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MutationTaster2: mutation prediction for the deep-sequencing age

Nature Methods volume 11, pages 361–362 (2014)Cite this article

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The majority of the gene variants discovered by next-generation sequencing (NGS) projects are either intronic or synonymous. These variants are difficult to interpret because their effects on protein expression and function tend to be less obvious than those of missense or nonsense variants. Here we present MutationTaster2 (http://www.mutationtaster.org/), the latest version of our web-based software MutationTaster1, which evaluates the pathogenic potential of DNA sequence alterations. It is designed to predict the functional consequences of not only amino acid substitutions but also intronic and synonymous alterations, short insertion and/or deletion (indel) mutations and variants spanning intron-exon borders.

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Acknowledgements

This work is supported by grants from the Deutsche Forschungsgemeinschaft (SFB665 TP-C4) to M.S., the Einsteinstiftung Berlin (A-2011-63) to J.M.S. and M.S. and the German Bundesministerium für Bildung und Forschung (mitoNET 01GM1113D) to D.S. and M.S. M.S. is a member of the NeuroCure Center of Excellence (Exc 257).

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Authors and Affiliations

  1. Department of Neuropediatrics, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Jana Marie Schwarz, Markus Schuelke & Dominik Seelow

  2. NeuroCure Clinical Research Center, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Jana Marie Schwarz, Markus Schuelke & Dominik Seelow

  3. Institute of Medical Genetics, Cardiff University, Cardiff, UK

    David N Cooper

Authors
  1. Jana Marie Schwarz
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  2. David N Cooper
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  3. Markus Schuelke
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  4. Dominik Seelow
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Corresponding author

Correspondence to Dominik Seelow.

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Competing interests

D.N.C. receives financial support from BIOBASE GmbH through a license agreement with Cardiff University.

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Supplementary Table 1–3 and Methods (PDF 213 kb)

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Schwarz, J., Cooper, D., Schuelke, M. et al. MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 11, 361–362 (2014). https://doi.org/10.1038/nmeth.2890

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