Sign in
GenomicsF1000Research
Peer-reviewed journal article

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

Selena C. Feng, Nathan C. SheffieldiD, Jianglin FengiD

Abstract

SummarySearching 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. Availabilityhttps://github.com/deepstanding/seqpare Contactjf.xtable@gmail.com

PDFDOICode

02 /Specification & Classification

Click any tag to browse matching papers
Citations
5
FWCI
0.32
Field percentile
49th percentile

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISRymuza '24Stovner '19Schäfer '25Teyssier '23Khan '17Mungall '14
57% semantic matchGenomics
Julia Rymuza, Yuchen Sun, Guangtao Zheng+5 more·Nucleic Acids Research·Jul 30, 2024· 6 citations