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Detail publikace
BENDL, J. ŠTOURAČ, J. ŠALANDA, O. PAVELKA, A. WIEBEN, E. ZENDULKA, J. BREZOVSKÝ, J. DAMBORSKÝ, J.
Originální název
PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
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
Klíčová slova
SNP, single nucleotide polymorphism, SNV, single nucleotide variant, pathogenicity prediction, disease-related mutations
Autoři
BENDL, J.; ŠTOURAČ, J.; ŠALANDA, O.; PAVELKA, A.; WIEBEN, E.; ZENDULKA, J.; BREZOVSKÝ, J.; DAMBORSKÝ, J.
Rok RIV
2014
Vydáno
16. 1. 2014
ISSN
1553-7358
Periodikum
PLoS Computational Biology
Ročník
10
Číslo
1
Stát
Spojené státy americké
Strany od
Strany do
11
Strany počet
URL
http://www.ploscompbiol.org/article/fetchObject.action?uri=info%3Adoi%2F10.1371%2Fjournal.pcbi.1003440&representation=PDF
BibTex
@article{BUT133482, author="Jaroslav {Bendl} and Jan {Štourač} and Ondřej {Šalanda} and Antonín {Pavelka} and Eric {Wieben} and Jaroslav {Zendulka} and Jan {Brezovský} and Jiří {Damborský}", title="PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations", journal="PLoS Computational Biology", year="2014", volume="10", number="1", pages="1--11", doi="10.1371/journal.pcbi.1003440", issn="1553-7358", url="http://www.ploscompbiol.org/article/fetchObject.action?uri=info%3Adoi%2F10.1371%2Fjournal.pcbi.1003440&representation=PDF" }
Dokumenty
predictsnp.pdf