FROG - Fingerprinting Genomic Variation Ontology

Genetic variations play a crucial role in differential phenotypic outcomes. Given the complexity in establishing this correlation and the enormous data available today, it is imperative to design machine-readable, efficient methods to store, label, search and analyze this data. A semantic approach,...

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Bibliographic Details
Published in:PloS one 2015-08, Vol.10 (8), p.e0134693-e0134693
Main Authors: Abinaya, E, Narang, Pankaj, Bhardwaj, Anshu
Format: Article
Language:English
Subjects:
Amphibians
Analysis
Animals
Annotations
Bioinformatics
Chromosomes - genetics
Consortia
Controlled vocabularies
Data processing
Deoxyribonucleic acid
Disease
DNA
DNA - genetics
DNA fingerprinting
DNA Fingerprinting - methods
Fingerprinting
Fingerprints
Frogs
Gene Ontology
Genes
Genetic diversity
Genetic Variation
Genome
Genomes
Genomics - methods
Humans
Insertion
Mitochondrial DNA
Mutation
Ontology
Phenotypes
Properties (attributes)
Proteins
Proteins - genetics
Ribonucleic acid
RNA
RNA - genetics
Semantic web
Semantics
Single nucleotide polymorphisms
Software
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Summary:Genetic variations play a crucial role in differential phenotypic outcomes. Given the complexity in establishing this correlation and the enormous data available today, it is imperative to design machine-readable, efficient methods to store, label, search and analyze this data. A semantic approach, FROG: "FingeRprinting Ontology of Genomic variations" is implemented to label variation data, based on its location, function and interactions. FROG has six levels to describe the variation annotation, namely, chromosome, DNA, RNA, protein, variations and interactions. Each level is a conceptual aggregation of logically connected attributes each of which comprises of various properties for the variant. For example, in chromosome level, one of the attributes is location of variation and which has two properties, allosomes or autosomes. Another attribute is variation kind which has four properties, namely, indel, deletion, insertion, substitution. Likewise, there are 48 attributes and 278 properties to capture the variation annotation across six levels. Each property is then assigned a bit score which in turn leads to generation of a binary fingerprint based on the combination of these properties (mostly taken from existing variation ontologies). FROG is a novel and unique method designed for the purpose of labeling the entire variation data generated till date for efficient storage, search and analysis. A web-based platform is designed as a test case for users to navigate sample datasets and generate fingerprints. The platform is available at http://ab-openlab.csir.res.in/frog.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0134693