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GIGGLE: a search engine for large-scale integrated genome analysis.

Ryan M Layer1,2, Brent S Pedersen1,2, Tonya DiSera1,2

  • 1Department of Human Genetics, University of Utah, Salt Lake City, Utah, USA.

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|January 9, 2018
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Summary
This summary is machine-generated.

GIGGLE is a fast genomics search engine that finds shared genomic regions across many datasets. This tool significantly speeds up analysis for large-scale genomics resources, aiding data integration and discovery.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic interval data is rapidly accumulating from large-scale projects like ENCODE, Roadmap Epigenomics, and GTEx.
  • Identifying shared genomic loci across diverse datasets is crucial for biological insight but computationally challenging.
  • Existing methods for querying genomic intervals are often slow and do not scale effectively.

Purpose of the Study:

  • To develop a highly scalable and efficient genomics search engine.
  • To enable rapid identification and ranking of significant genomic loci shared between query features and large interval datasets.
  • To facilitate data integration and hypothesis generation using publicly available genomics resources.

Main Methods:

  • Developed GIGGLE, a genomics search engine optimized for speed and scalability.
  • Implemented algorithms to efficiently index and query billions of genomic intervals.
  • Benchmarked GIGGLE against existing methods to quantify performance improvements.

Main Results:

  • GIGGLE identifies and ranks the significance of shared genomic loci.
  • The engine scales to billions of intervals.
  • GIGGLE is over three orders of magnitude faster than existing methods.

Conclusions:

  • GIGGLE provides a significant speed improvement for genomic interval analysis.
  • The tool enhances the accessibility and utility of large genomics datasets.
  • GIGGLE facilitates cross-resource data integration and accelerates biological hypothesis generation.