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.

Basic information

Original name

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

Authors

BENDL, Jaroslav (203 Czech Republic, belonging to the institution), Miloš MUSIL (203 Czech Republic, belonging to the institution), Jan ŠTOURAČ (203 Czech Republic, belonging to the institution), Jaroslav ZENDULKA (203 Czech Republic), Jiří DAMBORSKÝ (203 Czech Republic, guarantor, belonging to the institution) and Jan BREZOVSKÝ (203 Czech Republic, belonging to the institution)

Edition

PLOS COMPUTATIONAL BIOLOGY 12: e100496, 2016, 1553-734X

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10600 1.6 Biological sciences

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 4.542

RIV identification code

RIV/00216224:14310/16:00092839

Organization unit

Faculty of Science

DOI

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

UT WoS

000379348100043

Keywords in English

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

Abstract

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.

Links

LO1214, research and development project

Name: Centrum pro výzkum toxických látek v prostředí (Acronym: RECETOX)

Investor: Ministry of Education, Youth and Sports of the CR

676559, interní kód MU

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

Investor: European Union, RI Research Infrastructures (Excellent Science)

Citovat

BENDL, Jaroslav, Miloš MUSIL, Jan ŠTOURAČ, Jaroslav ZENDULKA, Jiří DAMBORSKÝ and 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, vol. 12, No 5, p. "nestrankovano", 18 pp. ISSN 1553-734X. Available from: 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 and 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, vol.~12, No~5, p.~''nestrankovano'', 18 pp. ISSN~1553-734X. Available from: https://dx.doi.org/10.1371/journal.pcbi.1004962.

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