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2023-09-24-【学习】使用GATK4-0找SNP.md

2023-09-24-【学习】使用GATK4-0找SNP.md 23 KB
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title: 【学习】使用GATK4.0找SNP urlname: shi-yong-GATK-zhao-SNP date: 2023-09-24 18:49:18 index_img: https://api.limour.top/randomImg?d=2023-09-24 18:49:18

tags: ['GATK', 'SNP', 'WGS']

配置环境

  • 基础编程环境
  • GitHub 下载加速

  • 可能需要用到的加速服务

    SRA工具

    conda create -n sra_tools -c bioconda sra-tools
conda activate sra_tools
conda install -c conda-forge lftp -y
conda install -c conda-forge pigz -y # 或许换成 pbgzip 更好，此时将 -p 换成 -n 来指定线程数
conda install -c bioconda pbgzip -y
prefetch
# vdb-config -i # 设置 HTTP 代理

    GATK4

    conda create -n GATK4 -c bioconda gatk4
conda activate GATK4
conda install -c bioconda samtools -y
conda install -c bioconda bwa -y
conda install -c bioconda pbgzip -y # 并行版bgzip，bgzip是修改过的gzip，更适合生信领域
conda install -c bioconda tabix -y # 操作VCF文件，与bgzip配套
# conda install -c bioconda fastqc -y # 改用fastp了
# conda install -c bioconda trimmomatic -y # 改用fastp了
conda install -c bioconda fastp -y
# conda install -c bioconda bcftools -y # 用于重命名染色体
# ln -s $CONDA_PREFIX/lib/libgsl.so $CONDA_PREFIX/lib/libgsl.so.25 # 无效，放弃
# conda create -n GATK4-VEP -c bioconda ensembl-vep -y # 根正苗红的突变注释软件，不懂有什么奇怪依赖，解析环境半天

  • BWA是DNA比对工具(不会跨外显子比对)，STAR是RNA比对工具

  • 找SNP不推荐用RNAseq的数据

  • 各种比对工具的说明

    准备数据

    参考数据

  • NCBI上各物种的参考序列，可以找到RefSeq，比如Human是GCF_000001405

  • 知道序号后可以到FTP上下载相应的genomic.fna.gz文件

  • 比如GCF_000001405，依次进入000/001/405即可找到对应的文件

    wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.40_GRCh38.p14/GCF_000001405.40_GRCh38.p14_genomic.fna.gz -O GRCh38.p14.fna.gz
conda run -n sra_tools pigz -d GRCh38.p14.fna.gz # 得到 GRCh38.p14.fna
# 创建索引
samtools faidx GRCh38.p14.fna # 得到 GRCh38.p14.fna.fai
# 查看一段序列
samtools faidx GRCh38.p14.fna NC_000001.11:1000000-1000200
# 创建比对索引
bwa index GRCh38.p14.fna # 会自动在 bwtsw, is or rb2 三种算法中选择合适的
# 创建dict
gatk CreateSequenceDictionary -R GRCh38.p14.fna

  • 最后得到的RefSeq目录结构如下

    # conda create -n linux -c conda-forge tree
# conda run -n linux tree -f -h --du
[8.5G]  .
├── [ 79K]  ./GRCh38.p14.dict
├── [3.1G]  ./GRCh38.p14.fna
├── [ 21K]  ./GRCh38.p14.fna.amb
├── [ 90K]  ./GRCh38.p14.fna.ann
├── [3.1G]  ./GRCh38.p14.fna.bwt
├── [ 26K]  ./GRCh38.p14.fna.fai
├── [786M]  ./GRCh38.p14.fna.pac
└── [1.5G]  ./GRCh38.p14.fna.sa

    已知SNP

  • GATK官网提供了一些数据

  • NCBI提供了dbSNP

  • lftp ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/，密码空，直接回车

  • 下载和参考数据相对应的indels.hg38.vcf

    wget https://ftp.ncbi.nlm.nih.gov/snp/latest_release/VCF/GCF_000001405.40.gz -O GRCh38.dbSNP.ncbi.vcf.gz

    转换染色体名称到NCBI的参考文件

  • assembly_report.txt 在下载NCBI参考数据FTP目录下

    wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.40_GRCh38.p14/GCF_000001405.40_GRCh38.p14_assembly_report.txt -O GRCh38.p14_assembly_report.txt 
grep -e '^[^#]' GRCh38.p14_assembly_report.txt | awk -F'\t' '{ print $NF, $7 }' | sed 's/\r / /g' > rename_file.txt
conda create -n something_fuck -c conda-forge mamba
conda activate something_fuck
mamba install -c bioconda bcftools
bcftools annotate --rename-chrs rename_file.txt -o Homo_sapiens_assembly38.known_indels.ncbi.vcf Homo_sapiens_assembly38.known_indels.vcf
bcftools annotate --rename-chrs rename_file.txt -o hapmap_3.3.hg38.ncbi.vcf hapmap_3.3.hg38.vcf
bcftools annotate --rename-chrs rename_file.txt -o Mills_and_1000G_gold_standard.indels.hg38.ncbi.vcf Mills_and_1000G_gold_standard.indels.hg38.vcf
conda run -n GATK4 pbgzip -n 4 *.ncbi.vcf # 似乎一次只压缩一个，多运行几次

    建立索引

    nano knownSitesIndex.sh && chmod +x knownSitesIndex.sh
./knownSitesIndex.sh

    #!/bin/bash
source activate GATK4
#设置knownSites数据存放目录
knownSites=/home/jovyan/upload/knownSites
for file in $knownSites/*.ncbi.vcf.gz
do
echo $file
gatk IndexFeatureFile \
-I $file
done

  • 最后得到的knownSites目录结构如下

    [ 25G]  .
├── [ 25G]  ./GRCh38.dbSNP.ncbi.vcf.gz
├── [4.2M]  ./GRCh38.dbSNP.ncbi.vcf.gz.tbi
├── [ 79K]  ./GRCh38.p14_assembly_report.txt
├── [ 61M]  ./hapmap_3.3.hg38.ncbi.vcf.gz
├── [2.1M]  ./hapmap_3.3.hg38.ncbi.vcf.gz.tbi
├── [ 58M]  ./Homo_sapiens_assembly38.known_indels.ncbi.vcf.gz
├── [2.1M]  ./Homo_sapiens_assembly38.known_indels.ncbi.vcf.gz.tbi
├── [ 20M]  ./Mills_and_1000G_gold_standard.indels.hg38.ncbi.vcf.gz
├── [2.0M]  ./Mills_and_1000G_gold_standard.indels.hg38.ncbi.vcf.gz.tbi
└── [ 23K]  ./rename_file.txt

    测序数据

  • WGS的DNA测序数据

  • 以SRX247249做学习的示例数据，感谢曾老师指路

  • 下载方式见SRA文件转FASTQ文件

  • 也可以到ENA数据库上下载

    conda run -n sra_tools prefetch --option-file SRR_Acc_List.txt
nano 11.sh && chmod +x 11.sh
./11.sh

    #!/bin/bash
source activate sra_tools
#任务名
TASKN=SRX247249
#设置SRA根目录, pwd是当前目录
ROOTDIR=`pwd`
#设置rawData存放目录
rawData=/home/jovyan/upload/rawData/$TASKN
mkdir -p $rawData
 
cd $ROOTDIR
for  file in `cat SRR_Acc_List.txt`
do
echo $file
mkdir $rawData/$file
cd $rawData/$file
fasterq-dump --split-3 $ROOTDIR/$file -e 6
pigz -p 6 *
done

rawData质控

  • 原始数据质量判断​

  • 原始数据过滤工具

    质量判断（可跳过）

    nano qc.sh && chmod +x qc.sh
./qc.sh

    #!/bin/bash
source activate GATK4
#任务名
TASKN=SRX247249
#设置rawData存放目录
rawData=/home/jovyan/upload/rawData/$TASKN
#设置qc结果的输出目录
QCDIR=/home/jovyan/upload/rawData/$TASKN"_fastqc"
mkdir -p $QCDIR
 
for file in $rawData/*
do
echo $file
SAMPLE=${file##*/}
echo $QCDIR"/"$SAMPLE
mkdir $QCDIR"/"$SAMPLE
fastqc -o $QCDIR"/"$SAMPLE --threads=6 `ls $rawData/$SAMPLE/*`
done

  • 对于PE而言，正向和反向reads的测量过程是独立的，将当成两次SE来处理

  • 最后的报告中：

  • Q20的碱基要在95%以上（最差不低于90%）

  • Q30要求大于85%（最差也不要低于80%）

  • 对于人类来说，GC含量应该在40%左右

    fastp一键质控

  • fastp的详细说明; 中文介绍

    nano qc.sh && chmod +x qc.sh
./qc.sh

    #!/bin/bash
source activate GATK4
#任务名
TASKN=SRX247249
#设置rawData存放目录
rawData=/home/jovyan/upload/rawData/$TASKN
#设置qc结果的输出目录
QCDIR=/home/jovyan/upload/rawData/$TASKN"_fastp"
mkdir -p $QCDIR
#设置cleanData的存放目录
CLEAN=/home/jovyan/upload/cleanData/$TASKN
mkdir -p $CLEAN
 
for file in $rawData/*
do
echo $file
SAMPLE=${file##*/}
echo $QCDIR"/"$SAMPLE
mkdir $QCDIR"/"$SAMPLE
echo $CLEAN"/"$SAMPLE
mkdir $CLEAN"/"$SAMPLE
cd $QCDIR"/"$SAMPLE
fastp -c -w 4 \
-o $CLEAN"/"$SAMPLE"/out.R1.fq.gz" \
-O $CLEAN"/"$SAMPLE"/out.R2.fq.gz" \
-h $QCDIR"/"$SAMPLE"/fastp.html" \
-j $QCDIR"/"$SAMPLE"/fastp.json" \
-i `ls $rawData/$SAMPLE/*_1.fastq.gz` \
-I `ls $rawData/$SAMPLE/*_2.fastq.gz`
done

  • 最后得到的cleanData目录结构如下

    [ 23G]  .
├── [9.1G]  ./SRR799559
│   ├── [4.4G]  ./SRR799559/out.R1.fq.gz
│   └── [4.7G]  ./SRR799559/out.R2.fq.gz
├── [7.0G]  ./SRR799560
│   ├── [3.4G]  ./SRR799560/out.R1.fq.gz
│   └── [3.5G]  ./SRR799560/out.R2.fq.gz
└── [7.4G]  ./SRR799561
├── [3.6G]  ./SRR799561/out.R1.fq.gz
└── [3.8G]  ./SRR799561/out.R2.fq.gz

    数据比对

    nano bwa_and_markdup.sh && chmod +x bwa_and_markdup.sh
./bwa_and_markdup.sh

    #!/bin/bash
source activate GATK4
#任务名
TASKN=SRX247249
#设置cleanData的存放目录
CLEAN=/home/jovyan/upload/cleanData/$TASKN
#设置RefSeq的存放目录
RefSeq=/home/jovyan/data/refseq/GRCh38.p14.fna
#设置Read Group信息，见 https://gatk.broadinstitute.org/hc/en-us/articles/360035890671-Read-groups
RGroup_PL=ILLUMINA # 所用的测序平台：ILLUMINA,SLX,SOLEXA,SOLID,454,LS454,COMPLETE,PACBIO,IONTORRENT,CAPILLARY,HELICOS或UNKNOWN。CG测序为COMPLETE
RGroup_SM=$TASKN # 样本ID，同一个样本可能有多个lane，此时用样本ID相关联
RGroup='PL:'$RGroup_PL'\tSM:'$RGroup_SM
#设置BAM的存放目录
BAM=/home/jovyan/upload/BAM/$TASKN
mkdir -p $BAM
 
for file in $CLEAN/*
do
 
echo $file
SAMPLE=${file##*/}
echo $BAM"/"$SAMPLE
mkdir $BAM"/"$SAMPLE
echo '@RG\tID:'$SAMPLE'\t'$RGroup
 
#1 比对
bwa mem -t 4 -M -R '@RG\tID:'$SAMPLE'\t'$RGroup $RefSeq `ls $CLEAN/$SAMPLE/*` \
| samtools view -Sb - > $BAM"/"$SAMPLE"/raw.bam"
 
#2 排序
samtools sort -@ 4 -m 4G -O bam -o $BAM"/"$SAMPLE"/sorted.bam" $BAM"/"$SAMPLE"/raw.bam"
rm $BAM"/"$SAMPLE"/raw.bam"
 
#3 标记PCR重复
gatk MarkDuplicates -I $BAM"/"$SAMPLE"/sorted.bam" \
-O $BAM"/"$SAMPLE"/sorted.markdup.bam" \
-M $BAM"/"$SAMPLE"/sorted.markdup_metrics.txt"
rm $BAM"/"$SAMPLE"/sorted.bam"
 
#4 创建比对索引文件
samtools index $BAM"/"$SAMPLE"/sorted.markdup.bam"
 
done

  • 最后得到的BAM目录结构如下

    [ 30G]  .
├── [ 11G]  ./SRR799559
│   ├── [ 11G]  ./SRR799559/sorted.markdup.bam
│   ├── [4.5M]  ./SRR799559/sorted.markdup.bam.bai
│   └── [3.7K]  ./SRR799559/sorted.markdup_metrics.txt
├── [8.8G]  ./SRR799560
│   ├── [8.8G]  ./SRR799560/sorted.markdup.bam
│   ├── [3.9M]  ./SRR799560/sorted.markdup.bam.bai
│   └── [3.7K]  ./SRR799560/sorted.markdup_metrics.txt
└── [9.6G]  ./SRR799561
├── [9.5G]  ./SRR799561/sorted.markdup.bam
├── [4.1M]  ./SRR799561/sorted.markdup.bam.bai
└── [3.7K]  ./SRR799561/sorted.markdup_metrics.txt

    同样本合并

    nano merge.sh && chmod +x merge.sh
./merge.sh

    #!/bin/bash
source activate GATK4
#任务名
TASKN=SRX247249
#设置BAM的存放目录
BAM=/home/jovyan/upload/BAM/$TASKN
#设置merge后的数据存放目录
MERGEDBAM=/home/jovyan/upload/merged/$TASKN/SAMPLE1
mkdir -p $MERGEDBAM
 
samtools merge $MERGEDBAM"/sorted.markdup.bam" \
`find "$BAM" -name "sorted.markdup.bam" -type f -exec readlink -f {} \;`
samtools index $MERGEDBAM"/sorted.markdup.bam"

  • 最后得到的MERGED目录结构如下

    [ 21G]  .
└── [ 21G]  ./SAMPLE1
├── [ 21G]  ./SAMPLE1/sorted.markdup.bam
└── [6.8M]  ./SAMPLE1/sorted.markdup.bam.bai

    局部重比对

  • 具体见黄树嘉博士的相关介绍

  • 因为本文是GATK 4.0的HaplotypeCaller模块，自带局部重比对，故用到的时候再写

    BQSR

    nano BQSR.sh && chmod +x BQSR.sh
./BQSR.sh

    #!/bin/bash
source activate GATK4
#任务名
TASKN=SRX247249
#设置merged数据存放目录
MERGED=/home/jovyan/upload/merged/$TASKN
#设置RefSeq的存放目录
RefSeq=/home/jovyan/data/refseq/GRCh38.p14.fna
#设置knownSites数据存放目录
knownSites=/home/jovyan/upload/knownSites
knownSites=$(echo $(ls $knownSites/*.ncbi.vcf.gz | sed 's/^/--known-sites /' | tr '\n' ' '))
echo $knownSites
 
for file in $MERGED/*
do
 
echo $file
SAMPLE=${file##*/}
echo $MERGED"/"$SAMPLE
 
gatk BaseRecalibrator $knownSites \
-R $RefSeq \
-I $MERGED"/"$SAMPLE"/sorted.markdup.bam" \
-O $MERGED"/"$SAMPLE"/recal_data.table"
 
gatk ApplyBQSR \
-R $RefSeq \
-I $MERGED"/"$SAMPLE"/sorted.markdup.bam" \
--bqsr-recal-file $MERGED"/"$SAMPLE"/recal_data.table" \
-O $MERGED"/"$SAMPLE"/sorted.markdup.BQSR.bam"
 
samtools index $MERGED"/"$SAMPLE"/sorted.markdup.BQSR.bam"
 
done

  • 最后得到的MERGED目录结构如下

    [ 54G]  .
└── [ 54G]  ./SAMPLE1
├── [2.5M]  ./SAMPLE1/recal_data.table
├── [ 21G]  ./SAMPLE1/sorted.markdup.bam
├── [6.8M]  ./SAMPLE1/sorted.markdup.bam.bai
├── [8.8M]  ./SAMPLE1/sorted.markdup.BQSR.bai
├── [ 33G]  ./SAMPLE1/sorted.markdup.BQSR.bam
└── [7.6M]  ./SAMPLE1/sorted.markdup.BQSR.bam.bai

    两步法变异检测

    HaplotypeCaller

    nano HaplotypeCaller.sh && chmod +x HaplotypeCaller.sh
./HaplotypeCaller.sh

    #!/bin/bash
source activate GATK4
 
#任务名
TASKN=SRX247249
#设置BQSR数据存放目录
MERGED=/home/jovyan/upload/merged/$TASKN
#设置RefSeq的存放目录
RefSeq=/home/jovyan/data/refseq/GRCh38.p14.fna
 
for file in $MERGED/*
do
 
echo $file
SAMPLE=${file##*/}
echo $MERGED"/"$SAMPLE
 
gatk --java-options "-Xmx4g" HaplotypeCaller -ERC GVCF \
-R $RefSeq \
-I $MERGED"/"$SAMPLE"/sorted.markdup.BQSR.bam" \
-O $MERGED"/"$SAMPLE"/HC.g.vcf.gz"
 
done

CombineGVCFs

单样本

VCFPATH=$MERGED'/SAMPLE1'

多样本

nano CombineGVCFs.sh && chmod +x CombineGVCFs.sh
./CombineGVCFs.sh

#!/bin/bash
source activate GATK4
 
#任务名
TASKN=SRX247249
#设置BQSR数据存放目录
MERGED=/home/jovyan/upload/merged/$TASKN
#设置RefSeq的存放目录
RefSeq=/home/jovyan/data/refseq/GRCh38.p14.fna
#设置最后输出的路径
VCFPATH=/home/jovyan/upload/VCF/$TASKN
mkdir -p $VCFPATH
 
variant=$(echo $(ls $MERGED/*/HC.g.vcf.gz | sed 's/^/--variant /' | tr '\n' ' '))
echo $variant
 
gatk CombineGVCFs $$variant \
    -R $RefSeq \
    -O $VCFPATH'/HC.g.vcf.gz'

GenotypeGVCFs

nano GenotypeGVCFs.sh && chmod +x GenotypeGVCFs.sh
./GenotypeGVCFs.sh

#!/bin/bash
source activate GATK4
 
#任务名
TASKN=SRX247249
#设置BQSR数据存放目录
MERGED=/home/jovyan/upload/merged/$TASKN
#设置RefSeq的存放目录
RefSeq=/home/jovyan/data/refseq/GRCh38.p14.fna
#设置最后输出的路径
VCFPATH=$MERGED'/SAMPLE1'
mkdir -p $VCFPATH
 
gatk --java-options "-Xmx4g" GenotypeGVCFs \
    -R $RefSeq \
    -V $VCFPATH'/HC.g.vcf.gz' \
    -O $VCFPATH'/HC.vcf.gz'

  • 最后得到的结果如下

    ├── [6.8G]  ./HC.g.vcf.gz
├── [5.0M]  ./HC.g.vcf.gz.tbi
├── [127M]  ./HC.vcf.gz
├── [2.0M]  ./HC.vcf.gz.tbi

    VQSR

    nano VQSR.sh && chmod +x VQSR.sh
./VQSR.sh

    #!/bin/bash
source activate GATK4
 
#任务名
TASKN=SRX247249
#设置BQSR数据存放目录
MERGED=/home/jovyan/upload/merged/$TASKN
#设置RefSeq的存放目录
RefSeq=/home/jovyan/data/refseq/GRCh38.p14.fna
#设置最后输出的路径
VCFPATH=$MERGED'/SAMPLE1'
#设置knownSites数据存放目录
knownSites=/home/jovyan/upload/knownSites
 
gatk VariantRecalibrator \
-R $RefSeq \
-V $VCFPATH'/HC.vcf.gz' \
--resource:hapmap,known=false,training=true,truth=true,prior=15.0 $knownSites/hapmap_3.3.hg38.ncbi.vcf.gz \
--resource:dbsnp,known=true,training=false,truth=false,prior=2.0 $knownSites/GRCh38.dbSNP.ncbi.vcf.gz \
-an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an SOR \
-mode SNP \
-O $VCFPATH/snp.recal \
--tranches-file $VCFPATH/snp.tranches \
--rscript-file $VCFPATH/snp.plots.R
 
gatk ApplyVQSR \
-R $RefSeq \
-V $VCFPATH'/HC.vcf.gz' \
-O $VCFPATH'/snp.VQSR.vcf.gz' \
--truth-sensitivity-filter-level 99.0 \
--tranches-file $VCFPATH/snp.tranches \
--recal-file $VCFPATH/snp.recal \
-mode SNP
 
gatk VariantRecalibrator \
-R $RefSeq \
-V $VCFPATH'/snp.VQSR.vcf.gz' \
--resource:dbindel,known=true,training=false,truth=false,prior=2.0 $knownSites/Homo_sapiens_assembly38.known_indels.ncbi.vcf.gz \
--resource:mills,known=true,training=true,truth=true,prior=12.0 $knownSites/Mills_and_1000G_gold_standard.indels.hg38.ncbi.vcf.gz \
-an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an SOR \
-mode INDEL --max-gaussians 6 \
-O $VCFPATH/snp.indel.recal \
--tranches-file $VCFPATH/snp.indel.tranches \
--rscript-file $VCFPATH/snp.indel.plots.R
 
gatk ApplyVQSR \
-R $RefSeq \
-V $VCFPATH'/snp.VQSR.vcf.gz' \
-O $VCFPATH'/snp.indel.VQSR.vcf.gz' \
--truth-sensitivity-filter-level 99.0 \
--tranches-file $VCFPATH/snp.indel.tranches \
--recal-file $VCFPATH/snp.indel.recal \
-mode INDEL

  • 最后得到的结果如下

    ├── [2.7M]  ./snp.plots.R
├── [6.2M]  ./snp.plots.R.pdf
├── [199M]  ./snp.recal
├── [7.5M]  ./snp.recal.idx
├── [ 584]  ./snp.tranches
├── [7.5K]  ./snp.tranches.pdf
├── [151M]  ./snp.VQSR.vcf.gz
├── [2.0M]  ./snp.VQSR.vcf.gz.tbi
├── [2.8M]  ./snp.indel.plots.R
├── [6.2M]  ./snp.indel.plots.R.pdf
├── [ 35M]  ./snp.indel.recal
├── [256K]  ./snp.indel.recal.idx
├── [ 595]  ./snp.indel.tranches
├── [153M]  ./snp.indel.VQSR.vcf.gz
├── [2.0M]  ./snp.indel.VQSR.vcf.gz.tbi

  • SNP内容示例

    # tabix snp.VQSR.vcf.gz  NC_000001.11 | head -n 5
NC_000001.11    16378   .       T       C       35.32   VQSRTrancheSNP99.90to100.00     AC=2;AF=1.00;AN=2;DP=2;ExcessHet=0.0000;FS=0.000;MLEAC=1;MLEAF=0.500;MQ=20.00;QD=17.66;SOR=2.303;VQSLOD=-1.018e+01;culprit=MQ GT:AD:DP:GQ:PL  1/1:0,2:2:6:47,6,0
NC_000001.11    17020   .       G       A       59.32   VQSRTrancheSNP99.90to100.00     AC=2;AF=1.00;AN=2;DP=2;ExcessHet=0.0000;FS=0.000;MLEAC=1;MLEAF=0.500;MQ=31.66;QD=29.66;SOR=0.693;VQSLOD=-5.480e+00;culprit=MQ GT:AD:DP:GQ:PL  1/1:0,2:2:6:71,6,0
NC_000001.11    17385   .       G       A       60.32   VQSRTrancheSNP99.90to100.00     AC=2;AF=1.00;AN=2;DP=2;ExcessHet=0.0000;FS=0.000;MLEAC=1;MLEAF=0.500;MQ=32.28;QD=30.16;SOR=2.303;VQSLOD=-2.357e+00;culprit=MQ GT:AD:DP:GQ:PL  1/1:0,2:2:6:72,6,0
NC_000001.11    20254   .       G       A       64.64   VQSRTrancheSNP99.90to100.00     AC=1;AF=0.500;AN=2;BaseQRankSum=2.37;DP=8;ExcessHet=0.0000;FS=0.000;MLEAC=1;MLEAF=0.500;MQ=24.89;MQRankSum=-2.030e-01;QD=8.08;ReadPosRankSum=-1.611e+00;SOR=1.034;VQSLOD=-1.317e+01;culprit=MQ      GT:AD:DP:GQ:PGT:PID:PL:PS       0|1:5,3:8:72:1|0:20250_T_C:72,0,126:20250
NC_000001.11    39230   .       G       A       83.64   VQSRTrancheSNP99.90to100.00     AC=1;AF=0.500;AN=2;BaseQRankSum=-1.078e+00;DP=15;ExcessHet=0.0000;FS=0.000;MLEAC=1;MLEAF=0.500;MQ=26.72;MQRankSum=2.20;QD=5.58;ReadPosRankSum=-1.917e+00;SOR=1.022;VQSLOD=-1.362e+01;culprit=MQ     GT:AD:DP:GQ:PL  0/1:10,5:15:91:91,0,239

    变异注释

    安装 VEP

  • VEP官网地址

    sudo docker pull ensemblorg/ensembl-vep
sudo docker run --rm -t -i -v ~/upload:/data:z ensemblorg/ensembl-vep pwd
sudo docker run --rm -t -i -v ~/upload:/data:z ensemblorg/ensembl-vep ls -al /opt/vep/
sudo mkdir -p ~/upload/vep && sudo chmod 777 ~/upload/vep
sudo docker run --rm -t -i -v ~/upload:/data:z -v ~/upload/vep:/opt/vep/.vep:z ensemblorg/ensembl-vep INSTALL.pl
# sudo docker run --rm -t -i -v ~/upload:/data:Z ensemblorg/ensembl-vep cat INSTALL.pl > INSTALL.pl
# 自行分析 INSTALL.pl，构造下载后的结构，以下是104版本的
# 太慢了，手动下载，请各显神通，下载地址来自上一步的输出
wget https://ftp.ensembl.org/pub/release-104/variation/indexed_vep_cache/homo_sapiens_vep_104_GRCh38.tar.gz -O ~/upload/vep
tar -zxvf homo_sapiens_vep_104_GRCh38.tar.gz

    进行注释

    mkdir -p ~/upload/VEP/SRX247249 && chmod -R 777 ~/upload/VEP/SRX247249
# mv ~/data/refseq ~/upload
# chmod -R 777 ~/upload/refseq
# chmod -R 777 ~/upload/vep
# chmod -R 777 ~/upload/merged/SRX247249
sudo docker run --rm -t -i -v ~/upload:/data:z ensemblorg/ensembl-vep \
vep --fasta /data/refseq/GRCh38.p14.fna \
    --format vcf --vcf --fork 4 --hgvs --force_overwrite --everything \
    --offline --dir_cache /data/vep \
    -i /data/merged/SRX247249/SAMPLE1/snp.indel.VQSR.vcf.gz \
    -o /data/merged/SRX247249/SAMPLE1/snp.indel.VQSR.VEP.vcf
# sudo chmod 777 ~/upload/merged/SRX247249/SAMPLE1/snp.indel.VQSR.VEP.vcf
# pbgzip -n 4 snp.indel.VQSR.VEP.vcf
# tabix -p vcf snp.indel.VQSR.VEP.vcf.gz

  • 最后得到的结果如下

    ├── [346M]  ./snp.indel.VQSR.VEP.vcf.gz
├── [210K]  ./snp.indel.VQSR.VEP.vcf.gz_summary.html
├── [1.6M]  ./snp.indel.VQSR.VEP.vcf.gz.tbi
├── [8.2K]  ./snp.indel.VQSR.VEP.vcf.gz_warnings.txt

    # tabix snp.indel.VQSR.VEP.vcf.gz  NC_000001.11 | head -n 2
NC_000001.11    16378   .       T       C       35.32   VQSRTrancheSNP99.90to100.00     AC=2;AF=1.00;AN=2;DP=2;ExcessHet=0.0000;FS=0.000;MLEAC=1;MLEAF=0.500;MQ=20.00;QD=17.66;SOR=2.303;VQSLOD=-1.018e+01;culprit=MQ;CSQ=C|downstream_gene_variant|MODIFIER|DDX11L1|ENSG00000223972|Transcript|ENST00000450305|transcribed_unprocessed_pseudogene||||||||||rs148220436|2708|1||SNV|HGNC|HGNC:37102|YES||||||||||||||||||||||||||||||||||||||||||||,C|downstream_gene_variant|MODIFIER|DDX11L1|ENSG00000223972|Transcript|ENST00000456328|processed_transcript||||||||||rs148220436|1969|1||SNV|HGNC|HGNC:37102||||1|||||||||||||||||||||||||||||||||||||||||,C|intron_variant&non_coding_transcript_variant|MODIFIER|WASH7P|ENSG00000227232|Transcript|ENST00000488147|unprocessed_pseudogene||8/10|ENST00000488147.1:n.1067+229A>G|||||||rs148220436||-1||SNV|HGNC|HGNC:38034|YES||||||||||||||||||||||||||||||||||||||||||||,C|downstream_gene_variant|MODIFIER|MIR6859-1|ENSG00000278267|Transcript|ENST00000619216|miRNA||||||||||rs148220436|991|-1||SNV|HGNC|HGNC:50039|YES||||||||||||||||||||||||||||||||||||||||||||,C|regulatory_region_variant|MODIFIER|||RegulatoryFeature|ENSR00000344266|CTCF_binding_site||||||||||rs148220436||||SNV|||||||||||||||||||||||||||||||||||||||||||||||,C|regulatory_region_variant|MODIFIER|||RegulatoryFeature|ENSR00001164745|promoter_flanking_region||||||||||rs148220436||||SNV||||||||||||||||||||||||||||||||||||||||||||||| GT:AD:DP:GQ:PL  1/1:0,2:2:6:47,6,0
NC_000001.11    17020   .       G       A       59.32   VQSRTrancheSNP99.90to100.00     AC=2;AF=1.00;AN=2;DP=2;ExcessHet=0.0000;FS=0.000;MLEAC=1;MLEAF=0.500;MQ=31.66;QD=29.66;SOR=0.693;VQSLOD=-5.480e+00;culprit=MQ;CSQ=A|downstream_gene_variant|MODIFIER|DDX11L1|ENSG00000223972|Transcript|ENST00000450305|transcribed_unprocessed_pseudogene||||||||||rs199740902|3350|1||SNV|HGNC|HGNC:37102|YES||||||||||||||||||||||||||||||||||||||||||||,A|downstream_gene_variant|MODIFIER|DDX11L1|ENSG00000223972|Transcript|ENST00000456328|processed_transcript||||||||||rs199740902|2611|1||SNV|HGNC|HGNC:37102||||1|||||||||||||||||||||||||||||||||||||||||,A|non_coding_transcript_exon_variant|MODIFIER|WASH7P|ENSG00000227232|Transcript|ENST00000488147|unprocessed_pseudogene|7/11||ENST00000488147.1:n.746C>T||746|||||rs199740902||-1||SNV|HGNC|HGNC:38034|YES||||||||||||||||||||||||||||||||||||||||||||,A|downstream_gene_variant|MODIFIER|MIR6859-1|ENSG00000278267|Transcript|ENST00000619216|miRNA||||||||||rs199740902|349|-1||SNV|HGNC|HGNC:50039|YES||||||||||||||||||||||||||||||||||||||||||||      GT:AD:DP:GQ:PL  1/1:0,2:2:6:71,6,0