用R语言对vcf文件进行数据挖掘.8 窗口缩放

目录

1. 前言
2. 方法简介
3. 从vcf文件里提取有用信息
4. tidy vcfR
5. vcf可视化1
6. vcf可视化2
7. 测序深度覆盖度
8. 窗口缩放
9. 如何单独分离染色体
10. 利用vcf信息判断物种染色体倍数
11. CNV分析

vcfR可以对单独的每一段染色体进行分析。比起全基因总体的变化，很多时候染色体单位的变化往往会更加引起我们的兴趣。

数据准备和导入

和之前文章里介绍的一样，先导入基础vcf数据

library(vcfR)
data(vcfR_example)
data("vcfR_test")

然后为了方便理解，自己虚拟三条染色体的参照序列和注释信息。

library(ape)

dna.l <- vector('list', length=3)

dna.l[[1]] <- as.character(dna[1,1:length(dna)])
set.seed(123)
dna.l[[2]] <- sample( c('a','c','g','t'), size = getPOS(vcfR_test)[length(getPOS(vcfR_test))], replace = TRUE )
dna.l[[3]] <- sample( c('a','c','g','t'), size = getPOS(vcfR_test)[length(getPOS(vcfR_test))], replace = TRUE )
dna.l <- as.DNAbin(dna.l)

names(dna.l)[1] <- "Supercontig_1.50"
names(dna.l)[2] <- "Supercontig_1.5"
names(dna.l)[3] <- "Supercontig_1.10"
dna.l

## 3 DNA sequences in binary format stored in a list.
## 
## Mean sequence length: 856378.3 
##    Shortest sequence: 100001 
##     Longest sequence: 1234567 
## 
## Labels:
## Supercontig_1.50
## Supercontig_1.5
## Supercontig_1.10
## 
## Base composition:
##    a    c    g    t 
## 0.25 0.25 0.25 0.25

分别虚拟三条染色体的注释文件，然后合并成一个整体。

gff2 <- gff[1:10,]
gff2[,1] <- "Supercontig_1.5"
gff3 <- gff[11:20,]
gff3[,1] <- "Supercontig_1.10"
gff <- rbind(gff, gff2, gff3)

写出三条染色体的vcf

write.vcf(vcf, file = "Supercontig_1.50.vcf.gz")
vcfR_test@fix[,'CHROM'] <- "Supercontig_1.5"
write.vcf(vcfR_test, file = "Supercontig_1.5.vcf.gz")
vcfR_test@fix[,'CHROM'] <- "Supercontig_1.10"
write.vcf(vcfR_test, file = "Supercontig_1.10.vcf.gz")

这样，三条染色体样本数据就准备好了。

缩放染色体

这样就可以自定义窗口尺寸，对染色体进行分析了。

myFiles <- list.files(".", pattern = "Supercontig.*vcf.gz$")
myChroms <- unlist( lapply( strsplit(myFiles, "\\.vcf"), function(x){ x[1] } ) )

myWin.size <- 1e4
i <- 1
myChrom <- myChroms[i]

dna1 <- dna.l[myChrom]
gff1 <- gff[ gff[,1] == myChrom,]

vcf <- read.vcfR(myFiles[i], verbose = FALSE)
    
chrom <- create.chromR(name=myChrom, vcf=vcf, seq=dna1, ann=gff1, verbose = FALSE)
chrom <- proc.chromR(chrom, win.size = myWin.size, verbose=FALSE)

write.var.info(chrom, file = "pinf_ref_vars.csv", APPEND = FALSE)
write.win.info(chrom, file = "pinf_ref_wins.csv", APPEND = FALSE)

for(i in 2:length(myFiles)){
  myChrom <- myChroms[i]
  cat(myChrom)
  cat("\n")
  dna1 <- dna.l[myChrom]
  gff1 <- gff[ gff[,1] == myChrom,]
  vcf <- read.vcfR(myFiles[i], verbose = FALSE)
  chrom <- create.chromR(name=myChrom, vcf=vcf, seq=dna1, ann=gff1, verbose = FALSE)
  chrom <- proc.chromR(chrom, win.size = myWin.size, verbose=FALSE)
  write.var.info(chrom, file = "pinf_ref_vars.csv", APPEND = TRUE)
  write.win.info(chrom, file = "pinf_ref_wins.csv", APPEND = TRUE)
}