偶然見發(fā)現(xiàn)了一R包，名曰vcfR，可高效處理vcf文件，并且可以做出好看的圖片
具體可查看說明文檔高鐵直達(dá)
安裝
BiocManager::install("vcfR")
測試數(shù)據(jù)
該軟件自帶測試數(shù)據(jù)，可進(jìn)行操練
# 讀取文件
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)
vcf <- read.vcfR( vcf_file, verbose = FALSE )
dna <- ape::read.dna(dna_file, format = "fasta")
gff <- read.table(gff_file, sep="\t", quote="")
讀取的vcf為一個對象
> vcf
***** Object of Class vcfR *****
18 samples
1 CHROMs
22,031 variants
Object size: 22.4 Mb
7.929 percent missing data
***** ***** *****
## 包含如下3個元素
meta
character vector for the meta information
fix
matrix for the fixed information
gt
matrix for the genotype information
3個元素包含的信息可以簡單查閱
# meta 頭信息
> head(vcf@meta)
[1] "##fileformat=VCFv4.1"
[2] "##source=\"GATK haplotype Caller, phased with beagle4\""
[3] "##FILTER=<ID=LowQual,Description=\"Low quality\">"
[4] "##FORMAT=<ID=AD,Number=.,Type=Integer,Description=\"Allelic depths for the ref and alt alleles in the order listed\">"
# fix 1-9列的信息
> head(vcf@fix[,1:7])
CHROM POS ID REF ALT QUAL FILTER
[1,] "Supercontig_1.50" "41" NA "AT" "A" "4784.43" NA
[2,] "Supercontig_1.50" "136" NA "A" "C" "550.27" NA
[3,] "Supercontig_1.50" "254" NA "T" "G" "774.44" NA
[4,] "Supercontig_1.50" "275" NA "A" "G" "714.53" NA
# gt 各個樣本的基因型信息
> head(vcf@gt[,1:3])
FORMAT BL2009P4_us23 DDR7602
[1,] "GT:AD:DP:GQ:PL" "1|1:0,7:7:21:283,21,0" "1|1:0,6:6:18:243,18,0"
[2,] "GT:AD:DP:GQ:PL" "0|0:12,0:12:36:0,36,427" "0|0:20,0:20:60:0,60,819"
[3,] "GT:AD:DP:GQ:PL" "0|0:27,0:27:81:0,81,1117" "0|0:26,0:26:78:0,78,1077"
[4,] "GT:AD:DP:GQ:PL" "0|0:29,0:29:87:0,87,1243" "0|0:27,0:27:81:0,81,1158"
既然已經(jīng)知道怎么調(diào)取相應(yīng)信息，則可將其存為數(shù)據(jù)框進(jìn)行任意操作
# 將fix內(nèi)容存為 info2
info2 <- vcf@fix
info2 <- as.data.frame(info2)
# 調(diào)取QUAL值存為qa
qa <- info2$QUAL
# 查看qual >30 的個數(shù)
qa <- as.numeric(as.character(qa))
table(qa > 30)
TRUE
22031
建立chromR
chrom <- create.chromR(name='Supercontig', vcf=vcf, seq=dna, ann=gff)
# 畫圖
plot(chrom)
該圖可以看出，深度分布非常突出，峰值可以表示測序深度的一個區(qū)域，也能看出有很長的尾巴，表示存在變異。同時(shí)MQ值都在60左右達(dá)到了峰值，所以可以根據(jù)改值進(jìn)行過濾。QUAL不太好看出時(shí)從那里開始逐漸到峰值
還可以根據(jù) 進(jìn)行過濾
chrom <- masker(chrom, min_QUAL = 1, min_DP = 300, max_DP = 700, min_MQ = 59.9, max_MQ = 60.1)
plot(chrom)
一旦我們對要考慮的高質(zhì)量變體感到滿意，就可以使用處理chromR對象
chrom <- proc.chromR(chrom, verbose=TRUE)
plot(chrom)
剛才我們已經(jīng)得到了變異文件，fasta文件，gff文件，并且過濾掉我們不滿意的數(shù)據(jù)，現(xiàn)在我們來對其進(jìn)行可視化
使用
chromoqc(chrom, dp.alpha=20)
也可以進(jìn)行調(diào)整X坐標(biāo)，篩選出我們想看的區(qū)域
chromoqc(chrom, xlim=c(5e+05, 6e+05))
最后導(dǎo)出數(shù)據(jù)可以使用
交流歡迎掃碼
