Rescuing Low Frequency Variants within Intra-Host Viral Populations directly from Oxford Nanopore sequencing data.

TitleRescuing Low Frequency Variants within Intra-Host Viral Populations directly from Oxford Nanopore sequencing data.
Publication TypeJournal Article
Year of Publication2021
AuthorsLiu, Y, Kearney, J, Mahmoud, M, Kille, B, Sedlazeck, FJ, Treangen, TJ
JournalbioRxiv
Date Published2021 Sep 06
Abstract

Infectious disease monitoring on Oxford Nanopore Technologies (ONT) platforms offers rapid turnaround times and low cost, exemplified by well over a half of million ONT SARS-COV-2 datasets. Tracking low frequency intra-host variants has provided important insights with respect to elucidating within host viral population dynamics and transmission. However, given the higher error rate of ONT, accurate identification of intra-host variants with low allele frequencies remains an open challenge with no viable solutions available. In response to this need, we present Variabel, a novel approach and first method designed for rescuing low frequency intra-host variants from ONT data alone. We evaluated Variabel on both within patient and across patient paired Illumina and ONT datasets; our results show that Variabel can accurately identify low frequency variants below 0.5 allele frequency, outperforming existing state-of-the-art ONT variant callers for this task. Variabel is open-source and available for download at: www.gitlab.com/treangenlab/variabel.

DOI10.1101/2021.09.03.458038
Alternate JournalbioRxiv
PubMed ID34518837
PubMed Central IDPMC8437309
Grant ListP01 AI152999 / AI / NIAID NIH HHS / United States
U19 AI144297 / AI / NIAID NIH HHS / United States