Your browser doesn't support javascript.
loading
PAIVS: prediction of avian influenza virus subtype.
Park, Hyeon-Chun; Shin, Juyoun; Cho, Sung-Min; Kang, Shinseok; Chung, Yeun-Jun; Jung, Seung-Hyun.
Afiliación
  • Park HC; Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
  • Shin J; Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
  • Cho SM; Integrated Research Center for Genome Polymorphism, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
  • Kang S; Department of Microbiology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
  • Chung YJ; Integrated Research Center for Genome Polymorphism, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
  • Jung SH; Chungbuk Veterinary Service Laboratory, Chungju 27336, Korea.
Genomics Inform ; 18(1): e5, 2020 Mar.
Article en En | MEDLINE | ID: mdl-32224838
Highly pathogenic avian influenza (HPAI) viruses have caused severe respiratory disease and death in poultry and human beings. Although most of the avian influenza viruses (AIVs) are of low pathogenicity and cause mild infections in birds, some subtypes including hemagglutinin H5 and H7 subtype cause HPAI. Therefore, sensitive and accurate subtyping of AIV is important to prepare and prevent for the spread of HPAI. Next-generation sequencing (NGS) can analyze the full-length sequence information of entire AIV genome at once, so this technology is becoming a more common in detecting AIVs and predicting subtypes. However, an analysis pipeline of NGS-based AIV sequencing data, including AIV subtyping, has not yet been established. Here, in order to support the pre-processing of NGS data and its interpretation, we developed a user-friendly tool, named prediction of avian influenza virus subtype (PAIVS). PAIVS has multiple functions that support the pre-processing of NGS data, reference-guided AIV subtyping, de novo assembly, variant calling and identifying the closest full-length sequences by BLAST, and provide the graphical summary to the end users.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Genomics Inform Año: 2020 Tipo del documento: Article Pais de publicación: Corea del Sur

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Genomics Inform Año: 2020 Tipo del documento: Article Pais de publicación: Corea del Sur