IGD: high-performance search for large-scale genomic interval datasets

Jianglin Feng; Nathan C Sheffield

doi:10.1093/bioinformatics/btaa1062

IGD: high-performance search for large-scale genomic interval datasets

Bioinformatics. 2021 Apr 9;37(1):118-120. doi: 10.1093/bioinformatics/btaa1062.

Authors

Jianglin Feng¹, Nathan C Sheffield^{1

2

3

4}

Affiliations

¹ Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA.
² Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA.
³ Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA.
⁴ Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA.

PMID: 33367484
DOI: 10.1093/bioinformatics/btaa1062

Abstract

Summary: Databases of large-scale genome projects now contain thousands of genomic interval datasets. These data are a critical resource for understanding the function of DNA. However, our ability to examine and integrate interval data of this scale is limited. Here, we introduce the integrated genome database (IGD), a method and tool for searching genome interval datasets more than three orders of magnitude faster than existing approaches, while using only one hundredth of the memory. IGD uses a novel linear binning method that allows us to scale analysis to billions of genomic regions.

Availabilityand implementation: https://github.com/databio/IGD.

Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract

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