Abstract
Metagenomic sequencing with the Oxford Nanopore MinION sequencer offers potential for point-of-care testing of infectious diseases in clinical settings. To improve cost-effectiveness, multiplexing of several, barcoded samples upon a single flow cell will be required during sequencing. We generated a unique sequencing dataset to assess the extent and source of cross barcode contamination caused by multiplex MinION sequencing. Sequencing libraries for three different viruses, including influenza A, dengue, and chikungunya, were prepared separately and sequenced on individual flow cells. We also pooled the respective libraries and performed multiplex sequencing. We identified 0.056% of total reads in the multiplex sequencing data that were assigned to incorrect barcodes. Chimeric reads were the predominant source of this error. Our findings highlight the need for careful filtering of multiplex sequencing data before downstream analysis, and the trade-off between sensitivity and specificity that applies to the barcode demultiplexing methods.
Highlights
Metagenomic sequencing has the potential to allow unbiased identification of pathogens from a clinical sample
The ultimate objective of our research is to develop a nanopore metagenomic sequencing based diagnostic assay that enables point-of-care testing for infectious diseases
Multiplex sequencing offers the opportunity to improve scalability and cut cost, cross sample contamination can lead to errors in the data and false interpretation of the results
Summary
Metagenomic sequencing has the potential to allow unbiased identification of pathogens from a clinical sample. The MinION is a palm-size, real-time, single-molecule genome sequencer developed by Oxford Nanopore Technologies (ONT). The MinION’s compact size and real-time nature could facilitate the application of metagenomic sequencing in point-of-care testing for infectious diseases, as demonstrated by several proof-of-concept studies, including identification of Chikungunya (CHIKV), Ebola (EBOV), and hepatitis C virus (HCV) from human clinical blood samples without target enrichment (Greninger et al, 2015), and detection of bacterial pathogens from urine samples (Schmidt et al, 2016) and respiratory samples, without the need for prior culture (Pendleton et al, 2017)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
