Your browser doesn't support javascript.
loading
DVsc: An Automated Framework for Efficiently Detecting Viral Infection from Single-cell Transcriptomics Data.
Leng, Fei; Mei, Song; Zhou, Xiaolin; Liu, Xuanshi; Yuan, Yefeng; Xu, Wenjian; Hao, Chongyi; Guo, Ruolan; Hao, Chanjuan; Li, Wei; Zhang, Peng.
Afiliación
  • Leng F; Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
  • Mei S; Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
  • Zhou X; Institute of Biomedical Engineering, University of Toronto, Toronto, M5S 3G9, Canada.
  • Liu X; Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
  • Yuan Y; Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
  • Xu W; Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
  • Hao C; Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
  • Guo R; Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
  • Hao C; Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
  • Li W; Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
  • Zhang P; Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute; MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
Genomics Proteomics Bioinformatics ; 22(2)2024 Jul 03.
Article en En | MEDLINE | ID: mdl-39215426
ABSTRACT
Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool for studying cellular heterogeneity in various fields, particularly in virological research. By studying the viral and cellular transcriptomes, the dynamics of viral infection can be investigated at a single-cell resolution. However, limited studies have been conducted to investigate whether RNA transcripts from clinical samples contain substantial amounts of viral RNAs, and a specific computational framework for efficiently detecting viral reads based on scRNA-seq data has not been developed. Hence, we introduce DVsc, an open-source framework for precise quantitative analysis of viral infection from single-cell transcriptomics data. When applied to approximately 200 diverse clinical samples that were infected by more than 10 different viruses, DVsc demonstrated high accuracy in systematically detecting viral infection across a wide array of cell types. This innovative bioinformatics pipeline could be crucial for addressing the potential effects of surreptitiously invading viruses on certain illnesses, as well as for designing novel medicines to target viruses in specific host cell subsets and evaluating the efficacy of treatment. DVsc supports the FASTQ format as an input and is compatible with multiple single-cell sequencing platforms. Moreover, it could also be applied to sequences from bulk RNA sequencing data. DVsc is available at http//62.234.32.335000/DVsc.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Virosis / Análisis de la Célula Individual Límite: Humans Idioma: En Revista: Genomics Proteomics Bioinformatics Asunto de la revista: BIOQUIMICA / GENETICA / INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Virosis / Análisis de la Célula Individual Límite: Humans Idioma: En Revista: Genomics Proteomics Bioinformatics Asunto de la revista: BIOQUIMICA / GENETICA / INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido