Filtering and handling VCFs
未找到原文所用数据,本文使用GATK4.0和全基因组数据分析实践(上)文章中的大肠杆菌基因组作为参考序列,使用wgsim
软件模拟生成双端150bp测序数据
wgsim -N 80000 -1 150 -2 150 ../Reference_genome/ecoli.fa sim_1_reads_R1.fastq sim_1_reads_R2.fastq
wgsim -N 80000 -1 150 -2 150 ../Reference_genome/ecoli.fa sim_2_reads_R1.fastq sim_2_reads_R2.fastq
wgsim -N 80000 -1 150 -2 150 ../Reference_genome/ecoli.fa sim_3_reads_R1.fastq sim_3_reads_R2.fastq
-N
指定生成reads的条数
-1 -2
生成reads的长度
接下来是参考序列
接下来是fastq文件的名字
这一部分参考文章
基本流程:
bwa
比对
samtools
变异检测
完整代码
###构建索引
bwa index Reference_genome/ecoli.fa
bwa mem -t 4 -R '@RG\tID:foo\tSM:sample1' Reference_genome/ecoli.fa sim_reads/sim_1_reads_R1.fastq sim_reads/sim_1_reads_R2.fastq -o output_results/sim_1_aligned.sam
bwa mem -t 4 -R '@RG\tID:foo\tSM:sample2' Reference_genome/ecoli.fa sim_reads/sim_2_reads_R1.fastq sim_reads/sim_2_reads_R2.fastq -o output_results/sim_2_aligned.sam
bwa mem -t 4 -R '@RG\tID:foo\tSM:sample3' Reference_genome/ecoli.fa sim_reads/sim_3_reads_R1.fastq sim_reads/sim_3_reads_R2.fastq -o output_results/sim_3_aligned.sam
这里遇到的问题:
-R
参数后面接的内容都是什么意思?cd output_results
#SAM装换为BAM
samtools view -S -b -o sim_1_aligned.bam sim_1_aligned.sam
samtools view -S -b -o sim_2_aligned.bam sim_2_aligned.sam
samtools view -S -b -o sim_3_aligned.bam sim_3_aligned.sam
#排序
samtools sort sim_1_aligned.bam -o sim_1_aligned.sorted.bam
samtools sort sim_2_aligned.bam -o sim_2_aligned.sorted.bam
samtools sort sim_3_aligned.bam -o sim_3_aligned.sorted.bam
#索引
samtools index sim_1_aligned.sorted.bam
samtools index sim_2_aligned.sorted.bam
samtools index sim_3_aligned.sorted.bam
#变异检测
time samtools mpileup -g -t DP,AD -f ../Reference_genome/ecoli.fa sim_1_aligned.sorted.bam sim_2_aligned.sorted.bam sim_3_aligned.sorted.bam > sim_variants_3sample.bcf
###其一
time bcftools call -v -c sim_variants_3sample.bcf > sim_variants_3sample.vcf
###其二
time bcftools call -f GQ,GP -vmO z sim_variants_3sample.bcf -o sim_variants_3sample_1.vcf.gz
这样就得到了最终的vcf格式的文件。
这里遇到的问题:samtools加上bcftools检测变异的各个参数的含义还不太明白!
bcftools view -H sim_variants_3sample.vcf | wc -l
6918
通常获得的vcf文件都比较大,可以通过随机取样的方法获得小的vcf文件用于后续的分析
过滤vcf文件通常考虑四点:
cd ../
mkdir vcf_handling
cd vcf_handling
vcftools --vcf ../output_results/sim_variants_3sample.vcf --freq2 --out sim_variant_AF
vcftools --vcf ../output_results/sim_variants_3sample.vcf --depth --out sim_variant_depth
vcftools --vcf ../output_results/sim_variants_3sample.vcf --site-mean-depth --out sim_variant_sitedepth
vcftools --vcf ../output_results/sim_variants_3sample.vcf --site-quality --out sim_variant_sitequality
vcftools --vcf ../output_results/sim_variants_3sample.vcf --missing-indv --out sim_variant_missingindv
五个文件分别是
sim_variant_missingindv.imiss
sim_variant_sitequality.lqual
sim_variant_sitedepth.ldepth.mean
sim_variant_depth.idepth
sim_variant_AF.frq
setwd("../../vcf_handling/")
library(tidyverse)
var_qual<-read_delim("sim_variant_sitequality.lqual",
delim="\t",
col_names=c("chr","pos","qual"),
skip=1)
library(ggplot2)
a<-ggplot(var_qual,aes(qual))+
geom_density(fill="dodgerblue1")+
theme_light()
a1<-a+xlim(0,100)
library(ggpubr)
ggarrange(a,a1,ncol=1,nrow=2)
image.png 从上图可以看出我们的位点质量值是偏低的,因为数据量比较小,位点质量值30代表检测出来的变异有千分之一的可能是错误的,推荐过滤变异的时候设置位点质量值大于30。
summary(var_depth$mean_depth)
var_depth<-read_delim("sim_variant_sitedepth.ldepth.mean",
delim="\t",col_names=c("chr","pos","mean_depth","var_depth"),
skip=1)
a<-ggplot(var_depth,aes(mean_depth))+
geom_density(fill="dodgerblue1",color="black",alpha=0.3)+
theme_light()
a
image.png 覆盖度的最小值设置(We could set our minimum coverage at the 5 and 95% quantiles这句话暂时还没看懂是什么意思!) 覆盖度最大值的设置:推荐设置为平均深度的两倍(Usually a good rule of thumb is something the mean depth * 2, so in this case we could set our maximum depth at 40x)
INDV N_DATA N_GENOTYPES_FILTERED N_MISS F_MISS
sample1 6918 0 0 0
sample2 6918 0 0 0
sample3 6918 0 0 0
本次分析中的缺失率都为0,暂时还没想到是什么原因
var_freq<-read_delim("sim_variant_AF.frq",delim="\t",
col_names=c("chr","pos","nalleles","nchr","a1","a2"),skip=1)
var_freq
var_freq$maf<-var_freq%>%
select(a1,a2)%>%
apply(1,function(z) min(z))
a<-ggplot(var_freq,aes(maf))+
geom_density(fill="dodgerblue1",alpha=0.3)+
theme_light()
a
image.png 这部分的解释自己还没有太看懂,留待后续分解
vcftools --vcf ../output_results/sim_variants_3sample.vcf --minQ 30 --min-meanDP 4 --max-meanDP 10 --minDP 4 --maxDP 10 --recode --stdout | gzip -c > sim_variants_filtered.vcf
参数含义:
--vcf or --gzvcf
输入未过滤的vcf文件
--minQ
位点质量值
--min-meanDP
位点最小平均深度
--max-meanDP
位点最大平均深度
minDP
the minimum depth allowed for a genotype
maxDP
the maximum depth allowed for a genotype
bcftools view -H sim_variants_filtered.vcf.gz | wc -l
2212
过滤掉了将近三分之二
这样一次完整的变异检测流程就完成了,当然这其中还有好多细节部分的知识点需要学习!