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J Virol Methods ; 327: 114938, 2024 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-38588779

RESUMEN

BACKGROUND: Advances in high-throughput sequencing (HTS) technologies and reductions in sequencing costs have revolutionised the study of genomics and molecular biology by making whole-genome sequencing (WGS) accessible to many laboratories. However, the analysis of WGS data requires significant computational effort, which is the major drawback in implementing WGS as a routine laboratory technique. OBJECTIVE: Automated pipelines have been developed to overcome this issue, but they do not exist for all organisms. This is the case for human respiratory syncytial virus (RSV), which is a leading cause of lower respiratory tract infections in infants, the elderly, and immunocompromised adults. RESULTS: We present RSV-GenoScan, a fast and easy-to-use pipeline for WGS analysis of RSV generated by HTS on Illumina or Nanopore platforms. RSV-GenoScan automates the WGS analysis steps directly from the raw sequence data. The pipeline filters the sequence data, maps the reads to the RSV reference genomes, generates a consensus sequence, identifies the RSV subgroup, and lists amino acid mutations, insertions and deletions in the F and G viral genes. This enables the rapid identification of mutations in these coding genes that are known to confer resistance to monoclonal antibodies. AVAILABILITY: RSV-GenoScan is freely available at https://github.com/AlexandreD-bio/RSV-GenoScan.


Asunto(s)
Genoma Viral , Secuenciación de Nucleótidos de Alto Rendimiento , Infecciones por Virus Sincitial Respiratorio , Virus Sincitial Respiratorio Humano , Secuenciación Completa del Genoma , Virus Sincitial Respiratorio Humano/genética , Virus Sincitial Respiratorio Humano/aislamiento & purificación , Humanos , Genoma Viral/genética , Infecciones por Virus Sincitial Respiratorio/virología , Infecciones por Virus Sincitial Respiratorio/diagnóstico , Secuenciación Completa del Genoma/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Biología Computacional/métodos , Mutación
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