Mutect2+scRNAseq+cancer cell

GATK4流程學(xué)習(xí)之背景知識(shí)與前期準(zhǔn)備 - 簡(jiǎn)書(shū)
GATK4流程學(xué)習(xí)之DNA-Seq variant calling(Germline：SNP+INDEL) - 簡(jiǎn)書(shū)
GATK4流程學(xué)習(xí)之RNA-Seq variant calling(SNP+INDEL) - 簡(jiǎn)書(shū)
補(bǔ)：Mutect2+scRNAseq+cancer cell - 簡(jiǎn)書(shū)

筆記主要內(nèi)容：2020年發(fā)表的一篇研究NSCLC非小細(xì)胞肺癌的cell文章結(jié)果之一是根據(jù)scRNA-seq測(cè)序結(jié)果注釋了細(xì)胞類(lèi)型（epithelial cells [n = 5,581], immune cells [n = 13,431], stromal cells
[n = 4,249]）郭膛；并結(jié)合CNV信息將上皮細(xì)胞分為cancer與non-concer兩種。基于此，利用上述數(shù)據(jù)學(xué)習(xí)使用mutect2用于rna-seq的variant calling流程分析。(注：仍然使用之間搭建的GATK conda環(huán)境鸟召，在筆記最后展示了目前的conda list信息)

Mutect2 for scRNA-seq pipeline

Step1: Get the cell_anno.rda

（1）獲取源信息

文章附件中有提供所有細(xì)胞的id與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
每個(gè)單細(xì)胞的SRA info
https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA591860&o=acc_s%3Aa
SRA對(duì)應(yīng)的aspera下載鏈接
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個(gè)cancer cell
noncancers=cell_anno$cell_id[cell_anno$inferCNV_annotation!="cancer cell"]
#1525個(gè)normal cell 
save(cell_anno,cancers,noncancers,file="cell_anno.rda")

cell_anno example

Step2: Download fastq data

（1）選擇10個(gè)cancer cells與10個(gè)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下載

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 ./

從multiqc的結(jié)果來(lái)看，測(cè)序質(zhì)量均在30以上。主要問(wèn)題在于duplication娄柳，在之后會(huì)處理。這里就不進(jìn)行針對(duì)fastq的過(guò)濾質(zhì)控了艘绍。

noncancer multiqc

Step3: Make processed bam file

首先利用star軟件進(jìn)行fatsq結(jié)果比對(duì)
然后對(duì)bam文件進(jìn)行去重Markduplicate赤拒、分割跨內(nèi)含子片段SplitNcard、增添SM信息诱鞠，以及堿基質(zhì)量校正BQSR
這一步與之前學(xué)習(xí)的Germline RNA-Seq variant calling的bam處理步驟基本一致挎挖。寫(xiě)成循環(huán)，包括steps所有步驟

#參考基因組
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比對(duì)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
#需要提供人類(lèi)常見(jiàn)已知變異注釋信息航夺，來(lái)自GATK bundle資源
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
#同上循環(huán)處理

noncancer processed bam

Step4: Mutect2 variant calling

（0）make interval.list

因?yàn)槭莝cRNA-seq測(cè)序數(shù)據(jù)蕉朵，即只考慮基因組中編碼基因的序列。而人類(lèi)基因組的編碼序列占總長(zhǎng)度很少阳掐，所以可以指定這些區(qū)域進(jìn)行targeted analysis可加快分析速度始衅。
同時(shí)為了示例學(xué)習(xí)，只考慮1號(hào)染色體區(qū)域缭保。

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

#人類(lèi)常見(jiàn)germline變異AF汛闸，來(lái)自GATK bundle資源
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

對(duì)3個(gè)tumor cell進(jìn)行嘗試

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

對(duì)比過(guò)濾前后，發(fā)現(xiàn)vcf記錄變異位點(diǎn)總數(shù)未變艺骂。變化在第七列添加了質(zhì)量注釋信息诸老。

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

主要參考鏈接
1、(How to) Call somatic mutations using GATK4 Mutect2 – GATK 钳恕；
2别伏、(How to) Call somatic mutations using GATK4 Mutect2 (Deprecated) – GATK ;
3、最新最全的mutect2教程（生信技能樹(shù)） - 云+社區(qū) - 騰訊云

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
libopenblas               0.3.12          pthreads_h4812303_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libpng                    1.6.37               h21135ba_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libprotobuf               3.14.0               h780b84a_0    conda-forge
libsolv                   0.7.17               h780b84a_0    conda-forge
libssh2                   1.9.0                hab1572f_5    conda-forge
libstdcxx-ng              9.3.0               h6de172a_18    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libtiff                   4.2.0                hdc55705_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libuuid                   2.32.1            h7f98852_1000    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libwebp-base              1.2.0                h7f98852_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libxml2                   2.9.10               h72842e0_3    conda-forge
lz4-c                     1.9.3                h9c3ff4c_0    conda-forge
lzo                       2.10              h516909a_1000    conda-forge
lzstring                  1.0.4                   py_1001    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
mamba                     0.7.14           py39h951de11_0    conda-forge
markdown                  3.3.3              pyh9f0ad1d_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
markupsafe                1.1.1            py39h3811e60_3    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
matplotlib-base           3.3.4            py39h2fa2bec_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
multidict                 5.1.0            py39h3811e60_1    conda-forge
multiqc                   1.9                        py_1    bioconda
ncbi-ngs-sdk              2.10.9               h3e72335_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
ncurses                   6.2                  h58526e2_4    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
networkx                  2.5                        py_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
numpy                     1.20.1           py39hdbf815f_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
olefile                   0.46               pyh9f0ad1d_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
openjdk                   8.0.265              h516909a_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
openssl                   1.1.1j               h7f98852_0    conda-forge
ossuuid                   1.6.2             hf484d3e_1000    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
perl                      5.26.2            h36c2ea0_1008    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
perl-app-cpanminus        1.7044                  pl526_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-business-isbn        3.004                   pl526_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-business-isbn-data   20140910.003            pl526_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-carp                 1.38                    pl526_3    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-constant             1.33                    pl526_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-data-dumper          2.173                   pl526_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-encode               2.88                    pl526_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-exporter             5.72                    pl526_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-extutils-makemaker   7.36                    pl526_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-file-path            2.16                    pl526_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-file-temp            0.2304                  pl526_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-mime-base64          3.15                    pl526_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-parent               0.236                   pl526_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-uri                  1.76                    pl526_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-xml-libxml           2.0132          pl526h7ec2d77_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-xml-namespacesupport 1.12                    pl526_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-xml-sax              1.02                    pl526_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-xml-sax-base         1.09                    pl526_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
perl-xsloader             0.24                    pl526_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
pillow                    8.1.0            py39hf95b381_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pip                       21.0.1             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
protobuf                  3.14.0           py39he80948d_1    conda-forge
pyasn1                    0.4.8                      py_0    conda-forge
pyasn1-modules            0.2.7                      py_0    conda-forge
pycosat                   0.6.3           py39h3811e60_1006    conda-forge
pycparser                 2.20               pyh9f0ad1d_2    conda-forge
pyopenssl                 20.0.1             pyhd8ed1ab_0    conda-forge
pyparsing                 2.4.7              pyh9f0ad1d_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pysocks                   1.7.1            py39hf3d152e_3    conda-forge
python                    3.9.1           hffdb5ce_5_cpython    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python-dateutil           2.8.1                      py_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
python_abi                3.9                      1_cp39    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pytz                      2021.1             pyhd8ed1ab_0    conda-forge
pyyaml                    5.4.1            py39h3811e60_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
readline                  8.0                  he28a2e2_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
reproc                    14.2.1               h36c2ea0_0    conda-forge
reproc-cpp                14.2.1               h58526e2_0    conda-forge
requests                  2.25.1             pyhd3deb0d_0    conda-forge
rsa                       4.7.1              pyh44b312d_0    conda-forge
ruamel_yaml               0.15.80         py39h3811e60_1004    conda-forge
sambamba                  0.6.6                         2    bioconda
samtools                  1.11                 h6270b1f_0    bioconda
seqtk                     1.3                  hed695b0_2    bioconda
setuptools                49.6.0           py39hf3d152e_3    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
simplejson                3.17.2           py39h3811e60_2    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
six                       1.15.0             pyh9f0ad1d_0    conda-forge
spectra                   0.0.11                     py_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
sqlite                    3.34.0               h74cdb3f_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
sra-tools                 2.10.9          pl526haddd2b5_0    bioconda
star                      2.7.1a                        0    bioconda
tk                        8.6.10               h21135ba_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tornado                   6.1              py39h3811e60_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tqdm                      4.56.2             pyhd8ed1ab_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
trimmomatic               0.39                          1    bioconda
typing-extensions         3.7.4.3                       0    conda-forge
typing_extensions         3.7.4.3                    py_0    conda-forge
tzdata                    2021a                he74cb21_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
urllib3                   1.26.3             pyhd8ed1ab_0    conda-forge
vcftools                  0.1.16               he513fc3_4    bioconda
wheel                     0.36.2             pyhd3deb0d_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
xz                        5.2.5                h516909a_1    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
yaml                      0.2.5                h516909a_0    conda-forge
yarl                      1.6.3            py39h3811e60_1    conda-forge
zipp                      3.4.0                      py_0    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zlib                      1.2.11            h516909a_1010    https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zstd                      1.4.8                ha95c52a_1    conda-forge

最后編輯于：2021.02.24 10:06:47

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Mutect2+scRNAseq+cancer cell

Step1: Get the cell_anno.rda

（1）獲取源信息

（2）整理(R)

Step2: Download fastq data

（1）選擇10個(gè)cancer cells與10個(gè)normal cell(R)

(2)ascp下載

(3)QC

Step3: Make processed bam file

Step4: Mutect2 variant calling

（0）make interval.list

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（3）filter

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