Quantitative profiling of pseudouridylation dynamics in native RNAs with nanopore sequencing

tRNA modifications: greasing the wheels of translation and beyond

Transfer RNA (tRNA) is one of the most abundant RNA types in cells, acting as an adaptor to bridge the genetic information in mRNAs with the amino acid sequence in proteins. Both tRNAs and small fragments processed from them play many nonconventional roles in addition to translation. tRNA molecules undergo various types of chemical modifications to ensure the accuracy and efficiency of translation and regulate their diverse functions beyond translation. In this review, we discuss the biogenesis and molecular mechanisms of tRNA modifications, including major tRNA modifications, writer enzymes, and their dynamic regulation. We also summarize the state-of-the-art technologies for measuring tRNA modification, with a particular focus on 2'-O-methylation (Nm), and discuss their limitations and remaining challenges. Finally, we highlight recent discoveries linking dysregulation of tRNA modifications with genetic diseases.

EIciRNAs in focus: current understanding and future perspectives

Circular RNAs (circRNAs) are a unique class of covalently closed single-stranded RNA molecules that play diverse roles in normal physiology and pathology. Among the major types of circRNA, exon-intron circRNA (EIciRNA) distinguishes itself by its sequence composition and nuclear localization. Recent RNA-seq technologies and computational methods have facilitated the detection and characterization of EIciRNAs, with features like circRNA intron retention (CIR) and tissue-specificity being characterized. EIciRNAs have been identified to exert their functions via mechanisms such as regulating gene transcription, and the physiological relevance of EIciRNAs has been reported. Within this review, we present a summary of the current understanding of EIciRNAs, delving into their identification and molecular functions. Additionally, we emphasize factors regulating EIciRNA biogenesis and the physiological roles of EIciRNAs based on recent research. We also discuss the future challenges in EIciRNA exploration, underscoring the potential for novel functions and functional mechanisms of EIciRNAs for further investigation.

RNA modification: A contemporary review of pseudouridine (Ψ) and its role in functional plant biology

Pseudouridine (Ψ) is a modified nucleoside present in diverse RNA species, including mRNA (messenger RNA), snRNA (small nuclear RNA), rRNA (ribosomal RNA) and tRNA (transfer RNA). In plants, Ψ serves a critical function in RNA modification, supporting the stability, structural integrity, and functionality of RNA molecules. This review provides the various roles that Ψ fulfils in the modification of plant RNA biology, encompassing effects on biosynthesis pathways, regulatory mechanisms, stability, and translation efficiency. Additionally, we discuss recent advancements in the dynamic regulation of Ψ deposition in response to environmental stimuli and stressors. Elucidating Ψ's roles contributes to the comprehension of plant biology and may facilitate developments in biotechnology and crop improvement.

Functions and therapeutic applications of pseudouridylation

The success of using pseudouridine (Ψ) and its methylation derivative in mRNA vaccines against SARS-CoV-2 has sparked a renewed interest in this RNA modification, known as the 'fifth nucleotide' of RNA. In this Review, we discuss the emerging functions of pseudouridylation in gene regulation, focusing on how pseudouridine in mRNA, tRNA and ribosomal RNA (rRNA) regulates translation. We also discuss the effects of pseudouridylation on RNA secondary structure, pre-mRNA splicing, and in vitro mRNA stability. In addition to nuclear-genome-encoded RNAs, pseudouridine is also present in mitochondria-encoded rRNA, mRNA and tRNA, where it has different distributions and functions compared with their nuclear counterparts. We then discuss the therapeutic potential of programmable pseudouridylation and mRNA vaccine optimization through pseudouridylation. Lastly, we briefly describe the latest quantitative pseudouridine detection methods. We posit that pseudouridine is a highly promising modification that merits further epitranscriptomics investigation and therapeutic application.

ReQuant: improved base modification calling by k-mer value imputation

Nanopore sequencing allows identification of base modifications, such as methylation, directly from raw current data. Prevailing approaches, including deep learning (DL) methods, require training data covering all possible sequence contexts. These data can be prohibitively expensive or impossible to obtain for some modifications. Hence, research into DNA modifications focuses on the most prevalent modification in human DNA: 5mC in a CpG context. Improved generalization is required to reach the technology's full potential: calling any modification from raw current values. We developed ReQuant, an algorithm to impute full, k-mer based, modification models from limited k-mer context training data. ReQuant is highly accurate for calling modifications (CpG/GpC methylation and CpG glucosylation) in Lambda Phage R9 data when fitting on ≤25% of all possible 6-mers with a modification and extends to human R10 data. The success of our approach shows that DNA modifications have a consistent and therefore predictable effect on Nanopore current levels, suggesting that interpretable rule-based imputation in unseen contexts is possible. Our approach circumvents the need for modification-specific DL tools and enables modification calling when not all sequence contexts can be obtained, opening a vast field of biological base modification research.

De novo non-canonical nanopore basecalling enables private communication using heavily-modified DNA data at single-molecule level

Hidden messages in DNA molecules by employing chemical modifications has been suggested for private data storage and transmission at high information density. However, rapidly decoding these "molecular keys" with corresponding basecallers remains challenging. We present DeepSME, a nanopore sequencing and deep-learning based framework towards single-molecule encryption, demonstrated by using 5-hydroxymethylcytosine (5hmC) substitution for individual nucleotide recognition rather than sequential interactions. This non-natural, motif-insensitive methylation disrupts ion current, resulting in a readout failure of 67.2%-100%, concealing the privacy within the DNAs. We further develop an alignment-free DeepSME basecaller as a key to reconstitute the digital information. Our three-stage training pipeline, expands k-mer size from 46 to 49, achieving over 92% precision and recall from scratch. DeepSME deciphers fully 5hmC concealed text and image within 16× coverage depth with an F1-score of 86.4%, surpassing all the state-of-the-art basecallers. Demonstrated on edge computing devices, DeepSME holds supreme potential for DNA-based private communications and broader bioengineering and medical applications.

Simultaneous profiling of ac4C and m5C modifications from nanopore direct RNA sequencing

N4-acetylcytidine (ac4C) and 5-methylcytidine (m5C) play important roles in mRNA stability, translation efficiency, and cellular stress responses. Current methods for detecting RNA modifications from nanopore sequencing data do not support simultaneous de novo identification of both modifications. In this study, we generate in vitro transcripts from a cDNA library with modifications, and develop modCnet, a deep learning frame utilizing nanopore direct RNA sequencing to identify ac4C and m5C from a single sample. We demonstrate the high performance of modCnet and apply it to detect ac4C and m5C sties on in vivo mRNAs from human cell lines, supported by RIP-seq, BisSeq and RedaC:T-seq. We further validate candidate ac4C sites by NaBH4-induced reverse transcription (RT) stop events. The versatility for simultaneous identification of different types of modified cytidines at the single-molecule level open a window for studying the biological function of the co-occurrence of ac4C and m5C modifications.

Determining the effects of pseudouridine incorporation on human tRNAs

Transfer RNAs (tRNAs) are ubiquitous non-coding RNA molecules required to translate mRNA-encoded sequence information into nascent polypeptide chains. Their relatively small size and heterogenous patterns of their RNA modifications have impeded the systematic structural characterization of individual tRNAs. Here, we use single-particle cryo-EM to determine the structures of four human tRNAs before and after incorporation of pseudouridines (Ψ). Following post-transcriptional modifications by distinct combinations of human pseudouridine synthases, we find that tRNAs become stabilized and undergo specific local structural changes. We establish interactions between the D- and T-arms as the key linchpin in the tertiary structure of tRNAs. Our structures of human tRNAs highlight the vast potential of cryo-EM combined with biophysical measurements and computational simulations for structure-function analyses of tRNAs and other small, folded RNA domains.

Probing enzyme-dependent pseudouridylation using direct RNA sequencing to assess epitranscriptome plasticity in a neuronal cell line

Chemical modifications in mRNAs, such as pseudouridine (psi), can control gene expression. Yet, we know little about how they are regulated, especially in neurons. We applied nanopore direct RNA sequencing to investigate psi dynamics in SH-SY5Y cells in response to two perturbations that model a natural and unnatural cellular state: retinoic-acid-mediated differentiation (healthy) and exposure to the neurotoxicant lead (unhealthy). We discovered that the expression of some psi writers changes significantly in response to physiological conditions. We also found that globally, lead-treated cells have more psi sites but lower relative occupancy than untreated cells and differentiated cells. Examples of highly plastic sites were accompanied by constant expression for psi writers, suggesting trans-regulation. Many positions were static throughout all three cellular states, suggestive of a "housekeeping" function. This study enables investigations into mechanisms that control psi modifications in neurons and their possible protective effects in response to cellular stress.

Rapid and accurate demultiplexing of direct RNA nanopore sequencing data with SeqTagger

Nanopore direct RNA sequencing (DRS) enables direct measurement of RNA molecules, including their native RNA modifications, without prior conversion to cDNA. However, commercial methods for molecular barcoding of multiple DRS samples are lacking, and community-driven efforts, such as DeePlexiCon, are not compatible with newer RNA chemistry flowcells and the latest generation of graphics processing units (GPUs). To overcome these limitations, we introduce SeqTagger, a rapid and robust method that can demultiplex DRS data sets with 99% precision and 95% recall. We demonstrate the applicability of SeqTagger in both RNA002/R9.4 and RNA004/RNA chemistries and show its robust performance both for long and short RNA libraries, including custom libraries that do not contain standard poly(A) tails, such as Nano-tRNAseq libraries. Finally, we demonstrate that increasing the multiplexing up to 96 barcodes yields highly accurate demultiplexing models. SeqTagger can be executed in a standalone manner or through the MasterOfPores NextFlow workflow. The availability of an efficient and simple multiplexing strategy improves the cost-effectiveness of this technology and facilitates the analysis of low-input biological samples.

The RMaP challenge of predicting RNA modifications by nanopore sequencing

The field of epitranscriptomics is undergoing a technology-driven revolution. During past decades, RNA modifications like N6-methyladenosine (m6A), pseudouridine (ψ), and 5-methylcytosine (m5C) became acknowledged for playing critical roles in cellular processes. Direct RNA sequencing by Oxford Nanopore Technologies (ONT) enabled the detection of modifications in native RNA, by detecting noncanonical RNA nucleosides properties in raw data. Consequently, the field's cutting edge has a heavy component in computer science, opening new avenues of cooperation across the community, as exchanging data is as impactful as exchanging samples. Therefore, we seize the occasion to bring scientists together within the RNA Modification and Processing (RMaP) challenge to advance solutions for RNA modification detection and discuss ideas, problems and approaches. We show several computational methods to detect the most researched mRNA modifications (m6A, ψ, and m5C). Results demonstrate that a low prediction error and a high prediction accuracy can be achieved on these modifications across different approaches and algorithms. The RMaP challenge marks a substantial step towards improving algorithms' comparability, reliability, and consistency in RNA modification prediction. It points out the deficits in this young field that need to be addressed in further challenges.

Structural Snapshots of Human tRNA Modifying Enzymes

Cells use a plethora of specialized enzymes to post-transcriptionally introduce chemical modifications into transfer RNA (tRNA) molecules. These modifications contribute novel chemical properties to the affected nucleotides and are crucial for the tRNA maturation process and for most other aspects of tRNA biology. Whereas, some of the modifications are ubiquitous and the respective modifying enzymes are conserved in all domains of life, other modifications are found only in specific organisms, in specific tRNAs or at specific positions of tRNAs. Despite the fact, that evolution has shaped a tremendous variety of tRNA modifications and the respective modification cascades, the clinical relevance of patient-derived mutations has recently led to an increased interest in the set of human tRNA modifying enzymes. Over decades macromolecular crystallography has immensely contributed to understand the enzymatic function of tRNA modifying enzymes at the molecular level. The advent of high resolution single-particle cryo-EM has recently led to structures of several clinically relevant human tRNA modifying enzymes in complex with tRNAs and a more fundamental understanding of the mechanistic consequences of specific disease-related mutations. Here, we aim to provide a comprehensive summary of the currently available experimentally determined structures of human tRNA modifying enzymes.

Current Analytical Strategies for mRNA-Based Therapeutics

Recent advancements in mRNA technology, utilized in vaccines, immunotherapies, protein replacement therapies, and genome editing, have emerged as promising and increasingly viable treatments. The rapid, potent, and transient properties of mRNA-encoded proteins make them attractive tools for the effective treatment of a variety of conditions, ranging from infectious diseases to cancer and single-gene disorders. The capability for rapid and large-scale production of mRNA therapeutics fueled the global response to the COVID-19 pandemic. For effective clinical implementation, it is crucial to deeply characterize and control important mRNA attributes such as purity/integrity, identity, structural quality features, and functionality. This implies the use of powerful and advanced analytical techniques for quality control and characterization of mRNA. Improvements in analytical techniques such as electrophoresis, chromatography, mass spectrometry, sequencing, and functionality assessments have significantly enhanced the quality and detail of information available for product and process characterization, as well as for routine stability and release testing. Here, we review the latest advancements in analytical techniques for the characterization of mRNA-based therapeutics, typically employed by the biopharmaceutical industry for eventual market release.

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.

Long-read RNA sequencing: A transformative technology for exploring transcriptome complexity in human diseases

Long-read RNA sequencing (RNA-seq) is emerging as a powerful and versatile technology for studying human transcriptomes. By enabling the end-to-end sequencing of full-length transcripts, long-read RNA-seq opens up avenues for investigating various RNA species and features that cannot be reliably interrogated by standard short-read RNA-seq methods. In this review, we present an overview of long-read RNA-seq, delineating its strengths over short-read RNA-seq, as well as summarizing recent advances in experimental and computational approaches to boost the power of long-read-based transcriptomics. We describe a wide range of applications of long-read RNA-seq, and highlight its expanding role as a foundational technology for exploring transcriptome variations in human diseases.

Toward the use of nanopore RNA sequencing technologies in the clinic: challenges and opportunities

RNA molecules have garnered increased attention as potential clinical biomarkers in recent years. While short-read sequencing and quantitative polymerase chain reaction have been the primary methods for quantifying RNA abundance, they typically fail to capture critical post-transcriptional regulatory elements, such as RNA modifications, which are often dysregulated in disease contexts. A promising cutting-edge technique sequencing method that addresses this gap is direct RNA sequencing, offered by Oxford Nanopore Technologies, which can simultaneously capture both RNA abundance and modification information. The rapid advancements in this platform, along with growing evidence of dysregulated RNA species in biofluids, presents a compelling clinical opportunity. In this review, we discuss the challenges and the emerging opportunities for the adoption of nanopore RNA sequencing technologies in the clinic, highlighting their potential to revolutionize personalized medicine and disease monitoring.

De novo basecalling of RNA modifications at single molecule and nucleotide resolution

RNA modifications influence RNA function and fate, but detecting them in individual molecules remains challenging for most modifications. Here we present a novel methodology to generate training sets and build modification-aware basecalling models. Using this approach, we develop the m6ABasecaller, a basecalling model that predicts m6A modifications from raw nanopore signals. We validate its accuracy in vitro and in vivo, revealing stable m6A modification stoichiometry across isoforms, m6A co-occurrence within RNA molecules, and m6A-dependent effects on poly(A) tails. Finally, we demonstrate that our method generalizes to other RNA and DNA modifications, paving the path towards future efforts detecting other modifications.

Advanced reactivity-based sequencing methods for mRNA epitranscriptome profiling

Currently, over 170 chemical modifications identified in RNA introduce an additional regulatory attribute to gene expression, known as the epitranscriptome. The development of detection methods to pinpoint the location and quantify these dynamic and reversible modifications has significantly expanded our understanding of their roles. This review goes deep into the latest progress in enzyme- and chemical-assisted sequencing methods, highlighting the opportunities presented by these reactivity-based techniques for detailed characterization of RNA modifications. Our survey provides a deeper understanding of the function and biological roles of RNA modification.

TRMT1L-catalyzed m22G27 on tyrosine tRNA is required for efficient mRNA translation and cell survival under oxidative stress

tRNA modifications are critical for several aspects of their functions, including decoding, folding, and stability. Using a multifaceted approach encompassing eCLIP-seq and nanopore tRNA-seq, we show that the human tRNA methyltransferase TRMT1L interacts with the component of the Rix1 ribosome biogenesis complex and binds to the 28S rRNA as well as to a subset of tRNAs. Mechanistically, we demonstrate that TRMT1L is responsible for catalyzing N2,N2-dimethylguanosine (m22G) solely at position 27 of tRNA-Tyr-GUA. Surprisingly, TRMT1L depletion also impaired the deposition of 3-(3-amino-3-carboxypropyl) uridine (acp3U) and dihydrouridine on tRNA-Tyr-GUA, Cys-GCA, and Ala-CGC. TRMT1L knockout cells have a marked decrease in tRNA-Tyr-GUA levels, coinciding with a reduction in global translation rates and hypersensitivity to oxidative stress. Our results establish TRMT1L as the elusive methyltransferase catalyzing the m22G27 modification on tRNA Tyr, resolving a long-standing gap of knowledge and highlighting its potential role in a tRNA modification circuit crucial for translation regulation and stress response.

Charting the epitranscriptomic landscape across RNA biotypes using native RNA nanopore sequencing

RNA modifications are conserved chemical features found in all domains of life and across diverse RNA biotypes, shaping gene expression profiles and enabling rapid responses to environmental changes. Their broad chemical diversity and dynamic nature pose significant challenges for studying them comprehensively. These limitations can now be addressed through direct RNA nanopore sequencing (DRS), which allows simultaneous identification of diverse RNA modification types at single-molecule and single-nucleotide resolution. Here, we review recent efforts pioneering the use of DRS to better understand the epitranscriptomic landscape. We highlight how DRS can be applied to investigate different RNA biotypes, emphasizing the use of specialized library preparation protocols and downstream bioinformatic workflows to detect both natural and synthetic RNA modifications. Finally, we provide a perspective on the future role of DRS in epitranscriptomic research, highlighting remaining challenges and emerging opportunities from improved sequencing yields and accuracy enabled by the latest DRS chemistry.

Training data diversity enhances the basecalling of novel RNA modification-induced nanopore sequencing readouts

Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a substantial challenge in nanopore sequencing bioinformatics. It has been extensively demonstrated that state-of-the-art basecallers are less compatible with modification-induced sequencing signals. A precise basecalling, on the other hand, serves as the prerequisite for virtually all the downstream analyses. Here, we report that basecallers exposed to diverse training modifications gain the generalizability to analyze novel modifications. With synthesized oligos as the model system, we precisely basecall various out-of-sample RNA modifications. From the representation learning perspective, we attribute this generalizability to basecaller representation space expanded by diverse training modifications. Taken together, we conclude increasing the training data diversity as a paradigm for building modification-tolerant nanopore sequencing basecallers.

Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation

As a fundamental mechanism for gene expression regulation, post-transcriptional RNA methylation plays versatile roles in various biological processes and disease mechanisms. Recent advances in single-cell technology have enabled simultaneous profiling of transcriptome-wide RNA methylation in thousands of cells, holding the promise to provide deeper insights into the dynamics, functions, and regulation of RNA methylation. However, it remains a major challenge to determine how to best analyze single-cell epitranscriptomics data. In this study, we developed SigRM, a computational framework for effectively mining single-cell epitranscriptomics datasets with a large cell number, such as those produced by the scDART-seq technique from the SMART-seq2 platform. SigRM not only outperforms state-of-the-art models in RNA methylation site detection on both simulated and real datasets but also provides rigorous quantification metrics of RNA methylation levels. This facilitates various downstream analyses, including trajectory inference and regulatory network reconstruction concerning the dynamics of RNA methylation.

Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

To date, over 40 epigenetic and 300 epitranscriptomic modifications have been identified. However, current short-read sequencing-based experimental methods can detect <10% of these modifications. Integrating long-read sequencing technologies with advanced computational approaches, including statistical analysis and machine learning, offers a promising new frontier to address this challenge. While supervised machine learning methods have achieved some success, their usefulness is restricted to a limited number of well-characterized modifications. Here, we introduce Modena, an innovative unsupervised learning approach utilizing long-read nanopore sequencing capable of detecting a broad range of modifications. Modena outperformed other methods in five out of six benchmark datasets, in some cases by a wide margin, while being equally competitive with the second best method on one dataset. Uniquely, Modena also demonstrates consistent accuracy on a DNA dataset, distinguishing it from other approaches. A key feature of Modena is its use of 'dynamic thresholding', an approach based on 1D score-clustering. This methodology differs substantially from the traditional statistics-based 'hard-thresholds.' We show that this approach is not limited to Modena but has broader applicability. Specifically, when combined with two existing algorithms, 'dynamic thresholding' significantly enhances their performance, resulting in up to a threefold improvement in F1-scores.

Epitranscriptomic rRNA fingerprinting reveals tissue-of-origin and tumor-specific signatures

Mammalian ribosomal RNA (rRNA) molecules are highly abundant RNAs, decorated with over 220 rRNA modifications. Previous works have shown that some rRNA modification types can be dynamically regulated; however, how and when the mammalian rRNA modification landscape is remodeled remains largely unexplored. Here, we employ direct RNA sequencing to chart the human and mouse rRNA epitranscriptome across tissues, developmental stages, cell types, and disease. Our analyses reveal multiple rRNA sites that are differentially modified in a tissue- and/or developmental stage-specific manner, including previously unannotated modified sites. We demonstrate that rRNA modification patterns can be used for tissue and cell-type identification, which we hereby term "epitranscriptomic fingerprinting." We then explore rRNA modification patterns in normal-tumor matched samples from lung cancer patients, finding that epitranscriptomic fingerprinting accurately classifies clinical samples into normal and tumor groups from only 250 reads per sample, demonstrating the potential of rRNA modifications as diagnostic biomarkers.

Mapping epigenetic modifications by sequencing technologies

The "epigenetics" concept was first described in 1942. Thus far, chemical modifications on histones, DNA, and RNA have emerged as three important building blocks of epigenetic modifications. Many epigenetic modifications have been intensively studied and found to be involved in most essential biological processes as well as human diseases, including cancer. Precisely and quantitatively mapping over 100 [1], 17 [2], and 160 [3] different known types of epigenetic modifications in histone, DNA, and RNA is the key to understanding the role of epigenetic modifications in gene regulation in diverse biological processes. With the rapid development of sequencing technologies, scientists are able to detect specific epigenetic modifications with various quantitative, high-resolution, whole-genome/transcriptome approaches. Here, we summarize recent advances in epigenetic modification sequencing technologies, focusing on major histone, DNA, and RNA modifications in mammalian cells.

High-throughput detection of RNA modifications at single base resolution

RNA is modified by > 170 chemical modifications that affect its structure and function. Accordingly, RNA modifications have been implicated in regulation of gene expression and cellular outcomes in a variety of species spanning the phylogenetic tree. The study of RNA modifications is accelerated by generation of high-throughput methods for detecting RNA modifications at single base resolution. Here, we review recent advancement in next generation sequencing based approaches for detection of 14 distinct RNA modifications present in rRNA, tRNA and mRNA. We further outline the molecular and computational principles underlying currently available methods.

The detection, function, and therapeutic potential of RNA 2'-O-methylation

RNA modifications play crucial roles in shaping RNA structure, function, and metabolism. Their dysregulation has been associated with many diseases, including cancer, developmental disorders, cardiovascular diseases, as well as neurological and immune-related conditions. A particular type of RNA modification, 2'-O-methylation (Nm) stands out due to its widespread occurrence on all four types of nucleotides (A, U, G, C) and in most RNA categories, e.g., mRNA, rRNA, tRNA, miRNA, snRNA, snoRNA, and viral RNA. Nm is the addition of a methyl group to the 2' hydroxyl of the ribose moiety of a nucleoside. Given its great biological significance and reported association with many diseases, we first reviewed the occurrences and functional implications of Nm in various RNA species. We then summarized the reported Nm detection methods, ranging from biochemical techniques in the 70's and 80's to recent methods based on Illumina RNA sequencing, artificial intelligence (AI) models for computational prediction, and the latest nanopore sequencing methods currently under active development. Moreover, we discussed the applications of Nm in the realm of RNA medicine, highlighting its therapeutic potential. At last, we present perspectives on potential research directions, aiming to offer insights for future investigations on Nm modification.

Simultaneous nanopore profiling of mRNA m6A and pseudouridine reveals translation coordination

N6-methyladenosine (m6A) and pseudouridine (Ψ) are the two most abundant modifications in mammalian messenger RNA, but the coordination of their biological functions remains poorly understood. We develop a machine learning-based nanopore direct RNA sequencing method (NanoSPA) that simultaneously analyzes m6A and Ψ in the human transcriptome. Applying NanoSPA to polysome profiling, we reveal opposing transcriptomic co-occurrence of m6A and Ψ and synergistic, hierarchical effects of m6A and Ψ on the polysome.

RNA methylation and breast cancer: insights into m6A, m7G and m5C

Breast cancer remains the most commonly diagnosed cancer in female worldwide, marked by its molecular diversity and complex subtypes. Despite progress in targeted therapies, tumor heterogeneity and treatment resistance continue to present major challenges. Recent studies emphasize the crucial role of RNA modifications in cancer biology, with nearly 200 distinct modifications identified. Among these, methylation is particularly significant, with methylation-related factors emerging as key regulators of RNA metabolism, influencing cancer progression, metastasis, and treatment resistance. This review focuses on the roles of key RNA methylation in breast cancer, particularly N6-methyladenosine (m6A), N7-methylguanosine (m7G), 5-methylcytosine (m5C), N1-methyladenosine (m1A), and N3-methylcytidine (m3C). We examine the functions of m6A "writers" like METTL3 and METTL14, and "readers" such as the YTH domain family in modulating tumor behavior. Dysregulation of m6A "erasers" like FTO and ALKBH5 are noticed too, highlighting their impact on cancer stem cell phenotypes, chemoresistance, and immune evasion. Additionally, the role of m7G modifications in mRNA stability and translation, facilitated by METTL1/WDR4 and RNMT, is discussed as a potential therapeutic target. The involvement of m5C, m1A, and m3C modifications, particularly those mediated by NSUN2 and NSUN6, in breast cancer tumorigenesis and prognosis is also reviewed. Despite coding RNAs, the interplay between these RNA methylations and non-coding RNAs, such as lncRNAs and miRNAs, is explored, shedding light on their roles in cancer cell proliferation, invasion, and immune response modulation. This review highlights the potential of RNA methylations as novel therapeutic targets in breast cancer, offering insights for precision medicine and improved patient outcomes.

Native RNA nanopore sequencing reveals antibiotic-induced loss of rRNA modifications in the A- and P-sites

The biological relevance and dynamics of mRNA modifications have been extensively studied; however, whether rRNA modifications are dynamically regulated, and under which conditions, remains unclear. Here, we systematically characterize bacterial rRNA modifications upon exposure to diverse antibiotics using native RNA nanopore sequencing. To identify significant rRNA modification changes, we develop NanoConsensus, a novel pipeline that is robust across RNA modification types, stoichiometries and coverage, with very low false positive rates, outperforming all individual algorithms tested. We then apply NanoConsensus to characterize the rRNA modification landscape upon antibiotic exposure, finding that rRNA modification profiles are altered in the vicinity of A and P-sites of the ribosome, in an antibiotic-specific manner, possibly contributing to antibiotic resistance. Our work demonstrates that rRNA modification profiles can be rapidly altered in response to environmental exposures, and provides a robust workflow to study rRNA modification dynamics in any species, in a scalable and reproducible manner.

mRNA psi profiling using nanopore DRS reveals cell type-specific pseudouridylation

Pseudouridine (psi) is one of the most abundant human mRNA modifications generated via psi synthases, including TRUB1 and PUS7. Nanopore direct RNA sequencing combined with our recently developed tool, Mod-p ID, enables psi mapping, transcriptome-wide, without chemical derivatization of the input RNA and/or conversion to cDNA. This method is sensitive for detecting differences in the positional occupancy of psi across cell types, which can inform our understanding of the impact of psi on gene expression. We sequenced, mapped, and compared the positional psi occupancy across six immortalized human cell lines derived from diverse tissue types. We found that lung-derived cells have the highest proportion of psi, while liver-derived cells have the lowest. Further, we find that conserved psi positions on mRNAs produce higher levels of protein than expected, suggesting a role in translation regulation. Interestingly, we identify cell type-specific sites of psi modification in ubiquitously expressed genes. Finally, we characterize transcripts with multiple psi modifications and found that these psi sites can be conserved or cell type-specific, including examples of multiple psi modifications within the same motif. Our data suggest that psi modifications contribute to regulating translation and that cell type-specific transacting factors play a major role in driving pseudouridylation.

Probing enzyme-dependent pseudouridylation using direct RNA sequencing to assess neuronal epitranscriptome plasticity

Chemical modifications in mRNAs, such as pseudouridine (psi), can control gene expression. Yet, we know little about how they are regulated, especially in neurons. We applied nanopore direct RNA sequencing to investigate psi dynamics in SH-SY5Y cells in response to two perturbations that model a natural and unnatural cellular state: retinoic-acid-mediated differentiation (healthy) and exposure to the neurotoxicant, lead (unhealthy). We discovered that the expression of some psi writers change significantly in response to physiological conditions. We also found that globally, lead-treated cells have more psi sites but lower relative occupancy than untreated cells and differentiated cells. Interestingly, examples of highly plastic sites were accompanied by constant expression for psi writers, suggesting trans-regulation. Many positions were static throughout all three cellular states, suggestive of a "housekeeping" function. This study enables investigations into mechanisms that control psi modifications in neurons and its possible protective effects in response to cellular stress.

Enhanced detection of RNA modifications and read mapping with high-accuracy nanopore RNA basecalling models

In recent years, nanopore direct RNA sequencing (DRS) became a valuable tool for studying the epitranscriptome, owing to its ability to detect multiple modifications within the same full-length native RNA molecules. Although RNA modifications can be identified in the form of systematic basecalling "errors" in DRS data sets, N6-methyladenosine (m6A) modifications produce relatively low "errors" compared with other RNA modifications, limiting the applicability of this approach to m6A sites that are modified at high stoichiometries. Here, we demonstrate that the use of alternative RNA basecalling models, trained with fully unmodified sequences, increases the "error" signal of m6A, leading to enhanced detection and improved sensitivity even at low stoichiometries. Moreover, we find that high-accuracy alternative RNA basecalling models can show up to 97% median basecalling accuracy, outperforming currently available RNA basecalling models, which show 91% median basecalling accuracy. Notably, the use of high-accuracy basecalling models is accompanied by a significant increase in the number of mapped reads-especially in shorter RNA fractions-and increased basecalling error signatures at pseudouridine (Ψ)- and N1-methylpseudouridine (m1Ψ)-modified sites. Overall, our work demonstrates that alternative RNA basecalling models can be used to improve the detection of RNA modifications, read mappability, and basecalling accuracy in nanopore DRS data sets.

Nanopore sequencing of intact aminoacylated tRNAs

Transfer RNAs (tRNA) are decorated during biogenesis with a variety of modifications that modulate their stability, aminoacylation, and decoding potential during translation. The complex landscape of tRNA modification presents significant analysis challenges and to date no single approach enables the simultaneous measurement of important but disparate chemical properties of individual, mature tRNA molecules. We developed a new, integrated approach to analyze the sequence, modification, and aminoacylation state of tRNA molecules in a high throughput nanopore sequencing experiment, leveraging a chemical ligation that embeds the charged amino acid in an adapted tRNA molecule. During nanopore sequencing, the embedded amino acid generates unique distortions in ionic current and translocation speed, enabling application of machine learning approaches to classify charging status and amino acid identity. Specific applications of the method indicate it will be broadly useful for examining relationships and dependencies between tRNA sequence, modification, and aminoacylation.

Quantitative detection of pseudouridine in RNA by mass spectrometry

Pseudouridine (Ψ) is one of the most prevalent and dynamic modification in RNA, and was shown to evade the host immune response in mRNA vaccines. Despite its significance, the biological role of Ψ remains poorly understood as certain key limitations and challenges in the detection of Ψ are yet to be overcome. In account of this, we report the usage of a chemical labelling strategy for the first quantitative detection of Ψ by mass spectrometry. We demonstrate a labelling efficiency exceeding 99% in isolated yeast tRNAs hosting multiple Ψs. LC-MS/MS analysis enables precise mapping of Ψ at single-base resolution, while simultaneously capturing a wide array of additional post-transcriptional modifications, which is not achieved with current sequencing technologies. This advancement may help unravel the dynamics and biological implications of Ψ, shedding light on its interplay with other modifications and deepening our understanding of its functional role.

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.

Combining Nanopore direct RNA sequencing with genetics and mass spectrometry for analysis of T-loop base modifications across 42 yeast tRNA isoacceptors

Transfer RNAs (tRNAs) contain dozens of chemical modifications. These modifications are critical for maintaining tRNA tertiary structure and optimizing protein synthesis. Here we advance the use of Nanopore direct RNA-sequencing (DRS) to investigate the synergy between modifications that are known to stabilize tRNA structure. We sequenced the 42 cytosolic tRNA isoacceptors from wild-type yeast and five tRNA-modifying enzyme knockout mutants. These data permitted comprehensive analysis of three neighboring and conserved modifications in T-loops: 5-methyluridine (m5U54), pseudouridine (Ψ55), and 1-methyladenosine (m1A58). Our results were validated using direct measurements of chemical modifications by mass spectrometry. We observed concerted T-loop modification circuits-the potent influence of Ψ55 for subsequent m1A58 modification on more tRNA isoacceptors than previously observed. Growing cells under nutrient depleted conditions also revealed a novel condition-specific increase in m1A58 modification on some tRNAs. A global and isoacceptor-specific classification strategy was developed to predict the status of T-loop modifications from a user-input tRNA DRS dataset, applicable to other conditions and tRNAs in other organisms. These advancements demonstrate how orthogonal technologies combined with genetics enable precise detection of modification landscapes of individual, full-length tRNAs, at transcriptome-scale.

Selective RNA pseudouridinylation in situ by circular gRNAs in designer organelles

RNA modifications play a pivotal role in the regulation of RNA chemistry within cells. Several technologies have been developed with the goal of using RNA modifications to regulate cellular biochemistry selectively, but achieving selective and precise modifications remains a challenge. Here, we show that by using designer organelles, we can modify mRNA with pseudouridine in a highly selective and guide-RNA-dependent manner. We use designer organelles inspired by concepts of phase separation, a central tenet in developing artificial membraneless organelles in living mammalian cells. In addition, we use circular guide RNAs to markedly enhance the effectiveness of targeted pseudouridinylation. Our studies introduce spatial engineering through optimized RNA editing organelles (OREO) as a complementary tool for targeted RNA modification, providing new avenues to enhance RNA modification specificity.

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.

TRMT1L-catalyzed m2 2G27 on tyrosine tRNA is required for efficient mRNA translation and cell survival under oxidative stress

tRNA modifications are critical for several aspects of their functions, including decoding, folding, and stability. Using a multifaceted approach encompassing eCLIP-seq and Nanopore tRNA-seq, we show that the human tRNA methyltransferase TRMT1L interacts with component of the Rix1 ribosome biogenesis complex and binds to the 28S rRNA, as well as to a subset of tRNAs. Mechanistically, we demonstrate that TRMT1L is responsible for catalyzing m2 2G solely at position 27 of tRNA-Tyr-GUA. Surprisingly, TRMT1L depletion also impaired the deposition of acp3U and dihydrouridine on tRNA-Tyr-GUA, Cys-GCA, and Ala-CGC. TRMT1L knockout cells have a marked decrease in tRNA-Tyr-GUA levels, coinciding with a reduction in global translation rates and hypersensitivity to oxidative stress. Our results establish TRMT1L as the elusive methyltransferase catalyzing the m2 2G27 modification on tRNA Tyr, resolving a long-standing gap of knowledge and highlighting its potential role in a tRNA modification circuit crucial for translation regulation and stress response.

Implications of RNA pseudouridylation for cancer biology and therapeutics: a narrative review

BACKGROUND

Pseudouridine (Ψ), a C5-glycoside isomer of uridine, stands as one of the most prevalent RNA modifications in all RNA types. Distinguishing from the C-N bond linking uridine to ribose, the link between Ψ and ribose is a C-C bond, endowing Ψ modified RNA distinct properties and functions in various biological processes. The conversion of uridine to Ψ is governed by pseudouridine synthases (PUSs). RNA pseudouridylation is implicated in cancer biology and therapeutics.

OBJECTIVES

In this review, we will summarize the methods for detecting Ψ, the process of Ψ generation, the impact of Ψ modification on RNA metabolism and gene expression, the roles of dysregulated Ψ and pseudouridine synthases in cancers, and the underlying mechanism.

METHODS

We conducted a comprehensive search of PubMed from its inception through February 2024. The search terms included "pseudouridine"; "pseudouridine synthase"; "PUS"; "dyskerin"; "cancer"; "tumor"; "carcinoma"; "malignancy"; "tumorigenesis"; "biomarker"; "prognosis" and "therapy". We included studies published in peer-reviewed journals that focused on Ψ detection, specific mechanisms involving Ψ and PUSs, and prognosis in cancer patients with high Ψ expression. We excluded studies lacking sufficient methodological details or appropriate controls.

RESULTS

Ψ has been recognized as a significant biomarker in cancer diagnosis and prognosis. Abnormal Ψ modifications mediated by various PUSs result in dysregulated RNA metabolism and impaired RNA function, promoting the development of various cancers. Overexpression of PUSs is common in cancer cells and predicts poor prognosis. PUSs inhibition arrests cell proliferation and enhances apoptosis in cancer cells, suggesting PUS-targeting cancer therapy may be a potential strategy in cancer treatment.

DISCUSSION

High Ψ levels in serum, urine, and saliva may suggest cancer, but do not specify the type, requiring additional lab markers and imaging for accurate diagnosis. Standardized detection methods are also crucial for reliable results. PUSs are linked to cancer, but more researches are needed to understand their mechanisms in different cancers. Anticancer treatments targeting PUSs are still under developed.

Nanopore signal deviations from pseudouridine modifications in RNA are sequence-specific: quantification requires dedicated synthetic controls

Chemical modifications to mRNA respond dynamically to environmental cues and are important modulators of gene expression. Nanopore direct RNA sequencing has been applied for assessing the presence of pseudouridine (ψ) modifications through basecalling errors and signal analysis. These approaches strongly depend on the sequence context around the modification, and the occupancies derived from these measurements are not quantitative. In this work, we combine direct RNA sequencing of synthetic RNAs bearing site-specific modifications and supervised machine learning models (ModQuant) to achieve near-analytical, site-specific ψ quantification. Our models demonstrate that the ionic current signal features important for accurate ψ classification are sequence dependent and encompass information extending beyond n + 2 and n - 2 nucleotides from the ψ site. This is contradictory to current models, which assume that accurate ψ classification can be achieved with signal information confined to the 5-nucleotide k-mer window (n + 2 and n - 2 nucleotides from the ψ site). We applied our models to quantitatively profile ψ occupancy in five mRNA sites in datasets from seven human cell lines, demonstrating conserved and variable sites. Our study motivates a wider pipeline that uses ground-truth RNA control sets with site-specific modifications for quantitative profiling of RNA modifications. The ModQuant pipeline and guide are freely available at https://github.com/wanunulab/ModQuant .

m6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data

N6-methyladenosine (m6A) is one of the most abundant and well-known modifications in messenger RNAs since its discovery in the 1970s. Recent studies have demonstrated that m6A is involved in various biological processes, such as alternative splicing and RNA degradation, playing an important role in a variety of diseases. To better understand the role of m6A, transcriptome-wide m6A profiling data are indispensable. In recent years, the Oxford Nanopore Technology Direct RNA Sequencing (DRS) platform has shown promise for RNA modification detection based on current disruptions measured in transcripts. However, decoding current intensity data into modification profiles remains a challenging task. Here, we introduce the m6A Transcriptome-wide Mapper (m6ATM), a novel Python-based computational pipeline that applies deep neural networks to predict m6A sites at a single-base resolution using DRS data. The m6ATM model architecture incorporates a WaveNet encoder and a dual-stream multiple-instance learning model to extract features from specific target sites and characterize the m6A epitranscriptome. For validation, m6ATM achieved an accuracy of 80% to 98% across in vitro transcription datasets containing varying m6A modification ratios and outperformed other tools in benchmarking with human cell line data. Moreover, we demonstrated the versatility of m6ATM in providing reliable stoichiometric information and used it to pinpoint PEG10 as a potential m6A target transcript in liver cancer cells. In conclusion, m6ATM is a high-performance m6A detection tool, and our results pave the way for future advancements in epitranscriptomic research.

Mass Spectrometry-Based Direct Sequencing of tRNAs De Novo and Quantitative Mapping of Multiple RNA Modifications

Despite the extensive use of next-generation sequencing (NGS) of RNA, simultaneous direct sequencing and quantitative mapping of multiple RNA nucleotide modifications remains challenging. Mass spectrometry (MS)-based sequencing can directly sequence all RNA modifications without being limited to specific ones, but it requires a perfect MS ladder that few tRNAs can provide. Here, we describe an MS ladder complementation sequencing approach (MLC-Seq) that circumvents the perfect ladder requirement, allowing de novo MS sequencing of full-length heterogeneous cellular tRNAs with multiple nucleotide modifications at single-nucleotide precision. Unlike NGS-based methods, which lose RNA modification information, MLC-Seq preserves RNA sequence diversity and modification information, revealing new detailed stoichiometric tRNA modification profiles and their changes upon treatment with the dealkylating enzyme AlkB. It can also be combined with reference sequences to provide quantitative analysis of diverse tRNAs and modifications in total tRNA samples. MLC-Seq enables systematic, quantitative, and site-specific mapping of RNA modifications, revealing the truly complete informational content of tRNA.

An Iterative Approach to Polish the Nanopore Sequencing Basecalling for Therapeutic RNA Quality Control

Nucleotide modifications deviate nanopore sequencing readouts, therefore generating artifacts during the basecalling of sequence backbones. Here, we present an iterative approach to polish modification-disturbed basecalling results. We show such an approach is able to promote the basecalling accuracy of both artificially-synthesized and real-world molecules. With demonstrated efficacy and reliability, we exploit the approach to precisely basecall therapeutic RNAs consisting of artificial or natural modifications, as the basis for quantifying the purity and integrity of vaccine mRNAs which are transcribed in vitro , and for determining modification hotspots of novel therapeutic RNA interference (RNAi) molecules which are bioengineered (BioRNA) in vivo .

Mitochondrial transcriptome of Candida albicans in flagranti - direct RNA sequencing reveals a new layer of information

BACKGROUND

Organellar transcriptomes are relatively under-studied systems, with data related to full-length transcripts and posttranscriptional modifications remaining sparse. Direct RNA sequencing presents the possibility of accessing a previously unavailable layer of information pertaining to transcriptomic data, as well as circumventing the biases introduced by second-generation RNA-seq platforms. Direct long-read ONT sequencing allows for the isoform analysis of full-length transcripts and the detection of posttranscriptional modifications. However, there are still relatively few projects employing this method specifically for studying organellar transcriptomes.

RESULTS

Candida albicans is a promising model for investigating nucleo-mitochondrial interactions. This work comprises ONT sequencing of the Candida albicans mitochondrial transcriptome along with the development of a dedicated data analysis pipeline. This approach allowed for the detection of complete transcript isoforms and posttranslational RNA modifications, as well as an analysis of C. albicans deletion mutants in genes coding for the 5' and 3' mitochondrial RNA exonucleases CaPET127 and CaDSS1. It also enabled for corrections to previous studies in terms of 3' and 5' transcript ends. A number of intermediate splicing isoforms was also discovered, along with mature and unspliced transcripts and changes in their abundances resulting from disruption of both 5' and 3' exonucleolytic processing. Multiple putative posttranscriptional modification sites have also been detected.

CONCLUSIONS

This preliminary work demonstrates the suitability of direct RNA sequencing for studying yeast mitochondrial transcriptomes in general and provides new insights into the workings of the C. albicans mitochondrial transcriptome in particular. It also provides a general roadmap for analyzing mitochondrial transcriptomic data from other organisms.

Deep learning and direct sequencing of labeled RNA captures transcriptome dynamics

In eukaryotes, genes produce a variety of distinct RNA isoforms, each with potentially unique protein products, coding potential or regulatory signals such as poly(A) tail and nucleotide modifications. Assessing the kinetics of RNA isoform metabolism, such as transcription and decay rates, is essential for unraveling gene regulation. However, it is currently impeded by lack of methods that can differentiate between individual isoforms. Here, we introduce RNAkinet, a deep convolutional and recurrent neural network, to detect nascent RNA molecules following metabolic labeling with the nucleoside analog 5-ethynyl uridine and long-read, direct RNA sequencing with nanopores. RNAkinet processes electrical signals from nanopore sequencing directly and distinguishes nascent from pre-existing RNA molecules. Our results show that RNAkinet prediction performance generalizes in various cell types and organisms and can be used to quantify RNA isoform half-lives. RNAkinet is expected to enable the identification of the kinetic parameters of RNA isoforms and to facilitate studies of RNA metabolism and the regulatory elements that influence it.

Training Data Diversity Enhances the Basecalling of Novel RNA Modification-Induced Nanopore Sequencing Readouts

Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a substantial challenge in nanopore sequencing bioinformatics. It has been extensively demonstrated that state-of-the-art basecallers are less compatible with modification-induced sequencing signals. A precise basecalling, on the other hand, serves as the prerequisite for virtually all the downstream analyses. Here, we report that basecallers exposed to diverse training modifications gain the generalizability to analyze novel modifications. With synthesized oligos as the model system, we precisely basecall various out-of-sample RNA modifications. From the representation learning perspective, we attribute this generalizability to basecaller representation space expanded by diverse training modifications. Taken together, we conclude increasing the training data diversity as a novel paradigm for building modification-tolerant nanopore sequencing basecallers.

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.

Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection

We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning (IL) technique to improve the basecalling of modification-rich sequences, which are usually of high biological interest. With sequence backbones resolved, we further run anomaly detection (AD) on individual nucleotides to determine their modification status. By this means, our pipeline promises the single-molecule, single-nucleotide, and sequence context-free detection of modifications. We benchmark the pipeline using control oligos, further apply it in the basecalling of densely-modified yeast tRNAs and E.coli genomic DNAs, the cross-species detection of N6-methyladenosine (m6A) in mammalian mRNAs, and the simultaneous detection of N1-methyladenosine (m1A) and m6A in human mRNAs. Our IL-AD workflow is available at: https://github.com/wangziyuan66/IL-AD .