Apr 10, 2020

Seqpare: a self-consistent metric of similarity between genomic interval sets

BioRxiv : the Preprint Server for Biology
Jianglin Feng, Jianglin Feng

Abstract

SummarySearching genomic interval sets produced by sequencing methods has been widely and routinely performed; however, a rigorous metric for quantifying similarities among interval sets is still lacking. 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, which directly represents the percentage of their effective overlap: an index of zero indicates totally unrelated interval sets, and an index of one means that the interval sets are equivalent. Analysis and tests confirm the effectiveness and self-consistency of the Seqpare metric, and an application to 100 DNase-seq interval sets shows that Seqpare can clearly identify the relationships among different tissue/cell types. Availability: https://github.com/deepstanding/seqpare] Contact: jf.xtable@gmail.com

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