GENESPACE优雅的绘制基因组共线性图
GENESPACE优雅的绘制基因组共线性图
R语言数据分析指南
发布于 2023-07-11 13:40:04
发布于 2023-07-11 13:40:04
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本节来介绍一个用于了解多个基因组的同线性和直系同源模式分析及可视化的R包GENESPACE，软件运行需要依赖其它软件如OrthoFinder、MCScanX等，分析环境配置可以一站式完成数据的分析及可视化同时具有很高的自定性。小编下面进行部分的结果展示，软件安装等更多的详细内容请参考作者的官方文档。
官方文档
(https://htmlpreview.github.io/?https://github.com/jtlovell/tutorials/blob/main/riparianGuide.html)
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软件安装
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("jtlovell/GENESPACE")
library(GENESPACE)定义软件路径
genomeRepo <- "~/path/to/store/rawGenomes"
wd <- "~/path/to/genespace/workingDirectory"
path2mcscanx <- "~/path/to/MCScanX/"下载基因组数据
urls <- c(
human ="000/001/405/GCF_000001405.40_GRCh38.p14/GCF_000001405.40_GRCh38.p14_",
mouse = "000/001/635/GCF_000001635.27_GRCm39/GCF_000001635.27_GRCm39_",
platypus = "004/115/215/GCF_004115215.2_mOrnAna1.pri.v4/GCF_004115215.2_mOrnAna1.pri.v4_",
chicken = "016/699/485/GCF_016699485.2_bGalGal1.mat.broiler.GRCg7b/GCF_016699485.2_bGalGal1.mat.broiler.GRCg7b_",
sandLizard = "009/819/535/GCF_009819535.1_rLacAgi1.pri/GCF_009819535.1_rLacAgi1.pri_")
genomes2run <- names(urls)
urls <- file.path("https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF", urls)
translatedCDS <- sprintf("%stranslated_cds.faa.gz", urls)
geneGff <- sprintf("%sgenomic.gff.gz", urls)
names(translatedCDS) <- genomes2run
names(geneGff) <- genomes2run
writeDirs <- file.path(genomeRepo, genomes2run)
names(writeDirs) <- genomes2run
for(i in genomes2run){
print(i)
if(!dir.exists(writeDirs[i]))
dir.create(writeDirs[i])
download.file(
url = geneGff[i],
destfile = file.path(writeDirs[i], basename(geneGff[i])))
download.file(
url = translatedCDS[i],
destfile = file.path(writeDirs[i], basename(translatedCDS[i])))
}解析基因组注释文件
genomes2run <- c("human", "mouse", "platypus", "chicken", "sandLizard")
parsedPaths <- parse_annotations(
rawGenomeRepo = genomeRepo,
genomeDirs = genomes2run,
genomeIDs = genomes2run,
presets = "ncbi",
genespaceWd = wd)软件运行
gpar <- init_genespace(
wd = wd,
ploidy = 1,
path2mcscanx = path2mcscanx)
out <- run_genespace(gpar, overwrite = T)数据可视化
ripDat <- plot_riparian(
gsParam = out,
refGenome = "human",
forceRecalcBlocks = FALSE)
ripDat <- plot_riparian(
out,
refGenome = "human",
useOrder = FALSE,
useRegions = FALSE)更改顺序
ripDat <- plot_riparian(
gsParam = out,
refGenome = "mouse",
genomeIDs = c("mouse", "human", "platypus", "chicken"),
forceRecalcBlocks = FALSE)调整排序
ripDat <- plot_riparian(
gsParam = out,
#reorderBySynteny = FALSE,
syntenyWeight = 0,
refGenome = "human")
自定义标签
ggthemes <- ggplot2::theme(
panel.background = ggplot2::element_rect(fill = "white"))
customPal <- colorRampPalette(
c("darkorange", "skyblue", "darkblue", "purple", "darkred", "salmon"))
ripDat <- plot_riparian(
gsParam = out,
palette = customPal,
braidAlpha = .75,
chrFill = "lightgrey",
addThemes = ggthemes,
refGenome = "human")
部分区域高亮展示
roi <- data.frame(
genome = c("human", "chicken"),
chr = c("X", "Z"),
color = c("#FAAA1D", "#17B5C5"))
ripDat <- plot_riparian(
gsParam = out,
highlightBed = roi,
refGenome = "human",
genomeIDs = c("sandLizard", "chicken", "human", "mouse", "platypus"),
customRefChrOrder = c("X", 1:22))
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