R build status

Generation of null ranges via bootstrapping or covariate matching

Modular package for generation of sets of genomic features representing the null hypothesis. These can take the form of block bootstrap samples of ranges using the framework of Bickel et al 2010, or sets of control ranges that are matched across one or more covariates with a focal set. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package.

An overview vignette can be found at the Get started tab above, including a decision tree informing which type of methods may be most appropriate, whether matching or bootstrapping.

Detailed vignettes on matching or bootstrapping can be found under Articles. The Reference tab lists function help pages.


This package can be installed via Bioconductor:


Installing nullranges and all of its dependencies on Mac or Windows with binaries will be very fast (a minute or so).

For installing on Ubuntu, note that many nullranges packages are available as binaries, which greatly speeds up installation. Follow the instructions for r2u, then install the following via apt:

r-cran-tidyverse r-cran-ks r-cran-speedglm r-cran-data.table 
r-cran-progress r-cran-ggridges r-cran-biocmanager 
r-bioc-rtracklayer r-bioc-genomicalignments r-bioc-interactionset


matchRanges paper:

Eric S. Davis, Wancen Mu, Stuart Lee, Mikhail G. Dozmorov, Michael I. Love, Douglas H. Phanstiel. (2023) “matchRanges: Generating null hypothesis genomic ranges via covariate-matched sampling.” Bioinformatics doi: 10.1093/bioinformatics/btad197

bootRanges paper:

Wancen Mu, Eric S. Davis, Stuart Lee, Mikhail G. Dozmorov, Douglas H. Phanstiel, Michael I. Love. (2023) “bootRanges: Flexible generation of null sets of genomic ranges for hypothesis testing.” Bioinformatics doi: 10.1093/bioinformatics/btad190

Tidy Ranges Tutorial

Additional tutorial material for performing tidy ranges analysis is currently being developed.


This work was funded by the Chan Zuckerberg Initiative as part of the EOSS grants.