A method to identify respiratory virus infections in clinical samples using next-generation sequencing

Talia Kustin, Guy Ling, Sivan Sharabi, Daniela Ram, Nehemya Friedman, Neta Zuckerman, Efrat Dahan Bucris, Aharona Glatman-Freedman, Adi Stern, Michal Mandelboim

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Respiratory virus infections are very common. Such infections impose an enormous economic burden and occasionally lead to death. Furthermore, every few decades, respiratory virus pandemics emerge, putting the entire world population at risk. Thus, there is an urgent need to quickly and precisely identify the infecting agent in a clinical setting. However, in many patients with influenza-like symptoms (ILS) the identity of the underlying pathogen remains unknown. In addition, it takes time and effort to individually identify the virus responsible for the ILS. Here, we present a new next-generation sequencing (NGS)-based method that enables rapid and robust identification of pathogens in a pool of clinical samples without the need for specific primers. The method is aimed at rapidly uncovering a potentially common pathogen affecting many samples with an unidentified source of disease.

Original languageEnglish
Article number2606
JournalScientific Reports
Volume9
Issue number1
DOIs
StatePublished - 22 Feb 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019, The Author(s).

Funding

This study was supported in part by a fellowship from the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University [to T.K. and G.L.].

FundersFunder number
Edmond J. Safra Center for Ethics, Harvard University

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