Title : A SIGNAL in early Canadian viral surveillance efforts for SARS-CoV-2
Abstract:
Sequencing technologies implemented across frontline and public healthcare settings were crucial in developing virus surveillance programs during the coronavirus disease 2019 (COVID19) pandemic caused by the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Through these programs, we have observed several variants of SARS-CoV-2, such as B.1.1.529 (i.e., Omicron), which arose from mutations altering the virus's core genome. Continual surveillance of these mutations remains necessary as additional variants capable of vaccine or diagnostic escape can arise if repeated transmission remains uncontrolled. To aid in SARS-CoV-2 surveillance, we present the evolution of a standardized bioinformatics Snakemake workflow for Illumina short-read sequencing platforms called the SARS-CoV-2 Illumina GeNome Assembly Line (SIGNAL). With SIGNAL, we can characterize mutations from assembled consensus sequences relative to the first SARS-CoV-2 genome sequence (MN908947.3) and observe divergence affecting vaccine, PCR, and diagnostic primer targets. As variants emerged, we leveraged iVar and FreeBayes to produce a consensus genome sequence and identify critical mutations. We then coupled in-depth reports using the Phylogenetic Assignment of Named Global Outbreak LINeages (PANGOLIN) and NCoV-Tools to assign phylogenetic lineages and perform quality control of samples. SIGNAL is currently the main workflow used by Canada’s National Microbiology Lab for processing Illumina SARS-CoV2 sequencing data. Through McMaster’s Sequencing Core and clinical/public health collaborations over the first year of the pandemic, SIGNAL processed about 50% of the Province of Ontario's data and about 20% of all Canadian SARS-CoV-2 data. SIGNAL has since contributed to routine surveillance of local communities, rapid outbreak responses, and academic studies on virus dynamics, including aerosol transmission. Tools like SIGNAL better equip us to handle future emergent viral threats through rapid, yet accurate, processing of large volumes of data.
