Open AccessAssessment of SARS-CoV-2 Genome Sequencing: Quality Criteria and Low-Frequency Variants
Author(s) -
Damien Jacot,
Trestan Pillonel,
Gilbert Greub,
Claire Bertelli
Publication year - 2021
Publication title -
journal of clinical microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.349
H-Index - 255
eISSN - 1070-633X
pISSN - 0095-1137
DOI - 10.1128/jcm.00944-21
Subject(s) - genome , deep sequencing , amplicon sequencing , biology , computational biology , whole genome sequencing , amplicon , genetics , dna sequencing , genomics , polymerase chain reaction , gene , 16s ribosomal rna
ABSTRACT Although many laboratories worldwide have developed their sequencing capacities in response to the need for SARS-CoV-2 genome-based surveillance of variants, only a few reported some quality criteria to ensure sequence quality before lineage assignment and submission to public databases. Hence, we aimed here to provide simple quality control criteria for SARS-CoV-2 sequencing to prevent erroneous interpretation of low-quality or contaminated data. We retrospectively investigated 647 SARS-CoV-2 genomes obtained over 10 tiled amplicons sequencing runs. We extracted 26 potentially relevant metrics covering the entire workflow from sample selection to bioinformatics analysis. Based on data distribution, critical values were established for 11 selected metrics to prompt further quality investigations for problematic samples, in particular those with a low viral RNA quantity. Low-frequency variants (<70% of supporting reads) can result from PCR amplification errors, sample cross contaminations, or presence of distinct SARS-CoV2 genomes in the sample sequenced. The number and the prevalence of low-frequency variants can be used as a robust quality criterion to identify possible sequencing errors or contaminations. Overall, we propose 11 metrics with fixed cutoff values as a simple tool to evaluate the quality of SARS-CoV-2 genomes, among which are cycle thresholds, mean depth, proportion of genome covered at least 10×, and the number of low-frequency variants combined with mutation prevalence data.