Assessment of Inter-Laboratory Differences in SARS-CoV-2 Consensus Genome Assemblies between Public Health Laboratories in Australia

PathoSeq-QC: a decision support bioinformatics workflow for robust genomic surveillance

MOTIVATION

Recommendations on the use of genomics for pathogens surveillance are evidence that high-throughput genomic sequencing plays a key role to fight global health threats. Coupled with bioinformatics and other data types (e.g., epidemiological information), genomics is used to obtain knowledge on health pathogenic threats and insights on their evolution, to monitor pathogens spread, and to evaluate the effectiveness of countermeasures. From a decision-making policy perspective, it is essential to ensure the entire process's quality before relying on analysis results as evidence. Available workflows usually offer quality assessment tools that are primarily focused on the quality of raw NGS reads but often struggle to keep pace with new technologies and threats, and fail to provide a robust consensus on results, necessitating manual evaluation of multiple tool outputs.

RESULTS

We present PathoSeq-QC, a bioinformatics decision support workflow developed to improve the trustworthiness of genomic surveillance analyses and conclusions. Designed for SARS-CoV-2, it is suitable for any viral threat. In the specific case of SARS-CoV-2, PathoSeq-QC: (i) evaluates the quality of the raw data; (ii) assesses whether the analysed sample is composed by single or multiple lineages; (iii) produces robust variant calling results via multi-tool comparison; (iv) reports whether the produced data are in support of a recombinant virus, a novel or an already known lineage. The tool is modular, which will allow easy functionalities extension.

AVAILABILITY AND IMPLEMENTATION

PathoSeq-QC is a command-line tool written in Python and R. The code is available at https://code.europa.eu/dighealth/pathoseq-qc.

Continued improvement in the development of the SARS-CoV-2 whole genome sequencing proficiency testing program

Application of whole genome sequencing (WGS) has allowed monitoring of the emergence of variants of concern (VOC) of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) globally. Genomic investigation of emerging variants and surveillance of clinical progress has reduced the public health impact of infection during the COVID-19 pandemic. These steps required developing and implementing a proficiency testing program (PTP), as WGS has been incorporated into routine reference laboratory practice. In this study, we describe how the PTP evaluated the capacity and capability of one New Zealand and 14 Australian public health laboratories to perform WGS of SARS-CoV-2 in 2022. The participants' performances in characterising a specimen panel of known SARS-CoV-2 isolates in the PTP were assessed based on: (1) genome coverage, (2) Pango lineage, and (3) sequence quality, with the choice of assessment metrics refined based on a previously reported assessment conducted in 2021. The participants' performances in 2021 and 2022 were also compared after reassessing the 2021 results using the more stringent metrics adopted in 2022. We found that more participants would have failed the 2021 assessment for all survey samples and a significantly higher fail rate per sample in 2021 compared to 2022. This study highlights the importance of choosing appropriate performance metrics to reflect better the laboratories' capacity to perform SARS-CoV-2 WGS, as was done in the 2022 PTP. It also displays the need for a PTP for WGS of SARS-CoV-2 to be available to public health laboratories ongoing, with continuous refinements in the design and provision of the PTP to account for the dynamic nature of the COVID-19 pandemic as SARS-CoV-2 continues to evolve.

Modeling infection from SARS-CoV-2 wastewater concentrations: promise, limitations, and future directions

Estimating total infection levels, including unreported and asymptomatic infections, is important for understanding community disease transmission. Wastewater can provide a pooled community sample to estimate total infections that is independent of case reporting biases toward individuals with moderate to severe symptoms and by test-seeking behavior and access. We derive three mechanistic models for estimating community infection levels from wastewater measurements based on a description of the processes that generate SARS-CoV-2 RNA signals in wastewater and accounting for the fecal strength of wastewater through endogenous microbial markers, daily flow, and per-capita wastewater generation estimates. The models are illustrated through two case studies of wastewater data collected during 2020-2021 in Virginia Beach, VA, and Santa Clara County, CA. Median simulated infection levels generally were higher than reported cases, but at times, were lower, suggesting a discrepancy between the reported cases and wastewater data, or inaccurate modeling results. Daily simulated infection estimates showed large ranges, in part due to dependence on highly variable clinical viral fecal shedding data. Overall, the wastewater-based mechanistic models are useful for normalization of wastewater measurements and for understanding wastewater-based surveillance data for public health decision-making but are currently limited by lack of robust SARS-CoV-2 fecal shedding data.