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A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants

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Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1415))

Abstract

Next-generation sequencing has provided extraordinary opportunities to investigate the massive human genetic variability. It helped identifying several kinds of genomic mismatches from the wild-type reference genome sequences and to explain the onset of several pathogenic phenotypes and diseases susceptibility. In this context, distinguishing pathogenic from functionally neutral amino acid changes turns out to be a task as useful as complex, expensive, and time-consuming.

Here, we present an exhaustive and up-to-dated survey of the algorithms and software packages conceived for the estimation of the putative pathogenicity of mutations, along with a description of the most popular mutation datasets that these tools used as training sets. Finally, we present and describe software for the prediction of cancer-related mutations.

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Authors and Affiliations

  1. IRCCS Casa Sollievo della Sofferenza, Viale Cappuccini 1, 71013, San Giovanni Rotondo (FG), Italy

    Stefano Castellana

  2. IRCCS Casa Sollievo della Sofferenza, Viale Regina Margherita 261, 00198, Rome, Italy

    Caterina Fusilli & Tommaso Mazza

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  1. Stefano Castellana
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  2. Caterina Fusilli
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  3. Tommaso Mazza
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Correspondence to Tommaso Mazza .

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Editors and Affiliations

  1. Max F. Perutz Laboratories GmbH, Universität Wien, Wien, Austria

    Oliviero Carugo

  2. Technology and Research (A*STAR), Agency for Science, Singapore, Singapore

    Frank Eisenhaber

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Castellana, S., Fusilli, C., Mazza, T. (2016). A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 1415. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3572-7_22

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  • DOI: https://doi.org/10.1007/978-1-4939-3572-7_22

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