Rescuing Low Frequency Variants Within Intra-Host Viral Populations Directly from Oxford Nanopore Sequencing Data

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. 

Learning Objectives:

1. Introduce variant detection of viral genomes. 

2. Explain consensus-level vs within-host variation of SARS-CoV-2.

3. Identify the current challenges of variant detection in Oxford Nanopore sequencing data.

4. Introduce a computational method to distinguish between ONT sequencing errors and ture biological variants.