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Integrated characterization of SARS-CoV-2 genome, microbiome, antibiotic resistance and host response from single throat swabs
Bo Lu; Yi Yan; Liting Dong; Lingling Han; Yawei Liu; Junping Yu; Jianjun Chen; Danyang Yi; Meiling Zhang; Chao Wang; Runkun Wang; Dengpeng Wang; Hongping Wei; Di Liu; Chengqi Yi.
Afiliación
  • Bo Lu; Peking University
  • Yi Yan; Chinese Academy of Sciences
  • Liting Dong; Peking University
  • Lingling Han; GrandOmics Biosciences
  • Yawei Liu; The First Medical Center of PLA General Hospital
  • Junping Yu; Chinese Academy of Sciences
  • Jianjun Chen; Chinese Academy of Sciences
  • Danyang Yi; Peking University
  • Meiling Zhang; Peking University
  • Chao Wang; GrandOmics Biosciences
  • Runkun Wang; GrandOmics Biosciences
  • Dengpeng Wang; GrandOmics Biosciences
  • Hongping Wei; Chinese Academy of Sciences
  • Di Liu; Chinese Academy of Sciences
  • Chengqi Yi; Peking University
Preprint en En | PREPRINT-BIORXIV | ID: ppbiorxiv-340794
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ABSTRACT
The ongoing coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, poses a severe threat to humanity. Rapid and comprehensive analysis of both pathogen and host sequencing data is critical to track infection and inform therapies. In this study, we performed unbiased metatranscriptomic analysis of clinical samples from COVID-19 patients using a newly-developed RNA-seq library construction method (TRACE-seq), which utilizes tagmentation activity of Tn5 on RNA/DNA hybrids. This approach avoids the laborious and time-consuming steps in traditional RNA-seq procedure, and hence is fast, sensitive and convenient. We demonstrated that TRACE-seq allowed integrated characterization of full genome information of SARS-CoV-2, putative pathogens causing coinfection, antibiotic resistance and host response from single throat swabs. We believe that the integrated information will deepen our understanding of pathogenesis and improve diagnostic accuracy for infectious diseases.
Licencia
cc_by_nc_nd
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-BIORXIV Tipo de estudio: Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Preprint
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-BIORXIV Tipo de estudio: Prognostic_studies Idioma: En Año: 2020 Tipo del documento: Preprint