VplotR

Introduction

Overview

VplotR is an R package streamlining the process of generating V-plots, i.e. two-dimensional paired-end fragment density plots. It contains functions to import paired-end sequencing bam files from any type of DNA accessibility experiments (e.g. ATAC-seq, DNA-seq, MNase-seq) and can produce V-plots and one-dimensional footprint profiles over single or aggregated genomic loci of interest. The R package is well integrated within the Bioconductor environment and easily fits in standard genomic analysis workflows. Integrating V-plots into existing analytical frameworks has already brought additional insights in chromatin organization (Serizay et al., 2020).

The main user-level functions of VplotR are getFragmentsDistribution(), plotVmat(), plotFootprint() and plotProfile().

getFragmentsDistribution() computes the distribution of fragment sizes over sets of genomic ranges;
plotVmat() is used to compute fragment density and generate V-plots;
plotFootprint() generates the MNase-seq or ATAC-seq footprint at a set of genomic ranges.
plotProfile() is used to plot the distribution of paired-end fragments at a single locus of interest.

Installation

VplotR can be installed from Bioconductor:

if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("VplotR")
library("VplotR")

Importing sequencing datasets

Using `importPEBamFiles()` function

Paired-end .bam files are imported using the importPEBamFiles() function as follows:

library(VplotR)
bamfile <- system.file("extdata", "ex1.bam", package = "Rsamtools")
fragments <- importPEBamFiles(
    bamfile, 
    shift_ATAC_fragments = TRUE
)
#> > Importing /home/biocbuild/bbs-3.13-bioc/R/library/Rsamtools/extdata/ex1.bam ...
#> > Filtering /home/biocbuild/bbs-3.13-bioc/R/library/Rsamtools/extdata/ex1.bam ...
#> > Shifting /home/biocbuild/bbs-3.13-bioc/R/library/Rsamtools/extdata/ex1.bam ...
#> > /home/biocbuild/bbs-3.13-bioc/R/library/Rsamtools/extdata/ex1.bam import completed.
fragments
#> GRanges object with 1572 ranges and 0 metadata columns:
#>          seqnames    ranges strand
#>             <Rle> <IRanges>  <Rle>
#>      [1]     seq1    41-215      +
#>      [2]     seq1    54-255      +
#>      [3]     seq1    56-258      +
#>      [4]     seq1    65-255      +
#>      [5]     seq1    65-265      +
#>      ...      ...       ...    ...
#>   [1568]     seq2 1326-1542      -
#>   [1569]     seq2 1336-1544      -
#>   [1570]     seq2 1358-1550      -
#>   [1571]     seq2 1380-1557      -
#>   [1572]     seq2 1353-1562      -
#>   -------
#>   seqinfo: 2 sequences from an unspecified genome; no seqlengths

Provided datasets

Several datasets are available for this package:

Sets of tissue-specific ATAC-seq experiments in young adult C. elegans (Serizay et al., 2020):

data(ce11_proms)
ce11_proms
#> GRanges object with 17458 ranges and 3 metadata columns:
#>           seqnames            ranges strand |   TSS.fwd   TSS.rev which.tissues
#>              <Rle>         <IRanges>  <Rle> | <numeric> <numeric>      <factor>
#>       [1]     chrI       11273-11423      + |     11294     11416       Intest.
#>       [2]     chrI       11273-11423      - |     11294     11416       Intest.
#>       [3]     chrI       26903-27053      - |     27038     26901       Ubiq.  
#>       [4]     chrI       36019-36169      - |     36112     36028       Intest.
#>       [5]     chrI       42127-42277      - |     42216     42119       Soma   
#>       ...      ...               ...    ... .       ...       ...           ...
#>   [17454]     chrX 17670496-17670646      + |  17670678  17670478  Muscle      
#>   [17455]     chrX 17684894-17685044      - |  17684919  17684932  Hypod.      
#>   [17456]     chrX 17686030-17686180      - |  17686189  17686064  Unclassified
#>   [17457]     chrX 17694789-17694939      + |  17694962  17694934  Intest.     
#>   [17458]     chrX 17711839-17711989      - |  17711974  17711854  Germline    
#>   -------
#>   seqinfo: 6 sequences from an unspecified genome; no seqlengths
data(ATAC_ce11_Serizay2020)
ATAC_ce11_Serizay2020
#> $Germline
#> GRanges object with 462371 ranges and 0 metadata columns:
#>            seqnames            ranges strand
#>               <Rle>         <IRanges>  <Rle>
#>        [1]     chrI           426-514      +
#>        [2]     chrI         3588-3854      +
#>        [3]     chrI         3640-3798      +
#>        [4]     chrI         3650-3694      +
#>        [5]     chrI         3732-3863      +
#>        ...      ...               ...    ...
#>   [462367]     chrX 17712277-17712469      -
#>   [462368]     chrX 17712279-17712342      -
#>   [462369]     chrX 17712282-17712565      -
#>   [462370]     chrX 17712285-17712384      -
#>   [462371]     chrX 17712287-17712576      -
#>   -------
#>   seqinfo: 7 sequences from an unspecified genome; no seqlengths
#> 
#> $Neurons
#> GRanges object with 367935 ranges and 0 metadata columns:
#>            seqnames            ranges strand
#>               <Rle>         <IRanges>  <Rle>
#>        [1]     chrI         4011-4241      +
#>        [2]     chrI         7397-7614      +
#>        [3]     chrI       11279-11502      +
#>        [4]     chrI       12744-12819      +
#>        [5]     chrI       14381-14433      +
#>        ...      ...               ...    ...
#>   [367931]     chrX 17687948-17687982      -
#>   [367932]     chrX 17699614-17699853      -
#>   [367933]     chrX 17706798-17706923      -
#>   [367934]     chrX 17708264-17708347      -
#>   [367935]     chrX 17709920-17710007      -
#>   -------
#>   seqinfo: 7 sequences from an unspecified genome; no seqlengths

MNase-seq experiment in yeast (Henikoff et al., 2011) and ABF1 binding sites:

data(ABF1_sacCer3)
ABF1_sacCer3
#> GRanges object with 567 ranges and 2 metadata columns:
#>         seqnames          ranges strand |  relScore       ID
#>            <Rle>       <IRanges>  <Rle> | <numeric>    <Rle>
#>     [1]    chrIV   392624-392639      + |  0.979866 MA0265.1
#>     [2]    chrIV 1196424-1196439      + |  0.979866 MA0265.1
#>     [3]   chrXIV   400687-400702      + |  0.979866 MA0265.1
#>     [4]    chrII   216540-216555      - |  0.978608 MA0265.1
#>     [5]   chrXVI     95317-95332      - |  0.974833 MA0265.1
#>     ...      ...             ...    ... .       ...      ...
#>   [563]    chrIV 1402786-1402801      + |  0.900182 MA0265.1
#>   [564]     chrX   545320-545335      + |  0.900182 MA0265.1
#>   [565]    chrXI   571301-571316      - |  0.900182 MA0265.1
#>   [566]   chrXIV   140631-140646      - |  0.900182 MA0265.1
#>   [567]   chrXVI   919179-919194      - |  0.900182 MA0265.1
#>   -------
#>   seqinfo: 17 sequences from an unspecified genome; no seqlengths
data(MNase_sacCer3_Henikoff2011)
MNase_sacCer3_Henikoff2011
#> GRanges object with 400000 ranges and 0 metadata columns:
#>            seqnames        ranges strand
#>               <Rle>     <IRanges>  <Rle>
#>        [1]     chrI         2-116      +
#>        [2]     chrI         14-66      +
#>        [3]     chrI        15-134      +
#>        [4]     chrI        54-167      +
#>        [5]     chrI        66-104      +
#>        ...      ...           ...    ...
#>   [399996]   chrXVI 920439-920471      -
#>   [399997]   chrXVI 920439-920486      -
#>   [399998]   chrXVI 920439-920528      -
#>   [399999]   chrXVI 920442-920659      -
#>   [400000]   chrXVI 920454-920683      -
#>   -------
#>   seqinfo: 17 sequences from an unspecified genome

Fragment size distribution

A preliminary control to check the distribution of fragment sizes (regardless of their location relative to genomic loci) can be performed using the getFragmentsDistribution() function.

df <- getFragmentsDistribution(
    MNase_sacCer3_Henikoff2011, 
    ABF1_sacCer3
)
#> Warning in as.cls(x): NAs introduced by coercion

#> Warning in as.cls(x): NAs introduced by coercion

#> Warning in as.cls(x): NAs introduced by coercion
p <- ggplot(df, aes(x = x, y = y)) + geom_line() + theme_ggplot2()
p
#> Warning: Removed 2 row(s) containing missing values (geom_path).

Vplot(s)

Single Vplot

Once data is imported, a V-plot of paired-end fragments over loci of interest is generated using the plotVmat() function:

p <- plotVmat(x = MNase_sacCer3_Henikoff2011, granges = ABF1_sacCer3)
#> Computing V-mat
#> Normalizing the matrix
#> No normalization applied
#> Smoothing the matrix
p

Multiple Vplots

The generation of multiple V-plots can be parallelized using a list of parameters:

list_params <- list(
    "MNase\n@ ABF1" = list(MNase_sacCer3_Henikoff2011, ABF1_sacCer3), 
    "MNase\n@ random loci" = list(
        MNase_sacCer3_Henikoff2011, sampleGRanges(ABF1_sacCer3)
    )
)
p <- plotVmat(
    list_params, 
    cores = 1
)
#> - Processing sample 1/2
#> - Processing sample 2/2
p

For instance, ATAC-seq fragment density can be visualized at different classes of ubiquitous and tissue-specific promoters in C. elegans.

list_params <- list(
    "Germline ATACseq\n@ Ubiq. proms" = list(
        ATAC_ce11_Serizay2020[['Germline']], 
        ce11_proms[ce11_proms$which.tissues == 'Ubiq.']
    ), 
    "Germline ATACseq\n@ Germline proms" = list(
        ATAC_ce11_Serizay2020[['Germline']], 
        ce11_proms[ce11_proms$which.tissues == 'Germline']
    ),
    "Neuron ATACseq\n@ Ubiq. proms" = list(
        ATAC_ce11_Serizay2020[['Neurons']], 
        ce11_proms[ce11_proms$which.tissues == 'Ubiq.']
    ), 
    "Neuron ATACseq\n@ Neuron proms" = list(
        ATAC_ce11_Serizay2020[['Neurons']], 
        ce11_proms[ce11_proms$which.tissues == 'Neurons']
    )
)
p <- plotVmat(
    list_params, 
    cores = 1,
    nrow = 2, ncol = 5
)
#> - Processing sample 1/4
#> - Processing sample 2/4
#> - Processing sample 3/4
#> - Processing sample 4/4
p

Vplots normalization

Different normalization approaches are available using the normFun argument.

Un-normalized raw counts can be plotted by specifying normFun = 'none'.

# No normalization 
p <- plotVmat(
    list_params, 
    cores = 1, 
    nrow = 2, ncol = 5, 
    verbose = FALSE,
    normFun = 'none'
)
#> Computing V-mat
#> Normalizing the matrix
#> No normalization applied
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> No normalization applied
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> No normalization applied
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> No normalization applied
#> Smoothing the matrix
p

By default, plots are normalized by the library depth of the sequencing run and by the number of loci used to compute fragment density.

# Library depth + number of loci of interest (default)
p <- plotVmat(
    list_params, 
    cores = 1, 
    nrow = 2, ncol = 5, 
    verbose = FALSE,
    normFun = 'libdepth+nloci'
)
#> Computing V-mat
#> Normalizing the matrix
#> Computing raw library depth
#> Dividing Vmat by its number of loci
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> Computing raw library depth
#> Dividing Vmat by its number of loci
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> Computing raw library depth
#> Dividing Vmat by its number of loci
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> Computing raw library depth
#> Dividing Vmat by its number of loci
#> Smoothing the matrix
p

Alternatively, heatmaps can be internally z-scored or scaled to a specific quantile.

# Zscore
p <- plotVmat(
    list_params, 
    cores = 1, 
    nrow = 2, ncol = 5, 
    verbose = FALSE,
    normFun = 'zscore'
)
#> Computing V-mat
#> Normalizing the matrix
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> Smoothing the matrix
p

# Quantile
p <- plotVmat(
    list_params, 
    cores = 1, 
    nrow = 2, ncol = 5, 
    verbose = FALSE,
    normFun = 'quantile', 
    s = 0.99
)
#> Computing V-mat
#> Normalizing the matrix
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> Smoothing the matrix
#> Computing V-mat
#> Normalizing the matrix
#> Smoothing the matrix
p

Footprints

VplotR also implements a function to profile the footprint from MNase or ATAC-seq over sets of genomic loci. For instance, CTCF is known for its ~40-bp large footprint at its binding loci.

p <- plotFootprint(
    MNase_sacCer3_Henikoff2011,
    ABF1_sacCer3
)
#> - Getting cuts
#> - Getting cut coverage
#> - Getting cut coverage / target
#> - Reformatting data into matrix
#> - Plotting footprint
p

Local fragment distribution

VplotR provides a function to plot the distribution of paired-end fragments over an individual genomic window.

data(MNase_sacCer3_Henikoff2011_subset)
genes_sacCer3 <- GenomicFeatures::genes(TxDb.Scerevisiae.UCSC.sacCer3.sgdGene::
    TxDb.Scerevisiae.UCSC.sacCer3.sgdGene
)
p <- plotProfile(
    fragments = MNase_sacCer3_Henikoff2011_subset,
    window = "chrXV:186,400-187,400", 
    loci = ABF1_sacCer3, 
    annots = genes_sacCer3,
    min = 20, max = 200, alpha = 0.1, size = 1.5
)
#> Filtering and resizing fragments
#> 32276 fragments mapped over 1001 bases
#> Filtering and resizing fragments
#> Generating plot
#> Warning: Removed 49 row(s) containing missing values (geom_path).
#> Warning: Removed 5176 rows containing missing values (geom_point).
#> Warning: Removed 19 row(s) containing missing values (geom_path).
p

Session Info

sessionInfo()
#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.2 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_GB              LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
#> [8] methods   base     
#> 
#> other attached packages:
#> [1] VplotR_1.2.0         magrittr_2.0.1       ggplot2_3.3.3       
#> [4] GenomicRanges_1.44.0 GenomeInfoDb_1.28.0  IRanges_2.26.0      
#> [7] S4Vectors_0.30.0     BiocGenerics_0.38.0 
#> 
#> loaded via a namespace (and not attached):
#>  [1] bitops_1.0-7                               
#>  [2] matrixStats_0.58.0                         
#>  [3] bit64_4.0.5                                
#>  [4] filelock_1.0.2                             
#>  [5] RColorBrewer_1.1-2                         
#>  [6] progress_1.2.2                             
#>  [7] httr_1.4.2                                 
#>  [8] tools_4.1.0                                
#>  [9] bslib_0.2.5.1                              
#> [10] utf8_1.2.1                                 
#> [11] R6_2.5.0                                   
#> [12] DBI_1.1.1                                  
#> [13] colorspace_2.0-1                           
#> [14] withr_2.4.2                                
#> [15] tidyselect_1.1.1                           
#> [16] prettyunits_1.1.1                          
#> [17] bit_4.0.4                                  
#> [18] curl_4.3.1                                 
#> [19] compiler_4.1.0                             
#> [20] Biobase_2.52.0                             
#> [21] DelayedArray_0.18.0                        
#> [22] rtracklayer_1.52.0                         
#> [23] labeling_0.4.2                             
#> [24] sass_0.4.0                                 
#> [25] scales_1.1.1                               
#> [26] rappdirs_0.3.3                             
#> [27] stringr_1.4.0                              
#> [28] digest_0.6.27                              
#> [29] Rsamtools_2.8.0                            
#> [30] rmarkdown_2.8                              
#> [31] XVector_0.32.0                             
#> [32] pkgconfig_2.0.3                            
#> [33] htmltools_0.5.1.1                          
#> [34] MatrixGenerics_1.4.0                       
#> [35] dbplyr_2.1.1                               
#> [36] fastmap_1.1.0                              
#> [37] highr_0.9                                  
#> [38] rlang_0.4.11                               
#> [39] RSQLite_2.2.7                              
#> [40] TxDb.Scerevisiae.UCSC.sacCer3.sgdGene_3.2.2
#> [41] jquerylib_0.1.4                            
#> [42] BiocIO_1.2.0                               
#> [43] farver_2.1.0                               
#> [44] generics_0.1.0                             
#> [45] zoo_1.8-9                                  
#> [46] jsonlite_1.7.2                             
#> [47] BiocParallel_1.26.0                        
#> [48] dplyr_1.0.6                                
#> [49] RCurl_1.98-1.3                             
#> [50] GenomeInfoDbData_1.2.6                     
#> [51] Matrix_1.3-3                               
#> [52] Rcpp_1.0.6                                 
#> [53] munsell_0.5.0                              
#> [54] fansi_0.4.2                                
#> [55] lifecycle_1.0.0                            
#> [56] stringi_1.6.2                              
#> [57] yaml_2.2.1                                 
#> [58] SummarizedExperiment_1.22.0                
#> [59] zlibbioc_1.38.0                            
#> [60] plyr_1.8.6                                 
#> [61] BiocFileCache_2.0.0                        
#> [62] grid_4.1.0                                 
#> [63] blob_1.2.1                                 
#> [64] crayon_1.4.1                               
#> [65] lattice_0.20-44                            
#> [66] Biostrings_2.60.0                          
#> [67] cowplot_1.1.1                              
#> [68] GenomicFeatures_1.44.0                     
#> [69] hms_1.1.0                                  
#> [70] KEGGREST_1.32.0                            
#> [71] knitr_1.33                                 
#> [72] pillar_1.6.1                               
#> [73] rjson_0.2.20                               
#> [74] reshape2_1.4.4                             
#> [75] biomaRt_2.48.0                             
#> [76] XML_3.99-0.6                               
#> [77] glue_1.4.2                                 
#> [78] evaluate_0.14                              
#> [79] vctrs_0.3.8                                
#> [80] png_0.1-7                                  
#> [81] gtable_0.3.0                               
#> [82] purrr_0.3.4                                
#> [83] assertthat_0.2.1                           
#> [84] cachem_1.0.5                               
#> [85] xfun_0.23                                  
#> [86] restfulr_0.0.13                            
#> [87] tibble_3.1.2                               
#> [88] GenomicAlignments_1.28.0                   
#> [89] AnnotationDbi_1.54.0                       
#> [90] memoise_2.0.0                              
#> [91] ellipsis_0.3.2

VplotR

Jacques Serizay

2021-05-21

Introduction

Overview

Installation

Importing sequencing datasets

Using `importPEBamFiles()` function

Provided datasets

Fragment size distribution

Vplot(s)

Single Vplot

Multiple Vplots

Vplots normalization

Footprints

Local fragment distribution

Session Info

VplotR

Jacques Serizay

2021-05-21

Introduction

Overview

Installation

Importing sequencing datasets

Using importPEBamFiles() function

Provided datasets

Fragment size distribution

Vplot(s)

Single Vplot

Multiple Vplots

Vplots normalization

Footprints

Local fragment distribution

Session Info

Using `importPEBamFiles()` function