutational hot spots, and to compare the signatures of mutagenic agents.
Zavolan
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Laboratory of Computational Genomics, The Rockefeller University, 1230 York Avenue, New York, New York 10021, USA. mihaela@genomes.rockefeller.edu
Kepler
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D019295:Computational Biology / Q000639:trends*; D016208:Databases, Factual; D014644:Genetic Variation; D015894:Genome, Human; D006801:Humans; D008969:Molecular Sequence Data; D009154:Mutation; D017422:Sequence Analysis, DNA / Q000592:standards*; D013223:Statistics as Topic