A comprehensive examination of Nanopore native RNA sequencing for characterization of complex transcriptomes

Host-pathogen protein interaction studies: quality control of cDNA libraries using nanopore sequencing

Protein-protein interactions (PPI) play a key role in host-pathogens interaction studies, as proteins are essential to many cellular mechanisms. The yeast two-hybrid (Y2H) approach is a well-established method for high-throughput PPI screening and mapping of protein interaction networks. The success of this approach partially depends on the quality and representativeness of the host cDNA library, which can be constructed from the transcriptomic content of a selected host cellular type. However, evaluating the relevance of the cDNA library content remains challenging, and one of the key limitations of this interactomic approach is the occurrence of false-negative results (i.e., the absence of detectable interactions). Here, we report a direct, long read, high-throughput sequencing method using Oxford Nanopore Technologies, to assess the completeness of the host cDNA library used in host-pathogen interactions Y2H screening. This approach enables easy identification of possible downstream screened genes in PPI assays, minimizing sequencing biases and bioinformatics handling of the data. This study was performed on a cDNA library, generated from A549 human lung carcinoma cells. We were able to identify 12,123 protein coding genes from the sequencing of whole plasmids containing the cDNA inserts, that were further analyzed via functional pathways enrichment for deeper characterization. This diversity and relative abundance evaluation method could be a first step when generating new cDNA libraries of interest for PPI studies, ensuring the validity and suitability of the host library before proceeding with all Y2H screening steps.

Utilizing Nanopore direct RNA sequencing of blood from patients with sepsis for discovery of co- and post-transcriptional disease biomarkers

BACKGROUND

RNA sequencing of whole blood has been increasingly employed to find transcriptomic signatures of disease states. These studies traditionally utilize short-read sequencing of cDNA, missing important aspects of RNA expression such as differential isoform abundance and poly(A) tail length variation.

METHODS

We used Oxford Nanopore Technologies sequencing to sequence native mRNA extracted from whole blood from 12 patients with definite bacterial and viral sepsis and compared with results from matching Illumina short-read cDNA sequencing data. Additionally, we explored poly(A) tail length variation, novel transcript identification, and differential transcript usage.

RESULTS

The correlation of gene count data between Illumina cDNA- and Nanopore RNA-sequencing strongly depended on the choice of analysis pipeline; NanoCount for Nanopore and Kallisto for Illumina data yielded the highest mean Pearson's correlation of 0.927 at the gene level and 0.736 at the transcript isoform level. We identified 2 genes with differential polyadenylation, 9 genes with differential expression and 4 genes with differential transcript usage between bacterial and viral infection. Gene ontology gene set enrichment analysis of poly(A) tail length revealed enrichment of long tails in mRNA of genes involved in signaling and short tails in oxidoreductase molecular functions. Additionally, we detected 240 non-artifactual novel transcript isoforms.

CONCLUSIONS

Nanopore RNA- and Illumina cDNA-gene counts are strongly correlated, indicating that both platforms are suitable for discovery and validation of gene count biomarkers. Nanopore direct RNA-seq provides additional advantages by uncovering additional post- and co-transcriptional biomarkers, such as poly(A) tail length variation and transcript isoform usage.

Unraveling the hidden complexity of cancer through long-read sequencing

Cancer is fundamentally a disease of the genome, characterized by extensive genomic, transcriptomic, and epigenomic alterations. Most current studies predominantly use short-read sequencing, gene panels, or microarrays to explore these alterations; however, these technologies can systematically miss or misrepresent certain types of alterations, especially structural variants, complex rearrangements, and alterations within repetitive regions. Long-read sequencing is rapidly emerging as a transformative technology for cancer research by providing a comprehensive view across the genome, transcriptome, and epigenome, including the ability to detect alterations that previous technologies have overlooked. In this Perspective, we explore the current applications of long-read sequencing for both germline and somatic cancer analysis. We provide an overview of the computational methodologies tailored to long-read data and highlight key discoveries and resources within cancer genomics that were previously inaccessible with prior technologies. We also address future opportunities and persistent challenges, including the experimental and computational requirements needed to scale to larger sample sizes, the hurdles in sequencing and analyzing complex cancer genomes, and opportunities for leveraging machine learning and artificial intelligence technologies for cancer informatics. We further discuss how the telomere-to-telomere genome and the emerging human pangenome could enhance the resolution of cancer genome analysis, potentially revolutionizing early detection and disease monitoring in patients. Finally, we outline strategies for transitioning long-read sequencing from research applications to routine clinical practice.

Notable challenges posed by long-read sequencing for the study of transcriptional diversity and genome annotation

Long-read sequencing (LRS) technologies have revolutionized transcriptomic research by enabling the comprehensive sequencing of full-length transcripts. Using these technologies, researchers have reported tens of thousands of novel transcripts, even in well-annotated genomes, while developing new algorithms and experimental approaches to handle the noisy data. The Long-read RNA-seq Genome Annotation Assessment Project community effort benchmarked LRS methods in transcriptomics and validated many novel, lowly expressed, often times sample-specific transcripts identified by long reads. These molecules represent deviations of the major transcriptional program that were overlooked by short-read sequencing methods but are now captured by the full-length, single-molecule approach. This Perspective discusses the challenges and opportunities associated with LRS' capacity to unravel this fraction of the transcriptome, in terms of both transcriptome biology and genome annotation. For transcriptome biology, we need to develop novel experimental and computational methods to effectively differentiate technology errors from rare but real molecules. For genome annotation, we must agree on the strategy to capture molecular variability while still defining reference annotations that are useful for the genomics community.

A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines

The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar and that remain difficult to quantify. To evaluate the ability to study RNA transcript expression, we profiled seven human cell lines with five different RNA-sequencing protocols, including short-read cDNA, Nanopore long-read direct RNA, amplification-free direct cDNA and PCR-amplified cDNA sequencing, and PacBio IsoSeq, with multiple spike-in controls, and additional transcriptome-wide N6-methyladenosine profiling data. We describe differences in read length, coverage, throughput and transcript expression, reporting that long-read RNA sequencing more robustly identifies major isoforms. We illustrate the value of the SG-NEx data to identify alternative isoforms, novel transcripts, fusion transcripts and N6-methyladenosine RNA modifications. Together, the SG-NEx data provide a comprehensive resource enabling the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.

Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment

Nanopore signal analysis enables detection of nucleotide modifications from native DNA and RNA sequencing, providing both accurate genetic or transcriptomic and epigenetic information without additional library preparation. At present, only a limited set of modifications can be directly basecalled (for example, 5-methylcytosine), while most others require exploratory methods that often begin with alignment of nanopore signal to a nucleotide reference. We present Uncalled4, a toolkit for nanopore signal alignment, analysis and visualization. Uncalled4 features an efficient banded signal alignment algorithm, BAM signal alignment file format, statistics for comparing signal alignment methods and a reproducible de novo training method for k-mer-based pore models, revealing potential errors in Oxford Nanopore Technologies' state-of-the-art DNA model. We apply Uncalled4 to RNA 6-methyladenine (m6A) detection in seven human cell lines, identifying 26% more modifications than Nanopolish using m6Anet, including in several genes where m6A has known implications in cancer. Uncalled4 is available open source at github.com/skovaka/uncalled4 .

Circular RNAs in neurological conditions - computational identification, functional validation, and potential clinical applications

Non-coding RNAs (ncRNAs) have gained significant attention in recent years due to advancements in biotechnology, particularly high-throughput total RNA sequencing. These developments have led to new understandings of non-coding biology, revealing that approximately 80% of non-coding regions in the genome possesses biochemical functionality. Among ncRNAs, circular RNAs (circRNAs), first identified in 1976, have emerged as a prominent research field. CircRNAs are abundant in most human cell types, evolutionary conserved, highly stable, and formed by back-splicing events which generate covalently closed ends. Notably, circRNAs exhibit high expression levels in neural tissue and perform diverse biochemical functions, including acting as molecular sponges for microRNAs, interacting with RNA-binding proteins to regulate their availability and activity, modulating transcription and splicing, and even translating into functional peptides in some cases. Recent advancements in computational and experimental methods have enhanced our ability to identify and validate circRNAs, providing valuable insights into their biological roles. This review focuses on recent developments in circRNA research as they related to neuropsychiatric and neurodegenerative conditions. We also explore their potential applications in clinical diagnostics, therapeutics, and future research directions. CircRNAs remain a relatively underexplored area of non-coding biology, particularly in the context of neurological disorders. However, emerging evidence supports their role as critical players in the etiology and molecular mechanisms of conditions such as schizophrenia, bipolar disorder, major depressive disorder, Alzheimer's disease, and Parkinson's disease. These findings suggest that circRNAs may provide a novel framework contributing to the molecular dysfunctions underpinning these complex neurological conditions.

Transcriptomics in the era of long-read sequencing

Transcriptome sequencing revolutionized the analysis of gene expression, providing an unbiased approach to gene detection and quantification that enabled the discovery of novel isoforms, alternative splicing events and fusion transcripts. However, although short-read sequencing technologies have surpassed the limited dynamic range of previous technologies such as microarrays, they have limitations, for example, in resolving full-length transcripts and complex isoforms. Over the past 5 years, long-read sequencing technologies have matured considerably, with improvements in instrumentation and analytical methods, enabling their application to RNA sequencing (RNA-seq). Benchmarking studies are beginning to identify the strengths and limitations of long-read RNA-seq, although there remains a need for comprehensive resources to guide newcomers through the intricacies of this approach. In this Review, we provide a comprehensive overview of the long-read RNA-seq workflow, from library preparation and sequencing challenges to core data processing, downstream analyses and emerging developments. We present an extensive inventory of experimental and analytical methods and discuss current challenges and prospects.

DEMINERS enables clinical metagenomics and comparative transcriptomic analysis by increasing throughput and accuracy of nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) is a powerful tool for RNA biology but suffers from low basecalling accuracy, low throughput, and high input requirements. We present DEMINERS, a novel DRS toolkit combining an RNA multiplexing workflow, a Random Forest-based barcode classifier, and an optimized convolutional neural network basecaller with species-specific training. DEMINERS enables accurate demultiplexing of up to 24 samples, reducing RNA input and runtime. Applications include clinical metagenomics, cancer transcriptomics, and parallel transcriptomic comparisons, uncovering microbial diversity in COVID-19 and m6A's role in malaria and glioma. DEMINERS offers a robust, high-throughput solution for precise transcript and RNA modification analysis.

Direct profiling of non-adenosines in poly(A) tails of endogenous and therapeutic mRNAs with Ninetails

Stability and translation of mRNAs, both endogenous and therapeutic, is determined by poly(A) tail. Direct RNA sequencing enables single-molecule measurements of poly(A) lengths, avoiding amplification bias. It also holds potential for observation of non-adenosines within poly(A), known to influence mRNA fate. However, there is no computational method to detect composite tails in Direct Sequencing data. To address this gap, we introduce the Ninetails, a neural network-based tool that accurately identifies and quantifies non-adenosines in poly(A) tails. Examination of different biological contexts revealed widespread non-adenosine decorations, with frequencies influenced by the origin of poly(A) tails differing by mRNA class, cell type, and species. Notably, substrates of cytoplasmic TENT5-polymerases and mitochondrially encoded mRNAs are enriched in composite tails. For mRNA therapeutics, we show that the composition of poly(A) tails in mRNA vaccines is dynamic during its cellular lifetime and that the manufacturing protocol of synthetic mRNAs affects the purity of poly(A) tails.

Shape-Shifting Mechanisms: Integrative Multi-Omics Insights Into Candida albicans Morphogenesis

The ability of Candida albicans to switch among yeast, hyphal, and pseudohyphal forms underlies its adaptability and pathogenicity. While cAMP-dependent signaling has long been considered central to hyphal growth, recent multi-omics studies show that cAMP-independent mechanisms also drive morphological changes. Basal PKA activity, cyclin-dependent kinases (e.g., Cdc28), and other regulators can promote shape-shifting even without classical cAMP pathways. In addition, N-acetylglucosamine (GlcNAc) acts as a potent signal that induces hyphal growth independently of its metabolic role, directly connecting environmental cues to morphological states. By integrating transcriptomic, proteomic, and phosphoproteomic data, this review exposes the intricate networks controlling C. albicans morphogenesis. A clearer understanding of these complex regulatory circuits lays the groundwork for future studies that employ advanced multi-omics analyses. Such approaches will help elucidate how these pathways converge, how they respond to changing environments, and how they might be harnessed or disrupted to influence fungal behavior.

Comprehensive analysis of poly(A) tails in mouse testes and ovaries using Nanopore Direct RNA Sequencing

Gametogenesis is a process in which dysfunctions lead to infertility, a growing health and social problem worldwide. In both spermatogenesis and oogenesis, post-transcriptional gene expression regulation is crucial. Essentially, all mRNAs possess non-templated poly(A) tails, whose composition and dynamics (elongation, shortening, and modifications) determine the fate of mRNA. Moreover, gametogenesis, especially oogenesis, represents a unique instance of the complexity of poly(A) tails metabolism, with oocyte-specific waves of cytoplasmic polyadenylation. In this context, we provide a comprehensive transcriptomic dataset focusing on mRNA poly(A) tail composition and dynamics in murine testes and ovaries. It consists of RNA samples isolated from wild-type and transgenic mice lacking TENT5 polymerases, which can extend poly(A) tails in the cytoplasm. TENT5 deficiencies have serious consequences. For instance, the defect of TENT5D causes infertility in humans. The data described here are generated mainly using the Oxford Nanopore Direct RNA Sequencing (DRS) method, which provides ground-truth information about mRNA molecules, including poly(A) tail length and nucleotide content. For instance, we show the prevalence of uridilated tails in testicular mRNAs.

Defining the True Native Ends of RNAs at Single-Molecule Level with TERA-Seq

Turnover of messenger RNAs (mRNAs) is a highly regulated process and serves to control expression of RNA molecules and to eliminate aberrant transcripts. Profiling mRNA decay using short-read sequencing methods that target either the 5' or 3' ends of RNAs, overlooks valuable information about the other end, which could provide significant insights into biological aspects and mechanisms of RNA decay. Oxford Nanopore Technology (ONT) is rapidly emerging as a powerful platform for direct sequencing of native, single-RNA molecules. However, as currently designed, the existing ONT platform is unable to sequence the very 5' ends of RNAs and is limited to polyadenylated molecules. Here, we present a detailed step-by-step experimental protocol for True End-to-end RNA Sequencing (TERA-Seq), a new method that addresses ONT's limitations, allowing accurate representation and characterization of RNAs at the level of single molecules. TERA-Seq describes both poly- and non-polyadenylated RNA molecules and accurately identifies their native ends by ligating uniquely designed adapters to the 5' ends (5TERA), the 3' ends (TERA3), or both ends (5TERA3) that are sequenced along with the transcripts.

Full-length direct RNA sequencing reveals extensive remodeling of RNA expression, processing and modification in aging Caenorhabditis elegans

Organismal aging is marked by decline in cellular function and anatomy, ultimately resulting in death. To inform our understanding of the mechanisms underlying this degeneration, we performed standard RNA sequencing (RNA-seq) and Oxford Nanopore Technologies direct RNA-seq over an adult time course in Caenorhabditis elegans. Long reads allowed for identification of hundreds of novel isoforms and age-associated differential isoform accumulation, resulting from alternative splicing and terminal exon choice. Genome-wide analysis reveals a decline in RNA processing fidelity. Finally, we identify thousands of inosine and hundreds of pseudouridine edits genome-wide. In this first map of pseudouridine modifications for C. elegans, we find that they largely reside in coding sequences and that the number of genes with this modification increases with age. Collectively, this analysis discovers transcriptomic signatures associated with age and is a valuable resource to understand the many processes that dictate altered gene expression patterns and post-transcriptional regulation in aging.

Adaptable and comprehensive approaches for long-read nanopore sequencing of polyadenylated and non-polyadenylated RNAs

The advent of long-read (LR) sequencing technologies has provided a direct opportunity to determine the structure of transcripts with potential for end-to-end sequencing of full-length RNAs. LR methods that have been described to date include commercial offerings from Oxford Nanopore Technologies (ONT) and Pacific Biosciences. These kits are based on selection of polyadenylated (polyA+) RNAs and/or oligo-dT priming of reverse transcription. Thus, these approaches do not allow comprehensive interrogation of the transcriptome due to their exclusion of non-polyadenylated (polyA-) RNAs. In addition, polyA + specificity also results in 3'-biased measurements of PolyA+ RNAs especially when the RNA input is partially degraded. To address these limitations of current LR protocols, we modified rRNA depletion protocols that have been used in short-read sequencing: one approach representing a ligation-based method and the other a template-switch cDNA synthesis-based method to append ONT-specific adaptor sequences and by removing any deliberate fragmentation/shearing of RNA/cDNA. Here, we present comparisons with poly+ RNA-specific versions of the two approaches including the ONT PCR-cDNA Barcoding kit. The rRNA depletion protocols displayed higher proportions (30%-50%) of intronic content compared to that of the polyA-specific protocols (5%-8%). In addition, the rRNA depletion protocols enabled ∼20-50% higher detection of expressed genes. Other metrics that were favourable to the rRNA depletion protocols include better coverage of long transcripts, and higher accuracy and reproducibility of expression measurements. Overall, these results indicate that the rRNA depletion-based protocols described here allow the comprehensive characterization of polyadenylated and non-polyadenylated RNAs. While the resulting reads are long enough to help decipher transcript structures, future endeavors are warranted to improve the proportion of individual reads representing end-to-end spanning of transcripts.

Challenges in identifying mRNA transcript starts and ends from long-read sequencing data

Long-read sequencing (LRS) technologies have the potential to revolutionize scientific discoveries in RNA biology through the comprehensive identification and quantification of full-length mRNA isoforms. Despite great promise, challenges remain in the widespread implementation of LRS technologies for RNA-based applications, including concerns about low coverage, high sequencing error, and robust computational pipelines. Although much focus has been placed on defining mRNA exon composition and structure with LRS data, less careful characterization has been done of the ability to assess the terminal ends of isoforms, specifically, transcription start and end sites. Such characterization is crucial for completely delineating full mRNA molecules and regulatory consequences. However, there are substantial inconsistencies in both start and end coordinates of LRS reads spanning a gene, such that LRS reads often fail to accurately recapitulate annotated or empirically derived terminal ends of mRNA molecules. Here, we describe the specific challenges of identifying and quantifying mRNA terminal ends with LRS technologies and how these issues influence biological interpretations of LRS data. We then review recent experimental and computational advances designed to alleviate these problems, with ideal use cases for each approach. Finally, we outline anticipated developments and necessary improvements for the characterization of terminal ends from LRS data.

Direct RNA sequencing in plants: Practical applications and future perspectives

The transcriptome serves as a bridge that links genomic variation to phenotypic diversity. A vast number of studies using next-generation RNA sequencing (RNA-seq) over the last 2 decades have emphasized the essential roles of the plant transcriptome in response to developmental and environmental conditions, providing numerous insights into the dynamic changes, evolutionary traces, and elaborate regulation of the plant transcriptome. With substantial improvement in accuracy and throughput, direct RNA sequencing (DRS) has emerged as a new and powerful sequencing platform for precise detection of native and full-length transcripts, overcoming many limitations such as read length and PCR bias that are inherent to short-read RNA-seq. Here, we review recent advances in dissecting the complexity and diversity of plant transcriptomes using DRS as the main technological approach, covering many aspects of RNA metabolism, including novel isoforms, poly(A) tails, and RNA modification, and we propose a comprehensive workflow for processing of plant DRS data. Many challenges to the application of DRS in plants, such as the need for machine learning tools tailored to plant transcriptomes, remain to be overcome, and together we outline future biological questions that can be addressed by DRS, such as allele-specific RNA modification. This technology provides convenient support on which the connection of distinct RNA features is tightly built, sustainably refining our understanding of the biological functions of the plant transcriptome.

A primordial DNA store and compute engine

Any modern information system is expected to feature a set of primordial features and functions: a substrate stably carrying data; the ability to repeatedly write, read, erase, reload and compute on specific data from that substrate; and the overall ability to execute such functions in a seamless and programmable manner. For nascent molecular information technologies, proof-of-principle realization of this set of primordial capabilities would advance the vision for their continued development. Here we present a DNA-based store and compute engine that captures these primordial capabilities. This system comprises multiple image files encoded into DNA and adsorbed onto ~50-μm-diameter, highly porous, hierarchically branched, colloidal substrate particles comprised of naturally abundant cellulose acetate. Their surface areas are over 200 cm2 mg-1 with binding capacities of over 1012 DNA oligos mg-1, 10 TB mg-1 or 104 TB cm-3. This 'dendricolloid' stably holds DNA files better than bare DNA with an extrapolated ability to be repeatedly lyophilized and rehydrated over 170 times compared with 60 times, respectively. Accelerated ageing studies project half-lives of ~6,000 and 2 million years at 4 °C and -18 °C, respectively. The data can also be erased and replaced, and non-destructive file access is achieved through transcribing from distinct synthetic promoters. The resultant RNA molecules can be directly read via nanopore sequencing and can also be enzymatically computed to solve simplified 3 × 3 chess and sudoku problems. Our study establishes a feasible route for utilizing the high information density and parallel computational advantages of nucleic acids.

Microbiome-induced reprogramming in post-transcriptional landscape using nanopore direct RNA sequencing

It has been widely recognized that the microbiota has the capacity to shape host gene expression and physiological functions. However, there remains a paucity of comprehensive study revealing the host transcriptional landscape regulated by the microbiota. Here, we comprehensively examined mRNA landscapes in mouse tissues (brain and cecum) from specific-pathogen-free and germ-free mice using nanopore direct RNA sequencing. Our results show that the microbiome has global influence on a host's RNA modifications (m6A, m5C, Ψ), isoform generation, poly(A) tail length, and transcript abundance in both brain and cecum tissues. Moreover, the microbiome exerts tissue-specific effects on various post-transcriptional regulatory processes. In addition, the microbiome impacts the coordination of multiple RNA modifications in host brain and cecum tissues. In conclusion, we establish the relationship between microbial regulation and gene expression. Our results help the understanding of the mechanisms by which the microbiome reprograms host gene expression.

Reference Materials for Improving Reliability of Multiomics Profiling

High-throughput technologies for multiomics or molecular phenomics profiling have been extensively adopted in biomedical research and clinical applications, offering a more comprehensive understanding of biological processes and diseases. Omics reference materials play a pivotal role in ensuring the accuracy, reliability, and comparability of laboratory measurements and analyses. However, the current application of omics reference materials has revealed several issues, including inappropriate selection and underutilization, leading to inconsistencies across laboratories. This review aims to address these concerns by emphasizing the importance of well-characterized reference materials at each level of omics, encompassing (epi-)genomics, transcriptomics, proteomics, and metabolomics. By summarizing their characteristics, advantages, and limitations along with appropriate performance metrics pertinent to study purposes, we provide an overview of how omics reference materials can enhance data quality and data integration, thus fostering robust scientific investigations with omics technologies.

Exploring the transcriptomic profile of human monkeypox virus via CAGE and native RNA sequencing approaches

In this study, we employed short- and long-read sequencing technologies to delineate the transcriptional architecture of the human monkeypox virus and to identify key regulatory elements that govern its gene expression. Specifically, we conducted a transcriptomic analysis to annotate the transcription start sites (TSSs) and transcription end sites (TESs) of the virus by utilizing Cap Analysis of gene expression sequencing on the Illumina platform and direct RNA sequencing on the Oxford Nanopore technology device. Our investigations uncovered significant complexity in the use of alternative TSSs and TESs in viral genes. In this research, we also detected the promoter elements and poly(A) signals associated with the viral genes. Additionally, we identified novel genes in both the left and right variable regions of the viral genome.IMPORTANCEGenerally, gaining insight into how the transcription of a virus is regulated offers insights into the key mechanisms that control its life cycle. The recent outbreak of the human monkeypox virus has underscored the necessity of understanding the basic biology of its causative agent. Our results are pivotal for constructing a comprehensive transcriptomic atlas of the human monkeypox virus, providing valuable resources for future studies.

An overview of recent technological developments in bovine genomics

Cattle are regarded as highly valuable animals because of their milk, beef, dung, fur, and ability to draft. The scientific community has tried a number of strategies to improve the genetic makeup of bovine germplasm. To ensure higher returns for the dairy and beef industries, researchers face their greatest challenge in improving commercially important traits. One of the biggest developments in the last few decades in the creation of instruments for cattle genetic improvement is the discovery of the genome. Breeding livestock is being revolutionized by genomic selection made possible by the availability of medium- and high-density single nucleotide polymorphism (SNP) arrays coupled with sophisticated statistical techniques. It is becoming easier to access high-dimensional genomic data in cattle. Continuously declining genotyping costs and an increase in services that use genomic data to increase return on investment have both made a significant contribution to this. The field of genomics has come a long way thanks to groundbreaking discoveries such as radiation-hybrid mapping, in situ hybridization, synteny analysis, somatic cell genetics, cytogenetic maps, molecular markers, association studies for quantitative trait loci, high-throughput SNP genotyping, whole-genome shotgun sequencing to whole-genome mapping, and genome editing. These advancements have had a significant positive impact on the field of cattle genomics. This manuscript aimed to review recent advances in genomic technologies for cattle breeding and future prospects in this field.

NERD-seq: a novel approach of Nanopore direct RNA sequencing that expands representation of non-coding RNAs

Non-coding RNAs (ncRNAs) are frequently documented RNA modification substrates. Nanopore Technologies enables the direct sequencing of RNAs and the detection of modified nucleobases. Ordinarily, direct RNA sequencing uses polyadenylation selection, studying primarily mRNA gene expression. Here, we present NERD-seq, which enables detection of multiple non-coding RNAs, excluded by the standard approach, alongside natively polyadenylated transcripts. Using neural tissues as a proof of principle, we show that NERD-seq expands representation of frequently modified non-coding RNAs, such as snoRNAs, snRNAs, scRNAs, srpRNAs, tRNAs, and rRFs. NERD-seq represents an RNA-seq approach to simultaneously study mRNA and ncRNA epitranscriptomes in brain tissues and beyond.

Current progress in strategies to profile transcriptomic m6A modifications

Various methods have been developed so far for detecting N 6-methyladenosine (m6A). The total m6A level or the m6A status at individual positions on mRNA can be detected and quantified through some sequencing-independent biochemical methods, such as LC/MS, SCARLET, SELECT, and m6A-ELISA. However, the m6A-detection techniques relying on high-throughput sequencing have more effectively advanced the understanding about biological significance of m6A-containing mRNA and m6A pathway at a transcriptomic level over the past decade. Various SGS-based (Second Generation Sequencing-based) methods with different detection principles have been widely employed for this purpose. These principles include m6A-enrichment using antibodies, discrimination of m6A from unmodified A-base by nucleases, a fusion protein strategy relying on RNA-editing enzymes, and marking m6A with chemical/biochemical reactions. Recently, TGS-based (Third Generation Sequencing-based) methods have brought a new trend by direct m6A-detection. This review first gives a brief introduction of current knowledge about m6A biogenesis and function, and then comprehensively describes m6A-profiling strategies including their principles, procedures, and features. This will guide users to pick appropriate methods according to research goals, give insights for developing novel techniques in varying areas, and continue to expand our boundary of knowledge on m6A.

Full-length direct RNA sequencing reveals extensive remodeling of RNA expression, processing and modification in aging Caenorhabditis elegans

Organismal aging is marked by decline in cellular function and anatomy, ultimately resulting in death. To inform our understanding of the mechanisms underlying this degeneration, we performed standard RNA sequencing and Nanopore direct RNA sequencing over an adult time course in Caenorhabditis elegans. Long reads allowed for identification of hundreds of novel isoforms and age-associated differential isoform accumulation, resulting from alternative splicing and terminal exon choice. Genome-wide analysis reveals a decline in RNA processing fidelity and a rise in inosine and pseudouridine editing events in transcripts from older animals. In this first map of pseudouridine modifications for C. elegans, we find that they largely reside in coding sequences and that the number of genes with this modification increases with age. Collectively, this analysis discovers transcriptomic signatures associated with age and is a valuable resource to understand the many processes that dictate altered gene expression patterns and post-transcriptional regulation in aging.

Enhanced ac4C detection in RNA via chemical reduction and cDNA synthesis with modified dNTPs

The functional analysis of epitranscriptomic modifications in RNA is constrained by a lack of methods that accurately capture their locations and levels. We previously demonstrated that the RNA modification N4-acetylcytidine (ac4C) can be mapped at base resolution through sodium borohydride reduction to tetrahydroacetylcytidine (tetrahydro-ac4C), followed by cDNA synthesis to misincorporate adenosine opposite reduced ac4C sites, culminating in C:T mismatches at acetylated cytidines (RedaC:T). However, this process is relatively inefficient, resulting in <20% C:T mismatches at a fully modified ac4C site in 18S rRNA. Considering that ac4C locations in other substrates including mRNA are unlikely to reach full penetrance, this method is not ideal for comprehensive mapping. Here, we introduce "RetraC:T" (reduction to tetrahydro-ac4C and reverse transcription with amino-dATP to induce C:T mismatches) as a method with enhanced ability to detect ac4C in cellular RNA. In brief, RNA is reduced through NaBH4 or the closely related reagent sodium cyanoborohydride (NaCNBH3) followed by cDNA synthesis in the presence of a modified DNA nucleotide, 2-amino-dATP, that preferentially binds to tetrahydro-ac4C. Incorporation of the modified dNTP substantially improved C:T mismatch rates, reaching stoichiometric detection of ac4C in 18S rRNA. Importantly, 2-amino-dATP did not result in truncated cDNA products nor increase mismatches at other locations. Thus, modified dNTPs are introduced as a new addition to the toolbox for detecting ac4C at base resolution.

Direct RNA sequencing of astronaut blood reveals spaceflight-associated m6A increases and hematopoietic transcriptional responses

The advent of civilian spaceflight challenges scientists to precisely describe the effects of spaceflight on human physiology, particularly at the molecular and cellular level. Newer, nanopore-based sequencing technologies can quantitatively map changes in chemical structure and expression at single molecule resolution across entire isoforms. We perform long-read, direct RNA nanopore sequencing, as well as Ultima high-coverage RNA-sequencing, of whole blood sampled longitudinally from four SpaceX Inspiration4 astronauts at seven timepoints, spanning pre-flight, day of return, and post-flight recovery. We report key genetic pathways, including changes in erythrocyte regulation, stress induction, and immune changes affected by spaceflight. We also present the first m6A methylation profiles for a human space mission, suggesting a significant spike in m6A levels immediately post-flight. These data and results represent the first longitudinal long-read RNA profiles and RNA modification maps for each gene for astronauts, improving our understanding of the human transcriptome's dynamic response to spaceflight.

Nanopore Translocation Reveals Electrophoretic Force on Noncanonical RNA:DNA Double Helix

Electrophoretic transport plays a pivotal role in advancing sensing technologies. So far, systematic studies have focused on the translocation of canonical B-form or A-form nucleic acids, while direct RNA analysis is emerging as the new frontier for nanopore sensing and sequencing. Here, we compare the less-explored dynamics of noncanonical RNA:DNA hybrids in electrophoretic transport to the well-researched transport of B-form DNA. Using DNA/RNA nanotechnology and solid-state nanopores, the translocation of RNA:DNA (RD) and DNA:DNA (DD) duplexes was examined. Notably, RD duplexes were found to translocate through nanopores faster than DD duplexes, despite containing the same number of base pairs. Our experiments reveal that RD duplexes present a noncanonical helix, with distinct transport properties from B-form DD molecules. We find that RD and DD molecules, with the same contour length, move with comparable velocity through nanopores. We examined the physical characteristics of both duplex forms using atomic force microscopy, atomistic molecular dynamics simulations, agarose gel electrophoresis, and dynamic light scattering measurements. With the help of coarse-grained and molecular dynamics simulations, we find the effective force per unit length applied by the electric field to a fragment of RD or DD duplex in nanopores with various geometries or shapes to be approximately the same. Our results shed light on the significance of helical form in nucleic acid translocation, with implications for RNA sensing, sequencing, and the molecular understanding of electrophoretic transport.

Sequencing accuracy and systematic errors of nanopore direct RNA sequencing

BACKGROUND

Direct RNA sequencing (dRNA-seq) on the Oxford Nanopore Technologies (ONT) platforms can produce reads covering up to full-length gene transcripts, while containing decipherable information about RNA base modifications and poly-A tail lengths. Although many published studies have been expanding the potential of dRNA-seq, its sequencing accuracy and error patterns remain understudied.

RESULTS

We present the first comprehensive evaluation of sequencing accuracy and characterisation of systematic errors in dRNA-seq data from diverse organisms and synthetic in vitro transcribed RNAs. We found that for sequencing kits SQK-RNA001 and SQK-RNA002, the median read accuracy ranged from 87% to 92% across species, and deletions significantly outnumbered mismatches and insertions. Due to their high abundance in the transcriptome, heteropolymers and short homopolymers were the major contributors to the overall sequencing errors. We also observed systematic biases across all species at the levels of single nucleotides and motifs. In general, cytosine/uracil-rich regions were more likely to be erroneous than guanines and adenines. By examining raw signal data, we identified the underlying signal-level features potentially associated with the error patterns and their dependency on sequence contexts. While read quality scores can be used to approximate error rates at base and read levels, failure to detect DNA adapters may be a source of errors and data loss. By comparing distinct basecallers, we reason that some sequencing errors are attributable to signal insufficiency rather than algorithmic (basecalling) artefacts. Lastly, we generated dRNA-seq data using the latest SQK-RNA004 sequencing kit released at the end of 2023 and found that although the overall read accuracy increased, the systematic errors remain largely identical compared to the previous kits.

CONCLUSIONS

As the first systematic investigation of dRNA-seq errors, this study offers a comprehensive overview of reproducible error patterns across diverse datasets, identifies potential signal-level insufficiency, and lays the foundation for error correction methods.

Biochemical-free enrichment or depletion of RNA classes in real-time during direct RNA sequencing with RISER

The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduces their respective read counts by more than 85%, resulting in an increase in sequencing depth of 47% on average for long non-coding RNAs. We also apply RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as an increase in non-globin reads by 16% on average. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISER's modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser .

Forensic Microbiology: When, Where and How

Forensic microbiology is a relatively new discipline, born in part thanks to the development of advanced methodologies for the detection, identification and characterization of microorganisms, and also in relation to the growing impact of infectious diseases of iatrogenic origin. Indeed, the increased application of medical practices, such as transplants, which require immunosuppressive treatments, and the growing demand for prosthetic installations, associated with an increasing threat of antimicrobial resistance, have led to a rise in the number of infections of iatrogenic origin, which entails important medico-legal issues. On the other hand, the possibility of detecting minimal amounts of microorganisms, even in the form of residual traces (e.g., their nucleic acids), and of obtaining gene and genomic sequences at contained costs, has made it possible to ask new questions of whether cases of death or illness might have a microbiological origin, with the possibility of also tracing the origin of the microorganisms involved and reconstructing the chain of contagion. In addition to the more obvious applications, such as those mentioned above related to the origin of iatrogenic infections, or to possible cases of infections not properly diagnosed and treated, a less obvious application of forensic microbiology concerns its use in cases of violence or violent death, where the characterization of the microorganisms can contribute to the reconstruction of the case. Finally, paleomicrobiology, e.g., the reconstruction and characterization of microorganisms in historical or even archaeological remnants, can be considered as a sister discipline of forensic microbiology. In this article, we will review these different aspects and applications of forensic microbiology.

Nanopore translocation reveals electrophoretic force on non-canonical RNA:DNA double helix

Electrophoretic transport plays a pivotal role in advancing sensing technologies. So far, systematic studies have focused on translocation of canonical B-form or A-form nucleic acids, while direct RNA analysis is emerging as the new frontier for nanopore sensing and sequencing. Here, we compare the less-explored dynamics of non-canonical RNA:DNA hybrids in electrophoretic transport with the well-researched transport of B-form DNA. Using DNA/RNA nanotechnology and solid-state nanopores, the translocation of RNA:DNA (RD) and DNA:DNA (DD) duplexes was examined. Notably, RD duplexes were found to translocate through nanopores faster than DD duplexes, despite containing the same number of base pairs. Our experiments reveal that RD duplexes present a non-canonical helix with distinct transport properties from B-form DD molecules. We find RD and DD molecules with the same contour length move with comparable velocity through nanopores. We examined the physical characteristics of both duplex forms using atomic force microscopy, atomistic molecular dynamics simulations, agarose gel electrophoresis, and dynamic light scattering measurements. With the help of coarse-grained and molecular dynamics simulations, we find the effective force per unit length applied by the electric field to a fragment of RD or DD duplex in nanopores with various geometries or shapes to be approximately the same within experimental errors. Our results shed light on the significance of helical form in nucleic acid translocation, with implications for RNA sensing, sequencing, and molecular understanding of electrophoretic transport.

SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms

SQANTI3 is a tool designed for the quality control, curation and annotation of long-read transcript models obtained with third-generation sequencing technologies. Leveraging its annotation framework, SQANTI3 calculates quality descriptors of transcript models, junctions and transcript ends. With this information, potential artifacts can be identified and replaced with reliable sequences. Furthermore, the integrated functional annotation feature enables subsequent functional iso-transcriptomics analyses.

Single-molecule epitranscriptomic analysis of full-length HIV-1 RNAs reveals functional roles of site-specific m6As

Although the significance of chemical modifications on RNA is acknowledged, the evolutionary benefits and specific roles in human immunodeficiency virus (HIV-1) replication remain elusive. Most studies have provided only population-averaged values of modifications for fragmented RNAs at low resolution and have relied on indirect analyses of phenotypic effects by perturbing host effectors. Here we analysed chemical modifications on HIV-1 RNAs at the full-length, single RNA level and nucleotide resolution using direct RNA sequencing methods. Our data reveal an unexpectedly simple HIV-1 modification landscape, highlighting three predominant N6-methyladenosine (m6A) modifications near the 3' end. More densely installed in spliced viral messenger RNAs than in genomic RNAs, these m6As play a crucial role in maintaining normal levels of HIV-1 RNA splicing and translation. HIV-1 generates diverse RNA subspecies with distinct m6A ensembles, and maintaining multiple of these m6As on its RNAs provides additional stability and resilience to HIV-1 replication, suggesting an unexplored viral RNA-level evolutionary strategy.

Multi-omics analysis reveals COVID-19 vaccine induced attenuation of inflammatory responses during breakthrough disease

The immune mechanisms mediating COVID-19 vaccine attenuation of COVID-19 remain undescribed. We conducted comprehensive analyses detailing immune responses to SARS-CoV-2 virus in blood post-vaccination with ChAdOx1 nCoV-19 or a placebo. Samples from randomised placebo-controlled trials (NCT04324606 and NCT04400838) were taken at baseline, onset of COVID-19-like symptoms, and 7 days later, confirming COVID-19 using nucleic amplification test (NAAT test) via real-time PCR (RT-PCR). Serum cytokines were measured with multiplexed immunoassays. The transcriptome was analysed with long, short and small RNA sequencing. We found attenuation of RNA inflammatory signatures in ChAdOx1 nCoV-19 compared with placebo vaccinees and reduced levels of serum proteins associated with COVID-19 severity. KREMEN1, a putative alternative SARS-CoV-2 receptor, was downregulated in placebo compared with ChAdOx1 nCoV-19 vaccinees. Vaccination ameliorates reductions in cell counts across leukocyte populations and platelets noted at COVID-19 onset, without inducing potentially deleterious Th2-skewed immune responses. Multi-omics integration links a global reduction in miRNA expression at COVID-19 onset to increased pro-inflammatory responses at the mRNA level. This study reveals insights into the role of COVID-19 vaccines in mitigating disease severity by abrogating pro-inflammatory responses associated with severe COVID-19, affirming vaccine-mediated benefit in breakthrough infection, and highlighting the importance of clinically relevant endpoints in vaccine evaluation.

Comprehensive gene profiling of the metabolic landscape of humanized livers in mice

BACKGROUND & AIMS

The human liver transcriptome is complex and highly dynamic, e.g. one gene may produce multiple distinct transcripts, each with distinct posttranscriptional modifications. Direct knowledge of transcriptome dynamics, however, is largely obscured by the inaccessibility of the human liver to treatments and the insufficient annotation of the human liver transcriptome at transcript and RNA modification levels.

METHODS

We generated mice that carry humanized livers of identical genetic background and subjected them to representative metabolic treatments. We then analyzed the humanized livers with nanopore single-molecule direct RNA sequencing to determine the expression level, m6A modification and poly(A) tail length of all RNA transcript isoforms. Our system allows for the de novo annotation of human liver transcriptomes to reflect metabolic responses and for the study of transcriptome dynamics in parallel.

RESULTS

Our analysis uncovered a vast number of novel genes and transcripts. Our transcript-level analysis of human liver transcriptomes also identified a multitude of regulated metabolic pathways that were otherwise invisible using conventional short-read RNA sequencing. We revealed for the first time the dynamic changes in m6A and poly(A) tail length of human liver transcripts, many of which are transcribed from key metabolic genes. Furthermore, we performed comparative analyses of gene regulation between humans and mice, and between two individuals using the liver-specific humanized mice, revealing that transcriptome dynamics are highly species- and genetic background-dependent.

CONCLUSION

Our work revealed a complex metabolic response landscape of the human liver transcriptome and provides a novel resource to understand transcriptome dynamics of the human liver in response to physiologically relevant metabolic stimuli (https://caolab.shinyapps.io/human_hepatocyte_landscape/).

IMPACT AND IMPLICATIONS

Direct knowledge of the human liver transcriptome is currently very limited, hindering the overall understanding of human liver pathophysiology. We combined a liver-specific humanized mouse model and long-read direct RNA sequencing technology to establish a de novo annotation of the human liver transcriptome and identified a multitude of regulated metabolic pathways that were otherwise invisible using conventional technologies. The extensive regulatory information on human genes we provided could enable basic scientists to infer the pathological relevance of their genes of interest and physician scientists to better pinpoint the changes in metabolic networks underlying a specific pathophysiology.

Long-read direct RNA sequencing of the mitochondrial transcriptome of Saccharomyces cerevisiae reveals condition-dependent intron abundance

Mitochondria fulfil many essential roles and have their own genome, which is expressed as polycistronic transcripts that undergo co- or posttranscriptional processing and splicing. Due to the inherent complexity and limited technical accessibility of the mitochondrial transcriptome, fundamental questions regarding mitochondrial gene expression and splicing remain unresolved, even in the model eukaryote Saccharomyces cerevisiae. Long-read sequencing could address these fundamental questions. Therefore, a method for the enrichment of mitochondrial RNA and sequencing using Nanopore technology was developed, enabling the resolution of splicing of polycistronic genes and the quantification of spliced RNA. This method successfully captured the full mitochondrial transcriptome and resolved RNA splicing patterns with single-base resolution and was applied to explore the transcriptome of S. cerevisiae grown with glucose or ethanol as the sole carbon source, revealing the impact of growth conditions on mitochondrial RNA expression and splicing. This study uncovered a remarkable difference in the turnover of Group II introns between yeast grown in either mostly fermentative or fully respiratory conditions. Whether this accumulation of introns in glucose medium has an impact on mitochondrial functions remains to be explored. Combined with the high tractability of the model yeast S. cerevisiae, the developed method enables to monitor mitochondrial transcriptome responses in a broad range of relevant contexts, including oxidative stress, apoptosis and mitochondrial diseases.

Noncontiguous operon atlas for the Staphylococcus aureus genome

Bacteria synchronize the expression of genes with related functions by organizing genes into operons so that they are cotranscribed together in a single polycistronic messenger RNA. However, some cellular processes may benefit if the simultaneous production of the operon proteins coincides with the inhibition of the expression of an antagonist gene. To coordinate such situations, bacteria have evolved noncontiguous operons (NcOs), a subtype of operons that contain one or more genes that are transcribed in the opposite direction to the other operon genes. This structure results in overlapping transcripts whose expression is mutually repressed. The presence of NcOs cannot be predicted computationally and their identification requires a detailed knowledge of the bacterial transcriptome. In this study, we used direct RNA sequencing methodology to determine the NcOs map in the Staphylococcus aureus genome. We detected the presence of 18 NcOs in the genome of S. aureus and four in the genome of the lysogenic prophage 80α. The identified NcOs comprise genes involved in energy metabolism, metal acquisition and transport, toxin-antitoxin systems, and control of the phage life cycle. Using the menaquinone operon as a proof of concept, we show that disarrangement of the NcO architecture results in a reduction of bacterial fitness due to an increase in menaquinone levels and a decrease in the rate of oxygen consumption. Our study demonstrates the significance of NcO structures in bacterial physiology and emphasizes the importance of combining operon maps with transcriptomic data to uncover previously unnoticed functional relationships between neighbouring genes.

Enriched atlas of lncRNA and protein-coding genes for the GRCg7b chicken assembly and its functional annotation across 47 tissues

Gene atlases for livestock are steadily improving thanks to new genome assemblies and new expression data improving the gene annotation. However, gene content varies across databases due to differences in RNA sequencing data and bioinformatics pipelines, especially for long non-coding RNAs (lncRNAs) which have higher tissue and developmental specificity and are harder to consistently identify compared to protein coding genes (PCGs). As done previously in 2020 for chicken assemblies galgal5 and GRCg6a, we provide a new gene atlas, lncRNA-enriched, for the latest GRCg7b chicken assembly, integrating "NCBI RefSeq", "EMBL-EBI Ensembl/GENCODE" reference annotations and other resources such as FAANG and NONCODE. As a result, the number of PCGs increases from 18,022 (RefSeq) and 17,007 (Ensembl) to 24,102, and that of lncRNAs from 5789 (RefSeq) and 11,944 (Ensembl) to 44,428. Using 1400 public RNA-seq transcriptome representing 47 tissues, we provided expression evidence for 35,257 (79%) lncRNAs and 22,468 (93%) PCGs, supporting the relevance of this atlas. Further characterization including tissue-specificity, sex-differential expression and gene configurations are provided. We also identified conserved miRNA-hosting genes with human counterparts, suggesting common function. The annotated atlas is available at gega.sigenae.org.

Transcription termination and readthrough in African swine fever virus

Introduction

African swine fever virus (ASFV) is a nucleocytoplasmic large DNA virus (NCLDV) that encodes its own host-like RNA polymerase (RNAP) and factors required to produce mature mRNA. The formation of accurate mRNA 3' ends by ASFV RNAP depends on transcription termination, likely enabled by a combination of sequence motifs and transcription factors, although these are poorly understood. The termination of any RNAP is rarely 100% efficient, and the transcriptional "readthrough" at terminators can generate long mRNAs which may interfere with the expression of downstream genes. ASFV transcriptome analyses reveal a landscape of heterogeneous mRNA 3' termini, likely a combination of bona fide termination sites and the result of mRNA degradation and processing. While short-read sequencing (SRS) like 3' RNA-seq indicates an accumulation of mRNA 3' ends at specific sites, it cannot inform about which promoters and transcription start sites (TSSs) directed their synthesis, i.e., information about the complete and unprocessed mRNAs at nucleotide resolution.

Methods

Here, we report a rigorous analysis of full-length ASFV transcripts using long-read sequencing (LRS). We systematically compared transcription termination sites predicted from SRS 3' RNA-seq with 3' ends mapped by LRS during early and late infection.

Results

Using in-vitro transcription assays, we show that recombinant ASFV RNAP terminates transcription at polyT stretches in the non-template strand, similar to the archaeal RNAP or eukaryotic RNAPIII, unaided by secondary RNA structures or predicted viral termination factors. Our results cement this T-rich motif (U-rich in the RNA) as a universal transcription termination signal in ASFV. Many genes share the usage of the same terminators, while genes can also use a range of terminators to generate transcript isoforms varying enormously in length. A key factor in the latter phenomenon is the highly abundant terminator readthrough we observed, which is more prevalent during late compared with early infection.

Discussion

This indicates that ASFV mRNAs under the control of late gene promoters utilize different termination mechanisms and factors to early promoters and/or that cellular factors influence the viral transcriptome landscape differently during the late stages of infection.

Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment

Nanopore signal analysis enables detection of nucleotide modifications from native DNA and RNA sequencing, providing both accurate genetic/transcriptomic and epigenetic information without additional library preparation. Presently, only a limited set of modifications can be directly basecalled (e.g. 5-methylcytosine), while most others require exploratory methods that often begin with alignment of nanopore signal to a nucleotide reference. We present Uncalled4, a toolkit for nanopore signal alignment, analysis, and visualization. Uncalled4 features an efficient banded signal alignment algorithm, BAM signal alignment file format, statistics for comparing signal alignment methods, and a reproducible de novo training method for k-mer-based pore models, revealing potential errors in ONT's state-of-the-art DNA model. We apply Uncalled4 to RNA 6-methyladenine (m6A) detection in seven human cell lines, identifying 26% more modifications than Nanopolish using m6Anet, including in several genes where m6A has known implications in cancer. Uncalled4 is available open-source at github.com/skovaka/uncalled4.

Downregulation of Ribosomal Protein Genes Is Revealed in a Model of Rat Hippocampal Neuronal Culture Activation with GABA(A)R/GlyRa2 Antagonist Picrotoxin

Long-read transcriptome sequencing provides us with a convenient tool for the thorough study of biological processes such as neuronal plasticity. Here, we aimed to perform transcriptional profiling of rat hippocampal primary neuron cultures after stimulation with picrotoxin (PTX) to further understand molecular mechanisms of neuronal activation. To overcome the limitations of short-read RNA-Seq approaches, we performed an Oxford Nanopore Technologies MinION-based long-read sequencing and transcriptome assembly of rat primary hippocampal culture mRNA at three time points after the PTX activation. We used a specific approach to exclude uncapped mRNAs during sample preparation. Overall, we found 23,652 novel transcripts in comparison to reference annotations, out of which ~6000 were entirely novel and mostly transposon-derived loci. Analysis of differentially expressed genes (DEG) showed that 3046 genes were differentially expressed, of which 2037 were upregulated and 1009 were downregulated at 30 min after the PTX application, with only 446 and 13 genes differentially expressed at 1 h and 5 h time points, respectively. Most notably, multiple genes encoding ribosomal proteins, with a high basal expression level, were downregulated after 30 min incubation with PTX; we suggest that this indicates redistribution of transcriptional resources towards activity-induced genes. Novel loci and isoforms observed in this study may help us further understand the functional mRNA repertoire in neuronal plasticity processes. Together with other NGS techniques, differential gene expression analysis of sequencing data obtained using MinION platform might provide a simple method to optimize further study of neuronal plasticity.

Bromine-mediated strategy endows efficient electrochemical oxidation of amine to nitrile

Conventional methods for nitrile synthesis bring inherent environmental risks due to their reliance on oxidants and harsh reaction conditions. Meanwhile, direct electrooxidation of amines to nitriles suffers from low current density. In this study, we propose an innovative indirect electrooxidation strategy for nitrile formation, mediated by Br-/Br2, utilizing a highly efficient CoS2/CoS@Graphite Felt (GF) electrode. Notably, the anodic nitrile generation can be synergistically coupled with the cathodic hydrogen evolution reaction (HER). Through meticulous optimization of reaction parameters, we achieve an impressive 98% selectivity for octanenitrile at a current density of 60 mA cm-2 with a remarkable faradaic efficiency (FE) of 87%. Furthermore, our approach demonstrates excellent versatility, as we successfully evaluate both aliphatic and aromatic primary amines, highlighting its promising potential for practical applications in the field.

Transcriptomics : Approaches to Quantifying Gene Expression and Their Application to Studying the Human Brain

To date, the field of transcriptomics has been characterized by rapid methods development and technological advancement, with new technologies continuously rendering older ones obsolete.This chapter traces the evolution of approaches to quantifying gene expression and provides an overall view of the current state of the field of transcriptomics, its applications to the study of the human brain, and its place in the broader emerging multiomics landscape.

An optimized workflow of full-length transcriptome sequencing for accurate fusion transcript identification

Next-generation sequencing has revolutionized cancer genomics by enabling high-throughput mutation screening yet detecting fusion genes reliably remains challenging. Long-read sequencing offers potential for accurate fusion transcript identification, though challenges persist. In this study, we present an optimized workflow using nanopore sequencing technology to precisely identify fusion transcripts. Our approach encompasses a tailored library preparation protocol, data processing, and fusion gene analysis pipeline. We evaluated the performance using Universal Human Reference RNA and human adenocarcinoma cell lines. Our optimized nanopore sequencing workflow generated high-quality full-length transcriptome data characterized by an extended length distribution and comprehensive transcript coverage. Validation experiments confirmed novel fusion events with potential clinical relevance. Our protocol aims to mitigate biases and enhance accuracy, facilitating increased adoption in clinical diagnostics. Continued advancements in long-read sequencing promise deeper insights into fusion gene biology and improved cancer diagnostics.

Leptospira transcriptome sequencing using long-read technology reveals unannotated transcripts and potential polyadenylation of RNA molecules

IMPORTANCE

Leptospirosis, caused by the spirochete bacteria Leptospira, is a zoonotic disease of humans and animals, accounting for over 1 million annual human cases and over 60,000 deaths. We have characterized operon transcriptional units, identified novel RNA coding regions, and reported evidence of potential posttranscriptional polyadenylation in the Leptospira transcriptomes for the first time using Oxford Nanopore Technology RNA sequencing protocols. The newly identified RNA coding regions and operon transcriptional units were detected only in the pathogenic Leptospira transcriptomes, suggesting their significance in virulence-related functions. This article integrates bioinformatics, infectious diseases, microbiology, molecular biology, veterinary sciences, and public health. Given the current knowledge gap in the regulation of leptospiral pathogenicity, our findings offer valuable insights to researchers studying leptospiral pathogenicity and provide both a basis and a tool for researchers focusing on prokaryotic molecular studies for the understanding of RNA compositions and prokaryotic polyadenylation for their organisms of interest.

SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark

Long-read RNA sequencing has emerged as a powerful tool for transcript discovery, even in well-annotated organisms. However, assessing the accuracy of different methods in identifying annotated and novel transcripts remains a challenge. Here, we present SQANTI-SIM, a versatile tool that wraps around popular long-read simulators to allow precise management of transcript novelty based on the structural categories defined by SQANTI3. By selectively excluding specific transcripts from the reference dataset, SQANTI-SIM effectively emulates scenarios involving unannotated transcripts. Furthermore, the tool provides customizable features and supports the simulation of additional types of data, representing the first multi-omics simulation tool for the lrRNA-seq field.

Quantitation of Circulating Mycobacterium tuberculosis Antigens by Nanopore Biosensing in Children Evaluated for Pulmonary Tuberculosis in South Africa

Nanopore sensing of proteomic biomarkers lacks accuracy due to the ultralow abundance of targets, a wide variety of interferents in clinical samples, and the mismatch between pore and analyte sizes. By converting antigens to DNA probes via click chemistry and quantifying their characteristic signals, we show a nanopore assay with several amplification mechanisms to achieve an attomolar level limit of detection that enables quantitation of the circulating Mycobacterium tuberculosis (Mtb) antigen ESAT-6/CFP-10 complex in human serum. The assay's nonsputum-based feature and low-volume sample requirements make it particularly well-suited for detecting pediatric tuberculosis (TB) disease, where establishing an accurate diagnosis is greatly complicated by the paucibacillary nature of respiratory secretions, nonspecific symptoms, and challenges with sample collection. In the clinical assessment, the assay was applied to analyze ESAT-6/CFP-10 levels in serum samples collected during baseline investigation for TB in 75 children, aged 0-12 years, enrolled in a diagnostic study conducted in Cape Town, South Africa. This nanopore assay showed superior sensitivity in children with confirmed TB (94.4%) compared to clinical "gold standard" diagnostic technologies (Xpert MTB/RIF 44.4% and Mtb culture 72.2%) and filled the diagnostic gap for children with unconfirmed TB, where these traditional technologies fell short. We envision that, in combination with automated sample processing and portable nanopore devices, this methodology will offer a powerful tool to support the diagnosis of pulmonary TB in children.

ConF: A Deep Learning Model Based on BiLSTM, CNN, and Cross Multi-Head Attention Mechanism for Noncoding RNA Family Prediction

This paper presents ConF, a novel deep learning model designed for accurate and efficient prediction of noncoding RNA families. NcRNAs are essential functional RNA molecules involved in various cellular processes, including replication, transcription, and gene expression. Identifying ncRNA families is crucial for comprehensive RNA research, as ncRNAs within the same family often exhibit similar functionalities. Traditional experimental methods for identifying ncRNA families are time-consuming and labor-intensive. Computational approaches relying on annotated secondary structure data face limitations in handling complex structures like pseudoknots and have restricted applicability, resulting in suboptimal prediction performance. To overcome these challenges, ConF integrates mainstream techniques such as residual networks with dilated convolutions and cross multi-head attention mechanisms. By employing a combination of dual-layer convolutional networks and BiLSTM, ConF effectively captures intricate features embedded within RNA sequences. This feature extraction process leads to significantly improved prediction accuracy compared to existing methods. Experimental evaluations conducted using a single, publicly available dataset and applying ten-fold cross-validation demonstrate the superiority of ConF in terms of accuracy, sensitivity, and other performance metrics. Overall, ConF represents a promising solution for accurate and efficient ncRNA family prediction, addressing the limitations of traditional experimental and computational methods.

Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures

The lack of benchmark data sets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (sequins). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that StringTie2 and bambu outperformed other tools from the six isoform detection tools tested, DESeq2, edgeR and limma-voom were best among the five differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the five tools compared, which suggests further methods development is needed for this application.