Motivation: Genetic variations play 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, tag, search and analyze this data.

Results: FROG offers a novel approach to tag variation data, based on its location, function and interactions. It further assigns bit scores to each property associated with the variants and generates a fingerprint based on the combination of these properties. FROG may be used to tag 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.

FROG ontology has been devised to capture genomic variations at various levels. The assigned ontology can also be converted to binary fingerprints to make large number of genomic variations computationally efficient in terms of memory requirement and faster retrieval. Some of the major features of FROG are:

  • Organism independent.
  • Developed with simpler sub-levels vocabularies which can be combined to propose a complex variation effect.
  • Sub-levels are placed so that they can include modifications in future.
  • Integration of tools like SIFT AND POLYPHEN so that the outcome of structural changes can be associated to pathogenicity.
  • Scalable in terms of computationally storage and search.

Availability and implementation:

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