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AccuVIR: an ACCUrate VIRal genome assembly tool for third-generation sequencing data.
Yu, Runzhou; Cai, Dehan; Sun, Yanni.
  • Yu R; Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR 000000, China.
  • Cai D; Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR 000000, China.
  • Sun Y; Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR 000000, China.
Bioinformatics ; 39(1)2023 01 01.
Article in English | MEDLINE | ID: covidwho-2188262
ABSTRACT
MOTIVATION RNA viruses tend to mutate constantly. While many of the variants are neutral, some can lead to higher transmissibility or virulence. Accurate assembly of complete viral genomes enables the identification of underlying variants, which are essential for studying virus evolution and elucidating the relationship between genotypes and virus properties. Recently, third-generation sequencing platforms such as Nanopore sequencers have been used for real-time virus sequencing for Ebola, Zika, coronavirus disease 2019, etc. However, their high per-base error rate prevents the accurate reconstruction of the viral genome.

RESULTS:

In this work, we introduce a new tool, AccuVIR, for viral genome assembly and polishing using error-prone long reads. It can better distinguish sequencing errors from true variants based on the key observation that sequencing errors can disrupt the gene structures of viruses, which usually have a high density of coding regions. Our experimental results on both simulated and real third-generation sequencing data demonstrated its superior performance on generating more accurate viral genomes than generic assembly or polish tools. AVAILABILITY AND IMPLEMENTATION The source code and the documentation of AccuVIR are available at https//github.com/rainyrubyzhou/AccuVIR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Zika Virus / Zika Virus Infection / COVID-19 Type of study: Observational study / Prognostic study Topics: Variants Limits: Humans Language: English Journal subject: Medical Informatics Year: 2023 Document Type: Article Affiliation country: Bioinformatics

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Zika Virus / Zika Virus Infection / COVID-19 Type of study: Observational study / Prognostic study Topics: Variants Limits: Humans Language: English Journal subject: Medical Informatics Year: 2023 Document Type: Article Affiliation country: Bioinformatics