▶ 肿瘤全外显子测序数据分析流程大放送

by 作者：Cavin Ma 休斯顿•易生活网站长 · August 28, 2018

这个一个肿瘤外显子项目的文章发表并且公布的公共数据，我这里给出全套分析流程代码。只需要你肯实践，就可以运行成功。

PS:有些后起之秀自己运营公众号或者博客喜欢批评我们这些老人，一味的堆砌代码不给解释，恶意揣测我们是因为不懂代码的原理。我表示很无语，我写了3千多篇教程，如果一篇篇都重复提到基础知识，我真的做不到。比如下面的流程，包括软件的用法，软件安装，注释数据库的下载，我博客都说过好几次了，直播我的基因组系列也详细解读过，我告诉你去哪里学，你却不珍惜，不当回事，呵呵。

文章解读

paper；https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4812767/a whole-exome sequencing (WES) study was performed in 161 NPC cases and 895 controls of Southern Chinese descent

测序概述：

We sequenced the blood samples from 161 NPC cases, including 39 EAO cases, 63 FH+ cases from 52 independent families, and 59 sporadic cases by WES and achieved an average coverage of 49-fold on target (range of 32- to 76-fold)

后来还扩大了验证样本集：

An additional 2,160 NPC cases and 2,433 healthy controls from Hong Kong were further examined for the selected candidate variants.

数据全部上传了：

Whole-exome sequencing data for the early-age onset cases have been deposited in the Sequence Read Archive (SRA) database (accession ID SRA291701).

了解WES数据分析步骤：2016-A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data

选择部分数据如下：

随便选择了5个样本，其ID号及描述如下,

SRX445405   MALE    NPC15   SRR1139956  NPC15F  NO  SRS540548   NPC15F-T
SRX445406   MALE    NPC15   SRR1139958  NPC15F  NO  SRS540549   NPC15F-N
SRX445407   MALE    NPC29   SRR1139966  NPC29F  YES SRS540550   NPC29F-T
SRX445408   MALE    NPC29   SRR1139973  NPC29F  YES SRS540551   NPC29F-N
SRX445409   FEMALE  NPC10   SRR1139999  NPC10F  NO  SRS540552   NPC10F-T
SRX445410   FEMALE  NPC10   SRR1140007  NPC10F  NO  SRS540553   NPC10F-N
SRX445411   FEMALE  NPC34   SRR1140015  NPC34F  NO  SRS540554   NPC34F-T
SRX445412   FEMALE  NPC34   SRR1140023  NPC34F  NO  SRS540555   NPC34F-N
SRX445413   MALE    NPC37   SRR1140044  NPC37F  YES SRS540556   NPC37F-T
SRX445414   MALE    NPC37   SRR1140045  NPC37F  YES SRS540557   NPC37F-N

从SRA数据库下载并转换为fastq测序数据文件

把上面的描述文本存为文件npc.sra.txt下载脚本如下：

cat npc.sra.txt | cut -f 4|while read id
do echo $id
wget -c ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByStudy/sra/SRP/SRP035/SRP035573/$id/$id.sra
done

转换脚本如下:

cat npc.sra.txt | while read id
do
array=($id)
echo  ${array[3]}.sra  ${array[7]} 
~/biosoft/sratoolkit/sratoolkit.2.8.2-1-centos_linux64/bin/fastq-dump --gzip --split-3 -A \  
${array[7]}  ${array[3]}.sra 
done

得到的fastq文件如下：

3.5G Aug 26 09:48 NPC10F-N_1.fastq.gz
3.6G Aug 26 09:48 NPC10F-N_2.fastq.gz
3.2G Aug 26 09:48 NPC10F-T_1.fastq.gz
3.3G Aug 26 09:48 NPC10F-T_2.fastq.gz
2.7G Aug 26 09:47 NPC15F-N_1.fastq.gz
2.8G Aug 26 09:47 NPC15F-N_2.fastq.gz
2.8G Aug 26 09:47 NPC15F-T_1.fastq.gz
2.9G Aug 26 09:47 NPC15F-T_2.fastq.gz
2.8G Aug 26 09:47 NPC29F-N_1.fastq.gz
2.9G Aug 26 09:47 NPC29F-N_2.fastq.gz
2.4G Aug 26 09:47 NPC29F-T_1.fastq.gz
2.5G Aug 26 09:47 NPC29F-T_2.fastq.gz
2.0G Aug 26 09:47 NPC34F-N_1.fastq.gz
2.0G Aug 26 09:47 NPC34F-N_2.fastq.gz
2.2G Aug 26 09:48 NPC34F-T_1.fastq.gz
2.3G Aug 26 09:48 NPC34F-T_2.fastq.gz
2.1G Aug 26 09:47 NPC37F-N_1.fastq.gz
2.1G Aug 26 09:47 NPC37F-N_2.fastq.gz
2.2G Aug 26 09:46 NPC37F-T_1.fastq.gz
2.2G Aug 26 09:46 NPC37F-T_2.fastq.gz

简单的走一下fastqc+multiqc 看看数据质量，一般都会很不错的，这个数据也不例外。

ls *.gz |xargs ~/biosoft/fastqc/FastQC/fastqc -o ./ -t 5

但是值得注意的是本文章中的测序reads的编码格式是phred+64 而不是传统的33

然后走WES的标准SNP-calling流程

选用的是经典的GATK best practice的流程，代码如下:


module load java/1.8.0_91
GENOME=/home/jianmingzeng/reference/genome/human_g1k_v37/human_g1k_v37.fasta
INDEX=/home/jianmingzeng/reference/index/bwa/human_g1k_v37
GATK=/home/jianmingzeng/biosoft/GATK/GenomeAnalysisTK.jar
PICARD=/home/jianmingzeng/biosoft/picardtools/2.9.2/picard.jar

DBSNP=/home/jianmingzeng/annotation/variation/human/dbSNP/All_20160601.vcf.gz
SNP=/home/jianmingzeng/biosoft/GATK/resources/bundle/b37/1000G_phase1.snps.high_confidence.b37.vcf.gz
INDEL=/home/jianmingzeng/biosoft/GATK/resources/bundle/b37/Mills_and_1000G_gold_standard.indels.b37.vcf.gz

KG_SNP=/home/jianmingzeng/biosoft/GATK/resources/bundle/b37/1000G_phase1.snps.high_confidence.b37.vcf.gz
Mills_indels=/home/jianmingzeng/biosoft/GATK/resources/bundle/b37/Mills_and_1000G_gold_standard.indels.b37.vcf
KG_indels=/home/jianmingzeng/biosoft/GATK/resources/bundle/b37/1000G_phase1.indels.b37.vcf

TMPDIR=/home/jianmingzeng/tmp/software
## samtools and bwa are in the environment
## samtools Version: 1.3.1 (using htslib 1.3.1)
## bwa Version: 0.7.15-r1140


#arr=($1)
#fq1=${arr[0]}
#fq2=${arr[1]}
#sample=${arr[2]}

fq1=$1
fq2=$2
sample=$3


#####################################################
################ Step 1 : Alignment #################
#####################################################
start=$(date +%s.%N)
echo bwa `date`
bwa mem -t 5 -M  -R "@RG\tID:$sample\tSM:$sample\tLB:WES\tPL:Illumina" $INDEX $fq1 $fq2 1>$sample.sam 2>/dev/null 
echo bwa `date`
dur=$(echo "$(date +%s.%N) - $start" | bc)
printf "Execution time for BWA : %.6f seconds" $dur
echo 

#####################################################
################ Step 2: Sort and Index #############
#####################################################
start=$(date +%s.%N)
echo SortSam `date`
java -Djava.io.tmpdir=$TMPDIR    -Xmx40g -jar $PICARD SortSam SORT_ORDER=coordinate INPUT=$sample.sam OUTPUT=$sample.bam
samtools index $sample.bam
echo SortSam `date`
dur=$(echo "$(date +%s.%N) - $start" | bc)
printf "Execution time for SortSam : %.6f seconds" $dur
echo 
rm $sample.sam 

#####################################################
################ Step 3: Basic Statistics ###########
#####################################################
start=$(date +%s.%N)
echo stats `date`
samtools flagstat $sample.bam > ${sample}.alignment.flagstat
samtools stats  $sample.bam > ${sample}.alignment.stat
echo plot-bamstats -p ${sample}_QC  ${sample}.alignment.stat
echo stats `date`
dur=$(echo "$(date +%s.%N) - $start" | bc)
printf "Execution time for Basic Statistics : %.6f seconds" $dur
echo 

#####################################################
####### Step 4: multiple filtering for bam files ####
#####################################################


###MarkDuplicates###
start=$(date +%s.%N)
echo MarkDuplicates `date`
java -Djava.io.tmpdir=$TMPDIR    -Xmx40g -jar $PICARD MarkDuplicates \
INPUT=$sample.bam OUTPUT=${sample}_marked.bam METRICS_FILE=$sample.metrics
echo MarkDuplicates `date`
dur=$(echo "$(date +%s.%N) - $start" | bc)
printf "Execution time for MarkDuplicates : %.6f seconds" $dur
echo 
rm $sample.bam  

###FixMateInfo###
start=$(date +%s.%N)
echo FixMateInfo `date`
java -Djava.io.tmpdir=$TMPDIR    -Xmx40g -jar $PICARD FixMateInformation \
INPUT=${sample}_marked.bam OUTPUT=${sample}_marked_fixed.bam SO=coordinate
samtools index ${sample}_marked_fixed.bam
echo FixMateInfo `date`
dur=$(echo "$(date +%s.%N) - $start" | bc)
printf "Execution time for FixMateInfo  : %.6f seconds" $dur
echo 


rm ${sample}_marked.bam 

#####################################################
####### Step 5: gatk process bam files ##############
#####################################################



### SplitNCigar ###
start=$(date +%s.%N)
echo SplitNCigar `date`
java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T SplitNCigarReads \
 -R $GENOME  -I ${sample}_marked_fixed.bam  -o ${sample}_marked_fixed_split.bam \
-rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 -U ALLOW_N_CIGAR_READS 
#--fix_misencoded_quality_scores
## --fix_misencoded_quality_scores only if phred 64 
echo SplitNCigar `date`
dur=$(echo "$(date +%s.%N) - $start" | bc)
printf "Execution time for SplitNCigar : %.6f seconds" $dur
echo 

rm ${sample}_marked_fixed.bam 
# rm ${sample}.sam ${sample}.bam ${sample}_marked.bam ${sample}_marked_fixed.bam


###RealignerTargetCreator###
start=$(date +%s.%N)
echo RealignerTargetCreator `date`
java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T RealignerTargetCreator \
-I ${sample}_marked_fixed_split.bam -R $GENOME -o ${sample}_target.intervals \
-known $Mills_indels -known $KG_indels -nt 5
echo RealignerTargetCreator `date`
dur=$(echo "$(date +%s.%N) - $start" | bc)
printf "Execution time for RealignerTargetCreator : %.6f seconds" $dur
echo 


###IndelRealigner###
start=$(date +%s.%N)
echo IndelRealigner `date`
java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T IndelRealigner \
-I ${sample}_marked_fixed_split.bam  -R $GENOME -targetIntervals ${sample}_target.intervals \
-o ${sample}_realigned.bam -known $Mills_indels -known $KG_indels
echo IndelRealigner `date`
dur=$(echo "$(date +%s.%N) - $start" | bc)
printf "Execution time for IndelRealigner : %.6f seconds" $dur
echo 

rm ${sample}_marked_fixed_split.bam

###BaseRecalibrator###
start=$(date +%s.%N)
echo BaseRecalibrator `date`
java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T BaseRecalibrator \
-I ${sample}_realigned.bam -R $GENOME -o ${sample}_temp.table -knownSites $DBSNP
echo BaseRecalibrator `date`
dur=$(echo "$(date +%s.%N) - $start" | bc)
printf "Execution time for BaseRecalibrator : %.6f seconds" $dur
echo 


###PrintReads###
start=$(date +%s.%N)
echo PrintReads `date`
java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T PrintReads \
-R $GENOME -I ${sample}_realigned.bam -o ${sample}_recal.bam -BQSR ${sample}_temp.table
samtools index ${sample}_recal.bam
echo PrintReads `date`
dur=$(echo "$(date +%s.%N) - $start" | bc)
printf "Execution time for PrintReads : %.6f seconds" $dur
echo 

rm  ${sample}_realigned.bam
chmod uga=r   ${sample}_recal.bam 
 
#####################################################
############## Step 6: gatk call snp/indel##########
#####################################################

### 
start=$(date +%s.%N)
echo HaplotypeCaller `date`
java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T HaplotypeCaller  \
-R $GENOME -I ${sample}_recal.bam --dbsnp $DBSNP  \
-stand_emit_conf 10 -o  ${sample}_raw.snps.indels.vcf
echo HaplotypeCaller `date`
dur=$(echo "$(date +%s.%N) - $start" | bc)
printf "Execution time for HaplotypeCaller : %.6f seconds" $dur
echo 


java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T SelectVariants  -R $GENOME  \
-selectType SNP \
-V ${sample}_raw.snps.indels.vcf -o ${sample}_raw_snps.vcf

java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T SelectVariants  -R $GENOME \
-selectType INDEL  \
-V ${sample}_raw.snps.indels.vcf   -o ${sample}_raw_indels.vcf


## 
:'
'
## for SNP

java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T VariantFiltration -R $GENOME  \
--filterExpression "QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0"  \
--filterName "my_snp_filter" \
-V ${sample}_raw_snps.vcf  -o ${sample}_filtered_snps.vcf   

java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T SelectVariants -R $GENOME  \
--excludeFiltered \
-V ${sample}_filtered_snps.vcf  -o  ${sample}_filtered_PASS.snps.vcf


java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T VariantEval -R $GENOME  \
-eval ${sample}_filtered_PASS.snps.vcf -o  ${sample}_filtered_PASS.snps.vcf.eval


## for  INDEL


java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T VariantFiltration -R $GENOME  \
--filterExpression "QD < 2.0 || FS > 200.0 || ReadPosRankSum < -20.0"  \
--filterName "my_indel_filter" \
-V ${sample}_raw_indels.vcf  -o ${sample}_filtered_indels.vcf   

java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T SelectVariants -R $GENOME  \
--excludeFiltered \
-V ${sample}_filtered_indels.vcf  -o  ${sample}_filtered_PASS.indels.vcf

java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK -T VariantEval -R $GENOME  \
-eval ${sample}_filtered_PASS.indels.vcf -o  ${sample}_filtered_PASS.indels.vcf.eval


:'
'
## bam文件的QC
bedtools coverage  -hist   -abam ${sample}.bam  \
-b ~/annotation/CCDS/human/hg19_exon.bed  >${sample}.exome.coverage.hist.txt
## 后面的几个质控，代码是我自己写的，所以大家可以不运行。
perl ~/scripts/calc_coverage_depth.pl ~/annotation/CCDS/human/CCDS.20110907.txt ${sample}.bam

比对成功后得到的bam文件如下；

7.7G Aug 26 19:22 NPC10F-N_marked_fixed.bam
7.7G Aug 26 22:59 NPC10F-N_marked_fixed_split.bam
7.7G Aug 26 23:57 NPC10F-N_realigned.bam
 15G Aug 27 03:45 NPC10F-N_recal.bam 
 
7.0G Aug 26 19:49 NPC10F-T_marked_fixed.bam
7.0G Aug 26 22:55 NPC10F-T_marked_fixed_split.bam
7.0G Aug 26 23:48 NPC10F-T_realigned.bam
 13G Aug 27 03:12 NPC10F-T_recal.bam

Snp-calling结束后得到的vcf如下：

    82576 NPC10F-N_raw_indels.vcf
   863243 NPC10F-N_raw_snps.vcf
   945753 NPC10F-N_recal_raw.snps.indels.vcf
    89604 NPC10F-T_raw_indels.vcf
   819657 NPC10F-T_raw_snps.vcf
   909190 NPC10F-T_recal_raw.snps.indels.vcf

这些vcf里面的变异位点还需要进行简单的过滤，或者只提取外显子区域的变异位点。

消耗时间如下；

# for NPC10F-N_1.fastq.gz NPC10F-N_2.fastq.gz NPC10F-N
bwa Sat Aug 26 16:04:44 CST 2017
bwa Sat Aug 26 17:07:11 CST 2017
SortSam Sat Aug 26 17:07:11 CST 2017
SortSam Sat Aug 26 17:45:04 CST 2017
stats Sat Aug 26 17:45:04 CST 2017
plot-bamstats -p NPC10F-N_QC NPC10F-N.alignment.stat
stats Sat Aug 26 17:56:07 CST 2017
MarkDuplicates Sat Aug 26 17:56:07 CST 2017
MarkDuplicates Sat Aug 26 18:40:25 CST 2017
FixMateInfo Sat Aug 26 18:40:25 CST 2017
FixMateInfo Sat Aug 26 19:26:39 CST 2017
SplitNCigar Sat Aug 26 22:20:48 CST 2017
SplitNCigar Sat Aug 26 22:59:32 CST 2017
RealignerTargetCreator Sat Aug 26 22:59:32 CST 2017
RealignerTargetCreator Sat Aug 26 23:17:35 CST 2017
IndelRealigner Sat Aug 26 23:17:35 CST 2017
IndelRealigner Sat Aug 26 23:57:35 CST 2017
BaseRecalibrator Sat Aug 26 23:57:35 CST 2017
BaseRecalibrator Sun Aug 27 01:27:39 CST 2017
PrintReads Sun Aug 27 01:27:39 CST 2017
PrintReads Sun Aug 27 03:52:03 CST 2017


#for NPC10F-T_1.fastq.gz NPC10F-T_2.fastq.gz NPC10F-T
bwa Sat Aug 26 16:54:14 CST 2017
bwa Sat Aug 26 17:48:07 CST 2017
SortSam Sat Aug 26 17:48:07 CST 2017
SortSam Sat Aug 26 18:20:48 CST 2017
stats Sat Aug 26 18:20:48 CST 2017
plot-bamstats -p NPC10F-T_QC NPC10F-T.alignment.stat
stats Sat Aug 26 18:30:45 CST 2017
MarkDuplicates Sat Aug 26 18:30:45 CST 2017
MarkDuplicates Sat Aug 26 19:11:40 CST 2017
FixMateInfo Sat Aug 26 19:11:40 CST 2017
FixMateInfo Sat Aug 26 19:52:37 CST 2017
SplitNCigar Sat Aug 26 22:20:54 CST 2017
SplitNCigar Sat Aug 26 22:55:53 CST 2017
RealignerTargetCreator Sat Aug 26 22:55:53 CST 2017
RealignerTargetCreator Sat Aug 26 23:14:19 CST 2017
IndelRealigner Sat Aug 26 23:14:19 CST 2017
IndelRealigner Sat Aug 26 23:48:43 CST 2017
BaseRecalibrator Sat Aug 26 23:48:43 CST 2017
BaseRecalibrator Sun Aug 27 01:10:26 CST 2017
PrintReads Sun Aug 27 01:10:26 CST 2017
PrintReads Sun Aug 27 03:18:33 CST 2017

外显子的QC结果是：

## for NPC10F-N
32541594    2392028455  0.982188933530586   73.5068004044301
18711840    934414537   0.971564415370185   49.9370739061471
17075251    505674931   0.895198983425405   29.6144947444696
15656543    241509710   0.819070615186704   15.4254812189383

## for NPC10F-T
32348763    2946257280  0.97636879840624    91.0778962398037
18182204    1054428864  0.94406442121146    57.9923569221861
16075260    555403212   0.842772759844003   34.5501853158207
14181528    265599059   0.741905340358179   18.7285219900141

可以看到T的测序深度高于N，符合实验设计。T的外显子区域平均测序深度高达91X，是比较好的数据了，而且外显子旁边50bp范围区域平均测序深度还有57X，旁边100bp区域还有34X，也说明这款芯片的捕获效率比较好

然后走走somatic mutation calling流程

因为是配对数据，还可以走somatic mutation calling流程

reference=/home/jianmingzeng/reference/genome/human_g1k_v37/human_g1k_v37.fasta
GENOME=/home/jianmingzeng/reference/genome/human_g1k_v37/human_g1k_v37.fasta
TMPDIR=/home/jianmingzeng/tmp/software
normal_bam=NPC10F-N_recal.bam
tumor_bam=NPC10F-T_recal.bam
sample=NPC10F

#####################################################
################### Step : Run VarScan #############
#####################################################
normal_pileup="samtools mpileup -q 1 -f $reference $normal_bam";
tumor_pileup="samtools mpileup -q 1 -f $reference $tumor_bam";
# Next, issue a system call that pipes input from these commands into VarScan :
java -Djava.io.tmpdir=$TMPDIR   -Xmx40g  -jar ~/biosoft/VarScan/VarScan.v2.3.9.jar \
somatic <($normal_pileup) <($tumor_pileup) $sample
java -jar ~/biosoft/VarScan/VarScan.v2.3.9.jar processSomatic $sample.snp

#####################################################
################### Step : Run Mutect2 #############
##################################################### 
java -Djava.io.tmpdir=$TMPDIR   -Xmx40g -jar $GATK  -T MuTect2 \
-R $GENOME -I:tumor $tumor_bam  -I:normal $normal_bam \
--dbsnp  $DBSNP   -o ${sample}-mutect2.vcf

#####################################################
################### Step : Run Muse#################
#####################################################
~/biosoft/muse/muse call -O $sample -f $reference $tumor_bam $normal_bam
~/biosoft/muse/muse sump -I ${sample}.MuSE.txt -E –O ${sample}.vcf –D $DBSNP

其中varscan耗费两个半小时，结果如下：

893539210 positions in tumor
891822458 positions shared in normal
79827518 had sufficient coverage for comparison
79718238 were called Reference
26 were mixed SNP-indel calls and filtered
102492 were called Germline
3703 were called LOH
2569 were called Somatic
490 were called Unknown
0 were called Variant

Reading input from NPC10F.snp
Opening output files: NPC10F.snp.Somatic NPC10F.snp.Germline NPC10F.snp.LOH
101352 VarScan calls processed
2342 were Somatic (556 high confidence)
95144 were Germline (4830 high confidence)
3502 were LOH (3484 high confidence)

这3个软件找到的somatic mutation可以互相对比一下，差异主要是在哪里，都值得自己去探究，这样最终确定的肿瘤外显子测序数据分析流程就更有把握。

需要安装的软件

软件太多了，我就不一一列出具体代码了，还有很多需要下载的参考基因组，变异数据库也是以前直播基因组的时候已经反复提到过，也不赘述啦。

conda install -c bioconda bedtools
conda install -c bioconda bwa
conda install -c bioconda samtools

cd ~/biosoft
mkdir sratoolkit &&  cd sratoolkit
wget https://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/current/sratoolkit.current-centos_linux64.tar.gz
tar zxvf sratoolkit.current-centos_linux64.tar.gz
~/biosoft/sratoolkit/sratoolkit.2.8.2-1-centos_linux64/bin/fastdump -h
## https://sourceforge.net/projects/picard/
## https://github.com/broadinstitute/picard
cd ~/biosoft
mkdir picardtools &&  cd picardtools
wget http://ncu.dl.sourceforge.net/project/picard/picard-tools/1.119/picard-tools-1.119.zip
unzip picard-tools-1.119.zip
mkdir 2.9.2 && cd 2.9.2 
wget https://github.com/broadinstitute/picard/releases/download/2.9.2/picard.jar


cd ~/biosoft
## https://sourceforge.net/projects/varscan/files/
## http://varscan.sourceforge.net/index.html
mkdir VarScan  &&  cd VarScan  
wget https://sourceforge.net/projects/varscan/files/VarScan.v2.3.9.jar 

cd ~/biosoft
mkdir SnpEff &&  cd SnpEff
##  http://snpeff.sourceforge.net/
##  http://snpeff.sourceforge.net/SnpSift.html 
##  http://snpeff.sourceforge.net/SnpEff_manual.html
wget http://sourceforge.net/projects/snpeff/files/snpEff_latest_core.zip 
## java -jar snpEff.jar download GRCh37.75
## java -Xmx4G -jar snpEff.jar -i vcf -o vcf GRCh37.75 example.vcf > example_snpeff.vcf
unzip snpEff_latest_core.zip

转载自： http://www.bio-info-trainee.com/2735.html

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作者：Cavin Ma 休斯顿•易生活网站长

来自哈尔滨，自2009年来休斯顿工作和生活。曾经久居医学中心7年之久，现定居Pearland梨城，熟知地产行情，太太是这两地经验丰富的地产经纪人。从一个人怀揣三千美金来美国，到现在全家四口买房定居，深知来美国打拼之艰辛。把自己在美国生活的经验和感受总结成“休斯顿•易生活网”，希望能够对华人朋友有所帮助。我做过博后，DIY过EB1A绿卡，爱烧烤，爱钓鱼、钓螃蟹，懂房地产，爱网购，爱总结回国礼物，对美国保健品也有相当的研究。有事您找我：咨询房地产微信：wull_123，其他业务微信：wecareshare 邮箱 ezthelife@gmai.com

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▶ 肿瘤全外显子测序数据分析流程大放送

文章解读

测序概述：

后来还扩大了验证样本集：

数据全部上传了：

选择部分数据如下：

从SRA数据库下载并转换为fastq测序数据文件

然后走WES的标准SNP-calling流程

然后走走somatic mutation calling流程

需要安装的软件

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▶ 肿瘤全外显子测序数据分析流程大放送

文章解读

测序概述：

后来还扩大了验证样本集：

数据全部上传了：

选择部分数据如下：

从SRA数据库下载并转换为fastq测序数据文件

然后走WES的标准SNP-calling流程

然后走走somatic mutation calling流程

需要安装的软件

Leave a Reply Cancel reply

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精华导读

最新评论

美国什么值得买