看到一個成熟的R包，所以推薦一下
- https://knausb.github.io/vcfR_documentation/
- https://cran.r-project.org/web/packages/vcfR/vignettes/intro_to_vcfR.html
最重要的使用方法，當然是讀入vcf文件啦，如下：
library(vcfR) vcf_file='/Users/jmzeng/germline/merge.dbsnp.vcf' vcf <- read.vcfR( vcf_file, verbose = FALSE )
十幾秒鐘就輕輕鬆鬆讀入一個300多M的vcf文件啦，成爲一個S4對象：
> vcf ***** Object of Class vcfR ***** 39 samples 24 CHROMs 212,875 variants Object size: 356.6 Mb 0 percent missing data ***** ***** ***** >
懂R的朋友就應該是知道如何去查看幫助文檔來學習它啦，最重要的兩句話是：
Objects of class vcfR can be manipulated with vcfR-method and extract.gt. Contents of the vcfR object can be visualized with the plot method.
最基本的3個元素是：
meta character vector for the meta information fix matrix for the fixed information gt matrix for the genotype information
其中meta存儲着vcf的頭文件，而fix存儲在vcf的固定列，gt存儲在樣本基因型信息。
最基本的操作函數如下：
show(object) colnames(vcf@fix) vcf@fix[1:4,1:4] colnames(vcf@gt) vcf@gt[1:4,1:4] head(x, n = 6, maxchar = 80) plot(x, y, ...) ## 主要查看突變位點的質量值的分佈情況 x[i, j, samples = NULL, ..., drop] dim(x) nrow(x)
解析出來了讀入的文件所變成的對象，後續分析就簡單多了。
包自帶的測試數據
pkg <- "pinfsc50"
vcf_file <- system.file("extdata", "pinf_sc50.vcf.gz", package = pkg)
dna_file <- system.file("extdata", "pinf_sc50.fasta", package = pkg)
gff_file <- system.file("extdata", "pinf_sc50.gff", package = pkg)
library(vcfR)
vcf <- read.vcfR( vcf_file, verbose = FALSE )
dna <- ape::read.dna(dna_file, format = "fasta")
gff <- read.table(gff_file, sep="\t", quote="")
library(vcfR)
chrom <- create.chromR(name='Supercontig', vcf=vcf, seq=dna, ann=gff)
plot(chrom)
chromoqc(chrom, dp.alpha=20)
chromoqc(chrom, xlim=c(5e+05, 6e+05))可以出一下還不錯的圖，我覺得顏值還行！
