Accelerated identification of disease-causing variants with ultra-rapid nanopore genome sequencing

Applications of Nanopore sequencing in precision cancer medicine

Oxford Nanopore Technologies sequencing, also referred to as Nanopore sequencing, stands at the forefront of a revolution in clinical genetics, offering the potential for rapid, long read, and real-time DNA and RNA sequencing. This technology is currently making sequencing more accessible and affordable. In this comprehensive review, we explore its potential regarding precision cancer diagnostics and treatment. We encompass a critical analysis of clinical cases where Nanopore sequencing was successfully applied to identify point mutations, splice variants, gene fusions, epigenetic modifications, non-coding RNAs, and other pivotal biomarkers that defined subsequent treatment strategies. Additionally, we address the challenges of clinical applications of Nanopore sequencing and discuss the current efforts to overcome them.

Local read haplotagging enables accurate long-read small variant calling

Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation simplifies long-read variant calling with DeepVariant.

mtDNA analysis using Mitopore

Mitochondrial DNA (mtDNA) analysis is crucial for the diagnosis of mitochondrial disorders, forensic investigations, and basic research. Existing pipelines are complex, expensive, and require specialized personnel. In many cases, including the diagnosis of detrimental single nucleotide variants (SNVs), mtDNA analysis is still carried out using Sanger sequencing. Here, we developed a simple workflow and a publicly available webserver named Mitopore that allows the detection of mtDNA SNVs, indels, and haplogroups. To simplify mtDNA analysis, we tailored our workflow to process noisy long-read sequencing data for mtDNA analysis, focusing on sequence alignment and parameter optimization. We implemented Mitopore with eliBQ (eliminate bad quality reads), an innovative quality enhancement that permits the increase of per-base quality of over 20% for low-quality data. The whole Mitopore workflow and webserver were validated using patient-derived and induced pluripotent stem cells harboring mtDNA mutations. Mitopore streamlines mtDNA analysis as an easy-to-use fast, reliable, and cost-effective analysis method for both long- and short-read sequencing data. This significantly enhances the accessibility of mtDNA analysis and reduces the cost per sample, contributing to the progress of mtDNA-related research and diagnosis.

The Application of Long-Read Sequencing to Cancer

Cancer is a multifaceted disease arising from numerous genomic aberrations that have been identified as a result of advancements in sequencing technologies. While next-generation sequencing (NGS), which uses short reads, has transformed cancer research and diagnostics, it is limited by read length. Third-generation sequencing (TGS), led by the Pacific Biosciences and Oxford Nanopore Technologies platforms, employs long-read sequences, which have marked a paradigm shift in cancer research. Cancer genomes often harbour complex events, and TGS, with its ability to span large genomic regions, has facilitated their characterisation, providing a better understanding of how complex rearrangements affect cancer initiation and progression. TGS has also characterised the entire transcriptome of various cancers, revealing cancer-associated isoforms that could serve as biomarkers or therapeutic targets. Furthermore, TGS has advanced cancer research by improving genome assemblies, detecting complex variants, and providing a more complete picture of transcriptomes and epigenomes. This review focuses on TGS and its growing role in cancer research. We investigate its advantages and limitations, providing a rigorous scientific analysis of its use in detecting previously hidden aberrations missed by NGS. This promising technology holds immense potential for both research and clinical applications, with far-reaching implications for cancer diagnosis and treatment.

Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: a review

Single locus (Mendelian) diseases are a leading cause of childhood hospitalization, intensive care unit (ICU) admission, mortality, and healthcare cost. Rapid genome sequencing (RGS), ultra-rapid genome sequencing (URGS), and rapid exome sequencing (RES) are diagnostic tests for genetic diseases for ICU patients. In 44 studies of children in ICUs with diseases of unknown etiology, 37% received a genetic diagnosis, 26% had consequent changes in management, and net healthcare costs were reduced by $14,265 per child tested by URGS, RGS, or RES. URGS outperformed RGS and RES with faster time to diagnosis, and higher rate of diagnosis and clinical utility. Diagnostic and clinical outcomes will improve as methods evolve, costs decrease, and testing is implemented within precision medicine delivery systems attuned to ICU needs. URGS, RGS, and RES are currently performed in <5% of the ~200,000 children likely to benefit annually due to lack of payor coverage, inadequate reimbursement, hospital policies, hospitalist unfamiliarity, under-recognition of possible genetic diseases, and current formatting as tests rather than as a rapid precision medicine delivery system. The gap between actual and optimal outcomes in children in ICUs is currently increasing since expanded use of URGS, RGS, and RES lags growth in those likely to benefit through new therapies. There is sufficient evidence to conclude that URGS, RGS, or RES should be considered in all children with diseases of uncertain etiology at ICU admission. Minimally, diagnostic URGS, RGS, or RES should be ordered early during admissions of critically ill infants and children with suspected genetic diseases.

Utility of long-read sequencing for All of Us

The All of Us (AoU) initiative aims to sequence the genomes of over one million Americans from diverse ethnic backgrounds to improve personalized medical care. In a recent technical pilot, we compare the performance of traditional short-read sequencing with long-read sequencing in a small cohort of samples from the HapMap project and two AoU control samples representing eight datasets. Our analysis reveals substantial differences in the ability of these technologies to accurately sequence complex medically relevant genes, particularly in terms of gene coverage and pathogenic variant identification. We also consider the advantages and challenges of using low coverage sequencing to increase sample numbers in large cohort analysis. Our results show that HiFi reads produce the most accurate results for both small and large variants. Further, we present a cloud-based pipeline to optimize SNV, indel and SV calling at scale for long-reads analysis. These results lead to widespread improvements across AoU.

Detection of mosaic and population-level structural variants with Sniffles2

Calling structural variations (SVs) is technically challenging, but using long reads remains the most accurate way to identify complex genomic alterations. Here we present Sniffles2, which improves over current methods by implementing a repeat aware clustering coupled with a fast consensus sequence and coverage-adaptive filtering. Sniffles2 is 11.8 times faster and 29% more accurate than state-of-the-art SV callers across different coverages (5-50×), sequencing technologies (ONT and HiFi) and SV types. Furthermore, Sniffles2 solves the problem of family-level to population-level SV calling to produce fully genotyped VCF files. Across 11 probands, we accurately identified causative SVs around MECP2, including highly complex alleles with three overlapping SVs. Sniffles2 also enables the detection of mosaic SVs in bulk long-read data. As a result, we identified multiple mosaic SVs in brain tissue from a patient with multiple system atrophy. The identified SV showed a remarkable diversity within the cingulate cortex, impacting both genes involved in neuron function and repetitive elements.

Fast Probing Amyloid Polymorphism via Nanopore Translocation

Neurodegenerative diseases are characterized by the presence of cross-β-sheet amyloid fibrils and a rich mesoscopic polymorphism, requiring noninvasive detection with high fidelity. Here, we introduce a methodology that can probe via a sensitive synthetic nanopore the complex polymorphism of amyloid fibrils by an automated and fast screening protocol. Statistically analyzing the translocation events on two model amyloid systems derived from β-lactoglobulin and lysozyme allows extracting the cross-sectional configuration of hydrated amyloid fibrils from current block amplitude and correlating dwell time with fibril length. These findings are consistent with the amyloid polymorphs observed in solution by atomic force microscopy. Furthermore, the ionic current signal of a single translocation event can reveal abnormally aggregated conformations of amyloid fibrils without potential artifacts associated with microscopy methods. This study introduces an effective approach to physically discriminating and separating amyloid and may serve in the rapid diagnosis of early aggregating pathological amyloidosis.

O’Brien T, Perucca P, Ge Z, Anderson A. Applications for Deep Learning in Epilepsy Genetic Research

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.

Local read haplotagging enables accurate long-read small variant calling

Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford nanopore technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation makes DeepVariant a universal variant calling solution for long-read sequencing platforms.

Slip-Coupled Electroosmosis and Electrophoresis Dictate DNA Translocation Speed in Solid-State Nanopores

Controlling the DNA translocation speed is critical in nanopore sequencing, but remains rather challenging in practice, as attributable to a complex coupling between nanoscale fluidics and electrically mediated migration of DNA in a dynamically evolving manner. One important factor influencing the translocation speed is the DNA-liquid slippage stemming from the hydrophobic nature of the oligonucleotide, an aspect that has been widely ignored in the reported literature. In an effort to circumvent this conceptual deficit, here we first develop an analytical model to bring out the slip-mediated coupling between the electroosmosis and DNA-electrophoresis in a solid-state nanopore at low surface charge limits, ignoring the end effects. Subsequently, we compare these results with the numerical simulation data on electrokinetically modulated DNA translocation in such a nanopore, albeit of finite length with due accommodation of the end effects, connecting two end reservoirs by deploying a fully coupled Poisson-Nernst-Plank-Stokes flow model. Both the numerical and analytical results indicate that the DNA translocation speed is a linearly increasing function of the slip length, with more than four-fold increase being observed for a slip length as minimal as 0.5 nm as compared to the no-slip scenario. Considering specific strategies on demand for arresting high translocation speeds for accurate DNA sequencing, the above results establish a theoretical proposition for the same, premised on an analytical expression of the DNA-hydrophobicity modulated enhancement in the translocation speed for designing a nanopore-based sequencing platform─a paradigm that remained to be underemphasized thus far.

Genomics in the long-read sequencing era

Long-read sequencing (LRS) technologies have provided extremely powerful tools to explore genomes. While in the early years these methods suffered technical limitations, they have recently made significant progress in terms of read length, throughput, and accuracy and bioinformatics tools have strongly improved. Here, we aim to review the current status of LRS technologies, the development of novel methods, and the impact on genomics research. We will explore the most impactful recent findings made possible by these technologies focusing on high-resolution sequencing of genomes and transcriptomes and the direct detection of DNA and RNA modifications. We will also discuss how LRS methods promise a more comprehensive understanding of human genetic variation, transcriptomics, and epigenetics for the coming years.

Scientific discovery in the age of artificial intelligence

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

Are We Ready for Whole Population Genomic Sequencing of Asymptomatic Newborns?

The introduction of genomic sequencing technologies into routine newborn screening programs in some form is not only inevitable but also already occurring in some settings. The question is therefore not "if" but "when and how" genomic newborn screening (GNBS) should be implemented. In April 2022, the Centre for Ethics of Paediatric Genomics held a one-day symposium exploring ethical issues relating to the use of genomic sequencing in a range of clinical settings. This review article synthesises the panel discussion and presents both the potential benefits of wide-scale implementation of genomic newborn screening, as well as its practical and ethical issues, including obtaining appropriate consent, and health system implications. A more in-depth understanding of the barriers associated with implementing genomic newborn screening is critical to the success of GNBS programs, both from a practical perspective and also in order to maintain public trust in an important public health initiative.

Integrated multi-omics for rapid rare disease diagnosis on a national scale

Critically ill infants and children with rare diseases need equitable access to rapid and accurate diagnosis to direct clinical management. Over 2 years, the Acute Care Genomics program provided whole-genome sequencing to 290 families whose critically ill infants and children were admitted to hospitals throughout Australia with suspected genetic conditions. The average time to result was 2.9 d and diagnostic yield was 47%. We performed additional bioinformatic analyses and transcriptome sequencing in all patients who remained undiagnosed. Long-read sequencing and functional assays, ranging from clinically accredited enzyme analysis to bespoke quantitative proteomics, were deployed in selected cases. This resulted in an additional 19 diagnoses and an overall diagnostic yield of 54%. Diagnostic variants ranged from structural chromosomal abnormalities through to an intronic retrotransposon, disrupting splicing. Critical care management changed in 120 diagnosed patients (77%). This included major impacts, such as informing precision treatments, surgical and transplant decisions and palliation, in 94 patients (60%). Our results provide preliminary evidence of the clinical utility of integrating multi-omic approaches into mainstream diagnostic practice to fully realize the potential of rare disease genomic testing in a timely manner.

Detection of Biological Molecules Using Nanopore Sensing Techniques

Modern biomedical sensing techniques have significantly increased in precision and accuracy due to new technologies that enable speed and that can be tailored to be highly specific for markers of a particular disease. Diagnosing early-stage conditions is paramount to treating serious diseases. Usually, in the early stages of the disease, the number of specific biomarkers is very low and sometimes difficult to detect using classical diagnostic methods. Among detection methods, biosensors are currently attracting significant interest in medicine, for advantages such as easy operation, speed, and portability, with additional benefits of low costs and repeated reliable results. Single-molecule sensors such as nanopores that can detect biomolecules at low concentrations have the potential to become clinically relevant. As such, several applications have been introduced in this field for the detection of blood markers, nucleic acids, or proteins. The use of nanopores has yet to reach maturity for standardization as diagnostic techniques, however, they promise enormous potential, as progress is made into stabilizing nanopore structures, enhancing chemistries, and improving data collection and bioinformatic analysis. This review offers a new perspective on current biomolecule sensing techniques, based on various types of nanopores, challenges, and approaches toward implementation in clinical settings.

Accelerated nanopore basecalling with SLOW5 data format

MOTIVATION

Nanopore sequencing is emerging as a key pillar in the genomic technology landscape but computational constraints limiting its scalability remain to be overcome. The translation of raw current signal data into DNA or RNA sequence reads, known as 'basecalling', is a major friction in any nanopore sequencing workflow. Here, we exploit the advantages of the recently developed signal data format 'SLOW5' to streamline and accelerate nanopore basecalling on high-performance computing (HPC) and cloud environments.

RESULTS

SLOW5 permits highly efficient sequential data access, eliminating a potential analysis bottleneck. To take advantage of this, we introduce Buttery-eel, an open-source wrapper for Oxford Nanopore's Guppy basecaller that enables SLOW5 data access, resulting in performance improvements that are essential for scalable, affordable basecalling.

AVAILABILITY AND IMPLEMENTATION

Buttery-eel is available at https://github.com/Psy-Fer/buttery-eel.

Flexible and efficient handling of nanopore sequencing signal data with slow5tools

Nanopore sequencing is being rapidly adopted in genomics. We recently developed SLOW5, a new file format with advantages for storage and analysis of raw signal data from nanopore experiments. Here we introduce slow5tools, an intuitive toolkit for handling nanopore data in SLOW5 format. Slow5tools enables lossless data conversion and a range of tools for interacting with SLOW5 files. Slow5tools uses multi-threading, multi-processing, and other engineering strategies to achieve fast data conversion and manipulation, including live FAST5-to-SLOW5 conversion during sequencing. We provide examples and benchmarking experiments to illustrate slow5tools usage, and describe the engineering principles underpinning its performance.

Navigating bottlenecks and trade-offs in genomic data analysis

Genome sequencing and analysis allow researchers to decode the functional information hidden in DNA sequences as well as to study cell to cell variation within a cell population. Traditionally, the primary bottleneck in genomic analysis pipelines has been the sequencing itself, which has been much more expensive than the computational analyses that follow. However, an important consequence of the continued drive to expand the throughput of sequencing platforms at lower cost is that often the analytical pipelines are struggling to keep up with the sheer amount of raw data produced. Computational cost and efficiency have thus become of ever increasing importance. Recent methodological advances, such as data sketching, accelerators and domain-specific libraries/languages, promise to address these modern computational challenges. However, despite being more efficient, these innovations come with a new set of trade-offs, both expected, such as accuracy versus memory and expense versus time, and more subtle, including the human expertise needed to use non-standard programming interfaces and set up complex infrastructure. In this Review, we discuss how to navigate these new methodological advances and their trade-offs.

Glass Capillary-Based Nanopores for Single Molecule/Single Cell Detection

A glass capillary-based nanopore (G-nanopore), due to its tapered tip, easy tunability in orifice size, and especially its flexible surface modifications that can be tailored to effectively capture and enhance the ionic current signal of single entities (single molecules, single cells, and single particles), offers a powerful and nanoconfined sensing platform for diverse biological measurements of single cells and single molecules. Compared with other artificial two-dimensional solid-state nanopores, its conical tip and high spatial and temporal resolution characteristics facilitate noninvasive single molecule and selected area (subcellular) single cell detections (e.g., DNA mutations, highly expressed proteins, and small molecule markers that reflect the change characteristics of the tumor), as a small G-nanopore (≤100 nm) does negligible damage to cell functions and cell membrane integrity when inserted through the cell membrane. In this brief review, we summarize the preparation of G-nanopores and discuss the advantages of them as solid-state sensing platforms for single molecule and single cell detection applications as well as for cancer diagnosis and treatment applications. We also describe the current bottlenecks that limit the widespread use of G-nanopores in clinical applications and provide an outlook on future developments. The brief review will provide the reader with a quick survey of this field and facilitate the rapid development of a G-nanopore sensing platform for future tumor diagnosis and personalized medicine based on single-molecule/single-cell bioassay.

The Current State of Nanopore Sequencing

Nanopore sensing is a disruptive, revolutionary way in which to sequence nucleic acids, including both native DNA and RNA molecules. First commercialized with the MinION^TM sequencer from Oxford Nanopore Technologies^TM in 2015, this review article looks at the current state of nanopore sequencing as of June 2022. Covering the unique characteristics of the technology and how it functions, we then go on to look at the ability of the platform to deliver sequencing at all scales-from personal to high-throughput devices-before looking at how the scientific community is applying the technology around the world to answer their biological questions.

Rapid genome sequencing for pediatrics

The advancements made in next-generation sequencing (NGS) technology over the past two decades have transformed our understanding of genetic variation in humans and had a profound impact on our ability to diagnose patients with rare genetic diseases. In this review, we discuss the recently developed application of rapid NGS techniques, used to diagnose pediatric patients with suspected rare diseases who are critically ill. We highlight the challenges associated with performing such clinical diagnostics tests in terms of the laboratory infrastructure, bioinformatic analysis pipelines, and the ethical considerations that need to be addressed. We end by looking at what future developments in this field may look like and how they can be used to augment the genetic data to further improve the diagnostic rates for these high-priority patients.

Long-read sequencing for molecular diagnostics in constitutional genetic disorders

Long-read sequencing (LRS) has been around for more than a decade, but widespread adoption of the technology has been slow due to the perceived high error rates and high sequencing cost. This is changing due to the recent advancements to produce highly accurate sequences and the reducing costs. LRS promises significant improvement over short read sequencing in four major areas: (1) better detection of structural variation (2) better resolution of highly repetitive or nonunique regions (3) accurate long-range haplotype phasing and (4) the detection of base modifications natively from the sequencing data. Several successful applications of LRS have demonstrated its ability to resolve molecular diagnoses where short-read sequencing fails to identify a cause. However, the argument for increased diagnostic yield from LRS remains to be validated. Larger cohort studies may be required to establish the realistic boundaries of LRS's clinical utility and analytical validity, as well as the development of standards for clinical applications. We discuss the limitations of the current standard of care, and contrast with the applications and advantages of two major LRS platforms, PacBio and Oxford Nanopore, for molecular diagnostics of constitutional disorders, and present a critical argument about the potential of LRS in diagnostic settings.

AutoNanopore: An Automated Adaptive and Robust Method to Locate Translocation Events in Solid-State Nanopore Current Traces

Solid-state nanopore sequencing has shown impressive performances in several research scenarios but is still challenging, mainly due to the ultrafast speed of DNA translocation and significant noises embedded in raw signals. Hence, event detection, aiming to locate precisely these translocation events, is the fundamental step of data analysis. However, existing event detection methods use either a user-defined global threshold or an adaptive threshold determined by the data, assuming the baseline current to be stable over time. These disadvantages limit their applications in real-world application scenarios, especially considering that the results of different methods are often inconsistent. In this study, we develop an automated adaptive method called AutoNanopore, for fast and accurate event detection in current traces. The method consists of three consecutive steps: current trace segmentation, current amplitude outlier identification by straightforward statistical analyses, and event characterization. Then we propose ideas/metrics on how to quantitatively evaluate the performance of an event detection method, followed by comparing the performance of AutoNanopore against two state-of-the-art methods, OpenNanopore and EventPro. Finally, we examine if one method can detect the overlapping events detected by the other two, demonstrating that AutoNanopore has the highest coverage ratio. Moreover, AutoNanopore also performs well in detecting challenging events: e.g., those with significantly varying baselines.

A method for multiplexed full-length single-molecule sequencing of the human mitochondrial genome

Methods to reconstruct the mitochondrial DNA (mtDNA) sequence using short-read sequencing come with an inherent bias due to amplification and mapping. They can fail to determine the phase of variants, to capture multiple deletions and to cover the mitochondrial genome evenly. Here we describe a method to target, multiplex and sequence at high coverage full-length human mitochondrial genomes as native single-molecules, utilizing the RNA-guided DNA endonuclease Cas9. Combining Cas9 induced breaks, that define the mtDNA beginning and end of the sequencing reads, as barcodes, we achieve high demultiplexing specificity and delineation of the full-length of the mtDNA, regardless of the structural variant pattern. The long-read sequencing data is analysed with a pipeline where our custom-developed software, baldur, efficiently detects single nucleotide heteroplasmy to below 1%, physically determines phase and can accurately disentangle complex deletions. Our workflow is a tool for studying mtDNA variation and will accelerate mitochondrial research.

Accurate analysis of mitochondrial DNA is important for mitochondrial disease clinical research and diagnostics. Here, authors present a method using Cas9 cleavage, nanopore sequencing and a custom pipeline to identify pathogenic variants, deletions and accurately quantify heteroplasmy to below 1%.

NanoCoV19: An analytical pipeline for rapid detection of severe acute respiratory syndrome coronavirus 2

Nanopore sequencing technology (NST) has become a rapid and cost-effective method for the diagnosis and epidemiological surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic. Compared with short-read sequencing platforms (e.g., Illumina’s), nanopore long-read sequencing platforms effectively shorten the time required to complete the detection process. However, due to the principles and data characteristics of NST, the accuracy of sequencing data has been reduced, thereby limiting monitoring and lineage analysis of SARS-CoV-2. In this study, we developed an analytical pipeline for SARS-CoV-2 rapid detection and lineage identification that integrates phylogenetic-tree and hotspot mutation analysis, which we have named NanoCoV19. This method not only can distinguish and trace the lineages contained in the alpha, beta, delta, gamma, lambda, and omicron variants of SARS-CoV-2 but is also rapid and efficient, completing overall analysis within 1 h. We hope that NanoCoV19 can be used as an auxiliary tool for rapid subtyping and lineage analysis of SARS-CoV-2 and, more importantly, that it can promote further applications of NST in public-health and -safety plans similar to those formulated to address the COVID-19 outbreak.

Dispatches from Biotech beginning BeginNGS: Rapid newborn genome sequencing to end the diagnostic and therapeutic odyssey

In this Dispatch from Biotech, we briefly review the urgent need for extensive expansion of newborn screening (NBS) by genomic sequencing, and the reasons why early attempts had limited success. During the next decade transformative developments will continue in society and in the pharmaceutical, biotechnology, informatics, and medical sectors that enable prompt addition of genetic disorders to NBS by rapid whole genome sequencing (rWGS) upon introduction of new therapies that qualify them according to the Wilson and Jungner criteria (Wilson, J. M. G., & Jungner, G., World Health Organization. (1968). Principles and Practice of Screening for Disease. World Health Organization. Retrieved from https://apps.who.int/iris/handle/10665/37650). Herein we describe plans, progress, and clinical trial designs for BeginNGS (Newborn Genome Sequencing to end the diagnostic and therapeutic odyssey), a new international, pre-competitive, public-private consortium that proposes to implement a self-learning healthcare delivery system for screening all newborns for over 400 hundred genetic diseases, diagnostic confirmation, implementation of effective treatment, and acceleration of orphan drug development. We invite investigators and stakeholders worldwide to join the consortium in a prospective, multi-center, international trial of the clinical utility and cost effectiveness of BeginNGS.