karyotype = chr.size # 设置染色体标签 染色体大小
chromosomes_order = Chr01A,Chr02A,Chr03A,Chr04A,Chr05A,Chr06A,Chr07A,Chr07B,Chr06B,Chr05B,Chr04B,Chr03B,Chr02B,Chr01B # 染色体排列顺序
chromosomes_scale = /Chr0[1234567]A/=0.5rn,/Chr0[1234567]B/=0.5rn
<ideogram>
<spacing>
#default = 0.005r
default = 8u # 默认染色体之间的间距
<pairwise Chr01A Chr01B> #设定两个染色体之间的距离
spacing = 35u
</pairwise>
# <pairwise Chr07A Chr07B> 设置这两号染色体之间空出来20U
# spacing = 20u
# </pairwise>
</spacing>
radius = 0.90r
thickness = 75p
fill = yes
fill_color = black
stroke_color = 196,196,196 # 符号的轮廓颜色
stroke_thickness = 5 p # 符号的轮廓的厚度，像素单位
# 设置标签
show_label = yes #展示label
label_font = light # 字体
label_radius = 1r + 170p #位置
label_size = 90 # 字体大小
label_parallel = yes # 是否平行
label_format = eval(sprintf("%s",var(chr))) # 格式
</ideogram>
<image>
#dir* = . # 输出文件夹
#radius* = 500p # 图片半径
#svg* = no # 是否输出svg
file* = circos2.png
radius* = 3000p
angle_offset* = -90
<<include etc/image.conf>>
</image>
<plots>
<plot> # 着丝粒折线图 137 红色
type = line
thickness = 12p
max_gap = 1u
file = ./string/137.txt
color = 251,106,74
r0 = 0.94r
r1 = 0.99r
</plot>
<plot>
type = line # 156 蓝色
thickness = 12p
max_gap = 1u
file = ./string/156.txt
color = 0,0,255
r0 = 0.94r
r1 = 0.99r
</plot>
<plot>
type = line # 148
thickness = 12p
max_gap = 1u
file = ./string/148.txt
color = 0,128,0
r0 = 0.94r
r1 = 0.99r
</plot>
<plot> # CG含量折线图
type = line
thickness = 9p
max_gap = 1u
file = CG.txt
color = 251,106,74
r0 = 0.85r
r1 = 0.92r
max = 0.6
orientation = out
<backgrounds>
<background>
color = 148,195,224
y0 = 0.3
y1 = 0.6
#show=no
</background>
</backgrounds>
</plot>
<plot> # SNP 图
type = histogram
file = SNP03.txt
color = 248,242,80
fill_color = 248,242,80
r0 = 0.76r
r1 = 0.83r
<backgrounds>
<background>
color = 153,204,153
y0 = 0
y1 = 700
</background>
</backgrounds>
</plot>
<plot>
type = heatmap
file = transf_TPM # 基因表达量
color = reds-9-seq
fill_color = 153,102,153
r0 = 0.52r
r1 = 0.59r
</plot>
<plot> # 基因密度图
type = histogram
file = gene02.txt
color = 80,176,214
fill_color = 80,176,214
r0 = 0.43r
r1 = 0.50r
<backgrounds>
<background>
color = 248,222,173
y0 = 0
y1 = 80
</background>
</backgrounds>
</plot>
</plots>
<links>
<link> # 基因共线性图
file = A.B.anchors.result
radius = 0.36r
color = 153,204,255
thickness = 3p
<rules>
<rule> # 设置link的颜色。如果右边的变量等于chr2，颜色等于249,141,59
condition = var(chr2) eq "Chr01B"
color=249,141,59
</rule>
<rule>
condition = var(chr2) eq "Chr02B"
color=248,242,80
</rule>
<rule>
condition = var(chr2) eq "Chr03B"
color=115,190,116
</rule>
<rule>
condition = var(chr2) eq "Chr04B"
color=67,130,178
</rule>
<rule>
condition = var(chr2) eq "Chr05B"
color=221,184,135
</rule>
<rule>
condition = var(chr2) eq "Chr06B"
color=23,165,187
</rule>
<rule>
condition = var(chr2) eq "Chr07B"
color=157,154,195
</rule>
</rules>
</link>
</links>
<<include etc/colors_fonts_patterns.conf>>
<<include etc/housekeeping.conf>>
<<include ticks.conf>>
最近做了两个物种的圈图， 也算是半个绘圈达人了。基因组圈图只能大概展示一些情况如 SNP数目 CG含量 基因密度 甲基化 转座子数量 。 按图的类型分为 条形图（数值越高，越长。 可以设定y0, y1有颜色参数和填充颜色参数） 折线图（数值越大，越长。可以设定y0, y1，有颜色参数和填充颜色参数 ） 密度图 （数值越大， 颜色越深）。 还有一个highlight区域。
我的划窗大小是 500000
划窗文件 win
根据划窗文件提取每个窗口 CG含量
bedtools nuc -fi /share/home/XXXXXX.chr.fasta -bed win | cut -f 1-3,5 > CG.bed
学习中我参考的资料以下
https://blog.csdn.net/u014182497/article/details/52513269
jcvi 鉴定共线性区块
https://xuzhougeng.blog.csdn.net/article/details/102804604?spm=1001.2101.3001.6661.1&utm_medium=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-102804604-blog-102804601.pc_relevant_paycolumn_v3&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-102804604-blog-102804601.pc_relevant_paycolumn_v3&utm_relevant_index=1
注意我们要使用 A.B.anchors 这个文件 而不是 A.B.anchors.sample文件。 前者是一小段一小段的共线性区域，后者是大块的区段共线
https://blog.csdn.net/weixin_43569478/article/details/108079782
