Mutect2+scRNAseq+cancer cell
2021-02-24 本文已影响0人
小贝学生信
GATK4流程学习之背景知识与前期准备 - 简书
GATK4流程学习之DNA-Seq variant calling(Germline：SNP+INDEL) - 简书
GATK4流程学习之RNA-Seq variant calling(SNP+INDEL) - 简书
补：Mutect2+scRNAseq+cancer cell - 简书
笔记主要内容：2020年发表的一篇研究NSCLC非小细胞肺癌的cell文章结果之一是根据scRNA-seq测序结果注释了细胞类型（epithelial cells [n = 5,581], immune cells [n = 13,431], stromal cells
[n = 4,249]）；并结合CNV信息将上皮细胞分为cancer与non-concer两种。基于此，利用上述数据学习使用mutect2用于rna-seq的variant calling流程分析。(注：仍然使用之间搭建的GATK conda环境，在笔记最后展示了目前的conda list信息)
Mutect2 for scRNA-seq pipeline
Step1: Get the cell_anno.rda
（1）获取源信息
- 文章附件中有提供所有细胞的id与type(cancer or not)信息
https://www.cell.com/cell/fulltext/S0092-8674(20)30882-5?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867420308825%3Fshowall%3Dtrue#supplementaryMaterial - 每个单细胞的SRA info
https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA591860&o=acc_s%3Aa - SRA对应的aspera下载链接
https://www.ebi.ac.uk/ena/browser/view/PRJNA591860
（2）整理(R)
celltype=read.csv("celltype.csv",header=T)
sra=read.csv("SraRunTable.txt",header=T)
sra2=sra[,c("Run","cell_ID")]
cell_anno=merge(celltype,sra2,by.x="cell_id",by.y="cell_ID")
library(stringr)
asp$Run=str_split(asp$fastq_aspera,"/",simplify = T)[,6]
cell_anno=merge(cell_anno,asp,by="Run")
cancers=cell_anno$cell_id[cell_anno$inferCNV_annotation=="cancer cell"]
#3671个cancer cell
noncancers=cell_anno$cell_id[cell_anno$inferCNV_annotation!="cancer cell"]
#1525个normal cell
save(cell_anno,cancers,noncancers,file="cell_anno.rda")
cell_anno example
Step2: Download fastq data
（1）选择10个cancer cells与10个normal cell(R)
sc=cancers[1:10]
asp_sc=cell_anno$fastq_aspera[cell_anno$cell_id %in% sc]
asp_sc=unlist(str_split(asp_sc,";"))
write.table(asp_sc,file="asp_sc.link",sep = "\n",quote = F, col.names = F, row.names = F)
snc=noncancers[1:10]
asp_snc=cell_anno$fastq_aspera[cell_anno$cell_id %in% snc]
asp_snc=unlist(str_split(asp_snc,";"))
write.table(asp_snc,file="asp_snc.link",sep = "\n",quote = F, col.names = F, row.names = F)
asp_sc.link
(2)ascp下载
conda activate GATK
mkdir cancers noncancers
cat asp_sc.link |while read sample
do
ascp -QT -l 300m -P33001 \
-i ~/miniconda3/envs/GATK/etc/asperaweb_id_dsa.openssh \
era-fasp@$sample ./cancers
done
cat asp_snc.link |while read sample
do
ascp -QT -l 300m -P33001 \
-i ~/miniconda3/envs/GATK/etc/asperaweb_id_dsa.openssh \
era-fasp@$sample ./noncancers
done
(3)QC
cd ./path/to/cancers/
mkdir qc
fastqc *.gz -o ./qc
cd ./qc
multiqc ./
cd ./path/to/noncancers/
mkdir qc
fastqc *.gz -o ./qc
cd ./qc
multiqc ./
从multiqc的结果来看，测序质量均在30以上。主要问题在于duplication，在之后会处理。这里就不进行针对fastq的过滤质控了。
noncancer multiqc
Step3: Make processed bam file
- 首先利用star软件进行fatsq结果比对
- 然后对bam文件进行去重Markduplicate、分割跨内含子片段SplitNcard、增添SM信息，以及碱基质量校正BQSR
- 这一步与之前学习的Germline RNA-Seq variant calling的bam处理步骤基本一致。写成循环，包括steps所有步骤
#参考基因组
GENOME=/home/data/gma49/GATK/ref/hg38/star-index/GRCh38_gencode_v22_CTAT_lib_Apr032020.plug-n-play/ctat_genome_lib_build_dir/ref_genome.fa
#star比对索引
star_index=/home/data/gma49/GATK/ref/hg38/star-index/GRCh38_gencode_v22_CTAT_lib_Apr032020.plug-n-play/ctat_genome_lib_build_dir/ref_genome.fa.star.idx
cd ./path/to/noncancers/
mkdir bam star_log bam_dealed
ls *gz | cut -d"_" -f 1 | uniq > sra.list
cat sra.list | while read sample
do
## (1)star mapping
fq1=${sample}_1.fastq.gz
fq2=${sample}_2.fastq.gz
STAR --runThreadN 4 --genomeDir $star_index \
--twopassMode Basic --outReadsUnmapped None --chimSegmentMin 12 \
--alignIntronMax 100000 --chimSegmentReadGapMax parameter 3 \
--alignSJstitchMismatchNmax 5 -1 5 5 \
--readFilesCommand zcat --readFilesIn $fq1 $fq2 --outFileNamePrefix ./star_log/${sample}_
samtools sort -o ./bam/${sample}.bam ./star_log/${sample}_Aligned.out.sam
rm ./star_log/${sample}_Aligned.out.sam
## (2)markduplicat
sambamba markdup --tmpdir='./' -r ./bam/$sample.bam ./bam_dealed/${sample}_rmd.bam
#--overflow-list-size 600000
## (3)SplitNCigarReads
gatk SplitNCigarReads -R $GENOME \
-I ./bam_dealed/${sample}_rmd.bam \
-O ./bam_dealed/${sample}_rmd_split.bam
rm ./bam_dealed/${sample}_rmd.bam*
## (4)Add SM
gatk AddOrReplaceReadGroups -I ./bam_dealed/${sample}_rmd_split.bam \
-O ./bam_dealed/${sample}_rmd_split_add.bam -LB $sample -PL ILLUMINA -PU $sample -SM $sample
rm ./bam_dealed/${sample}_rmd_split.bam
## (5)BQSR
#需要提供人类常见已知变异注释信息，来自GATK bundle资源
gatk BaseRecalibrator \
-I ./bam_dealed/${sample}_rmd_split_add.bam \
-R $GENOME \
--known-sites /home/data/gma49/GATK/ref/hg38/var_ref/dbsnp_146.hg38.vcf.gz \
--known-sites /home/data/gma49/GATK/ref/hg38/var_ref/Mills_and_1000G_gold_standard.indels.hg38.vcf.gz \
--known-sites /home/data/gma49/GATK/ref/hg38/var_ref/1000G_phase1.snps.high_confidence.hg38.vcf.gz \
-O ./bam_dealed/${sample}_recal.table
gatk ApplyBQSR \
--bqsr-recal-file ./bam_dealed/${sample}_recal.table \
-R $GENOME \
-I ./bam_dealed/${sample}_rmd_split_add.bam \
-O ./bam_dealed/${sample}_recal.bam
rm ./bam_dealed/*rmd*
done
cd ./path/to/noncancers/
mkdir bam star_log bam_dealed
ls *gz | cut -d"_" -f 1 | uniq > sra.list
#同上循环处理
noncancer processed bam
Step4: Mutect2 variant calling
（0）make interval.list
- 因为是scRNA-seq测序数据，即只考虑基因组中编码基因的序列。而人类基因组的编码序列占总长度很少，所以可以指定这些区域进行targeted analysis可加快分析速度。
- 同时为了示例学习，只考虑1号染色体区域。
cp /home/data/gma49/GATK/ref/hg38/star-index/GRCh38_gencode_v22_CTAT_lib_Apr032020.plug-n-play/ctat_genome_lib_build_dir/ref_annot.gtf.gene_spans ./
awk 'BEGIN{OFS="\t"}{print $2,$3,$4,$1,$7,$5}' ref_annot.gtf.gene_spans > tmp.bed
sort -k1,1 -k2,2n tmp.bed > ref_gtf.gene.bed
awk '$1=="chr1"{print}' ref_gtf.gene.bed > ref_chr1_gtf.gene.bed
interval.list
（1）creat Panel of Normals(pon.vcf.gz)
cd ./path/to/noncancers/
GENOME=/home/data/gma49/GATK/ref/hg38/star-index/GRCh38_gencode_v22_CTAT_lib_Apr032020.plug-n-play/ctat_genome_lib_build_dir/ref_genome.fa
cat sra.list | while read sample
do
gatk Mutect2 -R $GENOME -I ./bam_dealed/${sample}_recal.bam \
--max-mnp-distance 0 -L ../ref_chr1_gtf.gene.bed \
-O ./vcf/chr1.${sample}.vcf.gz
done
gatk GenomicsDBImport -R $GENOME -L ../ref_chr1_gtf.gene.bed \
--genomicsdb-workspace-path pon_db \
-V ./vcf/chr1.SRR10777234.vcf.gz \
-V ./vcf/chr1.SRR10777236.vcf.gz \
-V ./vcf/chr1.SRR10777248.vcf.gz \
-V ./vcf/chr1.SRR10777249.vcf.gz \
-V ./vcf/chr1.SRR10777250.vcf.gz \
-V ./vcf/chr1.SRR10777252.vcf.gz \
-V ./vcf/chr1.SRR10777280.vcf.gz \
-V ./vcf/chr1.SRR10777293.vcf.gz \
-V ./vcf/chr1.SRR10777310.vcf.gz \
-V ./vcf/chr1.SRR10777316.vcf.gz
#人类常见germline变异AF，来自GATK bundle资源
gnomad=/home/data/gma49/GATK/bundle/gatk-bundle/af-only-gnomad.hg38.vcf.gz
gatk CreateSomaticPanelOfNormals -R $GENOME \
--germline-resource $gnomad \
-V gendb://pon_db \
-O pon.vcf.gz
（2）mutect2 variant calling
- 对3个tumor cell进行尝试
cd ./path/to/noncancers/
GENOME=/home/data/gma49/GATK/ref/hg38/star-index/GRCh38_gencode_v22_CTAT_lib_Apr032020.plug-n-play/ctat_genome_lib_build_dir/ref_genome.fa
interval_list=/home/data/gma49/GATK/somatic/rawdata/ref_chr1_gtf.gene.bed
pon=/home/data/gma49/GATK/somatic/rawdata/noncancers/pon.vcf.gz
gnomad=/home/data/gma49/GATK/bundle/gatk-bundle/af-only-gnomad.hg38.vcf.gz
gatk Mutect2 -R $GENOME \
-L $interval_list \
-I ./bam_dealed/SRR10777226_recal.bam \
-I ./bam_dealed/SRR10777229_recal.bam \
-I ./bam_dealed/SRR10777230_recal.bam \
-germline-resource $gnomad \
-pon $pon \
--f1r2-tar-gz ./vcf/f1r2.tar.gz \
-O ./vcf/unfiltered.vcf
grep -v "#" unfiltered.vcf | wc
#4308 51696 1364983
unfiltered.vcf
（3）filter
small_exac_common=/home/data/gma49/GATK/bundle/gatk-bundle/small_exac_common_3.hg38.vcf.gz
mkdir GetPileupSummaries segments contamination
cat sra.list | while read sample
do
gatk GetPileupSummaries \
-I ./bam_dealed/${sample}_recal.bam \
-V $small_exac_common \
-L $small_exac_common \
-O ./vcf/GetPileupSummaries/getpileupsummaries.${sample}.table
gatk CalculateContamination \
-I ./vcf/GetPileupSummaries/getpileupsummaries.${sample}.table \
-tumor-segmentation ./vcf/segments/segments.${sample}.table \
-O ./vcf/contamination/contamination.${sample}.table
done
cd ./vcf
gatk LearnReadOrientationModel -I ./vcf/f1r2.tar.gz -O ./vcf/read-orientation-model.tar.gz
gatk FilterMutectCalls -R $GENOME -V unfiltered.vcf \
--tumor-segmentation ./segments/segments.SRR10777226.table \
--tumor-segmentation ./segments/segments.SRR10777229.table \
--tumor-segmentation ./segments/segments.SRR10777230.table \
--contamination-table contamination/contamination.SRR10777226.table \
--contamination-table contamination/contamination.SRR10777229.table \
--contamination-table contamination/contamination.SRR10777230.table \
--ob-priors read-orientation-model.tar.gz \
-O filtered.vcf
grep -v "#" filtered.vcf | wc
#4308 51696 1620981
-
对比过滤前后，发现vcf记录变异位点总数未变。变化在第七列添加了质量注释信息。
filtered.vcf
grep -v "#" filtered.vcf | cut -f 7 | sort | uniq -c |sed 's/ *//' | sort -t " " -n -r -k1
#####
1115 PASS
769 clustered_events;haplotype
371 clustered_events;fragment;haplotype
346 clustered_events;haplotype;weak_evidence
223 weak_evidence
222 germline
160 clustered_events
122 fragment
99 fragment;haplotype
90 clustered_events;fragment;haplotype;weak_evidence
45 clustered_events;germline
44 clustered_events;haplotype;panel_of_normals
44 clustered_events;germline;haplotype
42 haplotype
38 germline;haplotype
38 base_qual;clustered_events;haplotype
36 clustered_events;germline;haplotype;weak_evidence
33 fragment;haplotype;weak_evidence
29 fragment;weak_evidence
27 germline;weak_evidence
23 haplotype;weak_evidence
23 base_qual;haplotype
22 clustered_events;weak_evidence
20 fragment;germline
20 base_qual;clustered_events;haplotype;weak_evidence
18 germline;haplotype;weak_evidence
17 clustered_events;fragment
14 germline;slippage
13 clustered_events;fragment;germline;haplotype
12 position
11 panel_of_normals
10 slippage
10 haplotype;panel_of_normals
10 clustered_events;fragment;weak_evidence
9 slippage;weak_evidence
8 clustered_events;fragment;germline;haplotype;weak_evidence
8 base_qual;haplotype;weak_evidence
8 base_qual
6 multiallelic;slippage
6 germline;position
6 germline;multiallelic;slippage
6 clustered_events;germline;weak_evidence
6 base_qual;clustered_events;fragment;haplotype
5 base_qual;fragment;haplotype
4 germline;slippage;weak_evidence
4 fragment;germline;haplotype
4 clustered_events;multiallelic
4 clustered_events;haplotype;position
4 clustered_events;fragment;haplotype;panel_of_normals
4 base_qual;weak_evidence
4 base_qual;clustered_events
3 germline;panel_of_normals;slippage
3 germline;panel_of_normals
3 germline;multiallelic
3 fragment;panel_of_normals;weak_evidence
3 fragment;panel_of_normals
3 fragment;haplotype;position
3 fragment;germline;weak_evidence
3 fragment;germline;position
3 clustered_events;position
3 clustered_events;haplotype;panel_of_normals;weak_evidence
2 panel_of_normals;slippage
2 multiallelic;weak_evidence
2 multiallelic
2 haplotype;position
2 germline;multiallelic;slippage;weak_evidence
2 fragment;germline;haplotype;weak_evidence
2 clustered_events;panel_of_normals
2 clustered_events;germline;slippage;weak_evidence
2 clustered_events;germline;slippage
2 clustered_events;germline;multiallelic
2 clustered_events;germline;haplotype;panel_of_normals
2 clustered_events;fragment;germline
2 base_qual;germline;haplotype;weak_evidence
2 base_qual;germline
2 base_qual;fragment
2 base_qual;clustered_events;germline;haplotype;weak_evidence
1 orientation;weak_evidence
1 orientation
1 multiallelic;slippage;weak_evidence
1 multiallelic;panel_of_normals;weak_evidence
1 multiallelic;panel_of_normals;slippage
1 haplotype;slippage;weak_evidence
1 haplotype;position;weak_evidence
1 germline;panel_of_normals;weak_evidence
1 germline;haplotype;panel_of_normals;slippage
1 fragment;slippage;weak_evidence
1 fragment;position
1 fragment;germline;slippage
1 contamination;germline;haplotype;weak_evidence
1 contamination;germline;haplotype
1 contamination;germline
1 clustered_events;panel_of_normals;weak_evidence
1 clustered_events;multiallelic;weak_evidence
1 clustered_events;multiallelic;panel_of_normals
1 clustered_events;germline;multiallelic;slippage
1 clustered_events;germline;haplotype;position
1 clustered_events;fragment;panel_of_normals
1 clustered_events;fragment;haplotype;position;weak_evidence
1 clustered_events;fragment;haplotype;panel_of_normals;weak_evidence
1 clustered_events;fragment;germline;weak_evidence
1 base_qual;slippage
1 base_qual;haplotype;slippage;weak_evidence
1 base_qual;germline;weak_evidence
1 base_qual;fragment;weak_evidence
1 base_qual;fragment;haplotype;weak_evidence
1 base_qual;fragment;germline;haplotype;weak_evidence
1 base_qual;contamination;germline;weak_evidence
1 base_qual;clustered_events;weak_evidence
1 base_qual;clustered_events;germline;slippage
1 base_qual;clustered_events;germline
主要参考链接
1、(How to) Call somatic mutations using GATK4 Mutect2 – GATK ；
2、(How to) Call somatic mutations using GATK4 Mutect2 (Deprecated) – GATK ;
3、最新最全的mutect2教程（生信技能树） - 云+社区 - 腾讯云
conda list
# packages in environment at /home/data/gma49/miniconda3/envs/GATK:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
_openmp_mutex 4.5 1_gnu https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
aiohttp 3.7.3 py39h3811e60_1 conda-forge
aspera-cli 3.9.1 0 hcc
async-timeout 3.0.1 py_1000 conda-forge
attrs 20.3.0 pyhd3deb0d_0 conda-forge
brotlipy 0.7.0 py39h3811e60_1001 conda-forge
bwa 0.7.17 hed695b0_7 bioconda
bzip2 1.0.8 h7f98852_4 conda-forge
c-ares 1.17.1 h36c2ea0_0 conda-forge
ca-certificates 2020.12.5 ha878542_0 conda-forge
cachetools 4.2.1 pyhd8ed1ab_0 conda-forge
certifi 2020.12.5 py39hf3d152e_1 conda-forge
cffi 1.14.4 py39he32792d_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
chardet 3.0.4 py39h079e4ff_1008 conda-forge
click 7.1.2 pyh9f0ad1d_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
coloredlogs 15.0 py39hf3d152e_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
colormath 3.0.0 py_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
conda 4.9.2 py39hf3d152e_0 conda-forge
conda-package-handling 1.7.2 py39h38d8fee_0 conda-forge
cryptography 3.4.4 py39h95dcef6_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
curl 7.71.1 he644dc0_3 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
cycler 0.10.0 py_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
decorator 4.4.2 py_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
fastqc 0.11.9 0 bioconda
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
fontconfig 2.13.1 hba837de_1004 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
freetype 2.10.4 h0708190_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
future 0.18.2 py39hf3d152e_3 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
gatk4 4.1.9.0 py39_0 bioconda
google-api-core 1.25.1 pyh44b312d_0 conda-forge
google-auth 1.24.0 pyhd3deb0d_0 conda-forge
google-cloud-core 1.5.0 pyhd3deb0d_0 conda-forge
google-cloud-storage 1.19.0 py_0 conda-forge
google-crc32c 1.1.2 py39hb81f231_0 conda-forge
google-resumable-media 1.2.0 pyhd3deb0d_0 conda-forge
googleapis-common-protos 1.52.0 py39hf3d152e_1 conda-forge
grpcio 1.35.0 py39hff7568b_0 conda-forge
hdf5 1.10.5 nompi_h5b725eb_1114 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
htslib 1.11 hd3b49d5_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
humanfriendly 9.1 py39hf3d152e_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
icu 68.1 h58526e2_0 conda-forge
idna 2.10 pyh9f0ad1d_0 conda-forge
importlib-metadata 3.4.0 py39hf3d152e_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
jinja2 2.11.3 pyh44b312d_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
jpeg 9d h36c2ea0_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
kiwisolver 1.3.1 py39h1a9c180_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
krb5 1.17.2 h926e7f8_0 conda-forge
lcms2 2.12 hddcbb42_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
ld_impl_linux-64 2.35.1 hea4e1c9_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libarchive 3.5.1 h3f442fb_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libblas 3.9.0 8_openblas https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libcblas 3.9.0 8_openblas https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libcrc32c 1.1.1 h9c3ff4c_2 conda-forge
libcurl 7.71.1 hcdd3856_3 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libdeflate 1.7 h7f98852_5 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libedit 3.1.20191231 he28a2e2_2 conda-forge
libffi 3.3 h58526e2_2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgcc-ng 9.3.0 h2828fa1_18 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgfortran-ng 9.3.0 hff62375_18 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgfortran5 9.3.0 hff62375_18 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libgomp 9.3.0 h2828fa1_18 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libiconv 1.16 h516909a_0 conda-forge
liblapack 3.9.0 8_openblas https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
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