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GenoSurf: metadata driven semantic search system for integrated genomic datasets.

Arif Canakoglu1, Anna Bernasconi1, Andrea Colombo1

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GenoSurf unifies heterogeneous genomic metadata search, enabling easier access to valuable research data. This semantic search system enhances data discovery across multiple sources.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Data Science

Background:

  • Genomic datasets are valuable for research but suffer from heterogeneous, non-interoperable metadata search interfaces.
  • Existing search capabilities are often limited, hindering efficient secondary research and data discovery.

Purpose of the Study:

  • To develop GenoSurf, a multi-ontology semantic search system to consolidate and enhance access to genomic metadata.
  • To provide an interoperable and powerful search tool for diverse genomic datasets.

Main Methods:

  • Implemented a multi-ontology semantic search system integrating metadata from major genomic data sources.
  • Semantically enriched 10 key metadata attributes using relevant ontologies.
  • Developed interfaces for both attribute-based and keyword-based searches on consolidated and raw metadata.

Main Results:

  • GenoSurf integrates approximately 40 million metadata records from sources like TCGA, ENCODE, Roadmap Epigenomics, GENCODE, and RefSeq.
  • The system offers real-time search result updates and facilitates targeted data file identification.
  • Supports both standalone use and integration into complex query answering systems.

Conclusions:

  • GenoSurf significantly improves the discoverability and accessibility of genomic datasets.
  • The semantic enrichment and unified interface overcome limitations of heterogeneous metadata search systems.
  • Enables more efficient secondary research and complex data analysis in genomics.