plyranges: fluent genomic data analysis

plyranges provides a consistent interface for importing and wrangling genomics data from a variety of sources. The package defines a grammar of genomic data transformation based on dplyr and the Bioconductor packages IRanges, GenomicRanges, and rtracklayer. It does this by providing a set of verbs for developing analysis pipelines based on GRanges objects that represent genomic regions:

• Modify genomic regions with the mutate() and stretch() functions.
• Modify genomic regions while fixing the start/end/center coordinates

with the anchor_ family of functions.

• Sort genomic ranges with arrange().
• Modify, subset, and aggregate genomic data with the mutate(),

filter(), and summarise()functions.

• Any of the above operations can be performed on partitions of the data

with group_by().

• Find nearest neighbour genomic regions with the joinnearest family

of functions.

• Find overlaps between ranges with the joinoverlaps family of

functions.

• Add additional metadata between ranges and a table with the joinmcols

family of functions.

• Merge all overlapping and adjacent genomic regions with

reduce_ranges().

• Merge the end points of all genomic regions with disjoin_ranges().
• Import and write common genomic data formats with the read/write

family of functions.

Documentation

For more details on the features of plyranges, read the introductory vignette and the examples vignette.

For a complete case-study on using plyranges to combine ATAC-seq and RNA-seq results read the fluentGenomics workflow.

plyranges is part of the tidyomics project, providing a dplyr-based interface for many types of genomics datasets represented in Bioconductor.

Installation

plyranges can be installed from the latest Bioconductor release:

r
install.packages("BiocManager")
BiocManager::install("plyranges")

To install the development version from GitHub:

r
BiocManager::install("tidyomics/plyranges")

Learning more

In addition to the two package vignettes, see the following for more informtion:

• The fluentGenomics workflow

package shows how to combine differential gene expression and differential chromatin accessibility using plyranges.

• The extended vignette in the plyrangesWorkshops

package has a detailed walk through of using plyranges for coverage analysis.

• The collection of genomic range applications including plyranges:

tidy ranges tutorial.

Citation

If you found plyranges useful for your work please cite our paper:

@ARTICLE{Lee2019, title = "plyranges: a grammar of genomic data transformation", author = "Lee, Stuart and Cook, Dianne and Lawrence, Michael", journal = "Genome Biol.", volume = 20, number = 1, pages = "4", month = jan, year = 2019, url = "http://dx.doi.org/10.1186/s13059-018-1597-8", doi = "10.1186/s13059-018-1597-8", pmc = "PMC6320618" }

Contributing

We welcome contributions from the R/Bioconductor community. We ask that contributors follow the code of conduct and the guide outlined here.