Library Preparation Based on Transposase Assisted RNA/DNA Hybrid Co-Tagmentation for Next-Generation Sequencing of Human Noroviruses

Human noroviruses (HuNoVs) are one of the leading causes of foodborne illnesses globally. The viral genome is the most essential information for viral source tracing and viral transmission pattern monitoring. However, whole genome sequencing of HuNoVs is still challenging due to the sequence heterog...

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Bibliographic Details
Main Authors: Zilei Zhang, Danlei Liu, Dapeng Wang, Qingping Wu
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Viruses
Subjects:
Online Access:https://www.mdpi.com/1999-4915/13/1/65
Description
Summary:Human noroviruses (HuNoVs) are one of the leading causes of foodborne illnesses globally. The viral genome is the most essential information for viral source tracing and viral transmission pattern monitoring. However, whole genome sequencing of HuNoVs is still challenging due to the sequence heterogeneity among different genotypes and low titer in samples. To address this need, in this study, the Transposase assisted RNA/DNA hybrid Co-tagmentation (TRACE-seq) method was established for next generation sequencing library preparation of HuNoVs. Our data demonstrated that almost the whole HuNoVs genome (>7 kb) could be obtained from all of the 11 clinical samples tested. Twelve genotypes including GI.3, GI.4, GI.5, GI.8, GII.2, GII.3, GII.4, GII.6, GII.12, GII.13, GII.14, and GII.21 were involved. Compared with the traditional method for viral metagenomics library preparation, optimized TRACE-seq greatly reduced the interference from the host’s and bacterial RNAs. In addition, viral genome sequences can be assembled by using less raw data with sufficient depth along the whole genome. Therefore, for the high versatility and reliability, this method is promising for whole viral genome attainment. It is particularly applicable for the viruses with a low titer that are mixed with a complicated host background and are unable to be cultured in vitro, like the HuNoVs utilized in this study.
ISSN:1999-4915