PredictSNP2: A Unified Platform for Accurately Evaluating SNP
Effects by Exploiting the Different Characteristics of Variants
in Distinct Genomic Regions

J 2016

PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions

BENDL, Jaroslav, Miloš MUSIL, Jan ŠTOURAČ, Jaroslav ZENDULKA, Jiří DAMBORSKÝ et. al.

Základní údaje

Originální název

PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions

Autoři

BENDL, Jaroslav (203 Česká republika, domácí), Miloš MUSIL (203 Česká republika, domácí), Jan ŠTOURAČ (203 Česká republika, domácí), Jaroslav ZENDULKA (203 Česká republika), Jiří DAMBORSKÝ (203 Česká republika, garant, domácí) a Jan BREZOVSKÝ (203 Česká republika, domácí)

Vydání

PLOS COMPUTATIONAL BIOLOGY 12: e100496, 2016, 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: 4.542

Kód RIV

RIV/00216224:14310/16:00092839

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1371/journal.pcbi.1004962

UT WoS

000379348100043

Klíčová slova anglicky

GENETIC-VARIATION; REGULATORY VARIANTS; SEQUENCE VARIATION; PROTEIN FUNCTION; CAUSAL VARIANTS; COMPLEX TRAITS; MUTATIONS; DISEASE; CANCER; ELEMENTS

Štítky

AKR, rivok

Změněno: 9. 4. 2017 15:35, Ing. Andrea Mikešková

Anotace

V originále

An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors.The web server is freely available to the community at http://loschmidt.chemi.muni.cz/predictsnp2.

Návaznosti

LO1214, projekt VaV

Název: Centrum pro výzkum toxických látek v prostředí (Akronym: RECETOX)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Centrum pro výzkum toxických látek v prostředí

676559, interní kód MU

Název: ELIXIR-EXCELERATE: Fast-track ELIXIR implementation and drive early user exploitation across the life-sciences (Akronym: ELIXIR-EXCELERATE)

Investor: Evropská unie, ELIXIR-EXCELERATE: Fast-track ELIXIR implementation and drive early user exploitation across the life-sciences, RI Research Infrastructures (Excellent Science)

Citovat

BENDL, Jaroslav, Miloš MUSIL, Jan ŠTOURAČ, Jaroslav ZENDULKA, Jiří DAMBORSKÝ a Jan BREZOVSKÝ. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions. PLOS COMPUTATIONAL BIOLOGY 12: e100496. 2016, roč. 12, č. 5, s. "nestrankovano", 18 s. ISSN 1553-734X. Dostupné z: https://dx.doi.org/10.1371/journal.pcbi.1004962.

@article{1367553,
   author = {Bendl, Jaroslav and Musil, Miloš and Štourač, Jan and Zendulka, Jaroslav and Damborský, Jiří and Brezovský, Jan},
   article_number = {5},
   doi = {http://dx.doi.org/10.1371/journal.pcbi.1004962},
   keywords = {GENETIC-VARIATION; REGULATORY VARIANTS; SEQUENCE VARIATION; PROTEIN FUNCTION; CAUSAL VARIANTS; COMPLEX TRAITS; MUTATIONS; DISEASE; CANCER; ELEMENTS},
   language = {eng},
   issn = {1553-734X},
   journal = {PLOS COMPUTATIONAL BIOLOGY 12: e100496},
   title = {PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions},
   url = {https://loschmidt.chemi.muni.cz/peg/wp-content/uploads/2016/05/Bendl_2016pcbi.pdf},
   volume = {12},
   year = {2016}
}

TY  - JOUR
ID  - 1367553
AU  - Bendl, Jaroslav - Musil, Miloš - Štourač, Jan - Zendulka, Jaroslav - Damborský, Jiří - Brezovský, Jan
PY  - 2016
TI  - PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions
JF  - PLOS COMPUTATIONAL BIOLOGY 12: e100496
VL  - 12
IS  - 5
SP  - "nestrankovano"
EP  - "nestrankovano"
SN  - 1553734X
KW  - GENETIC-VARIATION
KW  - REGULATORY VARIANTS
KW  - SEQUENCE VARIATION
KW  - PROTEIN FUNCTION
KW  - CAUSAL VARIANTS
KW  - COMPLEX TRAITS
KW  - MUTATIONS
KW  - DISEASE
KW  - CANCER
KW  - ELEMENTS
UR  - https://loschmidt.chemi.muni.cz/peg/wp-content/uploads/2016/05/Bendl_2016pcbi.pdf
L2  - https://loschmidt.chemi.muni.cz/peg/wp-content/uploads/2016/05/Bendl_2016pcbi.pdf
N2  - An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors.The web server is freely available to the community at http://loschmidt.chemi.muni.cz/predictsnp2.
ER  -

BENDL, Jaroslav, Miloš MUSIL, Jan ŠTOURAČ, Jaroslav ZENDULKA, Jiří DAMBORSKÝ a Jan BREZOVSKÝ. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions. \textit{PLOS COMPUTATIONAL BIOLOGY 12: e100496}. 2016, roč.~12, č.~5, s.~''nestrankovano'', 18 s. ISSN~1553-734X. Dostupné z: https://dx.doi.org/10.1371/journal.pcbi.1004962.

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