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ClusterV-Web: a user-friendly tool for profiling HIV quasispecies and generating drug resistance reports from nanopore long-read data.
Su, Junhao; Li, Shumin; Zheng, Zhenxian; Lam, Tak-Wah; Luo, Ruibang.
Afiliación
  • Su J; Department of Computer Science, The University of Hong Kong, Hong Kong 999077, China.
  • Li S; Department of Computer Science, The University of Hong Kong, Hong Kong 999077, China.
  • Zheng Z; Department of Computer Science, The University of Hong Kong, Hong Kong 999077, China.
  • Lam TW; Department of Computer Science, The University of Hong Kong, Hong Kong 999077, China.
  • Luo R; Department of Computer Science, The University of Hong Kong, Hong Kong 999077, China.
Bioinform Adv ; 4(1): vbae006, 2024.
Article en En | MEDLINE | ID: mdl-38282975
ABSTRACT

Summary:

Third-generation long-read sequencing is an increasingly utilized technique for profiling human immunodeficiency virus (HIV) quasispecies and detecting drug resistance mutations due to its ability to cover the entire viral genome in individual reads. Recently, the ClusterV tool has demonstrated accurate detection of HIV quasispecies from Nanopore long-read sequencing data. However, the need for scripting skills and a computational environment may act as a barrier for many potential users. To address this issue, we have introduced ClusterV-Web, a user-friendly web-based application that enables easy configuration and execution of ClusterV, both remotely and locally. Our tool provides interactive tables and data visualizations to aid in the interpretation of results. This development is expected to democratize access to long-read sequencing data analysis, enabling a wider range of researchers and clinicians to efficiently profile HIV quasispecies and detect drug resistance mutations. Availability and implementation ClusterV-Web is freely available and open source, with detailed documentation accessible at http//www.bio8.cs.hku.hk/ClusterVW/. The standalone Docker image and source code are also available at https//github.com/HKU-BAL/ClusterV-Web.

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Bioinform Adv Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Bioinform Adv Año: 2024 Tipo del documento: Article País de afiliación: China