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

J 2014

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

BENDL, Jaroslav; Jan ŠTOURAČ; O. SALANDA; Antonín PAVELKA; E.D. WIEBEN et al.

Základní údaje

Originální název

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

Autoři

BENDL, Jaroslav; Jan ŠTOURAČ; O. SALANDA; Antonín PAVELKA; E.D. WIEBEN; J. ZENDULKA; Jan BREZOVSKÝ a Jiří DAMBORSKÝ

Vydání

PLOS Computational Biology, 2014, 1553-734X

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10600 1.6 Biological sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 4.620

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/14:00077795

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

Meta-SNP; PredictSNP

Štítky

AKR, rivok

Změněno: 21. 3. 2017 08:06, prof. Mgr. Jiří Damborský, Dr.

Anotace

V originále

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.

Návaznosti

EE2.3.30.0037, projekt VaV

Název: Zaměstnáním nejlepších mladých vědců k rozvoji mezinárodní spolupráce

Citovat

BENDL, Jaroslav; Jan ŠTOURAČ; O. SALANDA; Antonín PAVELKA; E.D. WIEBEN; J. ZENDULKA; Jan BREZOVSKÝ a Jiří DAMBORSKÝ. PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations. PLOS Computational Biology. 2014, roč. 10, č. 1, s. "nestrankovano", 11 s. ISSN 1553-734X. Dostupné z: https://doi.org/10.1371/journal.pcbi.1003440.

@article{1213470,
   author = {Bendl, Jaroslav and Štourač, Jan and Salanda, O. and Pavelka, Antonín and Wieben, E.D. and Zendulka, J. and Brezovský, Jan and Damborský, Jiří},
   article_number = {1},
   doi = {https://doi.org/10.1371/journal.pcbi.1003440},
   keywords = {Meta-SNP; PredictSNP},
   language = {eng},
   issn = {1553-734X},
   journal = {PLOS Computational Biology},
   title = {PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations.},
   url = {http://loschmidt.chemi.muni.cz/peg/wp-content/uploads/2014/01/ploscb2014.pdf},
   volume = {10},
   year = {2014}
}

TY  - JOUR
ID  - 1213470
AU  - Bendl, Jaroslav - Štourač, Jan - Salanda, O. - Pavelka, Antonín - Wieben, E.D. - Zendulka, J. - Brezovský, Jan - Damborský, Jiří
PY  - 2014
TI  - PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations.
JF  - PLOS Computational Biology
VL  - 10
IS  - 1
SP  - "nestrankovano"
EP  - "nestrankovano"
SN  - 1553734X
KW  - Meta-SNP
KW  - PredictSNP
UR  - http://loschmidt.chemi.muni.cz/peg/wp-content/uploads/2014/01/ploscb2014.pdf
N2  - 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.
ER  -

BENDL, Jaroslav; Jan ŠTOURAČ; O. SALANDA; Antonín PAVELKA; E.D. WIEBEN; J. ZENDULKA; Jan BREZOVSKÝ a Jiří DAMBORSKÝ. PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations. \textit{PLOS Computational Biology}. 2014, roč.~10, č.~1, s.~''nestrankovano'', 11 s. ISSN~1553-734X. Dostupné z: https://doi.org/10.1371/journal.pcbi.1003440.

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