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FROG - Fingerprinting Genomic Variation Ontology.

Abinaya E, Narang P, Bhardwaj A - PLoS ONE (2015)

Bottom Line: Genetic variations play a crucial role in differential phenotypic outcomes.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, SASTRA University, Thanjavur, Tamil Nadu, India.

ABSTRACT
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.

No MeSH data available.


The attributes, ontology terms and their relationship within (A) RNA and (B) Protein level.
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pone.0134693.g003: The attributes, ontology terms and their relationship within (A) RNA and (B) Protein level.

Mentions: The RNA level includes nine attributes and 58 properties: variation kind (indel, transition, transversion inversion, translocation), Transition (pyrmidine to pyrimidine transition or purine to purine transition), Transversion (nucleotide changes), the secondary structure changes (loop changes—hairpin, interior loop, multiloop, complex structure changes—stem, bulge, pseudoknots), the levels affected (composition, frame (in or out), coding regions (introns or exons), splicing changes (cis- and trans- splicing), property changes (folding, stability, abundance etc.) and mechanism changes (silent and nonsense changes) as shown in Fig 3A.


FROG - Fingerprinting Genomic Variation Ontology.

Abinaya E, Narang P, Bhardwaj A - PLoS ONE (2015)

The attributes, ontology terms and their relationship within (A) RNA and (B) Protein level.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4526677&req=5

pone.0134693.g003: The attributes, ontology terms and their relationship within (A) RNA and (B) Protein level.
Mentions: The RNA level includes nine attributes and 58 properties: variation kind (indel, transition, transversion inversion, translocation), Transition (pyrmidine to pyrimidine transition or purine to purine transition), Transversion (nucleotide changes), the secondary structure changes (loop changes—hairpin, interior loop, multiloop, complex structure changes—stem, bulge, pseudoknots), the levels affected (composition, frame (in or out), coding regions (introns or exons), splicing changes (cis- and trans- splicing), property changes (folding, stability, abundance etc.) and mechanism changes (silent and nonsense changes) as shown in Fig 3A.

Bottom Line: Genetic variations play a crucial role in differential phenotypic outcomes.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, SASTRA University, Thanjavur, Tamil Nadu, India.

ABSTRACT
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.

No MeSH data available.