Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-scale Sequencing Data

Abstract

Understanding the evolution of viral genomes is essential for elucidating how viruses adapt and change over time. Analyzing intra-host single nucleotide variants (iSNVs) provides key insights into the mechanisms driving the emergence of new viral lineages, which is crucial for predicting and mitigating future viral threats. Despite the potential of next-generation sequencing (NGS) to capture these iSNVs, the process is fraught with challenges, particularly the risk of capturing sequencing artifacts that may result in false iSNVs. To tackle this issue, we developed a two-step workflow designed to enhance the reliability of iSNV detection in large heterogeneous collections of NGS libraries. We use over 130,000 publicly available SARS-CoV-2 NGS libraries to show how our comprehensive workflow effectively distinguishes emerging viral mutations from sequencing errors. This approach incorporates rigorous bioinformatics protocols, stringent quality control metrics, and innovative usage of dimensionality reduction methods to generate insightful representations of this high-dimensional dataset. We identified and mitigated notable batch effects linked to specific sequencing centers around the world and introduced quality control metrics such as the Strand Bias Likelihood that considers strand coverage imbalance, enhancing iSNV reliability. Additionally, we pioneer the application of the PHATE visualization approach to genomic data and introduce a methodology that quantifies how closely related groups of data points are within a two-dimensional space, enhancing our ability to explain clustering patterns based on their shared genetic characteristics. Our workflow not only sheds light on the complexities of viral genomic analysis with state-of-the-art sequencing technologies but also advances the detection of accurate intra-host mutations, opening the door for an enhanced understanding of viral adaptation mechanisms.