Title | Rescuing Low Frequency Variants within Intra-Host Viral Populations directly from Oxford Nanopore sequencing data. |
Publication Type | Journal Article |
Year of Publication | 2021 |
Authors | Liu, Y, Kearney, J, Mahmoud, M, Kille, B, Sedlazeck, FJ, Treangen, TJ |
Journal | bioRxiv |
Date Published | 2021 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. |
DOI | 10.1101/2021.09.03.458038 |
Alternate Journal | bioRxiv |
PubMed ID | 34518837 |
PubMed Central ID | PMC8437309 |
Grant List | P01 AI152999 / AI / NIAID NIH HHS / United States U19 AI144297 / AI / NIAID NIH HHS / United States |