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Seqpare: a self-consistent metric of similarity between genomic interval sets [version 1; peer review: 1 approved, 1 approved with reservations]

Authors :
Selena C. Feng
Nathan C. Sheffield
Jianglin Feng
Author Affiliations :
<relatesTo>1</relatesTo>Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA<br /><relatesTo>2</relatesTo>Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA<br /><relatesTo>3</relatesTo>Deepstanding LLC, Crozet, VA, 22932, USA
Source :
F1000Research. 9:581
Publication Year :
2020
Publisher :
London, UK: F1000 Research Limited, 2020.

Abstract

Searching genomic interval sets produced by sequencing methods has been widely and routinely performed; however, existing metrics for quantifying similarities among interval sets are inconsistent. Here we introduce Seqpare, a self-consistent and effective metric of similarity and tool for comparing sequences based on their interval sets. With this metric, the similarity of two interval sets is quantified by a single index, the ratio of their effective overlap over the union: an index of zero indicates unrelated interval sets, and an index of one means that the interval sets are identical. Analysis and tests confirm the effectiveness and self-consistency of the Seqpare metric.

Details

ISSN :
20461402
Volume :
9
Database :
F1000Research
Journal :
F1000Research
Notes :
[version 1; peer review: 1 approved, 1 approved with reservations]
Publication Type :
Academic Journal
Accession number :
edsfor.10.12688.f1000research.23390.1
Document Type :
software-tool
Full Text :
https://doi.org/10.12688/f1000research.23390.1