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PLoS Comput Biol. 2014 Jan;10(1):e1003440. doi: 10.1371/journal.pcbi.1003440. Epub 2014 Jan 16.

PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.

Author information

1
Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic ; Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic ; Center of Biomolecular and Cellular Engineering, International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czech Republic.
2
Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic ; Center of Biomolecular and Cellular Engineering, International Centre for Clinical Research, St. Anne's University Hospital Brno, Brno, Czech Republic.
3
Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic.
4
Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic.
5
Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, New York, United States of America.

Abstract

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

PMID:
24453961
PMCID:
PMC3894168
DOI:
10.1371/journal.pcbi.1003440
[Indexed for MEDLINE]
Free PMC Article

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