Publication Details
PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations
Štourač Jan
Šalanda Ondřej, Ing.
Pavelka Antonín
Wieben Eric
Zendulka Jaroslav, doc. Ing., CSc. (UIFS)
Brezovský Jan
Damborský Jiří, prof. Mgr., Dr. (UMEL)
SNP, single nucleotide polymorphism, SNV, single nucleotide variant,
pathogenicity prediction, disease-related mutations
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
@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"
}