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

Author:

Feng Jianglin1,Sheffield Nathan C1234ORCID

Affiliation:

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

Abstract

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.

Funder

University of Virginia School of Medicine

University of Virginia 4-VA program

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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