Metagenomic Nanopore sequencing of influenza virus direct from clinical respiratory samples

Kuiama Lewandowski; Yifei Xu; Steven T. Pullan; Sheila F. Lumley; Dona Foster; Nicholas Sanderson; Alison Vaughan; Marcus Morgan; Nicole Bright; James Kavanagh; Richard Vipond; Miles Carroll; Anthony C. Marriott; Karen E Gooch; Monique Andersson; Katie Jeffery; Timothy EA Peto; Derrick W. Crook; A Sarah Walker; Philippa C. Matthews

Detection of the control virus (Hazara at 10 4 genome copies/ml) was highly variable, 173 demonstrating that levels of background non-target RNA are a major source of inter-sample 174 variation. Hazara reads per sample ranged from 0 to 13.5x10 3 (0-3.5x10 4 RPM) with a median 175 . CC-BY-NC 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/676155 doi: bioRxiv preprint of 70 (160 RPM) and mean of 706 (1.7x10 3 RPM) (Table S1) The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/676155 doi: bioRxiv preprint data for influenza diagnostics and genome characterisation, whilst also detecting and 226 sequencing other common RNA viruses. 227 228 Despite time reductions in wet laboratory processing, this method requires further modification 229 to simplify and accelerate the protocol if it is to become viable as a near-to-patient test. High 230 error rates are a recognised concern in Nanopore sequence data, and cross-barcode 231 contamination can create challenges when low and high titre samples are batched together [22] . 232