A general approach to identify low-frequency variants within influenza samples collected during routine surveillance.

Influenza viruses exhibit considerable diversity between hosts. Additionally, different quasispecies can be found within the same host. High-throughput sequencing technologies can be used to sequence a patient-derived virus population at sufficient depths to identify low-frequency variants (LFV) present in a quasispecies, but many challenges remain for reliable LFV detection because of experimental errors introduced during sample preparation and sequencing. High genomic copy numbers and extensive sequencing depths are required to differentiate false positive from real LFV, especially at low allelic frequencies (AFs). This study proposes a general approach for identifying LFV in patient-derived samples obtained during routine surveillance. Firstly, validated thresholds were determined for LFV detection, whilst balancing both the cost and feasibility of reliable LFV detection in clinical samples. Using a genetically well-defined population of influenza A viruses, thresholds of at least 104 genomes per microlitre and AF of ≥5 % were established as detection limits. Secondly, a subset of 59 retained influenza A (H3N2) samples from the 2016-2017 Belgian influenza season was composed. Thirdly, as a proof of concept for the added value of LFV for routine influenza monitoring, potential associations between patient data and whole genome sequencing data were investigated. A significant association was found between a high prevalence of LFV and disease severity. This study provides a general methodology for influenza LFV detection, which can also be adopted by other national influenza reference centres and for other viruses such as SARS-CoV-2. Additionally, this study suggests that the current relevance of LFV for routine influenza surveillance programmes might be undervalued.

Strategy and Performance Evaluation of Low-Frequency Variant Calling for SARS-CoV-2 Using Targeted Deep Illumina Sequencing.

The ongoing COVID-19 pandemic, caused by SARS-CoV-2, constitutes a tremendous global health issue. Continuous monitoring of the virus has become a cornerstone to make rational decisions on implementing societal and sanitary measures to curtail the virus spread. Additionally, emerging SARS-CoV-2 variants have increased the need for genomic surveillance to detect particular strains because of their potentially increased transmissibility, pathogenicity and immune escape. Targeted SARS-CoV-2 sequencing of diagnostic and wastewater samples has been explored as an epidemiological surveillance method for the competent authorities. Currently, only the consensus genome sequence of the most abundant strain is taken into consideration for analysis, but multiple variant strains are now circulating in the population. Consequently, in diagnostic samples, potential co-infection(s) by several different variants can occur or quasispecies can develop during an infection in an individual. In wastewater samples, multiple variant strains will often be simultaneously present. Currently, quality criteria are mainly available for constructing the consensus genome sequence, and some guidelines exist for the detection of co-infections and quasispecies in diagnostic samples. The performance of detection and quantification of low-frequency variants using whole genome sequencing (WGS) of SARS-CoV-2 remains largely unknown. Here, we evaluated the detection and quantification of mutations present at low abundances using the mutations defining the SARS-CoV-2 lineage B.1.1.7 (alpha variant) as a case study. Real sequencing data were in silico modified by introducing mutations of interest into raw wild-type sequencing data, or by mixing wild-type and mutant raw sequencing data, to construct mixed samples subjected to WGS using a tiling amplicon-based targeted metagenomics approach and Illumina sequencing. As anticipated, higher variation and lower sensitivity were observed at lower coverages and allelic frequencies. We found that detection of all low-frequency variants at an abundance of 10, 5, 3, and 1%, requires at least a sequencing coverage of 250, 500, 1500, and 10,000×, respectively. Although increasing variability of estimated allelic frequencies at decreasing coverages and lower allelic frequencies was observed, its impact on reliable quantification was limited. This study provides a highly sensitive low-frequency variant detection approach, which is publicly available at https://galaxy.sciensano.be, and specific recommendations for minimum sequencing coverages to detect clade-defining mutations at certain allelic frequencies. This approach will be useful to detect and quantify low-frequency variants in both diagnostic (e.g., co-infections and quasispecies) and wastewater [e.g., multiple variants of concern (VOCs)] samples.