Optimization and Validation of SARS-CoV2 Sequencing from Clinical and Wastewater Samples

The emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in December 2019 triggered a global pandemic, causing the urgent need for effective surveillance measures to combat its spread and monitor the evolution of new variants. Sequencing SARS-CoV-2 is an essential tool for surveilling the circulating and emerging variants. This thesis addresses key challenges and proposes advancements in sequencing SARS-CoV-2, focusing on both clinical and wastewater samples.The primary objective of this thesis is to optimize sequencing protocols for SARS-CoV-2 variants from clinical and wastewater samples, specifically targeting improved sequencing capabilities for low viral concentrations using the Oxford Nanopore Promethion platform. Through protocol modifications and refinements, we achieved notable enhancements in sequencing output metrics, such as amplicon amplification, sequencing depth, and the generation of high-quality consensus sequences. The second objective evaluates the performance of wastewater deconvolution software for identifying SARS-CoV-2 variants, employing a meticulous assessment approach with controlled mixtures of synthetic variants and amplicon-based sequencing. In this objective we highlight the effectiveness of Freyja, a widely utilized tool, in producing variant abundance calls closely aligned with expected ratios. In the third objective, we investigate factors contributing to ambiguous variant calls in next-generation sequencing data from two distinct platforms, shedding light on potential sources of variability in variant abundance estimation. Through comprehensive analysis, significant disparities in genome coverage and mutation profiles between platforms were identified, suggesting possible biases or variations in error rates. While Freyja demonstrates excellent performance with controlled datasets, challenges arise with real-world wastewater samples. Through these objectives, the thesis aims to offer insights into optimizing sequencing protocols, enhancing variant detection algorithms, and improving data reproducibility across different sequencing technologies. Ultimately, this research contributes to ongoing efforts in infectious disease surveillance by advancing our understanding of SARS-CoV-2 sequencing from diverse sample sources and providing valuable guidance for future research in viral pathogen sequencing.