新冠病毒分型和突变分析（SARS-CoV2_ARTIC_Nanopore）

原创

SliverWorkspace

修改于 2022-12-13 23:07:42

8540

修改于 2022-12-13 23:07:42

文章被收录于专栏：图形化开放式生信分析系统开发图形化开放式生信分析系统开发

一. 本文使用Artic官方提供环境对Nanopore minion SARS-Cov-2测序数据，对新冠病毒突变及分型鉴定

二. 概览：按照惯例，先上一张概览图，浏览下分析流程步骤

流程输入	SRR14800265.fastq.gz 测试数据下载 SRX11133330: Nanopore sequencing of SARS-CoV-2: V-22 1 OXFORD_NANOPORE (MinION) run: 277,605 spots, 139.3M bases, 133.2Mb downloads 使用NCBI官方工具sra-toolkit拆分成fastq.gz文件 fastq-dump SRR14800265 --gzip 得到SRR14800265.fastq.gz 参考文件，默认路径/opt/ref下 Artic-ncov2019 artic-ncov2019 primer&参考序列分析流程文件（可一键导入sliverworkspace运行）及报告文件，conda环境文件下载，导入操作
运行环境	docker image based on ubuntu21.04 Conda Mamba（默认使用清华源） ssh 获取镜像代码见下文段落
分析软件	- artic=1.2.1 - artic-network::rampart=1.2.0 - snakemake-minimal=5.8.1 - pangolin=4.1.3
输出结果	按照序列一致性组装的新冠病毒序列 SRR14800265.consensus.fa Panglin 根据组装的序列分析得出病毒分型信息 lineage_report.csv 根据primertrim.bam获的新冠病毒突变信息，过滤后得到 SRR14800265.pass.vcf.gz

环境搭建: 为了快速完成环境搭建，节省95%以上时间。

本文使用docker + conda (mamba) 作为基础分析环境，镜像获取：docker/docker-compoes 的安装及镜像构建见《基于docker的生信基础环境镜像构建》,docker镜像基于ubuntu21.04构建，并安装有conda/mamba，ssh服务。并尝试初次运行时初始化安装所需软件下载所需文件（作为代价首次运行时间会较长，切需网络通畅），即实现自动初始化的分析流程。

备注：docker运行的操作系统，推荐为Linux，windows，macOS系统下docker可能部分功能（网络）不能正常运行

 # 拉取docker镜像
 docker     pull     doujiangbaozi/sliverworkspace:latest
 
 # 查看docker 镜像
 docker     images

基础环境配置，docker-compose.yml 配置文件，可以根据需要自行修改调整

 version: "3"
 services:
   SarsCov2:
     image: doujiangbaozi/sliverworkspace:latest
     container_name: SarsCov2
     volumes:
       - /media/sliver/Data/data:/opt/data:rw                               #挂载input数据，artic目录下
       - /media/sliver/Manufacture/SC2/envs:/root/mambaforge-pypy3/envs:rw  #挂载envs conda环境目录
       - /media/sliver/Manufacture/SC2/config:/opt/config:rw                #挂载config,conda配置文件目录
       - /media/sliver/Manufacture/SC2/ref:/opt/ref:rw                      #挂载reference目录
       - /media/sliver/Manufacture/SC2/result:/opt/result:rw                #挂载中间文件和输出结果目录
     ports:
       - "9024:9024"                                                        #ssh连接端口可以按需修改
     environment:
       - TZ=Asia/Shanghai                                                   #设置时区
       - PS=20191124                                                        #修改默认ssh密码
       - PT=9024                                                            #修改默认ssh端口

基础环境运行

 # docker-compose.yml 所在目录下运行
 docker-compose up -d
 
 # 或者 
 docker-compose up -d -f /路径/docker-compose.yaml
 
 # 查看docker是否正常运行,docker-compose.yaml目录下运行
 docker-compose ps
 
 # 或者
 docker ps

docker 容器使用，类似于登录远程服务器

 # 登录docker，使用的是ssh服务，可以本地或者远程部署使用
 ssh root@192.168.6.6 -p9024
 
 # 看到如下，显示如下提示即正常登录
 (base) root@SliverWorkstation:~#

三. 分析流程：本流程包含artic流程详细步骤，不感兴趣可以直接跳至文章末尾

1. 变量设置

 #样本编号
 export sn=SRR14800265
 #数据输入目录
 export data=/opt/data
 #数据输出、中间文件目录
 export result=/opt/result
 #conda安装的环境目录
 export envs=/root/mambaforge-pypy3/envs 
 #artic primer 版本V1,V2,V3,V4,V4.1
 export artic_primer_version=4.1
 #设置可用线程数
 export threads=8

2. 数据过滤

 #首次运行下载artic-ncov2019
 if  [ ! -d "/opt/ref/artic-ncov2019" ]; then
     apt-get install -y git
     git clone https://github.com/artic-network/artic-ncov2019.git "/opt/ref/artic-ncov2019"
 fi
 
 #conda检测环境是否存在，首次运行不存在创建该环境并安装软件
 if [ ! -d "${envs}/artic-ncov2019" ]; then
   mamba env create -f /opt/ref/artic-ncov2019/environment.yml
   mamba install muscle=3.8
 fi
 
 source activate artic-ncov2019
 
 cp    -f ${data}/artic/${sn}.fastq.gz ${result}/${sn}/
 
 mkdir -p ${result}/${sn}/clean
 
 artic guppyplex --min-length 400 --max-length 700 \
     --directory ${result}/${sn}/ \
     --output ${result}/${sn}/clean/${sn}.clean.fastq
 
 conda  deactivate

3. 比对到参考基因组上，得到bam文件并排序

 source activate artic-ncov2019
 
 if [ ! -f /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.reference.fasta ]; then
     cp  -f /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/SARS-CoV-2.reference.fasta \
         /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.reference.fasta
 fi
 
 cd ${result}/${sn}
 
 mkdir -p ${result}/${sn}/aligned
 
 minimap2 -a -x map-ont -t ${threads} \
     /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.reference.fasta \
     ${result}/${sn}/clean/${sn}.clean.fastq  | samtools view -bS -F 4 - | samtools sort -o ${result}/${sn}/aligned/${sn}.sorted.bam -
 
 samtools index ${result}/${sn}/aligned/${sn}.sorted.bam
 
 conda  deactivate

4. trim bam / samtools coverage

 source activate artic-ncov2019
 
 cd ${result}/${sn}
 
 if [ ! -f /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.scheme.bed ]; then
     cp  -r /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/SARS-CoV-2.scheme.bed \
         /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.scheme.bed
 fi
 
 align_trim --normalise 200 /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.scheme.bed \
     --start --remove-incorrect-pairs --report ${result}/${sn}/aligned/${sn}.alignreport.txt < ${result}/${sn}/aligned/${sn}.sorted.bam \
     2> ${result}/${sn}/aligned/${sn}.alignreport.er | \
     samtools sort -T ${result}/${sn}/aligned/temp - -o ${result}/${sn}/aligned/${sn}.trimmed.rg.sorted.bam
 
 align_trim --normalise 200 /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.scheme.bed \
     --remove-incorrect-pairs --report ${result}/${sn}/aligned/${sn}.alignreport.txt < ${result}/${sn}/aligned/${sn}.sorted.bam \
     2> ${result}/${sn}/aligned/${sn}.alignreport.er| \
     samtools sort -T ${result}/${sn}/aligned/temp - -o ${result}/${sn}/aligned/${sn}.primertrimmed.rg.sorted.bam
 
 samtools index ${result}/${sn}/aligned/${sn}.trimmed.rg.sorted.bam
 samtools index ${result}/${sn}/aligned/${sn}.primertrimmed.rg.sorted.bam
 
 samtools coverage ${result}/${sn}/aligned/${sn}.primertrimmed.rg.sorted.bam -o ${result}/${sn}/aligned/${sn}.samcov.tsv
 
 conda  deactivate

5. medaka variant

 source activate artic-ncov2019
 
 if [ ! -f /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.reference.fasta ]; then
     cp  -f /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/SARS-CoV-2.reference.fasta \
         /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.reference.fasta
 fi
 
 mkdir -p ${result}/${sn}/vcf
 
 if [ -f ${result}/${sn}/vcf/${sn}.nCoV-2019_1.hdf ];then
     rm -f ${result}/${sn}/vcf/${sn}.nCoV-2019_1.hdf
 fi
 
 if [ -f ${result}/${sn}/vcf/${sn}.nCoV-2019_2.hdf ];then
     rm -f ${result}/${sn}/vcf/${sn}.nCoV-2019_2.hdf
 fi
 
 medaka consensus --model r941_min_high_g351 \
     --threads ${threads} --chunk_len 800 --chunk_ovlp 400 \
     --RG 1 ${result}/${sn}/aligned/${sn}.trimmed.rg.sorted.bam \
     ${result}/${sn}/vcf/${sn}.nCoV-2019_1.hdf
 
 medaka variant /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.reference.fasta \
     ${result}/${sn}/vcf/${sn}.nCoV-2019_1.hdf ${result}/${sn}/vcf/${sn}.nCoV-2019_1.vcf
 
 medaka consensus --model r941_min_high_g351 \
     --threads ${threads} --chunk_len 800 --chunk_ovlp 400 \
     --RG 2 ${result}/${sn}/aligned/${sn}.trimmed.rg.sorted.bam \
     ${result}/${sn}/vcf/${sn}.nCoV-2019_2.hdf
 
 medaka variant /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.reference.fasta \
     ${result}/${sn}/vcf/${sn}.nCoV-2019_2.hdf ${result}/${sn}/vcf/${sn}.nCoV-2019_2.vcf
 
 artic_vcf_merge ${result}/${sn}/vcf/${sn} /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.scheme.bed \
     2> ${result}/${sn}/vcf/${sn}.primersitereport.txt \
     nCoV-2019_1:${result}/${sn}/vcf/${sn}.nCoV-2019_1.vcf nCoV-2019_2:${result}/${sn}/vcf/${sn}.nCoV-2019_2.vcf
 
 bgzip -f ${result}/${sn}/vcf/${sn}.merged.vcf
 tabix -f -p vcf ${result}/${sn}/vcf/${sn}.merged.vcf.gz
 
 conda  deactivate

6. longshot vcf

 source activate artic-ncov2019
 
 longshot -P 0 -F -A --no_haps \
     --bam ${result}/${sn}/aligned/${sn}.primertrimmed.rg.sorted.bam \
     --ref /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.reference.fasta \
     --out ${result}/${sn}/vcf/${sn}.longshoted.vcf \
     --potential_variants ${result}/${sn}/vcf/${sn}.merged.vcf.gz
 
 conda  deactivate

7. artic_vcf_filter 过滤variant vcf

 source activate artic-ncov2019
 
 artic_vcf_filter --medaka ${result}/${sn}/vcf/${sn}.longshoted.vcf \
     ${result}/${sn}/vcf/${sn}.pass.vcf \
     ${result}/${sn}/vcf/${sn}.fail.vcf
 bgzip -f ${result}/${sn}/vcf/${sn}.pass.vcf
 tabix -p vcf ${result}/${sn}/vcf/${sn}.pass.vcf.gz
 
 conda  deactivate

8. depth mask

 source activate artic-ncov2019
 
 artic_make_depth_mask --store-rg-depths \
     /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.reference.fasta \
     ${result}/${sn}/aligned/${sn}.trimmed.rg.sorted.bam \
     ${result}/${sn}/${sn}.coverage_mask.txt
 
 artic_mask /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.reference.fasta \
     ${result}/${sn}/${sn}.coverage_mask.txt \
     ${result}/${sn}/vcf/${sn}.fail.vcf \
     ${result}/${sn}/${sn}.preconsensus.fasta
 
 conda  deactivate

9. 使用 bcftools 获取一致性序列

 source activate artic-ncov2019
 
 bcftools consensus \
     -f ${result}/${sn}/${sn}.preconsensus.fasta ${result}/${sn}/vcf/${sn}.pass.vcf.gz \
     -m ${result}/${sn}/${sn}.coverage_mask.txt \
     -o ${result}/${sn}/${sn}.consensus.fasta
 
 artic_fasta_header ${result}/${sn}/${sn}.consensus.fasta "${sn}"
 
 conda  deactivate

10. 使用Pangolin获取序列分型信息

 #conda检测环境是否存在，首次运行不存在创建该环境并安装软件
 if [ ! -d "${envs}/pangolin" ]; then
   mamba env create -f /opt/config/pangolin.yaml
 fi
 
 source activate pangolin
 
 pangolin ${result}/${sn}/${sn}.consensus.fasta --outdir ${result}/${sn}
 
 conda  deactivate

11. muscle （可选）

 source activate artic-ncov2019
 
 cat ${result}/${sn}/${sn}.consensus.fasta \
     /opt/ref/artic-ncov2019/primer_schemes/nCoV-2019/${artic_primer_version}/nCoV-2019.reference.fasta \
     > ${result}/${sn}/${sn}.muscle.in.fasta
 
 muscle  -in ${result}/${sn}/${sn}.muscle.in.fasta -out ${result}/${sn}/${sn}.muscle.out.fasta
 
 conda  deactivate

12. 报告(可选)

11. 使用IGV Browser查看突变信息（可选）

四. 参考链接

https://github.com/artic-network/artic-ncov2019

 #官方建议分析过程
 
 #数据过滤
 artic guppyplex --min-length 400 --max-length 700 \
   --directory /opt/result/SRR14800265/ \
   --output /opt/result/SRR14800265/clean/SRR14800265.clean.fastq
 #获取一致性序列和突变数据
 artic minion --normalise 200 --threads 32 \
   --medaka --medaka-model r941_min_high_g351 \
   --scheme-directory /opt/ref/artic-ncov2019/primer_schemes \
   --read-file /opt/result/SRR14800265/clean/SRR14800265.clean.fastq nCoV-2019/V4.1 SRR14800265

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

容器镜像服务