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GenometriCorr specifications


Unique identifier OMICS_10180
Name GenometriCorr
Alternative name Genometric Correlation
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
Computer skills Advanced
Stability Stable
Maintained Yes


No version available


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Publication for Genometric Correlation

GenometriCorr citations


De Novo Assembly and Phasing of Dikaryotic Genomes from Two Isolates of Puccinia coronata f. sp. avenae, the Causal Agent of Oat Crown Rust

PMCID: 5821079
PMID: 29463655
DOI: 10.1128/mBio.01650-17

[…] y was assessed by the method of Saunders et al. (), with updates from density-Mapr ( to plot the 5′ and 3′ intergenic distance for each gene. The R package GenometriCorr () was used to test for associations between effectors and various categories of repeats within 10-kbp regions using default parameters. […]


Epigenetic regulation of gene expression in cancer: techniques, resources and analysis

Brief Funct Genomics
PMCID: 5860551
PMID: 28968850
DOI: 10.1093/bfgp/elx018

[…] ed analysis tailored to annotating regions of epigenetic regulation. More general genome-wide correlation techniques can perform additional integration to infer more complex regulatory relationships. GenometriCorr [], which computes the correlation between sets of genomic intervals, can be used to integrate different types of data—such as the location of gene promoters and transcription factor-bin […]


valr: Reproducible genome interval analysis in R

PMCID: 5506536
PMID: 28751969
DOI: 10.5256/f1000research.12975.r23916

[…] s. bedr is a CRAN-distributed package that provides wrapper R functions to call the BEDtools, BEDOPS, and tabix command-line utilities, providing out-of-memory support for interval analysis. Finally, GenometriCorr provides a set of statistical tests to determine the relationships between interval sets using IRanges data structures. These packages provide functionality for processing and statistica […]


Condensin II is anchored by TFIIIC and H3K4me3 in the mammalian genome and supports the expression of active dense gene clusters

Sci Adv
PMCID: 5479651
PMID: 28691095
DOI: 10.1126/sciadv.1700191
call_split See protocol

[…] h was used to identify the number and insulation score of the nonclustered CTS-associated TSS that are found in TAD boundaries.The correlation between CTS and other genomic domains was computed using GenometriCorr algorithm (), which is based on Jaccard index and statistical tests that aim to identify spatial correlations between the two sets of genome features.J(A,B)=|A∩B||A∪B|DNA sequence motifs […]


Potential energy landscapes identify the information theoretic nature of the epigenome

Nat Genet
PMCID: 5565269
PMID: 28346445
DOI: 10.1038/ng.3811

[…] r-1, livernormal-2, livercancer-2, livernormal-3, livercancer-3, lungnormal-1, lungcancer-1, lungnormal-2, lungcancer-2, lungnormal-3, lungcancer-3, brain-1, brain-2), and analyzed our results using “GenometriCorr”, a statistical package for evaluating the correlation of genome-wide data with given genomic features. Finally, we considered a boundary prediction to be “correct” when the distance of […]


Goldmine integrates information placing genomic ranges into meaningful biological contexts

Nucleic Acids Res
PMCID: 4937336
PMID: 27257071
DOI: 10.1093/nar/gkw477

[…] tatistical enrichment calculations globally between a query set of ranges and a reference set of ranges to determine if range overlaps occur more than expected by chance. These tools include LOLA (), GenometriCorr () and regioneR (). Goldmine can work together with these existing tools for global enrichment calculations by providing a companion annotation that details the exact overlap for each in […]


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GenometriCorr institution(s)
Department of Oncology, Division of Biostatistics and Bioinformatics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
GenometriCorr funding source(s)
The work was supported by Russian Foundation for Basic Research (grant 11-04-02016-a to AF, 09-04-92742); by the Presidium of Russian Academy of Science Program in Molecular and Cell Biology and in Basic Science for Medicine; by “Russian Ministry of Science and Education State Contract (07.514.11.4005)”; by National Center for Research Resources grant (UL2RR025005 and supplement, UL1RR025005); by the Johns Hopkins University Framework for the Future; by the Commonwealth Foundation.

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