High-Throughput Adaptable SARS-CoV-2 Screening for Rapid Identification of Dominant and Emerging Regional Variants

ABSTRACTObjectivesEmerging SARS-CoV-2 variant strains can be associated with increased transmissibility, more severe disease, and reduced effectiveness of treatments. To improve the availability of regional variant surveillance, we describe a variant genotyping system that is rapid, accurate, adaptable, and able to detect new low-level variants built with existing hospital infrastructure.MethodsWe use a tiered high-throughput SARS-CoV-2 screening program to characterizes variants in a supra-regional health system over 76 days. Combining targeted qPCR and selective sequencing, we screen positive SARS-CoV-2 samples from all hospitals within our health care system for genotyping dominant and emerging variants.ResultsThe median turnaround for genotyping was two days using the high-throughput qPCR-based screen, allowing us to rapidly characterize the emerging Delta variant. In our population, the Delta variant is associated with a lower CT value, lower age at infection, and increased vaccine breakthrough cases. Detection of low-level and potentially emerging variants highlights the utility of a tiered approach.ConclusionsThese findings underscore the need for fast, low-cost, high-throughput monitoring of regional viral sequences as the pandemic unfolds and the emergence of SARS-CoV-2 variants increases. Combing qPCR-based screening with selective sequencing allows for rapid genotyping of variants and dynamic system improvement.Key messagesA tiered approach that uses qPCR-based screening to identify dominant variants and sequencing for unique variants maximizes throughput, turnaround time, and information gleaned from each sample.In our population, the Delta variant became dominant in less than a month and is associated with lower CT, lower age at infection, and increased breakthrough cases.We identified low-level variants, including the variant of interest B.1.621 and a Delta variant with an E484K mutation in our population using existing hospital infrastructure.

Ecogenomics of Groundwater Phages Suggests Niche Differentiation Linked to Specific Environmental Tolerance

To our knowledge, this is the first study to identify the bacteriophage distribution in a groundwater ecosystem shedding light on their prevalence and distribution across metal-contaminated and background sites. Our study is uniquely based on selective sequencing of solely the extrachromosomal elements of a microbiome followed by analysis for viral signatures, thus establishing a more focused approach for phage identifications.

Real-time selective sequencing using nanopores and deep learning

Nanopore sequencing is an emerging technology that utilizes a unique method of reading nucleic acid sequences and, at the same time, it detects various chemical modifications. Deep learning has increased in popularity as a useful technique to solve many complex computational tasks. Selective sequencing has been widely used in genomic research; although it introduces several caveats to the process of sequencing, its advantages supersede them. In this study we demonstrate an alternative method of software-based selective sequencing that is performed in real time by combining nanopore sequencing and deep learning. Our results show the feasibility of using deep learning for classifying signals from only the first 200 nucleotides in a raw nanopore sequencing signal format. Using custom deep learning models and a script utilizing "Read-Until" framework to target mitochondrial molecules in real time from a human cell line sample, we achieved a significant separation and enrichment ability of more than 2-fold. In a series of very short sequencing runs (10, 30, and 120 minutes), we identified genomic and mitochondrial reads with accuracy above 90%, although mitochondrial DNA comprises only 0.1% of the total input material. We believe that our results will lay the foundation for rapid and selective sequencing using nanopore technology and will pave the way for future clinical applications using nanopore sequencing data.

BOSS-RUNS: a flexible and practical dynamic read sampling framework for nanopore sequencing

Real-time selective sequencing of individual DNA fragments, or ‘Read Until’, allows the focusing of Oxford Nanopore Technology sequencing on pre-selected genomic regions. This can lead to large improvements in DNA sequencing performance in many scenarios where only part of the DNA content of a sample is of interest. This approach is based on the idea of deciding whether to sequence a fragment completely after having sequenced only a small initial part of it. If, based on this small part, the fragment is not deemed of (sufficient) interest it is rejected and sequencing is continued on a new fragment. To date, only simple decision strategies based on location within a genome have been proposed to determine what fragments are of interest. We present a new mathematical model and algorithm for the real-time assessment of the value of prospective fragments. Our decision framework is based not only on which genomic regions are a priori interesting, but also on which fragments have so far been sequenced, and so on the current information available regarding the genome being sequenced. As such, our strategy can adapt dynamically during each run, focusing sequencing efforts in areas of highest uncertainty (typically areas currently low coverage). We show that our approach can lead to considerable savings of time and materials, providing high-confidence genome reconstruction sooner than a standard sequencing run, and resulting in more homogeneous coverage across the genome, even when entire genomes are of interest.Author SummaryAn existing technique called ‘Read Until’ allows selective sequencing of DNA fragments with an Oxford Nanopore Technology (ONT) sequencer. With Read Until it is possible to enrich coverage of areas of interest within a sequenced genome. We propose a new use of this technique: combining a mathematical model of read utility and an algorithm to select an optimal dynamic decision strategy (i.e. one that can be updated in real time, and so react to the data generated so far in an experiment), we show that it possible to improve the efficiency of a sequencing run by focusing effort on areas of highest uncertainty.

Publisher Correction: Real-Time Selective Sequencing with RUBRIC: Read Until with Basecall and Reference-Informed Criteria

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Real-Time Selective Sequencing with RUBRIC: Read Until with Basecall and Reference-Informed Criteria

The Oxford MinION, the first commercial nanopore sequencer, is also the first to implement molecule-by-molecule real-time selective sequencing or “Read Until”. As DNA transits a MinION nanopore, real-time pore current data can be accessed and analyzed to provide active feedback to that pore. Fragments of interest are sequenced by default, while DNA deemed non-informative is rejected by reversing the pore bias to eject the strand, providing a novel means of background depletion and/or target enrichment. In contrast to the previously published pattern-matching Read Until approach, our RUBRIC method is the first example of real-time selective sequencing where on-line basecalling enables alignment against conventional nucleic acid references to provide the basis for sequence/reject decisions. We evaluate RUBRIC performance across a range of optimizable parameters, apply it to mixed human/bacteria and CRISPR/Cas9-cut samples, and present a generalized model for estimating real-time selection performance as a function of sample composition and computing configuration.