首先安装软件
先安装conda，使用清华的conda，说明书：https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/
然后下载安装miniconda，位置：https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/ ，
在国内需要更改镜像配置（我租用了一台美国HPC超算中心的服务器，不用设置镜像）
下载安装软件之前先搜索是否存在 https://bioconda.github.io/recipes.html
更改镜像源配置参考卖萌哥持续更新的conda教程：
然后就可以根据流程来使用conda安装一系列软件
首先搭建分析环境
为了做好目录整理，可以先创建文件夹，以存放各种软件包、数据库文件，以及分析过程中产生的结果
## 首先在用户的主目录下创建 wes_cancer 文件夹作为工作目录
mkdir ~/wes_cancer
cd ~/wes_cancer
## 在 ~/wes_cancer 中创建 biosoft project data 三个文件夹
## biosoft 存放软件安装包
## project 存放分析过程产生的文件
## data 存放数据库文件
mkdir biosoft project data
cd project
## 在 project 文件夹中创建若干个文件夹，分别存放每一步产生的文件
mkdir -p 0.sra 1.raw_fq 2.clean_fq 3.qc/{raw_qc,clean_qc} 4.align/{qualimap,flagstat,stats} 5.gatk/gvcf 6.mutect 7.annotation/{vep,annovar,funcotator,snpeff} 8.cnv/{gatk,cnvkit,gistic,facet} 9.pyclone 10.signature
2个重要的软件安装相关问题，参考之前的学习笔记GATK下载安装相关,ANNOVAR | 注释
熟悉参考基因组及必备数据库
gatk_hg38系列由BWA生成,构建索引需要下载很多文件，费时较久：
/bundle/hg38/索引：
cd ~/wes_cancer/data/
wget -c ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/hg38/Homo_sapiens_assembly38.fasta.gz
gatk需要用到的其他文件
因为比较多，可以使用nohup...&的形式将下载的命令都提交到后台（如果网络不好，可能会下载失败，下载后使用前需要检查）
## gatk
nohup wget -c ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/hg38/dbsnp_146.hg38.vcf.gz &
nohup wget -c ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/hg38/dbsnp_146.hg38.vcf.gz.tbi &
nohup wget -c ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/hg38/Mills_and_1000G_gold_standard.indels.hg38.vcf.gz &
nohup wget -c ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/hg38/Mills_and_1000G_gold_standard.indels.hg38.vcf.gz.tbi &
nohup wget -c ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/hg38/Homo_sapiens_assembly38.fasta.gz &
nohup wget -c ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/hg38/Homo_sapiens_assembly38.fasta.fai &
nohup wget -c ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/hg38/Homo_sapiens_assembly38.dict &
nohup wget -c ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/hg38/1000G_phase1.snps.high_confidence.hg38.vcf.gz &
nohup wget -c ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/hg38/1000G_phase1.snps.high_confidence.hg38.vcf.gz.tbi &
nohup wget -c ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/funcotator/funcotator_dataSources.v1.6.20190124s.tar.gz &
其它数据库文件：
## bed
wget ftp://ftp.ncbi.nlm.nih.gov/pub/CCDS/current_human/CCDS.current.txt
cat CCDS.current.txt | grep "Public" | perl -alne '{/\[(.*?)\]/;next unless $1;$gene=$F[2];$exons=$1;$exons=~s/\s//g;$exons=~s/-/\t/g;print "$F[0]\t$_\t$gene" foreach split/,/,$exons;}'|sort -u |bedtools sort -i |awk '{if($3>$2) print "chr"$0"\t0\t+"}' > hg38.exon.bed
构建索引使用的命令：
conda activate wes
cd ~/wes_cancer/data
gunzip Homo_sapiens_assembly38.fasta.gz
time bwa index -a bwtsw -p gatk_hg38 ~/wes_cancer/data/Homo_sapiens_assembly38.fasta
cd ~/wes_cancer/project
构建索引过程如下：
(base) root@1100150:~# conda activate wes
(wes) root@1100150:~# cd ~/wes_cancer/data
(wes) root@1100150:~/wes_cancer/data# time bwa index -a bwtsw -p gatk_hg38 ~/wes_cancer/data/Homo_sapiens_assembly38.fasta
[bwa_index] Pack FASTA... 52.77 sec
[bwa_index] Construct BWT for the packed sequence...
[BWTIncCreate] textLength=6434693834, availableWord=464768632
[BWTIncConstructFromPacked] 10 iterations done. 99999994 characters processed.
[BWTIncConstructFromPacked] 20 iterations done. 199999994 characters processed.
[BWTIncConstructFromPacked] 30 iterations done. 299999994 characters processed.
[BWTIncConstructFromPacked] 40 iterations done. 399999994 characters processed.
...
[bwt_gen] Finished constructing BWT in 711 iterations.
[bwa_index] 5822.96 seconds elapse.
[bwa_index] Update BWT... 33.52 sec
[bwa_index] Pack forward-only FASTA... 39.86 sec
[bwa_index] Construct SA from BWT and Occ... 1753.57 sec
[main] Version: 0.7.17-r1188
[main] CMD: bwa index -a bwtsw -p gatk_hg38 /root/wes_cancer/data/Homo_sapiens_assembly38.fasta
[main] Real time: 7785.516 sec; CPU: 7702.678 sec
real 129m45.519s
user 126m56.258s
sys 1m26.421s
完成后的索引文件目录（大约100G的数据库文件）
由于单位网络屏蔽了远程服务器的IP，暂时没能链接上，用了曾老师的教程内容
/public/biosoft/GATK/resources/bundle/hg38/bwa_index/
|-- [ 20K] gatk_hg38.amb
|-- [445K] gatk_hg38.ann
|-- [3.0G] gatk_hg38.bwt
|-- [767M] gatk_hg38.pac
|-- [1.5G] gatk_hg38.sa
|-- [6.2K] hg38.bwa_index.log
`-- [ 566] run.sh
/public/biosoft/GATK/resources/bundle/hg38/
|-- [1.8G] 1000G_phase1.snps.high_confidence.hg38.vcf.gz
|-- [2.0M] 1000G_phase1.snps.high_confidence.hg38.vcf.gz.tbi
|-- [3.2G] dbsnp_146.hg38.vcf.gz
|-- [3.0M] dbsnp_146.hg38.vcf.gz.tbi
|-- [ 59M] hapmap_3.3.hg38.vcf.gz
|-- [1.5M] hapmap_3.3.hg38.vcf.gz.tbi
|-- [568K] Homo_sapiens_assembly38.dict
|-- [3.0G] Homo_sapiens_assembly38.fasta
|-- [157K] Homo_sapiens_assembly38.fasta.fai
|-- [ 20M] Mills_and_1000G_gold_standard.indels.hg38.vcf.gz
|-- [1.4M] Mills_and_1000G_gold_standard.indels.hg38.vcf.gz.tbi
通过流量连接上服务器后查看自己下载的索引及数据库文件(没有分开为2个目录，且少了hapmap_3.3.hg38.vcf.gz，可能会对后续分析造成影响)：
(base) root@1100150:~# cd wes_cancer/data/
(base) root@1100150:~/wes_cancer/data# ll
total 26715496
drwxr-xr-x 2 root root 20 Jun 19 02:15 ./
drwxr-xr-x 5 root root 5 Jun 18 08:37 ../
-rw-r--r-- 1 root root 1888262073 Jun 18 12:28 1000G_phase1.snps.high_confidence.hg38.vcf.gz
-rw-r--r-- 1 root root 2128536 Jun 18 12:25 1000G_phase1.snps.high_confidence.hg38.vcf.gz.tbi
-rw-r--r-- 1 root root 9839781 Jun 18 12:26 CCDS.current.txt
-rw-r--r-- 1 root root 581712 Jun 18 12:25 Homo_sapiens_assembly38.dict
-rw-r--r-- 1 root root 3249912778 Jun 18 12:25 Homo_sapiens_assembly38.fasta
-rw-r--r-- 1 root root 160928 Jun 18 12:25 Homo_sapiens_assembly38.fasta.fai
-rw-r--r-- 1 root root 20685880 Jun 18 12:25 Mills_and_1000G_gold_standard.indels.hg38.vcf.gz
-rw-r--r-- 1 root root 1500013 Jun 18 12:25 Mills_and_1000G_gold_standard.indels.hg38.vcf.gz.tbi
-rw-r--r-- 1 root root 3411143311 Jun 18 12:29 dbsnp_146.hg38.vcf.gz
-rw-r--r-- 1 root root 2466606 Jun 18 12:25 dbsnp_146.hg38.vcf.gz.tbi
-rw-r--r-- 1 root root 15381038028 Jun 18 12:33 funcotator_dataSources.v1.6.20190124s.tar.gz
-rw-r--r-- 1 root root 20199 Jun 19 01:46 gatk_hg38.amb
-rw-r--r-- 1 root root 455474 Jun 19 01:46 gatk_hg38.ann
-rw-r--r-- 1 root root 3217347004 Jun 19 01:45 gatk_hg38.bwt
-rw-r--r-- 1 root root 804336731 Jun 19 01:46 gatk_hg38.pac
-rw-r--r-- 1 root root 1608673512 Jun 19 02:15 gatk_hg38.sa
-rw-r--r-- 1 root root 6813165 Jun 18 12:27 hg38.exon.bed
-rw------- 1 root root 32237249 Jun 18 12:33 nohup.out
第一步是QC
由于我的示例数据是clean.fq这一步就省略了
包括使用fasqc和multiqc两个软件查看测序质量，以及使用trim_galore软件进行过滤低质量reads和去除接头。
mkdir ~/project/boy
wkd=/home/jmzeng/project/boy
mkdir {raw,clean,qc,align,mutation}
cd qc
find /public/project/clinical/beijing_boy -name *gz |grep -v '\._'|xargs fastqc -t 10 -o ./
假设质量很差，就过滤：
### step3: filter the bad quality reads and remove adaptors.
mkdir $wkd/clean
cd $wkd/clean
find /public/project/clinical/beijing_boy -name *gz |grep -v '\._'|grep 1.fastq.gz > 1
find /public/project/clinical/beijing_boy -name *gz |grep -v '\._'|grep 2.fastq.gz > 2
paste 1 2 > config
### 打开文件 qc.sh ，并且写入内容如下：
source activate wes
bin_trim_galore=trim_galore
dir=$wkd/clean
cat config |while read id
do
arr=(${id})
fq1=${arr[0]}
fq2=${arr[1]}
echo $dir $fq1 $fq2
nohup $bin_trim_galore -q 25 --phred33 --length 36 -e 0.1 --stringency 3 --paired -o $dir $fq1 $fq2 &
done
source deactivate
读质量较好的测序数据进行比对
先走测试数据
## 先提取小的fq
source activate wes
find /public/project/clinical/beijing_boy -name *gz |grep -v '\._' > fq.txt
cat fq.txt |while read id ;do (zcat $id|head -10000 > $(basename $id ".gz"));done
## 然后一个个小fq文件比对
sample='7E5239'
bwa mem -t 5 -R "@RG\tID:$sample\tSM:$sample\tLB:WGS\tPL:Illumina" /public/biosoft/GATK/resources/bundle/hg38/bwa_index/gatk_hg38 7E5239.L1_1.fastq 7E5239.L1_2.fastq | samtools sort -@ 5 -o 7E5239.bam -
sample='7E5240'
bwa mem -t 5 -R "@RG\tID:$sample\tSM:$sample\tLB:WGS\tPL:Illumina" /public/biosoft/GATK/resources/bundle/hg38/bwa_index/gatk_hg38 7E5240_L1_A001.L1_1.fastq 7E5240_L1_A001.L1_2.fastq | samtools sort -@ 5 -o 7E5240.bam -
sample='7E5241'
bwa mem -t 5 -R "@RG\tID:$sample\tSM:$sample\tLB:WGS\tPL:Illumina" /public/biosoft/GATK/resources/bundle/hg38/bwa_index/gatk_hg38 7E5241.L1_1.fastq 7E5241.L1_2.fastq | samtools sort -@ 5 -o 7E5241.bam -
## 或者循环比对
# 7E5239 7E5239.L1_1.fastq 7E5239.L1_2.fastq
# 7E5240 7E5240_L1_A001.L1_1.fastq 7E5240_L1_A001.L1_2.fastq
# 7E5241 7E5241.L1_1.fastq 7E5241.L1_2.fastq
INDEX=/public/biosoft/GATK/resources/bundle/hg38/bwa_index/gatk_hg38
cat config |while read id
do
arr=($id)
fq1=${arr[1]}
fq2=${arr[2]}
sample=${arr[0]}
bwa mem -t 5 -R "@RG\tID:$sample\tSM:$sample\tLB:WGS\tPL:Illumina" $INDEX $fq1 $fq2 | samtools sort -@ 5 -o $sample.bam -
done
模仿这个例子完成小bam提取和比对：
(base) root@1100150:~/wes_cancer/project# source activate wes
(wes) root@1100150:~/wes_cancer/project# find ~/wes_cancer/project/2.clean_fq/ -name *gz
/root/wes_cancer/project/2.clean_fq/9Y1640.R1.clean.fastq.gz
/root/wes_cancer/project/2.clean_fq/9Y1640.R2.clean.fastq.gz
(wes) root@1100150:~/wes_cancer/project# find ~/wes_cancer/project/2.clean_fq/ -name *gz > fq.txt
(wes) root@1100150:~/wes_cancer/project# cat fq.txt |while read id ; do (zcat $id|head -10000 > $(basename $id ".gz")); done
(wes) root@1100150:~/wes_cancer/project# cat fq.txt
/root/wes_cancer/project/2.clean_fq/9Y1640.R1.clean.fastq.gz
/root/wes_cancer/project/2.clean_fq/9Y1640.R2.clean.fastq.gz
(wes) root@1100150:~/wes_cancer/project# ll
total 791
drwxr-xr-x 13 root root 16 Jun 22 06:59 ./
drwxr-xr-x 5 root root 5 Jun 18 08:37 ../
drwxr-xr-x 2 root root 2 Jun 18 09:02 0.sra/
drwxr-xr-x 2 root root 2 Jun 18 09:02 1.raw_fq/
drwxr-xr-x 2 root root 2 Jun 18 09:02 10.signature/
drwxr-xr-x 2 root root 4 Jun 21 06:48 2.clean_fq/
drwxr-xr-x 4 root root 4 Jun 18 09:02 3.qc/
drwxr-xr-x 5 root root 6 Jun 21 06:59 4.align/
drwxr-xr-x 3 root root 3 Jun 18 09:02 5.gatk/
drwxr-xr-x 2 root root 2 Jun 18 09:02 6.mutect/
drwxr-xr-x 6 root root 6 Jun 18 09:02 7.annotation/
drwxr-xr-x 6 root root 6 Jun 18 09:02 8.cnv/
drwxr-xr-x 2 root root 2 Jun 18 09:02 9.pyclone/
-rw-r--r-- 1 root root 874855 Jun 22 07:05 9Y1640.R1.clean.fastq
-rw-r--r-- 1 root root 874855 Jun 22 07:05 9Y1640.R2.clean.fastq
-rw-r--r-- 1 root root 122 Jun 22 06:57 fq.txt
(wes) root@1100150:~/wes_cancer/project# sample='9Y1640'
(wes) root@1100150:~/wes_cancer/project# bwa mem -t 5 -R "@RG\tID:$sample\tSM:$sample\tLB:WGS\tPL:Illumina" ~/wes_cancer/data/gatk_hg38 9Y1640.R1.clean.fastq 9Y1640.R2.clean.fastq | samtools sort -@ 5 -o 7E5239.bam -
[M::bwa_idx_load_from_disk] read 0 ALT contigs
[M::process] read 5000 sequences (708078 bp)...
[M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (2, 2045, 0, 0)
[M::mem_pestat] skip orientation FF as there are not enough pairs
[M::mem_pestat] analyzing insert size distribution for orientation FR...
[M::mem_pestat] (25, 50, 75) percentile: (142, 182, 238)
[M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 430)
[M::mem_pestat] mean and std.dev: (193.44, 69.83)
[M::mem_pestat] low and high boundaries for proper pairs: (1, 526)
[M::mem_pestat] skip orientation RF as there are not enough pairs
[M::mem_pestat] skip orientation RR as there are not enough pairs
[M::mem_process_seqs] Processed 5000 reads in 1.654 CPU sec, 0.360 real sec
[main] Version: 0.7.17-r1188
[main] CMD: bwa mem -t 5 -R @RG\tID:9Y1640\tSM:9Y1640\tLB:WGS\tPL:Illumina /root/wes_cancer/data/gatk_hg38 9Y1640.R1.clean.fastq 9Y1640.R2.clean.fastq
[main] Real time: 31.238 sec; CPU: 14.275 sec
[bam_sort_core] merging from 0 files and 5 in-memory blocks...
(wes) root@1100150:~/wes_cancer/project# ll
total 1250
drwxr-xr-x 13 root root 17 Jun 22 07:10 ./
drwxr-xr-x 5 root root 5 Jun 18 08:37 ../
drwxr-xr-x 2 root root 2 Jun 18 09:02 0.sra/
drwxr-xr-x 2 root root 2 Jun 18 09:02 1.raw_fq/
drwxr-xr-x 2 root root 2 Jun 18 09:02 10.signature/
drwxr-xr-x 2 root root 4 Jun 21 06:48 2.clean_fq/
drwxr-xr-x 4 root root 4 Jun 18 09:02 3.qc/
drwxr-xr-x 5 root root 6 Jun 21 06:59 4.align/
drwxr-xr-x 3 root root 3 Jun 18 09:02 5.gatk/
drwxr-xr-x 2 root root 2 Jun 18 09:02 6.mutect/
drwxr-xr-x 6 root root 6 Jun 18 09:02 7.annotation/
-rw-r--r-- 1 root root 451049 Jun 22 07:10 7E5239.bam
drwxr-xr-x 6 root root 6 Jun 18 09:02 8.cnv/
drwxr-xr-x 2 root root 2 Jun 18 09:02 9.pyclone/
-rw-r--r-- 1 root root 874855 Jun 22 07:05 9Y1640.R1.clean.fastq
-rw-r--r-- 1 root root 874855 Jun 22 07:05 9Y1640.R2.clean.fastq
-rw-r--r-- 1 root root 122 Jun 22 06:57 fq.txt
Jun 22生成的就是模仿后的结果，说明这个过程是OK的（这里复制曾老师的代码有的名称忘记修改了7E5239.bam，后面跑完整数据的时候进行修改）
如果是样本很多需要写循环，然后走正常的数据
ls /home/jmzeng/project/boy/clean/*1.fq.gz >1
ls /home/jmzeng/project/boy/clean/*2.fq.gz >2
cut -d"/" -f 7 1 |cut -d"_" -f 1 > 0
paste 0 1 2 > config
source activate wes
INDEX=/public/biosoft/GATK/resources/bundle/hg38/bwa_index/gatk_hg38
cat config |while read id
do
arr=($id)
fq1=${arr[1]}
fq2=${arr[2]}
sample=${arr[0]}
echo $sample $fq1 $fq2
bwa mem -t 5 -R "@RG\tID:$sample\tSM:$sample\tLB:WGS\tPL:Illumina" $INDEX $fq1 $fq2 | samtools sort -@ 5 -o $sample.bam -
done
而我只有一个样本，则只需要像示例数据那样完成即可：
(wes) root@1100150:~# cd wes_cancer/project/2.clean_fq/
(wes) root@1100150:~/wes_cancer/project/2.clean_fq# ll
total 5561163
drwxr-xr-x 2 root root 4 Jun 21 06:48 ./
drwxr-xr-x 13 root root 14 Jun 22 07:13 ../
-rw-r--r-- 1 root root 2897369062 Jun 21 06:48 9Y1640.R1.clean.fastq.gz
-rw-r--r-- 1 root root 2798611010 Jun 21 06:50 9Y1640.R2.clean.fastq.gz
(wes) root@1100150:~/wes_cancer/project/2.clean_fq# sample='9Y1640'
(wes) root@1100150:~/wes_cancer/project/2.clean_fq# bwa mem -t 5 -R "@RG\tID:$sample\tSM:$sample\tLB:WGS\tPL:Illumina" ~/wes_cancer/data/gatk_hg38 9Y1640.R1.clean.fastq 9Y1640.R2.clean.fastq | samtools sort -@ 5 -o 9Y1640.bam -
[M::bwa_idx_load_from_disk] read 0 ALT contigs
[E::main_mem] fail to open file `9Y1640.R1.clean.fastq'.
(wes) root@1100150:~/wes_cancer/project/2.clean_fq# sample='9Y1640'
(wes) root@1100150:~/wes_cancer/project/2.clean_fq# bwa mem -t 5 -R "@RG\tID:$sample\tSM:$sample\tLB:WGS\tPL:Illumina" ~/wes_cancer/data/gatk_hg38 9Y1640.R1.clean.fastq.gz 9Y1640.R2.clean.fastq.gz | samtools sort -@ 5 -o 9Y1640.bam -
[M::bwa_idx_load_from_disk] read 0 ALT contigs
[M::process] read 354436 sequences (50000086 bp)...
[M::process] read 353998 sequences (50000030 bp)...
[M::mem_pestat] # candidate unique pairs for (FF, FR, RF, RR): (113, 144879, 0, 173)
[M::mem_pestat] analyzing insert size distribution for orientation FF...
[M::mem_pestat] (25, 50, 75) percentile: (128, 187, 267)
[M::mem_pestat] low and high boundaries for computing mean and std.dev: (1, 545)
[M::mem_pestat] mean and std.dev: (197.98, 92.21)
[M::mem_pestat] low and high boundaries for proper pairs: (1, 684)
...
由于WES数据量很大，即使一个样本也要运行很久，就不记录全程了(真的太久了，1个小时了还没有结束的迹象，以后这种指令还是要放在后台运行)。
运行完成结果如下：
[M::mem_process_seqs] Processed 63974 reads in 26.351 CPU sec, 5.102 real sec
[main] Version: 0.7.17-r1188
[main] CMD: bwa mem -t 5 -R @RG\tID:9Y1640\tSM:9Y1640\tLB:WGS\tPL:Illumina /root/wes_cancer/data/gatk_hg38 9Y1640.R1.clean.fastq.gz 9Y1640.R2.clean.fastq.gz
[main] Real time: 5758.605 sec; CPU: 28791.178 sec
[bam_sort_core] merging from 30 files and 5 in-memory blocks...
(wes) root@1100150:~/wes_cancer/project/2.clean_fq# ll
total 8745086
drwxr-xr-x 2 root root 5 Jun 22 10:13 ./
drwxr-xr-x 13 root root 14 Jun 22 07:13 ../
-rw-r--r-- 1 root root 2897369062 Jun 21 06:48 9Y1640.R1.clean.fastq.gz
-rw-r--r-- 1 root root 2798611010 Jun 21 06:50 9Y1640.R2.clean.fastq.gz
-rw-r--r-- 1 root root 3261116989 Jun 22 10:13 9Y1640.bam
最简单的找变异流程
如果要理解参数的意思，参考曾老师的 【直播】我的基因组25：用bcftools来call variation
ref=/public/biosoft/GATK/resources/bundle/hg38/Homo_sapiens_assembly38.fasta
source activate wes
time samtools mpileup -ugf $ref *.bam | bcftools call -vmO z -o out.vcf.gz
ls *.bam |xargs -i samtools index {}
## 没有去除PCR重复
去除PCR重复
理解参数和教程为什么会过时（生物信息日新月异，要明白代码的逻辑，会复用即可）
# samtools markdup -r 7E5241.bam 7E5241.rm.bam
# samtools markdup -S 7E5241.bam 7E5241.mk.bam
完善的GATK流程
source activate wes
GATK=/home/jmzeng/biosoft/gatk4/gatk-4.0.6.0/gatk
ref=/public/biosoft/GATK/resources/bundle/hg38/Homo_sapiens_assembly38.fasta
snp=/public/biosoft/GATK/resources/bundle/hg38/dbsnp_146.hg38.vcf.gz
indel=/public/biosoft/GATK/resources/bundle/hg38/Mills_and_1000G_gold_standard.indels.hg38.vcf.gz
for sample in {7E5239.L1,7E5240,7E5241.L1}
do
echo $sample
# Elapsed time: 7.91 minutes
$GATK --java-options "-Xmx20G -Djava.io.tmpdir=./" MarkDuplicates \
-I $sample.bam \
-O ${sample}_marked.bam \
-M $sample.metrics \
1>${sample}_log.mark 2>&1
## Elapsed time: 13.61 minutes
$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
samtools index ${sample}_marked_fixed.bam
## 17.2 minutes
$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
检查感兴趣基因区域内比对和找变异情况
通过IGV可视化来加深自己对这个流程的把握和理解。
chr17 HAVANA gene 43044295 43170245
3.5G Jul 21 18:01 7E5240.bam
7.1G Jul 21 21:40 7E5240_bqsr.bam
4.7G Jul 21 20:28 7E5240_marked.bam
4.8G Jul 21 20:44 7E5240_marked_fixed.bam
把这些bam里面的BRCA1基因的reads拿出来：
samtools view -h 7E5240.bam chr17:43044295-43170245 |samtools sort -o 7E5240.brca1.bam -
samtools view -h 7E5240_bqsr.bam chr17:43044295-43170245 |samtools sort -o 7E5240_bqsr.brca1.bam -
samtools view -h 7E5240_marked.bam chr17:43044295-43170245 |samtools sort -o 7E5240_marked.brca1.bam -
samtools view -h 7E5240_marked_fixed.bam chr17:43044295-43170245 |samtools sort -o 7E5240_marked_fixed.brca1.bam -
ls *brca1.bam|xargs -i samtools index {}
有了这些特定基因区域的bam，就可以针对特定基因找变异
source activate wes
ref=/public/biosoft/GATK/resources/bundle/hg38/Homo_sapiens_assembly38.fasta
samtools mpileup -ugf $ref 7E5240_bqsr.brca1.bam | bcftools call -vmO z -o 7E5240_bqsr.vcf.gz
所有样本走samtools mpileup 和bcftools call 流程
仍然是参考我的直播基因组 【直播】我的基因组25：用bcftools来call variation
source activate wes
wkd=/home/jmzeng/project/boy
cd $wkd/align
ls *_bqsr.bam |xargs -i samtools index {}
ref=/public/biosoft/GATK/resources/bundle/hg38/Homo_sapiens_assembly38.fasta
nohup samtools mpileup -ugf $ref *_bqsr.bam | bcftools call -vmO z -o all_bqsr.vcf.gz &
比对及找变异结果的质控
raw和clean的fastq文件都需要使用fastqc质控。
比对的各个阶段的bam文件都可以质控。
使用qualimap对wes的比对bam文件总结测序深度及覆盖度。
source activate wes
wkd=/home/jmzeng/project/boy
cd $wkd/clean
ls *.gz |xargs fastqc -t 10 -o ./
cd $wkd/align
rm *_marked*.bam
ls *.bam |xargs -i samtools index {}
ls *.bam | while read id ;do (samtools flagstat $id > $(basename $id ".bam").stat);done
conda install bedtools
cat /public/annotation/CCDS/human/CCDS.20160908.txt |perl -alne '{/\[(.*?)\]/;next unless $1;$gene=$F[2];$exons=$1;$exons=~s/\s//g;$exons=~s/-/\t/g;print "$F[0]\t$_\t$gene" foreach split/,/,$exons;}'|sort -u |bedtools sort -i |awk '{print "chr"$0"\t0\t+"}' > $wkd/align/hg38.exon.bed
exon_bed=hg38.exon.bed
ls *_bqsr.bam | while read id;
do
sample=${id%%.*}
echo $sample
qualimap bamqc --java-mem-size=20G -gff $exon_bed -bam $id &
done
### featureCounts
gtf=/public/reference/gtf/gencode/gencode.v25.annotation.gtf.gz
featureCounts -T 5 -p -t exon -g gene_id \
-a $gtf *_bqsr.bam -o all.id.txt 1>counts.id.log 2>&1 &
比较两个找变异工具的区别
chr1 139213 . A G 388 .
DP=984;VDB=0.831898;SGB=-223.781;RPB=0.599582;MQB=0.0001971;MQSB=0.00457372;BQB=2.59396e-09;MQ0F=0.281504;ICB=0.333333;HOB=0.5;AC=3;AN=6;DP4=371,169,160,84;MQ=21
GT:PL 0/1:198,0,255 0/1:176,0,255 0/1:50,0,158
chr1 139213 rs370723703 A G 3945.77 . AC=1;AF=0.500;AN=2;BaseQRankSum=-2.999;ClippingRankSum=0.000;DB;DP=244;ExcessHet=3.0103;FS=2.256;MLEAC=1;MLEAF=0.500;MQ=29.33;MQRankSum=-0.929;QD=16.17;ReadPosRankSum=1.462;SOR=0.863 GT:AD:DP:GQ:PL 0/1:136,108:244:99:3974,0,6459
chr1 139213 rs370723703 A G 2261.77 . AC=1;AF=0.500;AN=2;BaseQRankSum=-1.191;ClippingRankSum=0.000;DB;DP=192;ExcessHet=3.0103;FS=9.094;MLEAC=1;MLEAF=0.500;MQ=32.03;MQRankSum=-0.533;QD=11.78;ReadPosRankSum=0.916;SOR=0.321 GT:AD:DP:GQ:PL 0/1:126,66:192:99:2290,0,7128
chr1 139213 rs370723703 A G 2445.77 . AC=1;AF=0.500;AN=2;BaseQRankSum=-2.495;ClippingRankSum=0.000;DB;DP=223;ExcessHet=3.0103;FS=10.346;MLEAC=1;MLEAF=0.500;MQ=30.18;MQRankSum=0.486;QD=10.97;ReadPosRankSum=-0.808;SOR=0.300 GT:AD:DP:GQ:PL 0/1:152,71:223:99:2474,0,7901
VCF下游分析
主要是：注释和过滤
注释
VEP,snpEFF,ANNOVAR
1.Annovar使用记录 (http://www.bio-info-trainee.com/641.html)
2.用annovar对snp进行注释 (http://www.bio-info-trainee.com/441.html)
3.对感兴趣的基因call variation(http://www.bio-info-trainee.com/2013.html)
4.WES（六）用annovar注释(http://www.bio-info-trainee.com/1158.html)
cd ~/biosoft/
ln -s /public/biosoft/ANNOVAR/ ANNOVAR
source activate wes
wkd=/home/jmzeng/project/boy
cd $wkd/mutation
mv ../align/*.vcf ./
~/biosoft/ANNOVAR/annovar/convert2annovar.pl -format vcf4old 7E5240_raw.vcf >7E5240.annovar
~/biosoft/ANNOVAR/annovar/annotate_variation.pl \
-buildver hg38 \
--outfile 7E5240.anno \
7E5240.annovar \
~/biosoft/ANNOVAR/annovar/humandb/
其实并不一定需要vcf文件，软件需要的只是染色体加上坐标即可，对于我们的vcf格式的变异文件， 软件通常会进行一定程度的格式化之后再进行注释。这里的注释主要有三种方式，分别是：
-
基于基因的注释,
exonic,splicing,ncRNA,UTR5,UTR3,intronic,upstream,downstream,intergenic -
基于区域的注释,
cytoBand,TFBS,SV,bed,GWAS,ENCODE,enhancers, repressors, promoters -
基于数据库的过滤,
dbSNP,ExAC,ESP6500,cosmic,gnomad,1000genomes,clinvar
如果要使用annovar一次性注释多个数据库
dir=/home/jianmingzeng/biosoft/ANNOVAR/annovar
db=$dir/humandb/
ls $db
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/archive_2.0/2018/clinvar_20180603.vcf.gz
perl $dir/annotate_variation.pl --downdb --webfrom annovar --buildver hg38
clinvar_20180603 $db
# perl $bin --downdb --webfrom annovar --buildver hg38 gnomad_genome $db
mkdir annovar_results
$dir/convert2annovar.pl -format vcf4old highQ.vcf 1> highQ.avinput 2>/dev/null
perl $dir/annotate_variation.pl -buildver hg38 -filter -dbtype clinvar_20180603 --outfile annovar_results/highQ_clinvar highQ.avinput $db
perl $dir/table_annovar.pl \
-buildver hg38 \
highQ.avinput $db \
-out test \
-remove -protocol \
refGene,clinvar_20170905 \
-operation g,r \
-nastring NA
补充作业
使用 Variant Effect Predictor 对所有遗传变异进行注释。过滤掉 dbSNP 数据库和千人基因组计划数据库中已知的 SNP。
应用 OMIM 数据库(http://omim.org/)查询蛋白 的结构及功能。利用 SIFT ，PolyPhen-2 以及 PROVEAN 软件, 预测 SNV 对蛋白质功能的影响 程度，仅当 3 种软件均预测同一遗传变异对蛋白质 的功能影响较大时，才认定该遗传变异具有高危害 性。利用 PROVEAN 软件 预测 Indel 对蛋白质功 能的影响。
其实dbNSFP数据库，就注释了这些变异对蛋白功能的影响。
变异位点的过滤
使用 GATK的 Joint Calling ， 过滤参考：https://mp.weixin.qq.com/s/W8Vfv1WmW6M7U0tIcPtlng
注意很多资料会过时
比如虽然可以找到了gatk3代码：http://baserecalibrator1.rssing.com/chan-10751514/all_p13.html 但是已经可以抛弃了，也就是说教程经常会过时。
