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 join_nearest_ family of functions.
• Find overlaps between ranges with the join_overlaps_ family of functions.
• Add additional metadata between ranges and a table with the join_mcols_ 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:

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

To install the development version from GitHub:

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.