Nanopore techniques offer a low-cost, label-free, and high-throughput platform that could be used in single-molecule biosensing and in particular DNA sequencing. Since 2010, graphene and other ...two-dimensional (2D) materials have attracted considerable attention as membranes for producing nanopore devices, owing to their subnanometer thickness that can in theory provide the highest possible spatial resolution of detection. Moreover, 2D materials can be electrically conductive, which potentially enables alternative measurement schemes relying on the transverse current across the membrane material itself and thereby extends the technical capability of traditional ionic current-based nanopore devices. In this review, we discuss key advances in experimental and computational research into DNA sensing with nanopores built from 2D materials, focusing on both the ionic current and transverse current measurement schemes. Challenges associated with the development of 2D material nanopores toward DNA sequencing are further analyzed, concentrating on lowering the noise levels, slowing down DNA translocation, and inhibiting DNA fluctuations inside the pores. Finally, we overview future directions of research that may expedite the emergence of proof-of-concept DNA sequencing with 2D material nanopores.
The solid‐state nanopore has attracted much attention as a next‐generation DNA sequencing tool or a single‐molecule biosensor platform with its high sensitivity of biomolecule detection. The platform ...has advantages of processability, robustness of the device, and flexibility in the nanopore dimensions as compared with the protein nanopore, but with the limitation of insufficient spatial and temporal resolution to be utilized in DNA sequencing. Here, the fundamental principles of the solid‐state nanopore are summarized to illustrate the novelty of the device, and improvements in the performance of the platform in terms of device fabrication are explained. The efforts to reduce the electrical noise of solid‐state nanopore devices, and thus to enhance the sensitivity of detection, are presented along with detailed descriptions of the noise properties of the solid‐state nanopore. Applications of 2D materials including graphene, h‐BN, and MoS2 as a nanopore membrane to enhance the spatial resolution of nanopore detection, and organic coatings on the nanopore membranes for the addition of chemical functionality to the nanopore are summarized. Finally, the recently reported applications of the solid‐state nanopore are categorized and described according to the target biomolecules: DNA‐bound proteins, modified DNA structures, proteins, and protein oligomers.
The solid‐state nanopore has attracted much attention as a next‐generation DNA sequencing tool or a single‐molecule biosensor platform with its high sensitivity of biomolecule detection. The history, fundamental principles, improvements in the performance, and the recently reported applications of the solid‐state nanopore are highlighted, with a focus on devices, materials, and fabrication methods.
Abstract
Nanopore sequencing technology has the potential to render other sequencing technologies obsolete with its ability to generate long reads and provide portability. However, high error rates ...of the technology pose a challenge while generating accurate genome assemblies. The tools used for nanopore sequence analysis are of critical importance, as they should overcome the high error rates of the technology. Our goal in this work is to comprehensively analyze current publicly available tools for nanopore sequence analysis to understand their advantages, disadvantages and performance bottlenecks. It is important to understand where the current tools do not perform well to develop better tools. To this end, we (1) analyze the multiple steps and the associated tools in the genome assembly pipeline using nanopore sequence data, and (2) provide guidelines for determining the appropriate tools for each step. Based on our analyses, we make four key observations: (1) the choice of the tool for basecalling plays a critical role in overcoming the high error rates of nanopore sequencing technology. (2) Read-to-read overlap finding tools, GraphMap and Minimap, perform similarly in terms of accuracy. However, Minimap has a lower memory usage, and it is faster than GraphMap. (3) There is a trade-off between accuracy and performance when deciding on the appropriate tool for the assembly step. The fast but less accurate assembler Miniasm can be used for quick initial assembly, and further polishing can be applied on top of it to increase the accuracy, which leads to faster overall assembly. (4) The state-of-the-art polishing tool, Racon, generates high-quality consensus sequences while providing a significant speedup over another polishing tool, Nanopolish. We analyze various combinations of different tools and expose the trade-offs between accuracy, performance, memory usage and scalability. We conclude that our observations can guide researchers and practitioners in making conscious and effective choices for each step of the genome assembly pipeline using nanopore sequence data. Also, with the help of bottlenecks we have found, developers can improve the current tools or build new ones that are both accurate and fast, to overcome the high error rates of the nanopore sequencing technology.
Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the ...characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain.
DNA and RNA modifications have important functions, including the regulation of gene expression. Existing methods based on short-read sequencing for the detection of modifications show difficulty in ...determining the modification patterns of single chromosomes or an entire transcript sequence. Furthermore, the kinds of modifications for which detection methods are available are very limited. The Nanopore sequencer is a single-molecule, long-read sequencer that can directly sequence RNA as well as DNA. Moreover, the Nanopore sequencer detects modifications on long DNA and RNA molecules. In this review, we mainly focus on base modification detection in the DNA and RNA of mammals using the Nanopore sequencer. We summarize current studies of modifications using the Nanopore sequencer, detection tools using statistical tests or machine learning, and applications of this technology, such as analyses of open chromatin, DNA replication, and RNA metabolism.
Rapid advances in nanopore technologies for sequencing single long DNA and RNA molecules have led to substantial improvements in accuracy, read length and throughput. These breakthroughs have ...required extensive development of experimental and bioinformatics methods to fully exploit nanopore long reads for investigations of genomes, transcriptomes, epigenomes and epitranscriptomes. Nanopore sequencing is being applied in genome assembly, full-length transcript detection and base modification detection and in more specialized areas, such as rapid clinical diagnoses and outbreak surveillance. Many opportunities remain for improving data quality and analytical approaches through the development of new nanopores, base-calling methods and experimental protocols tailored to particular applications.
In the past years, circular RNAs (circRNAs) became a major focus of many studies in animals and plants. circRNAs are generated by backsplicing from the same linear transcripts that are canonically ...spliced to produce, for example, mature mRNAs. They exhibit tissue-specific expression pattern and are potentially involved in many diseases, among them cardiovascular diseases. However, despite the tremendous efforts to establish circRNA catalogues, much less is known about the biological function of the vast majority of circRNAs. We have previously introduced Lexo-circSeq, a targeted RNA sequencing approach that can profile up to 110 circRNAs and their corresponding linear transcripts in one experiment from low amounts of input material on the Illumina platform. Here, we present an improved protocol for Lexo-circSeq and now extend our approach to Nanopore sequencing, which allows the structural assessment of small- and medium-sized circRNAs. Employing human-induced pluripotent stem-cell-derived cardiomyocytes originating from healthy controls or patients suffering from hypertrophic cardiomyopathy, we identify deregulated circRNAs and alternative exon usage.
The Oxford Nanopore Technologies (ONT) MinION is a new sequencing technology that potentially offers read lengths of tens of kilobases (kb) limited only by the length of DNA molecules presented to ...it. The device has a low capital cost, is by far the most portable DNA sequencer available, and can produce data in real-time. It has numerous prospective applications including improving genome sequence assemblies and resolution of repeat-rich regions. Before such a technology is widely adopted, it is important to assess its performance and limitations in respect of throughput and accuracy. In this study we assessed the performance of the MinION by re-sequencing three bacterial genomes, with very different nucleotide compositions ranging from 28.6% to 70.7%; the high G+C strain was underrepresented in the sequencing reads. We estimate the error rate of the MinION (after base calling) to be 38.2%. Mean and median read lengths were 2kb and 1kb respectively, while the longest single read was 98kb. The whole length of a 5kb rRNA operon was covered by a single read. As the first nanopore-based single molecule sequencer available to researchers, the MinION is an exciting prospect; however, the current error rate limits its ability to compete with existing sequencing technologies, though we do show that MinION sequence reads can enhance contiguity of de novo assembly when used in conjunction with Illumina MiSeq data.
Nanopore sequencing provides signal data corresponding to the nucleotide motifs sequenced. Through machine learning-based methods, these signals are translated into long-read sequences that overcome ...the read size limit of short-read sequencing. However, analyzing the raw nanopore signal data provides many more opportunities beyond just sequencing genomes and transcriptomes: algorithms that use machine learning approaches to extract biological information from these signals allow the detection of DNA and RNA modifications, the estimation of poly(A) tail length, and the prediction of RNA secondary structures. In this review, we discuss how developments in machine learning methodologies contributed to more accurate basecalling and lower error rates, and how these methods enable new biological discoveries. We argue that direct nanopore sequencing of DNA and RNA provides a new dimensionality for genomics experiments and highlight challenges and future directions for computational approaches to extract the additional information provided by nanopore signal data.
Nanopore sequencing accuracy has increased to 98.3% as new-generation base callers replace early generation hidden Markov model basecalling algorithms with neural network algorithms.Machine learning methods can classify sequences in real-time, allowing targeted sequencing with nanopore’s ReadUntil feature.Machine learning and statistical testing tools can detect DNA modifications by analyzing ion current signals from nanopore direct DNA sequencing.Nanopore direct RNA sequencing profiles RNAs with their modification retained, which influences the ion current signals emitted from the nanopore.Machine learning and statistical testing tools analyze ion current signals from direct RNA sequencing, enabling RNA modification detection, RNA secondary structure prediction, and poly(A) tail length estimation.
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