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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) The screenshot of FROG Interface displaying ontology terms and associated fingerprints (B) The search tool interface to query the DNA variation using ontology (C) Sample output of the search tool.
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pone.0134693.g006: (A) The screenshot of FROG Interface displaying ontology terms and associated fingerprints (B) The search tool interface to query the DNA variation using ontology (C) Sample output of the search tool.

Mentions: The rapidly increasing amount of genomic variations data poses a challenge for storage and searching complex queries efficiently. An example dataset of 29,241 RNA and 1,13,255 protein variants from MitoLSDB [18] have been used as a test case to generate binary fingerprints using FROG. These fingerprints are stored in a database wrapped by Galaxy framework [19]. The framework allows visualization of FROG ontologies terms and their associated fingerprints (Fig 6A). Within the framework, FROG search tools are available that streamline querying variations data using combinations of variation properties (Fig 6B and 6C). As an example, if one retrieves all protein variations caused by thymine to cytosine transition in DNA. This query can be performed using DNA fingerprints search tool provided under Protein variations search category by selecting transition type as T->C. If no other filters are applied, query results in ~21000 protein variations. These results can further be filtered for their association with a phenotype. The hierarchy of FROG fingerprints into different levels can also be visualized through framework, available at http://ab-openlab.csir.res.in/frog. FROG framework also offers a tool to generate fingerprints of different levels for user-supplied data. Thus, the interface facilitates understanding of the variation and the fingerprinting method with help of examples.


FROG - Fingerprinting Genomic Variation Ontology.

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

(A) The screenshot of FROG Interface displaying ontology terms and associated fingerprints (B) The search tool interface to query the DNA variation using ontology (C) Sample output of the search tool.
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Related In: Results  -  Collection

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pone.0134693.g006: (A) The screenshot of FROG Interface displaying ontology terms and associated fingerprints (B) The search tool interface to query the DNA variation using ontology (C) Sample output of the search tool.
Mentions: The rapidly increasing amount of genomic variations data poses a challenge for storage and searching complex queries efficiently. An example dataset of 29,241 RNA and 1,13,255 protein variants from MitoLSDB [18] have been used as a test case to generate binary fingerprints using FROG. These fingerprints are stored in a database wrapped by Galaxy framework [19]. The framework allows visualization of FROG ontologies terms and their associated fingerprints (Fig 6A). Within the framework, FROG search tools are available that streamline querying variations data using combinations of variation properties (Fig 6B and 6C). As an example, if one retrieves all protein variations caused by thymine to cytosine transition in DNA. This query can be performed using DNA fingerprints search tool provided under Protein variations search category by selecting transition type as T->C. If no other filters are applied, query results in ~21000 protein variations. These results can further be filtered for their association with a phenotype. The hierarchy of FROG fingerprints into different levels can also be visualized through framework, available at http://ab-openlab.csir.res.in/frog. FROG framework also offers a tool to generate fingerprints of different levels for user-supplied data. Thus, the interface facilitates understanding of the variation and the fingerprinting method with help of examples.

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.