Liu Boxiang, Liu Kaibo, Zhang He, Zhang Liang, Bian Yuchen, Huang Liang
Baidu Research, Sunnyvale, CA, United States.
School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, United States.
J Med Internet Res. 2020 Oct 2;22(10):e22299. doi: 10.2196/22299.
COVID-19 became a global pandemic not long after its identification in late 2019. The genomes of SARS-CoV-2 are being rapidly sequenced and shared on public repositories. To keep up with these updates, scientists need to frequently refresh and reclean data sets, which is an ad hoc and labor-intensive process. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes.
To address these challenges, we developed CoV-Seq, an integrated web server that enables simple and rapid analysis of SARS-CoV-2 genomes.
CoV-Seq is implemented in Python and JavaScript. The web server and source code URLs are provided in this article.
Given a new sequence, CoV-Seq automatically predicts gene boundaries and identifies genetic variants, which are displayed in an interactive genome visualizer and are downloadable for further analysis. A command-line interface is available for high-throughput processing. In addition, we aggregated all publicly available SARS-CoV-2 sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID), National Center for Biotechnology Information (NCBI), European Nucleotide Archive (ENA), and China National GeneBank (CNGB), and extracted genetic variants from these sequences for download and downstream analysis. The CoV-Seq database is updated weekly.
We have developed CoV-Seq, an integrated web service for fast and easy analysis of custom SARS-CoV-2 sequences. The web server provides an interactive module for the analysis of custom sequences and a weekly updated database of genetic variants of all publicly accessible SARS-CoV-2 sequences. We believe CoV-Seq will help improve our understanding of the genetic underpinnings of COVID-19.
2019年末新冠病毒被发现后不久，COVID-19就成为了全球大流行病。严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的基因组正在被快速测序并在公共数据库中共享。为了跟上这些更新，科学家们需要频繁刷新和重新清理数据集，这是一个临时且耗费人力的过程。此外，生物信息学或编程知识有限的科学家可能会发现分析SARS-CoV-2基因组很困难。
为应对这些挑战，我们开发了CoV-Seq，这是一个集成的网络服务器，可实现对SARS-CoV-2基因组的简单快速分析。
CoV-Seq用Python和JavaScript实现。本文提供了网络服务器和源代码的网址。
给定一个新序列，CoV-Seq会自动预测基因边界并识别基因变异，这些变异会显示在交互式基因组可视化工具中，并且可供下载以进行进一步分析。还有一个命令行界面可用于高通量处理。此外，我们汇总了来自全球共享禽流感数据倡议组织（GISAID）、美国国立生物技术信息中心（NCBI）、欧洲核苷酸档案馆（ENA）和中国国家基因库（CNGB）的所有公开可用的SARS-CoV-2序列，并从这些序列中提取基因变异以供下载和下游分析。CoV-Seq数据库每周更新一次。
我们开发了CoV-Seq，这是一个用于快速轻松分析定制SARS-CoV-2序列的集成网络服务。该网络服务器提供了一个用于分析定制序列的交互式模块以及一个每周更新的所有可公开获取的SARS-CoV-2序列的基因变异数据库。我们相信CoV-Seq将有助于增进我们对COVID-19遗传基础的理解。
