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Nucleic Acids Research Advance Access published online on May 25, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm238
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational Biology

SNAP: predict effect of non-synonymous polymorphisms on function

Yana Bromberg1,2,4,* and Burkhard Rost1,2,3

1Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY 10032, USA, 2Columbia University Center for Computational Biology and Bioinformatics (C2B2), 1130 St. Nicholas Ave. Rm. 802 New York, NY 10032, USA 3NorthEast Structural Genomics Consortium (NESG), 1130 St. Nicholas Ave. Rm. 802, New York, NY 10032, USA and 4Department of Biomedical Informatics, Columbia University, 630 West 168th Street, New York, NY 10032, USA.

*To whom correspondence should be addressed. Tel: +1 212 851 4669; Fax: +1 212 305 7932; Email: bromberg{at}rostlab.org, http://www.rostlab.org

Received December 27, 2006. Revised March 28, 2007. Accepted March 30, 2007.

Many genetic variations are single nucleotide polymorphisms (SNPs). Non-synonymous SNPs are ‘neutral’ if the resulting point-mutated protein is not functionally discernible from the wild type and ‘non-neutral’ otherwise. The ability to identify non-neutral substitutions could significantly aid targeting disease causing detrimental mutations, as well as SNPs that increase the fitness of particular phenotypes. Here, we introduced comprehensive data sets to assess the performance of methods that predict SNP effects. Along we introduced SNAP (screening for non-acceptable polymorphisms), a neural network-based method for the prediction of the functional effects of non-synonymous SNPs. SNAP needs only sequence information as input, but benefits from functional and structural annotations, if available. In a cross-validation test on over 80 000 mutants, SNAP identified 80% of the non-neutral substitutions at 77% accuracy and 76% of the neutral substitutions at 80% accuracy. This constituted an important improvement over other methods; the improvement rose to over ten percentage points for mutants for which existing methods disagreed. Possibly even more importantly SNAP introduced a well-calibrated measure for the reliability of each prediction. This measure will allow users to focus on the most accurate predictions and/or the most severe effects. Available at http: http://www.rostlab.org/services/SNAP


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