ViralFlow: an automated workflow for SARS-CoV-2 genome assembly, lineage assignment, mutations and intrahost variants detection

The COVID-19 pandemic, a disease caused by the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), emerged in 2019 and quickly spread worldwide. Genomic surveillance has become the gold standard methodology to monitor and study this emerging virus. The current deluge of SARS-CoV-2 genomic data being generated worldwide has put additional pressure on the urgent need for streamlined bioinformatics workflows for data analysis. Here, we describe a workflow developed by our group to process and analyze large-scale SARS-CoV-2 Illumina amplicon sequencing data. This workflow automates all the steps involved in SARS-CoV-2 genomic analysis: data processing, genome assembly, PANGO lineage assignment, mutation analysis and the screening of intrahost variants. The workflow presented here (https://github.com/dezordi/ViralFlow) is available through Docker or Singularity images, allowing implementation in laptops for small scale analyses or in high processing capacity servers or clusters. Moreover, the low requirements for memory and CPU cores makes it a versatile tool for SARS-CoV-2 genomic analysis.由严重急性呼吸综合征冠状病毒2（Severe Acute Respiratory Syndrome coronavirus 2，SARS-CoV-2）引发的新型冠状病毒肺炎（Corona Virus Disease 2019，COVID-19）大流行于2019年暴发，并迅速在全球范围内蔓延。基因组监测已成为监测与研究该新型病毒的金标准方法。当前全球范围内持续产出的海量SARS-CoV-2基因组数据，进一步凸显了对简化生物信息学分析流程的迫切需求。本文介绍了本团队开发的、用于处理与分析大规模SARS-CoV-2 Illumina扩增子测序数据的工作流程。该流程可自动化完成SARS-CoV-2基因组分析的全部步骤：数据处理、基因组组装、PANGO谱系分型（PANGO lineage assignment）、突变分析以及宿主内变异筛选。本文所提出的流程（访问地址：https://github.com/dezordi/ViralFlow）可通过Docker或Singularity镜像获取，既可部署于笔记本电脑以开展小规模分析，也可在高算力服务器或集群中运行。此外，其对内存与CPU核心的低资源需求使其成为一款适配SARS-CoV-2基因组分析的通用工具。