VplotR

Jacques Serizay

2020-02-09

VplotR

Introduction

This R package makes the process of generating fragment density plots (also known as “V-plots”) straightforward.
V-plots have been introduced for the first time by the Henikoff lab in 2011. Recently, V-plots have proven to be very instructive to understand the molecular organization of the chromatin. For instance, the nucleoATAC package relies on cross-correlation of ATAC-seq fragment density plots to accurately map nucleosome occupancy along the genome.
VplotR aim is to streamline the process of generating V-plots. It contains wrapping functions to import paired-end sequencing bam files and generate V-plots around genomic loci of interest.
VplotR is designed around ggplot2 and makes full use of its potential. As such, it is easy to generate V-plots in batches and combine them with other plots to make publication-ready figures.

Installation

VplotR can be installed from Github as follows:

install.packages("devtools")
devtools::install_github("js2264/VplotR")
library(VplotR)

Quick V-plot

Firstly BAM files are read using the importPEBamFiles() function and loci of interest from a BED file, for instance.

granges <- rtracklayer::import('loci_of_interest.bed')
fragments <- importPEBamFiles(
    'ATAC-seq-mapped-fragments.bam', 
    where = GenomicRanges::resize(granges, width = 2000, fix = 'center'), 
    shift_ATAC_fragments = TRUE
)

Note: to allow for a background normalization, the where argument should be omitted.

Then V-plot of ATAC-seq fragments (fragments) over the loci of interest (granges) is generated using the plotVmat() function:

plotVmat(fragments, granges)

The generation of multiple V-plots can be parallelized as follow:

list_params <- list(
    "sample_1" = list("bam" = fragments_1, "granges" = granges_1), 
    "sample_2" = list("bam" = bam_2, "granges" = granges_2), 
    ..., 
    "sample_N" = list("bam" = bam_N, "granges" = granges_N)
)
plotVmat(
    list_params, 
    cores = length(list_params)
) + ggplot2::facet_wrap(~Cond.)

Finally, the nucleosomeEnrichment() function is useful to statistically quantify and compare nucleosome enrichment (e.g. flanking nucleosome at promoters). To do so:

nuc_enrich <- nucleosomeEnrichment(
    bam_granges = fragments, 
    granges = granges
)

For more details and additional functions, read the Introduction vignette.

Building package

This package was built by executing the following commands in R:

devtools::document()
devtools::build()
pkgdown::build_site()

Session Info

#> R version 3.5.2 (2018-12-20)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.2 LTS
#> 
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
#> 
#> locale:
#>  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
#>  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
#>  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] Rcpp_1.0.1       rprojroot_1.3-2  crayon_1.3.4     digest_0.6.18   
#>  [5] assertthat_0.2.1 MASS_7.3-51.1    R6_2.4.0         backports_1.1.3 
#>  [9] magrittr_1.5     evaluate_0.13    stringi_1.3.1    rlang_0.4.2     
#> [13] fs_1.3.1         rmarkdown_1.12.6 pkgdown_1.4.1    desc_1.2.0      
#> [17] tools_3.5.2      stringr_1.4.0    yaml_2.2.0       xfun_0.5        
#> [21] compiler_3.5.2   memoise_1.1.0    htmltools_0.3.6  knitr_1.22