Tracking and curating putative SARS-CoV-2 recombinants with RIVET.

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad538

Kyle Smith, Cheng Ye, Yatish Turakhia

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引用次数: 0

Abstract

Motivation: Identifying and tracking recombinant strains of SARS-CoV-2 is critical to understanding the evolution of the virus and controlling its spread. But confidently identifying SARS-CoV-2 recombinants from thousands of new genome sequences that are being shared online every day is quite challenging, causing many recombinants to be missed or suffer from weeks of delay in being formally identified while undergoing expert curation.

Results: We present RIVET-a software pipeline and visual platform that takes advantage of recent algorithmic advances in recombination inference to comprehensively and sensitively search for potential SARS-CoV-2 recombinants and organize the relevant information in a web interface that would help greatly accelerate the process of identifying and tracking recombinants.

Availability and implementation: RIVET-based web interface displaying the most updated analysis of potential SARS-CoV-2 recombinants is available at https://rivet.ucsd.edu/. RIVET's frontend and backend code is freely available under the MIT license at https://github.com/TurakhiaLab/rivet and the documentation for RIVET is available at https://turakhialab.github.io/rivet/. The inputs necessary for running RIVET's backend workflow for SARS-CoV-2 are available through a public database maintained and updated daily by UCSC (https://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/).

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用RIVET追踪和培养假定的SARS-CoV-2重组病毒。

动机:识别和追踪重组SARS-CoV-2菌株对于了解病毒的进化和控制其传播至关重要。但是，从每天在网上共享的数千个新基因组序列中自信地识别SARS-CoV-2重组是相当具有挑战性的，导致许多重组被遗漏，或者在接受专家管理时延迟数周才被正式识别。结果:我们提出了rivet -一个软件管道和可视化平台，利用重组推断的最新算法进展，全面、灵敏地搜索潜在的SARS-CoV-2重组体，并在web界面中组织相关信息，这将有助于大大加快识别和跟踪重组体的过程。可用性和实施:基于rivet的web界面显示对潜在SARS-CoV-2重组体的最新分析，可在https://rivet.ucsd.edu/上获得。RIVET的前端和后端代码在MIT许可下可在https://github.com/TurakhiaLab/rivet免费获得，RIVET的文档可在https://turakhialab.github.io/rivet/获得。运行RIVET针对SARS-CoV-2的后端工作流程所需的输入可通过UCSC每天维护和更新的公共数据库(https://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/UShER_SARS-CoV-2/)获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.