Publication Details

PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

BENDL Jaroslav, ŠTOURAČ Jan, ŠALANDA Ondřej, PAVELKA Antonín, WIEBEN Eric, ZENDULKA Jaroslav, BREZOVSKÝ Jan and DAMBORSKÝ Jiří. PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations. PLoS Computational Biology, vol. 10, no. 1, 2014, pp. 1-11. ISSN 1553-7358. Available from: http://www.ploscompbiol.org/article/fetchObject.action?uri=info%3Adoi%2F10.1371%2Fjournal.pcbi.1003440&representation=PDF
Czech title
PredictSNP: robustní a přesný klasifikátor pro predikci mutací asociovaných se vznikem onemocnění
Type
journal article
Language
english
Authors
Bendl Jaroslav, Ing. (DIFS FIT BUT)
Štourač Jan (LL)
Šalanda Ondřej, Ing. (FIT BUT)
Pavelka Antonín, RNDr. (LL)
Wieben Eric, Ph.D. (MAYO)
Zendulka Jaroslav, doc. Ing., CSc. (DIFS FIT BUT)
Brezovský Jan, Mgr., Ph.D. (LL)
Damborský Jiří, prof. Mgr., Dr. (LL)
URL
Keywords

SNP, single nucleotide polymorphism, SNV, single nucleotide variant, pathogenicity prediction, disease-related mutations

Abstract

Single nucleotide polymorphisms represent very prevalent form of genetic variation. Mutations in coding regions are frequently associated with the development of various diseases. Computational tools for prediction of effect of mutations are becoming very important for the initial analysis of single nucleotide polymorphisms and their consequent prioritization for experimental characterization due to recent massive increase in the number of known mutations. Many computational tools are already widely employed. Unfortunately, their comparison and further improvement is hindered by large overlaps between their training datasets and potential benchmark datasets, which lead to biased and overly optimistic performances. We constructed the independent benchmark dataset from five large datasets by removing all duplicities or inconsistencies, and subtracting all mutations present at any position used in the training of the evaluated tools or in any of the two external testing datasets. The final independent MetaSNP dataset containing of over 40,000 mutations was then employed in the unbiased evaluation of eight well-established prediction tools - i.e. MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. Consequently, the six best performing tools were combined into a consensus classifier MetaSNP. In the evaluation on two other independent external testing datasets, MetaSNP outperformed all integrated prediction tools. This comparison shows that MetaSNP represents a robust alternative to prediction by individual tool. Finally, we developed an easy-to-use web interface to allow an access to all eight prediction tools and consensus classifier MetaSNP. Predictions are supplemented by experimental annotations form Protein mutant and UniProt databases. The interface is available at: http://loschmidt.chemi.muni.cz/metasnp

Published
2014
Pages
1-11
Journal
PLoS Computational Biology, vol. 10, no. 1, ISSN 1553-7358
Publisher
Public Library of Science
DOI
UT WoS
000337948500040
EID Scopus
BibTeX
@ARTICLE{FITPUB10343,
   author = "Jaroslav Bendl and Jan \v{S}toura\v{c} and Ond\v{r}ej \v{S}alanda and Anton\'{i}n Pavelka and Eric Wieben and Jaroslav Zendulka and Jan Brezovsk\'{y} and Ji\v{r}\'{i} Damborsk\'{y}",
   title = "PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations",
   pages = "1--11",
   journal = "PLoS Computational Biology",
   volume = 10,
   number = 1,
   year = 2014,
   ISSN = "1553-7358",
   doi = "10.1371/journal.pcbi.1003440",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/10343"
}
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