Treffer: CoV-Seq, a New Tool for SARS-CoV-2 Genome Analysis and Visualization: Development and Usability Study.

Title:
CoV-Seq, a New Tool for SARS-CoV-2 Genome Analysis and Visualization: Development and Usability Study.
Authors:
Liu B; Baidu Research, Sunnyvale, CA, United States., Liu K; Baidu Research, Sunnyvale, CA, United States., Zhang H; Baidu Research, Sunnyvale, CA, United States., Zhang L; Baidu Research, Sunnyvale, CA, United States.; School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, United States., Bian Y; Baidu Research, Sunnyvale, CA, United States., Huang L; Baidu Research, Sunnyvale, CA, United States.; School of Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, United States.
Source:
Journal of medical Internet research [J Med Internet Res] 2020 Oct 02; Vol. 22 (10), pp. e22299. Date of Electronic Publication: 2020 Oct 02.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE
Imprint Name(s):
Publication: <2011- > : Toronto : JMIR Publications
Original Publication: [Pittsburgh, PA? : s.n., 1999-
MeSH Terms:
Data Visualization* , Databases, Genetic* , Software*, Betacoronavirus/*genetics , Coronavirus Infections/*virology , Genome, Viral/*genetics , Pneumonia, Viral/*virology, COVID-19 ; Computational Biology ; Coronavirus Infections/epidemiology ; Humans ; Pandemics ; Pneumonia, Viral/epidemiology ; SARS-CoV-2
References:
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Bioinformatics. 2018 Dec 1;34(23):4121-4123. (PMID: 29790939)
Lancet Infect Dis. 2020 May;20(5):533-534. (PMID: 32087114)
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Contributed Indexing:
Keywords: COVID-19; SARS-CoV-2; bioinformatics; data sets; genetics; genome; programming; sequence; virus; web server
Entry Date(s):
Date Created: 20200915 Date Completed: 20201009 Latest Revision: 20201218
Update Code:
20250114
PubMed Central ID:
PMC7537720
DOI:
10.2196/22299
PMID:
32931441
Database:
MEDLINE

Weitere Informationen

Background: 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.
Objective: To address these challenges, we developed CoV-Seq, an integrated web server that enables simple and rapid analysis of SARS-CoV-2 genomes.
Methods: CoV-Seq is implemented in Python and JavaScript. The web server and source code URLs are provided in this article.
Results: 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.
Conclusions: 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.
(©Boxiang Liu, Kaibo Liu, He Zhang, Liang Zhang, Yuchen Bian, Liang Huang. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 02.10.2020.)