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Conceptual Thinking for In Silico Prioritization of Candidate Disease Genes

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In Silico Tools for Gene Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 760))

Abstract

Prioritization of most likely etiological genes entails predicting and defining a set of characteristics that are most likely to fit the underlying disease gene and scoring candidates according to their fit to this “perfect disease gene” profile. This requires a full understanding of the disease phenotype, characteristics, and any available data on the underlying genetics of the disease. Public databases provide enormous and ever-growing amounts of information that can be relevant to the prioritization of etiological genes. Computational approaches allow this information to be retrieved in an automated and exhaustive way and can therefore facilitate the comprehensive mining of this information, including its combination with sets of empirically generated data, in the process of identifying most likely candidate disease genes.

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Acknowledgments

This work was funded by the Medical Research Council of South Africa.

Author information

Authors and Affiliations

  1. The South African National Bioinformatics Institute, University of the Western Cape, 7925, Belville, Cape Town, South Africa

    Nicki Tiffin

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  1. Nicki Tiffin
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Correspondence to Nicki Tiffin .

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Editors and Affiliations

  1. Royal Prince Alfred Hospital, Dept. of Molecular & Clinical Genetics, University of Sydney, Missenden Road, Camperdown, 2050, New South Wales, Australia

    Bing Yu

  2. Royal Prince Alfred Hospital, Dept. Molecular & Clinical Genetics, University of Sydney, Missenden Road, Camperdown, 2050, New South Wales, Australia

    Marcus Hinchcliffe

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Tiffin, N. (2011). Conceptual Thinking for In Silico Prioritization of Candidate Disease Genes. In: Yu, B., Hinchcliffe, M. (eds) In Silico Tools for Gene Discovery. Methods in Molecular Biology, vol 760. Humana Press. https://doi.org/10.1007/978-1-61779-176-5_11

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  • DOI: https://doi.org/10.1007/978-1-61779-176-5_11

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-175-8

  • Online ISBN: 978-1-61779-176-5

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