Rescuing low frequency variants within intra-host viral populations directly from Oxford Nanopore sequencing data

Yunxi Liu, Joshua Kearney, Medhat Mahmoud, Bryce Kille, Fritz J. Sedlazeck and Todd J. Treangen ()
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Yunxi Liu: Rice University
Joshua Kearney: Rice University
Medhat Mahmoud: Human Genome Sequencing Center, Baylor College of Medicine
Bryce Kille: Rice University
Fritz J. Sedlazeck: Rice University
Todd J. Treangen: Rice University

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Infectious disease monitoring on Oxford Nanopore Technologies (ONT) platforms offers rapid turnaround times and low cost. Tracking low frequency intra-host variants provides 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 computational 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 evaluate Variabel on both synthetic data (SARS-CoV-2) and patient derived datasets (Ebola virus, norovirus, SARS-CoV-2); 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 .

Date: 2022
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DOI: 10.1038/s41467-022-28852-1

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