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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.


(A) Summary of the fingerprints designed to represent ontology terms in the six levels (B) As an example, the 15 attributes of the Variation level are listed along with their bit annotation (C) Example in Table 2 is explained with help of color coded bits.
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pone.0134693.g005: (A) Summary of the fingerprints designed to represent ontology terms in the six levels (B) As an example, the 15 attributes of the Variation level are listed along with their bit annotation (C) Example in Table 2 is explained with help of color coded bits.

Mentions: As discussed above, FROG fingerprints comprise of a set of 102 binary bits representing 278 properties of genomic variation. These fingerprints are broadly divided into six levels including Chromosome level wherein 13 bits are used for representing 38 properties at chromosome level. Likewise, in DNA level 36 properties are represented using 15 bits, in RNA level 58 properties with 23 bits, Protein level represents 78 properties in 22 bits, variation with 36 properties in 18 bits and Interaction level with 32 properties using 11 bits (Fig 5A).


FROG - Fingerprinting Genomic Variation Ontology.

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

(A) Summary of the fingerprints designed to represent ontology terms in the six levels (B) As an example, the 15 attributes of the Variation level are listed along with their bit annotation (C) Example in Table 2 is explained with help of color coded bits.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0134693.g005: (A) Summary of the fingerprints designed to represent ontology terms in the six levels (B) As an example, the 15 attributes of the Variation level are listed along with their bit annotation (C) Example in Table 2 is explained with help of color coded bits.
Mentions: As discussed above, FROG fingerprints comprise of a set of 102 binary bits representing 278 properties of genomic variation. These fingerprints are broadly divided into six levels including Chromosome level wherein 13 bits are used for representing 38 properties at chromosome level. Likewise, in DNA level 36 properties are represented using 15 bits, in RNA level 58 properties with 23 bits, Protein level represents 78 properties in 22 bits, variation with 36 properties in 18 bits and Interaction level with 32 properties using 11 bits (Fig 5A).

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.