6 GATK4完整流程

0定义变量

source activate wes
#GATK=~/biosoft/gatk/gatk-4.1.2.0/gatk
ref=/mnt/f/kelly/bioTree/server/wesproject/hg38/Homo_sapiens_assembly38.fasta
snp=/mnt/f/kelly/bioTree/server/wesproject/hg38/dbsnp_146.hg38.vcf.gz
indel=/mnt/f/kelly/bioTree/server/wesproject/hg38/Mills_and_1000G_gold_standard.indels.hg38.vcf.gz  

1 标记PCR重复reads

sample=SRR7696207
echo $sample
gatk --java-options "-Xmx20G -Djava.io.tmpdir=./" MarkDuplicates \
-I $sample.bam -O ${sample}_marked.bam \
-M $sample.metrics \
1>log.mark 2>&1

运行结束后的文件如下

├── [ 17K]  log.mark
├── [3.8G]  SRR7696207.bam
├── [5.0G]  SRR7696207_marked.bam
├── [3.3K]  SRR7696207.metrics

2 FixMateInformation

gatk --java-options "-Xmx20G -Djava.io.tmpdir=./" FixMateInformation \
    -I ${sample}_marked.bam \
    -O ${sample}_marked_fixed.bam \
    -SO coordinate \
    1>${sample}_log.fix 2>&1 

这样就得到marked_fixed.bam文件。
接着进行index

samtools index ${sample}_marked_fixed.bam

3 BaseRecalibrator 碱基矫正

gatk --java-options "-Xmx20G -Djava.io.tmpdir=./"  BaseRecalibrator \
    -R $ref  \
    -I ${sample}_marked_fixed.bam  \
    --known-sites $snp \
    --known-sites $indel \
    -O ${sample}_recal.table \
    1>${sample}_log.recal 2>&1 

gatk --java-options "-Xmx20G -Djava.io.tmpdir=./"   ApplyBQSR \
    -R $ref  \
    -I ${sample}_marked_fixed.bam  \
    -bqsr ${sample}_recal.table \
    -O ${sample}_bqsr.bam \
    1>${sample}_log.ApplyBQSR  2>&1 

此时文件结构如下

├── [7.2M]  SRR7696207_bqsr.bai
├── [8.1G]  SRR7696207_bqsr.bam
├── [ 13K]  SRR7696207_log.ApplyBQSR
├── [  24]  SRR7696207_log.fix
├── [ 30K]  SRR7696207_log.HC
├── [ 39K]  SRR7696207_log.recal
├── [5.0G]  SRR7696207_marked.bam
├── [5.0G]  SRR7696207_marked_fixed.bam
├── [3.3M]  SRR7696207_marked_fixed.bam.bai
├── [3.3K]  SRR7696207.metrics
├── [246K]  SRR7696207_recal.table

这里同样包含了两个步骤：
第一步，BaseRecalibrator，这里计算出了所有需要进行重校正的read和特征值，然后把这些信息输出为一份校准表文件（sample_name.recal_data.table）
第二步，PrintReads，这一步利用第一步得到的校准表文件（sample_name.recal_data.table）重新调整原来BAM文件中的碱基质量值，并使用这个新的质量值重新输出一份新的BAM文件。
注意，因为BQSR实际上是为了（尽可能）校正测序过程中的系统性错误，因此，在执行的时候是按照不同的测序lane或者测序文库来进行的，这个时候@RG信息（BWA比对时所设置的）就显得很重要了，算法就是通过@RG中的ID来识别各个独立的测序过程，这也是我开始强调其重要性的原因。

第4节构建WGS主流程

4 HaplotypeCaller命令

gatk --java-options "-Xmx20G -Djava.io.tmpdir=./" HaplotypeCaller \
     -R $ref  \
     -I ${sample}_bqsr.bam \
      --dbsnp $snp \
      -O ${sample}_raw.vcf \
      1>${sample}_log.HC 2>&1

......
13:37:48.966 INFO  ProgressMeter - Starting traversal
13:37:48.966 INFO  ProgressMeter -        Current Locus  Elapsed Minutes     Regions Processed   Regions/Minute
13:37:58.966 INFO  ProgressMeter -         chr1:1221125              0.2                  5110          30660.0
13:38:09.200 INFO  ProgressMeter -         chr1:1705254              0.3                  7670          22743.9
13:38:19.204 INFO  ProgressMeter -         chr1:2899974              0.5                 13300          26390.6
13:38:29.224 INFO  ProgressMeter -         chr1:3950942              0.7                 18600          27721.2
13:38:39.236 INFO  ProgressMeter -         chr1:5289141              0.8                 25380          30292.4
13:38:49.236 INFO  ProgressMeter -         chr1:6707838              1.0                 32080          31936.3
13:38:59.241 INFO  ProgressMeter -         chr1:8292545              1.2                 39780          33963.7
13:39:09.243 INFO  ProgressMeter -         chr1:9939444              1.3                 47690          35644.1
13:39:19.261 INFO  ProgressMeter -        chr1:11517156              1.5                 55250          36713.0
13:39:29.284 INFO  ProgressMeter -        chr1:12845200              1.7                 61470          36765.1
13:39:39.418 INFO  ProgressMeter -        chr1:13371271              1.8                 63630          34565.2
13:39:49.440 INFO  ProgressMeter -        chr1:15196274              2.0                 72310          36013.0
13:39:59.443 INFO  ProgressMeter -        chr1:16167558              2.2                 77060          35436.1
......

这样就得到vcf文件

以上可以批量进行

#设置环境和变量
source activate wes
#如果把gatk加到了环境变量 就直接按下面走，否则
#gatk=~/biosoft/gatk/gatk-4.1.2.0/gatk
#下面gatk改为$gatk
#下面都设置为你自己的路径
ref=/mnt/f/kelly/bioTree/server/wesproject/hg38/Homo_sapiens_assembly38.fasta
snp=/mnt/f/kelly/bioTree/server/wesproject/hg38/dbsnp_146.hg38.vcf.gz
indel=/mnt/f/kelly/bioTree/server/wesproject/hg38/Mills_and_1000G_gold_standard.indels.hg38.vcf.gz  ```

for sample in {file1.sam,file2.sam,file3.sam...}
do
echo $sample
#mark dupulicates
gatk --java-options "-Xmx20G -Djava.io.tmpdir=./" MarkDuplicates \
-I $sample.bam -O ${sample}_marked.bam \
-M $sample.metrics \
1>log.mark 2>&1
#fixmateinformation
gatk --java-options "-Xmx20G -Djava.io.tmpdir=./" FixMateInformation \
-I ${sample}_marked.bam \
-O ${sample}_marked_fixed.bam \
-SO coordinate \
1>log.fix 2>&1 
#index
samtools index ${sample}_marked_fixed.bam
#baserecalibrator
gatk --java-options "-Xmx20G -Djava.io.tmpdir=./"  BaseRecalibrator \
    -R $ref  \
    -I ${sample}_marked_fixed.bam  \
    --known-sites $snp \
    --known-sites $indel \
    -O ${sample}_recal.table \
    1>${sample}_log.recal 2>&1 
    
gatk --java-options "-Xmx20G -Djava.io.tmpdir=./"   ApplyBQSR \
    -R $ref  \
    -I ${sample}_marked_fixed.bam  \
    -bqsr ${sample}_recal.table \
    -O ${sample}_bqsr.bam \
    1>${sample}_log.ApplyBQSR  2>&1 
    
## 使用GATK的HaplotypeCaller命令
gatk --java-options "-Xmx20G -Djava.io.tmpdir=./" HaplotypeCaller \
     -R $ref  \
     -I ${sample}_bqsr.bam \
      --dbsnp $snp \
      -O ${sample}_raw.vcf \
      1>${sample}_log.HC 2>&1  
done

可以把上面内容写入脚本，比如

cat gatk4.sh

然后运行就可以了