肿瘤全外显子测序数据分析流程大放送

Original 曾健明生信技能树 2022-06-07

收录于合集 #找变异 31个

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3000多篇教程，要求我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

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。(从WGS测序得到的VCF文件里面提取位于外显子区域的【直播】我的基因组84)

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(这个是我自己写的脚本)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

老规矩，点击下面的阅读原文，链接都是可以点击的。

另外：不要点击广告，不要点击广告，不要点击广告

李尚福、魏凤和双双被拿下，与美国一份报告是否有关？

这样的洞庭湖决堤，实在让人同情不起来

有的人走了，却永远活着

圈内疯传某谣言

不要放过这些人渣

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

您可能也对以下帖子感兴趣

李尚福、魏凤和双双被拿下，与美国一份报告是否有关？

这样的洞庭湖决堤，实在让人同情不起来

有的人走了，却永远活着

圈内疯传某谣言

不要放过这些人渣

生成图片，分享到微信朋友圈

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

您可能也对以下帖子感兴趣