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SEMANTIC-ENABLED HYBRID GENETIC DISEASE DIAGNOSTICS IN NEXT-GENERATION SEQUENCED DATA
Author(s) -
Emilia Zawadzka-Gosk,
Krzysztof Wołk
Publication year - 2018
Publication title -
computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.145
H-Index - 5
eISSN - 2300-7036
pISSN - 1508-2806
DOI - 10.7494/csci.2018.19.2.2319
Subject(s) - computer science , prioritization , scope (computer science) , data science , dna sequencing , computational biology , gene , genetics , biology , management science , economics , programming language
Next Generation Sequencing is a technology for genome sequencing used in genetics for diseased diagnosis. NGS provides the list of all mutations in a genome, so identifying the one which causes a disease is not trivial. A number of applications for variant prioritization was developed, but the data they provide is rather a suggestion than a diagnosis, moreover they suffer from issues as identifying nonpathogenic variant as a causal one or inability to identify the causal gene. These issues inspired us to create a strategy for variant prioritization which includes the use of Exomiser and OmimExplorer result sets improved by semantic analysis of abstracts and articles freely available from PubMed and PubMed Central databases. For the wider scope of scientific articles Google Scholar repository will be used. Described approach enables to present latest and most accurate information about potential pathogenic variants.

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