VplotR

Jacques Serizay

2020-10-28

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().

Installation

VplotR can be installed from Bioconductor:

Importing sequencing datasets

Using importPEBamFiles() function

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

Provided datasets

Several datasets are available for this package:

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

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:

Multiple Vplots

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

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

Vplots normalization

Different normalization approaches are available using the normFun argument.

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 4.0.3 (2020-10-10)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.5 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        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_1.0.0         magrittr_1.5         ggplot2_3.3.2       
#> [4] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0  IRanges_2.24.0      
#> [7] S4Vectors_0.28.0     BiocGenerics_0.36.0 
#> 
#> loaded via a namespace (and not attached):
#>  [1] MatrixGenerics_1.2.0                       
#>  [2] Biobase_2.50.0                             
#>  [3] httr_1.4.2                                 
#>  [4] bit64_4.0.5                                
#>  [5] assertthat_0.2.1                           
#>  [6] askpass_1.1                                
#>  [7] BiocFileCache_1.14.0                       
#>  [8] blob_1.2.1                                 
#>  [9] GenomeInfoDbData_1.2.4                     
#> [10] Rsamtools_2.6.0                            
#> [11] yaml_2.2.1                                 
#> [12] progress_1.2.2                             
#> [13] pillar_1.4.6                               
#> [14] RSQLite_2.2.1                              
#> [15] lattice_0.20-41                            
#> [16] glue_1.4.2                                 
#> [17] digest_0.6.27                              
#> [18] RColorBrewer_1.1-2                         
#> [19] XVector_0.30.0                             
#> [20] colorspace_1.4-1                           
#> [21] cowplot_1.1.0                              
#> [22] htmltools_0.5.0                            
#> [23] Matrix_1.2-18                              
#> [24] plyr_1.8.6                                 
#> [25] XML_3.99-0.5                               
#> [26] pkgconfig_2.0.3                            
#> [27] biomaRt_2.46.0                             
#> [28] zlibbioc_1.36.0                            
#> [29] TxDb.Scerevisiae.UCSC.sacCer3.sgdGene_3.2.2
#> [30] purrr_0.3.4                                
#> [31] scales_1.1.1                               
#> [32] BiocParallel_1.24.0                        
#> [33] tibble_3.0.4                               
#> [34] openssl_1.4.3                              
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#> [37] ellipsis_0.3.1                             
#> [38] withr_2.3.0                                
#> [39] SummarizedExperiment_1.20.0                
#> [40] GenomicFeatures_1.42.0                     
#> [41] crayon_1.3.4                               
#> [42] memoise_1.1.0                              
#> [43] evaluate_0.14                              
#> [44] xml2_1.3.2                                 
#> [45] tools_4.0.3                                
#> [46] prettyunits_1.1.1                          
#> [47] hms_0.5.3                                  
#> [48] lifecycle_0.2.0                            
#> [49] matrixStats_0.57.0                         
#> [50] stringr_1.4.0                              
#> [51] munsell_0.5.0                              
#> [52] DelayedArray_0.16.0                        
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#> [55] compiler_4.0.3                             
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#> [67] R6_2.4.1                                   
#> [68] GenomicAlignments_1.26.0                   
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#> [70] rtracklayer_1.50.0                         
#> [71] knitr_1.30                                 
#> [72] dplyr_1.0.2                                
#> [73] bit_4.0.4                                  
#> [74] stringi_1.5.3                              
#> [75] Rcpp_1.0.5                                 
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#> [77] dbplyr_1.4.4                               
#> [78] tidyselect_1.1.0                           
#> [79] xfun_0.18