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Comparative Genomics

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Handbook of Statistical Bioinformatics

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Abstract

Comparative genomics was previously misguided by the naïve dogma that what is true in E. coli is also true in the elephant. With the rejection of such a dogma, comparative genomics has been positioned in proper evolutionary context. Here I numerically illustrate the application of phylogeny-based comparative methods in comparative genomics involving both continuous and discrete characters to solve problems from characterizing functional association of genes to detection of horizontal gene transfer and viral genome recombination, together with a detailed explanation and numerical illustration of statistical significance tests based on the false discovery rate (FDR). FDR methods are essential for multiple comparisons associated with almost any large-scale comparative genomic studies. I discuss the strength and weakness of the methods and provide some guidelines on their proper applications.

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Acknowledgements

I thank J. Felsenstein and M. Pagel for identifying ambiguities and errors in the manuscript and for their many suggestions to improve the manuscript. S. Aris-Brosou, Y. B. Fu and G. Palidwor, as well as two anonymous reviewers, provided comments and references. I am supported by the Strategic Research, Discovery and Research Tools and Instrument Grants of Natural Science and Engineering Research Council of Canada.

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  1. Department of Biology, University of Ottawa, Ottawa, Canada

    Xuhua Xia

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  1. , Institute of Statistics, National Chiao Tung University, Ta Hsueh Road 1001, Hsinchu, 30050, Taiwan, Taiwan R.O.C.

    Henry Horng-Shing Lu

  2. , Department of Empirical Inference, MPI for Intelligent Systems, Spemannstraße 38, Tübingen, 72076, Germany

    Bernhard Schölkopf

  3. School of Medicine, Dept. Epidemiology & Public Health, Yale University, College Street 60, New Haven, 06520, Connecticut, USA

    Hongyu Zhao

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Xia, X. (2011). Comparative Genomics. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_26

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