In silicoanalyses identifies sequence contamination thresholds for Nanopore-generated SARS-CoV2 sequences

Abstract

AbstractThe SARS-CoV-2 pandemic has brought molecular biology and genomic sequencing into the public consciousness and lexicon. With an emphasis on rapid turnaround, genomic data has been used to inform both diagnostic and surveillance decisions for the current pa ndemic at a previously unheard-of scale. The surge in the submission of genomic data to publicly-available databases has proved essential as comparing different genome sequences offers a wealth of knowledge, including phylogenetic links, modes of transmission, rates of evolution, and the impact of mutations on infection and disease severity. However, the scale of the pandemic has meant that once sequencing runs are performed, they are rarely repeated due to limited sample material and/or the availability of sequencing resources, resulting in some imperfect runs being uploaded to public repositories. As a result, it is crucial to investigate the data obtained from these imperfect runs to determine whether the results are reliable. Numerous studies have identified a variety of sources of contamination in public next-generation sequencing (NGS) data as the number of NGS studies increases along with the diversity of sequencing technologies and procedures [1–3]. For this study, we conducted anin silicoexperiment with known SARS-CoV-2 sequences produced from Oxford Nanopore Technologies sequencing to investigate the effect of contamination on lineage calls and single nucleotide variations (SNVs). Through a series of analyses, we identified a contamination threshold below which runs are expected to generate accurate lineage calls and maintain genomic sequence integrity. Together, these findings provide a benchmark below which imperfect runs may be considered robust for reporting results to both stakeholders and public repositories and reduce the need for repeat or wasted runs.Author SummaryLarge-scale genomic comparisons provide a wealth of knowledge, including modes of transmission, rates of evolution, and the impact of mutations on infection, disease severity, and treatment effectiveness. As a result, the public release of genomic data has proven to be crucial. However, studies continue to show that some of the genomic data in public repositories are contaminated due to a variety of reasons. For instance, in the case of SARS-CoV-2 sequences, the pandemic prevented many sequencing runs from being repeated, resulting in some imperfect runs being uploaded to public repositories. It is of note that when genomic data is contaminated, both scientific decisions/studies and public health measures may be compromised. To identify genome contamination threshold(s) for SARS-CoV-2 sequences generated by Nanopore sequencing, computational biology techniques were utilized to generate artificially subsampled contaminated genomes. This is the first study of its kind and so our hope is that the results obtained provide a starting point for the investigation of reporting contamination of NGS data.