BENDL, Jaroslav, Miloš MUSIL, Jan ŠTOURAČ, Jaroslav ZENDULKA, Jiří DAMBORSKÝ and Jan BREZOVSKÝ. PredictSNP 2.0. 2015.
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Basic information
Original name PredictSNP 2.0
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 2015.
Other information
Original language English
Type of outcome Software
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
WWW URL User guide
RIV identification code RIV/00216224:14310/15:00080423
Organization unit Faculty of Science
Keywords (in Czech) nukleotidový polymorfismus; predikce škodlivosti mutací; SNP predikce; analýza mutací
Keywords in English SNP effect; deleteriousness prediction; SNP prediction; mutation analysis; Mendelian diseases
Technical parameters A unified web platform and consensus classifier for accurate evaluation of SNP effect by exploiting different characteristics of variants in distinct genomic regions.
Tags AKR, rivok
Changed by Changed by: Ing. Andrea Mikešková, učo 137293. Changed: 30/3/2016 15:06.
Abstract
This tool estimates the deleteriousness of single-nucleotide mutations in the context of the development of Mendelian diseases. The predictions are based on the results of existing tools: CADD, DANN, FATHMM, FunSeq2 and GWAVA. To achieve the highest possible accuracy, developed consensual functions based on category optimal decision thresholds differ according to the category of variants. These general categories are recognized: (i) regulatory, (ii) splicing, (iii) synonymous, (iv) missense and (v)nonsense variants. The evaluation on large datasets revealed a marked benefit of this approach while the web interface provides easily interpretable results for all individual tools and our consensual prediction together with the links to the relevant databases and on-line services.
Links
TA04021380, research and development projectName: Biosenzor pro monitorování toxických látek v životním prostředí
Investor: Technology Agency of the Czech Republic
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