Chapter 3 Draw chromosome ideograms

A chromosomal ideogram is an idealized graphic representation of chromosomes. The common different shades of color positions on chromosome represent densities of gene. Here is a simple illustration:

• “gneg” refers to a chromosomal band that stains lightly with Giemsa stain.
• “gpos25”, “gpos50”, “gpos75”, and “gpos100” refer to chromosomal bands that stain progressively darker with Giemsa stain. These terms represent increasing proportions of GC-rich DNA in the band.
• “acen” refers to the centromere or the specialized region of a chromosome that plays a role in cell division.
• “gvar” refers to a chromosomal band that has structural variation compared to the reference genome.
• “stalk” can have different meanings depending on the context, but in cytogenetics, it refers to a short arm protrusion that extends from the centromere.

3.1 Downloading related data

mm10, hg38 and hg19 related ideogram data have been downloaded and saved in BioSeqUtils. You can use catchIdeoData function to download other species data. Here is the data strcuture of “hg38_obj” object:

data("hg38_obj")

str(hg38_obj)

# List of 3
#  $ plot_df     :'data.frame': 814 obs. of  6 variables:
#   ..$ chr        : chr [1:814] "chr1" "chr1" "chr1" "chr1" ...
#   ..$ chromStart : num [1:814] 0 2300000 5300000 7100000 9100000 12500000 15900000 20100000 23600000 27600000 ...
#   ..$ chromEnd   : num [1:814] 2300000 5300000 7100000 9100000 12500000 15900000 20100000 23600000 27600000 29900000 ...
#   ..$ name       : chr [1:814] "p36.33" "p36.32" "p36.31" "p36.23" ...
#   ..$ gieStain   : chr [1:814] "gneg" "gpos25" "gneg" "gpos25" ...
#   ..$ gieStainCol: chr [1:814] "white" "gray75" "white" "gray75" ...
#  $ border_df   : gropd_df [48 × 4] (S3: grouped_df/tbl_df/tbl/data.frame)
#   ..$ chr : chr [1:48] "chr1" "chr1" "chr10" "chr10" ...
#   ..$ pos : chr [1:48] "left" "right" "left" "right" ...
#   ..$ xmin: num [1:48] 0.00 1.25e+08 0.00 4.16e+07 0.00 ...
#   ..$ xmax: num [1:48] 1.22e+08 2.49e+08 3.80e+07 1.34e+08 5.10e+07 ...
#   ..- attr(*, "groups")= tibble [24 × 2] (S3: tbl_df/tbl/data.frame)
#   .. ..$ chr  : chr [1:24] "chr1" "chr10" "chr11" "chr12" ...
#   .. ..$ .rows: list<int> [1:24]
#   .. .. ..$ : int [1:2] 1 2
#   .. .. ..$ : int [1:2] 3 4
#   ...
#   .. .. ..@ ptype: int(0)
#   .. ..- attr(*, ".drop")= logi TRUE
#  $ acen_plot_df:'data.frame': 96000 obs. of  4 variables:
#   ..$ xpos: num [1:96000] 1.25e+08 1.25e+08 1.25e+08 1.25e+08 1.25e+08 ...
#   ..$ ypos: num [1:96000] 0 0.00025 0.000501 0.000751 0.001002 ...
#   ..$ type: num [1:96000] 1 1 1 1 1 1 1 1 1 1 ...
#   ..$ chr : chr [1:96000] "chr1" "chr1" "chr1" "chr1" ...

If we download “mm10” related ideogram data online:

mm10_obj <- catchIdeoData(genome = "mm10")

3.2 Making plot

drawChromosome function allows you to plot chromosome ideogram structure:

drawChromosome(ideogram_obj = hg38_obj,
               chromosomes = unique(hg38_obj$plot_df$chr),
               facet_params = list(scales = "fixed"))

Selecting some chromosomes:

drawChromosome(ideogram_obj = hg38_obj,
               chromosomes = c("chr1","chr2","chr3"))

Zooming a region on chromosomes with a genomic position:

drawChromosome(ideogram_obj = hg38_obj,
               chromosomes = c("chr1","chr2","chr3"),
               zoom_region = c(10^8,10^8+300000))

Multiple regions are also accepted:

data("mm10_obj")

drawChromosome(ideogram_obj = mm10_obj,
               chromosomes = c("chr1","chr2","chr3"),
               zoom_region = list(chr1 = c(10^8,10^8+300000),
                                  chr2 = c(0.6*10^8 + 300000,0.6*10^8+900000),
                                  chr3 = c(0.1*10^8,0.1*10^8+450000)),
               facet_params = list(strip.position = "bottom"),
               zoom_col = c("white","#82CD47"))

3.3 Drawing gene density manually

If you want to calculate and plot gene densities by manual, please refer to 手把手教你绘制染色体结构图.