Literature DB >> 32931441

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

Boxiang Liu1, Kaibo Liu1, He Zhang1, Liang Zhang1,2, Yuchen Bian1, Liang Huang1,2.   

Abstract

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.

Entities:  

Keywords:  COVID-19; SARS-CoV-2; bioinformatics; data sets; genetics; genome; programming; sequence; virus; web server

Mesh:

Year:  2020        PMID: 32931441      PMCID: PMC7537720          DOI: 10.2196/22299

Source DB:  PubMed          Journal:  J Med Internet Res        ISSN: 1438-8871            Impact factor:   5.428


  10 in total

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Journal:  Nucleic Acids Res       Date:  2005-01-01       Impact factor: 16.971

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  10 in total
  10 in total

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2.  High Performance Integration Pipeline for Viral and Epitope Sequences.

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4.  NGS data vectorization, clustering, and finding key codons in SARS-CoV-2 variations.

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5.  An in-silico study of the mutation-associated effects on the spike protein of SARS-CoV-2, Omicron variant.

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Review 6.  Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: lessons from the pandemic and preparing for future health crises.

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7.  IDbSV: An Open-Access Repository for Monitoring SARS-CoV-2 Variations and Evolution.

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8.  Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning.

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Review 9.  Transcriptomics and RNA-Based Therapeutics as Potential Approaches to Manage SARS-CoV-2 Infection.

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10.  Mutation in a SARS-CoV-2 Haplotype from Sub-Antarctic Chile Reveals New Insights into the Spike's Dynamics.

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  10 in total

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