GenomicPlot 1.5.0
Visualization of next generation sequencing (NGS) data at various genomic features on a genome-wide scale provides an effective way of exploring and communicating experimental results on one hand, yet poses as a tremendous challenge on the other hand, due to the huge amount of data to be processed. Existing software tools like deeptools, ngs.plot, CoverageView and metagene2, while having attractive features and perform reasonably well in relatively simple scenarios, like plotting coverage profiles of fixed genomic loci or regions, have serious limitations in terms of efficiency and flexibility. For instance, deeptools requires 3 steps (3 sub-programs to be run) to generate plots from input files: first, convert .bam files to .bigwig format; second, compute coverage matrix; and last, plot genomic profiles. Huge amount of intermediate data are generated along the way and additional efforts have to be made to integrate these 3 closely related steps. All of them focus on plotting signals within genomic regions or around genomic loci, but not within or around combinations of genomic features. None of them have the capability of performing statistical tests on the data displayed in the profile plots.
To meet the diverse needs of experimental biologists, we have developed GenomicPlot
using rich resources available on the R platform (particularly, the Bioconductor). Our GenomicPlot
has the following major features:
The following packages are prerequisites:
GenomicRanges (>= 1.46.1), GenomicFeatures, Rsamtools, ggplot2 (>= 3.3.5), tidyr, rtracklayer (>= 1.54.0), plyranges (>= 1.14.0), dplyr (>= 1.0.8), cowplot (>= 1.1.1), VennDiagram, ggplotify, GenomeInfoDb, IRanges, ComplexHeatmap, RCAS (>= 1.20.0), scales (>= 1.2.0), GenomicAlignments (>= 1.30.0), edgeR, forcats, circlize, viridis, ggsignif (>= 0.6.3), ggsci (>= 2.9), genomation (>= 1.26.0), ggpubr
You can install the current release version from Bioconductor:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("GenomicPlot")
or the development version from Github with:
if (!require("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("shuye2009/GenomicPlot",
build_manual = TRUE,
build_vignettes = TRUE)
The lengths of each part of the genes are prorated based on the median length of 5’UTR, CDS and 3’UTR of protein-coding genes in the genome. The total length (including upstream and downstream extensions) are divided into the specified number of bins. Subsets of genes can be plotted as overlays for comparison.
suppressPackageStartupMessages(library(GenomicPlot, quietly = TRUE))
## Warning: replacing previous import 'Biostrings::pattern' by 'grid::pattern'
## when loading 'genomation'
txdb <- AnnotationDbi::loadDb(system.file("extdata", "txdb.sql",
package = "GenomicPlot"))
## Loading required package: GenomicFeatures
## Loading required package: AnnotationDbi
## Loading required package: Biobase
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
data(gf5_meta)
queryfiles <- system.file("extdata", "treat_chr19.bam",
package = "GenomicPlot")
names(queryfiles) <- "clip_bam"
inputfiles <- system.file("extdata", "input_chr19.bam",
package = "GenomicPlot")
names(inputfiles) <- "clip_input"
bamimportParams <- setImportParams(
offset = -1, fix_width = 0, fix_point = "start", norm = TRUE,
useScore = FALSE, outRle = TRUE, useSizeFactor = FALSE, genome = "hg19"
)
plot_5parts_metagene(
queryFiles = queryfiles,
gFeatures_list = list("metagene" = gf5_meta),
inputFiles = inputfiles,
scale = FALSE,
verbose = FALSE,
transform = NA,
smooth = TRUE,
stranded = TRUE,
outPrefix = NULL,
importParams = bamimportParams,
heatmap = TRUE,
rmOutlier = 0,
nc = 2
)
## 565 [set_seqinfo]
## 418 [set_seqinfo]
## 75% of values are not unique, heatmap may not show
## signals effectively
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## [draw_matrix_heatmap] finished!
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## 75% of values are not unique, heatmap may not show
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## 75% of values are not unique, heatmap may not show
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