Detection of m6A from direct RNA sequencing using a multiple instance learning framework

Targeted DNA-seq and RNA-seq of Reference Samples with Short-read and Long-read Sequencing

Next-generation sequencing (NGS) has revolutionized genomic research by enabling high-throughput, cost-effective genome and transcriptome sequencing accelerating personalized medicine for complex diseases, including cancer. Whole genome/transcriptome sequencing (WGS/WTS) provides comprehensive insights, while targeted sequencing is more cost-effective and sensitive. In comparison to short-read sequencing, which still dominates the field due to high speed and cost-effectiveness, long-read sequencing can overcome alignment limitations and better discriminate similar sequences from alternative transcripts or repetitive regions. Hybrid sequencing combines the best strengths of different technologies for a more comprehensive view of genomic/transcriptomic variations. Understanding each technology's strengths and limitations is critical for translating cutting-edge technologies into clinical applications. In this study, we sequenced DNA and RNA libraries of reference samples using various targeted DNA and RNA panels and the whole transcriptome on both short-read and long-read platforms. This study design enables a comprehensive analysis of sequencing technologies, targeting protocols, and library preparation methods. Our expanded profiling landscape establishes a reference point for assessing current sequencing technologies, facilitating informed decision-making in genomic research and precision medicine.

R2Dtool: integration and visualization of isoform-resolved RNA features

MOTIVATION

Long-read RNA sequencing enables the mapping of RNA modifications, structures, and protein-interaction sites at the resolution of individual transcript isoforms. To understand the functions of these RNA features, it is critical to analyze them in the context of transcriptomic and genomic annotations, such as open reading frames and splice junctions.

RESULTS

We have developed R2Dtool, a bioinformatics tool that integrates transcript-mapped information with transcript and genome annotations, allowing for the isoform-resolved analytics and graphical representation of RNA features in their genomic context. We illustrate R2Dtool's capability to integrate and expedite RNA feature analysis using epitranscriptomics data. R2Dtool facilitates the comprehensive analysis and interpretation of alternative transcript isoforms.

AVAILABILITY AND IMPLEMENTATION

R2Dtool is freely available under the MIT license at github.com/comprna/R2Dtool.

Molecular and physiological changes in the SpaceX Inspiration4 civilian crew

Human spaceflight has historically been managed by government agencies, such as in the NASA Twins Study1, but new commercial spaceflight opportunities have opened spaceflight to a broader population. In 2021, the SpaceX Inspiration4 mission launched the first all-civilian crew to low Earth orbit, which included the youngest American astronaut (aged 29), new in-flight experimental technologies (handheld ultrasound imaging, smartwatch wearables and immune profiling), ocular alignment measurements and new protocols for in-depth, multi-omic molecular and cellular profiling. Here we report the primary findings from the 3-day spaceflight mission, which induced a broad range of physiological and stress responses, neurovestibular changes indexed by ocular misalignment, and altered neurocognitive functioning, some of which match those of long-term spaceflight2, but almost all of which did not differ from baseline (pre-flight) after return to Earth. Overall, these preliminary civilian spaceflight data suggest that short-duration missions do not pose a significant health risk, and moreover present a rich opportunity to measure the earliest phases of adaptation to spaceflight in the human body at anatomical, cellular, physiological and cognitive levels. Finally, these methods and results lay the foundation for an open, rapidly expanding biomedical database for astronauts3, which can inform countermeasure development for both private and government-sponsored space missions.

Nanopore guided annotation of transcriptome architectures

Nanopore direct RNA sequencing (DRS) enables the capture and full-length sequencing of native RNAs, without recoding or amplification bias. Resulting data sets may be interrogated to define the identity and location of chemically modified ribonucleotides, as well as the length of poly(A) tails, on individual RNA molecules. The success of these analyses is highly dependent on the provision of high-resolution transcriptome annotations in combination with workflows that minimize misalignments and other analysis artifacts. Existing software solutions for generating high-resolution transcriptome annotations are poorly suited to small gene-dense genomes of viruses due to the challenge of identifying distinct transcript isoforms where alternative splicing and overlapping RNAs are prevalent. To resolve this, we identified key characteristics of DRS data sets that inform resulting read alignments and developed the nanopore guided annotation of transcriptome architectures (NAGATA) software package (https://github.com/DepledgeLab/NAGATA). We demonstrate, using a combination of synthetic and original DRS data sets derived from adenoviruses, herpesviruses, coronaviruses, and human cells, that NAGATA outperforms existing transcriptome annotation software and yields a consistently high level of precision and recall when reconstructing both gene sparse and gene-dense transcriptomes. Finally, we apply NAGATA to generate the first high-resolution transcriptome annotation of the neglected pathogen human adenovirus type F41 (HAdV-41) for which we identify 77 distinct transcripts encoding at least 23 different proteins.

IMPORTANCE

The transcriptome of an organism denotes the full repertoire of encoded RNAs that may be expressed. This is critical to understanding the biology of an organism and for accurate transcriptomic and epitranscriptomic-based analyses. Annotating transcriptomes remains a complex task, particularly in small gene-dense organisms such as viruses which maximize their coding capacity through overlapping RNAs. To resolve this, we have developed a new software nanopore guided annotation of transcriptome architectures (NAGATA) which utilizes nanopore direct RNA sequencing (DRS) datasets to rapidly produce high-resolution transcriptome annotations for diverse viruses and other organisms.

[Advances in mapping analysis of ribonucleic acid modifications through sequencing]

Over 170 chemical modifications have been discovered in various types of ribonucleic acids (RNAs), including messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and small nuclear RNA (snRNA). These RNA modifications play crucial roles in a wide range of biological processes such as gene expression regulation, RNA stability maintenance, and protein translation. RNA modifications represent a new dimension of gene expression regulation known as the "epitranscriptome". The discovery of RNA modifications and the relevant writers, erasers, and readers provides an important basis for studies on the dynamic regulation and physiological functions of RNA modifications. Owing to the development of detection technologies for RNA modifications, studies on RNA epitranscriptomes have progressed to the single-base resolution, multilayer, and full-coverage stage. Transcriptome-wide methods help discover new RNA modification sites and are of great importance for elucidating the molecular regulatory mechanisms of epitranscriptomics, exploring the disease associations of RNA modifications, and understanding their clinical applications. The existing RNA modification sequencing technologies can be categorized according to the pretreatment approach and sequencing principle as direct high-throughput sequencing, antibody-enrichment sequencing, enzyme-assisted sequencing, chemical labeling-assisted sequencing, metabolic labeling sequencing, and nanopore sequencing technologies. These methods, as well as studies on the functions of RNA modifications, have greatly expanded our understanding of epitranscriptomics. In this review, we summarize the recent progress in RNA modification detection technologies, focusing on the basic principles, advantages, and limitations of different methods. Direct high-throughput sequencing methods do not require complex RNA pretreatment and allow for the mapping of RNA modifications using conventional RNA sequencing methods. However, only a few RNA modifications can be analyzed by high-throughput sequencing. Antibody enrichment followed by high-throughput sequencing has emerged as a crucial approach for mapping RNA modifications, significantly advancing the understanding of RNA modifications and their regulatory functions in different species. However, the resolution of antibody-enrichment sequencing is limited to approximately 100-200 bp. Although chemical crosslinking techniques can achieve single-base resolution, these methods are often complex, and the specificity of the antibodies used in these methods has raised concerns. In particular, the issue of off-target binding by the antibodies requires urgent attention. Enzyme-assisted sequencing has improved the accuracy of the localization analysis of RNA modifications and enables stoichiometric detection with single-base resolution. However, the enzymes used in this technique show poor reactivity, specificity, and sequence preference. Chemical labeling sequencing has become a widely used approach for profiling RNA modifications, particularly by altering reverse transcription (RT) signatures such as RT stops, misincorporations, and deletions. Chemical-assisted sequencing provides a sequence-independent RNA modification detection strategy that enables the localization of multiple RNA modifications. Additionally, when combined with the biotin-streptavidin affinity method, low-abundance RNA modifications can be enriched and detected. Nevertheless, the specificity of many chemical reactions remains problematic, and the development of specific reaction probes for particular modifications should continue in the future to achieve the precise localization of RNA modifications. As an indirect localization method, metabolic labeling sequencing specifically localizes the sites at which modifying enzymes act, which is of great significance in the study of RNA modification functions. However, this method is limited by the intracellular labeling of RNA and cannot be applied to biological samples such as clinical tissues and blood samples. Nanopore sequencing is a direct RNA-sequencing method that does not require RT or the polymerase chain reaction (PCR). However, challenges in analyzing the data obtained from nanopore sequencing, such as the high rate of false positives, must be resolved. Discussing sequencing analysis methods for various types of RNA modifications is instructive for the future development of novel RNA modification mapping technologies, and will aid studies on the functions of RNA modifications across the entire transcriptome.

Nanopore Direct RNA Sequencing Reveals Virus-Induced Changes in the Transcriptional Landscape in Human Bronchial Epithelial Cells

Direct RNA nanopore sequencing reveals changes in gene expression, polyadenylation, splicing, m6A methylation, and pseudouridylation in response to influenza virus exposure in primary human bronchial epithelial cells. This study focuses on the epitranscriptomic profile of genes in the host immune response. In addition to polyadenylated noncoding RNA, we purified and sequenced nonpolyadenylated noncoding RNA and observed changes in expression, N6-methyl-adenosine (m6A), and pseudouridylation (Ψ) in these novel RNA. Two recently discovered lincRNA with roles in immune response, Chaserr and LEADR , became highly methylated in response to influenza exposure. Several H/ACA type snoRNAs that guide pseudouridylation are decreased in expression in response to influenza, and there is a corresponding decrease in the pseudouridylation of two novel lncRNA. Thus, novel epitranscriptomic changes revealed by direct RNA sequencing with nanopore technology provides unique insights into the host epitranscriptomic changes in epithelial gene networks that respond to influenza virus infection.

Multicellular, IVT-derived, unmodified human transcriptome for nanopore-direct RNA analysis

Nanopore direct RNA sequencing (DRS) enables measurements of RNA modifications. Modification-free transcripts are a practical and targeted control for DRS, providing a baseline measurement for canonical nucleotides within a matched and biologically-derived sequence context. However, these controls can be challenging to generate and carry nanopore-specific nuances that can impact analyses. We produced DRS datasets using modification-free transcripts from in vitro transcription of cDNA from six immortalized human cell lines. We characterized variation across cell lines and demonstrated how these may be interpreted. These data will serve as a versatile control and resource to the community for RNA modification analyses of human transcripts.

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.

RNA m6A detection using raw current signals and basecalling errors from Nanopore direct RNA sequencing reads

MOTIVATION

Nanopore direct RNA sequencing (DRS) enables the detection of RNA N6-methyladenosine (m6A) without extra laboratory techniques. A number of supervised or comparative approaches have been developed to identify m6A from Nanopore DRS reads. However, existing methods typically utilize either statistical features of the current signals or basecalling-error features, ignoring the richer information of the raw signals of DRS reads.

RESULTS

Here, we propose RedNano, a deep-learning method designed to detect m6A from Nanopore DRS reads by utilizing both raw signals and basecalling errors. RedNano processes the raw-signal feature and basecalling-error feature through residual networks. We validated the effectiveness of RedNano using synthesized, Arabidopsis, and human DRS data. The results demonstrate that RedNano surpasses existing methods by achieving higher area under the ROC curve (AUC) and area under the precision-recall curve (AUPRs) in all three datasets. Furthermore, RedNano performs better in cross-species validation, demonstrating its robustness. Additionally, when detecting m6A from an independent dataset of Populus trichocarpa, RedNano achieves the highest AUC and AUPR, which are 3.8%-9.9% and 5.5%-13.8% higher than other methods, respectively.

AVAILABILITY AND IMPLEMENTATION

The source code of RedNano is freely available at https://github.com/Derryxu/RedNano.

Big data and deep learning for RNA biology

The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.

m6A regulates heterochromatin in mammalian embryonic stem cells

As the most well-studied modification in mRNA, m6A has been shown to regulate multiple biological processes, including RNA degradation, processing, and translation. Recent studies showed that m6A modification is enriched in chromatin-associated RNAs and nascent RNAs, suggesting m6A might play regulatory roles in chromatin contexts. Indeed, in the past several years, a number of studies have clarified how m6A and its modulators regulate different types of chromatin states. Specifically, in the past 2-3 years, several studies discovered the roles of m6A and/or its modulators in regulating constitutive and facultative heterochromatin, shedding interesting lights on RNA-dependent heterochromatin formation in mammalian cells. This review will summarize and discuss the mechanisms underlying m6A's regulation in different types of heterochromatin, with a specific emphasis on the regulation in mammalian embryonic stem cells, which exhibit distinct features of multiple heterochromatin marks.

Effects and mechanisms of N6-methyladenosine RNA methylation in environmental pollutant-induced carcinogenesis

Environmental pollution, including air pollution, plastic contamination, and heavy metal exposure, is a pressing global issue. This crisis contributes significantly to pollution-related diseases and is a critical risk factor for chronic health conditions, including cancer. Mounting evidence underscores the pivotal role of N6-methyladenosine (m6A) as a crucial regulatory mechanism in pathological processes and cancer progression. Governed by m6A writers, erasers, and readers, m6A orchestrates alterations in target gene expression, consequently playing a vital role in a spectrum of RNA processes, covering mRNA processing, translation, degradation, splicing, nuclear export, and folding. Thus, there is a growing need to pinpoint specific m6A-regulated targets in environmental pollutant-induced carcinogenesis, an emerging area of research in cancer prevention. This review consolidates the understanding of m6A modification in environmental pollutant-induced tumorigenesis, explicitly examining its implications in lung, skin, and bladder cancer. We also investigate the biological mechanisms that underlie carcinogenesis originating from pollution. Specific m6A methylation pathways, such as the HIF1A/METTL3/IGF2BP3/BIRC5 network, METTL3/YTHDF1-mediated m6A modification of IL 24, METTL3/YTHDF2 dynamically catalyzed m6A modification of AKT1, METTL3-mediated m6A-modified oxidative stress, METTL16-mediated m6A modification, site-specific ATG13 methylation-mediated autophagy, and the role of m6A in up-regulating ribosome biogenesis, all come into play in this intricate process. Furthermore, we discuss the direction regarding the interplay between pollutants and RNA metabolism, particularly in immune response, providing new information on RNA modifications for future exploration.

NanoMUD: Profiling of pseudouridine and N1-methylpseudouridine using Oxford Nanopore direct RNA sequencing

Nanopore direct RNA sequencing provided a promising solution for unraveling the landscapes of modifications on single RNA molecules. Here, we proposed NanoMUD, a computational framework for predicting the RNA pseudouridine modification (Ψ) and its methylated analog N1-methylpseudouridine (m1Ψ), which have critical application in mRNA vaccination, at single-base and single-molecule resolution from direct RNA sequencing data. Electric signal features were fed into a bidirectional LSTM neural network to achieve improved accuracy and predictive capabilities. Motif-specific models (NNUNN, N = A, C, U or G) were trained based on features extracted from designed dataset and achieved superior performance on molecule-level modification prediction (Ψ models: min AUC = 0.86, max AUC = 0.99; m1Ψ models: min AUC = 0.87, max AUC = 0.99). We then aggregated read-level predictions for site stoichiometry estimation. Given the observed sequence-dependent bias in model performance, we trained regression models based on the distribution of modification probabilities for sites with known stoichiometry. The distribution-based site stoichiometry estimation method allows unbiased comparison between different contexts. To demonstrate the feasibility of our work, three case studies on both in vitro and in vivo transcribed RNAs were presented. NanoMUD will make a powerful tool to facilitate the research on modified therapeutic IVT RNAs and provides useful insight to the landscape and stoichiometry of pseudouridine and N1-pseudouridine on in vivo transcribed RNA species.

Nanopore Current Events Magnifier (nanoCEM): a novel tool for visualizing current events at modification sites of nanopore sequencing

Nanopore sequencing technologies have enabled the direct detection of base modifications in DNA or RNA molecules. Despite these advancements, the tools for visualizing electrical current, essential for analyzing base modifications, are often lacking in clarity and compatibility with diverse nanopore pipelines. Here, we present Nanopore Current Events Magnifier (nanoCEM, https://github.com/lrslab/nanoCEM), a Python command-line tool designed to facilitate the identification of DNA/RNA modification sites through enhanced visualization and statistical analysis. Compatible with the four preprocessing methods including 'f5c resquiggle', 'f5c eventalign', 'Tombo' and 'move table', nanoCEM is applicable to RNA and DNA analysis across multiple flow cell types. By utilizing rescaling techniques and calculating various statistical features, nanoCEM provides more accurate and comparable visualization of current events, allowing researchers to effectively observe differences between samples and showcase the modified sites.

Ramifications of m6A Modification on ncRNAs in Cancer

N6-methyladenosine (m6A) is an RNA modification wherein the N6-position of adenosine is methylated. It is one of the most prevalent internal modifications of RNA and regulates various aspects of RNA metabolism. M6A is deposited by m6A methyltransferases, removed by m6A demethylases, and recognized by reader proteins, which modulate splicing, export, translation, and stability of the modified mRNA. Recent evidence suggests that various classes of non- coding RNAs (ncRNAs), including microRNAs (miRNAs), circular RNAs (circRNAs), and long con-coding RNAs (lncRNAs), are also targeted by this modification. Depending on the ncRNA species, m6A may affect the processing, stability, or localization of these molecules. The m6A- modified ncRNAs are implicated in a number of diseases, including cancer. In this review, the author summarizes the role of m6A modification in the regulation and functions of ncRNAs in tumor development. Moreover, the potential applications in cancer prognosis and therapeutics are discussed.

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.

Deep Learning for Elucidating Modifications to RNA-Status and Challenges Ahead

RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations along with their preferred contexts and sequence-based determinants. Recently, deep learning approaches have significantly advanced in this field. These methods can predict the presence or absence of modification at specific genomic regions based on diverse features, particularly sequence and secondary structure, allowing us to decipher the highly non-linear sequence patterns and structures that underlie site preferences. This article provides an overview of how deep learning is being applied to this area, with a particular focus on the problem of mRNA-RBP binding, while also considering other types of chemical modification to RNA. It discusses how different types of model can handle sequence-based and/or secondary-structure-based inputs, the process of model training, including choice of negative regions and separating sets for testing and training, and offers recommendations for developing biologically relevant models. Finally, it highlights four key areas that are crucial for advancing the field.

Navigating the landscape of epitranscriptomics and host immunity

RNA modifications, also termed epitranscriptomic marks, encompass chemical alterations to individual nucleotides, including processes such as methylation and editing. These marks contribute to a wide range of biological processes, many of which are related to host immune system defense. The functions of immune-related RNA modifications can be categorized into three main groups: regulation of immunogenic RNAs, control of genes involved in innate immune response, and facilitation of adaptive immunity. Here, we provide an overview of recent research findings that elucidate the contributions of RNA modifications to each of these processes. We also discuss relevant methods for genome-wide identification of RNA modifications and their immunogenic substrates. Finally, we highlight recent advances in cancer immunotherapies that aim to reduce cancer cell viability by targeting the enzymes responsible for RNA modifications. Our presentation of these dynamic research avenues sets the stage for future investigations in this field.

Epitranscriptome insights into Riccia fluitans L. (Marchantiophyta) aquatic transition using nanopore direct RNA sequencing

BACKGROUND

Riccia fluitans, an amphibious liverwort, exhibits a fascinating adaptation mechanism to transition between terrestrial and aquatic environments. Utilizing nanopore direct RNA sequencing, we try to capture the complex epitranscriptomic changes undergone in response to land-water transition.

RESULTS

A significant finding is the identification of 45 differentially expressed genes (DEGs), with a split of 33 downregulated in terrestrial forms and 12 upregulated in aquatic forms, indicating a robust transcriptional response to environmental changes. Analysis of N6-methyladenosine (m6A) modifications revealed 173 m6A sites in aquatic and only 27 sites in the terrestrial forms, indicating a significant increase in methylation in the former, which could facilitate rapid adaptation to changing environments. The aquatic form showed a global elongation bias in poly(A) tails, which is associated with increased mRNA stability and efficient translation, enhancing the plant's resilience to water stress. Significant differences in polyadenylation signals were observed between the two forms, with nine transcripts showing notable changes in tail length, suggesting an adaptive mechanism to modulate mRNA stability and translational efficiency in response to environmental conditions. This differential methylation and polyadenylation underline a sophisticated layer of post-transcriptional regulation, enabling Riccia fluitans to fine-tune gene expression in response to its living conditions.

CONCLUSIONS

These insights into transcriptome dynamics offer a deeper understanding of plant adaptation strategies at the molecular level, contributing to the broader knowledge of plant biology and evolution. These findings underscore the sophisticated post-transcriptional regulatory strategies Riccia fluitans employs to navigate the challenges of aquatic versus terrestrial living, highlighting the plant's dynamic adaptation to environmental stresses and its utility as a model for studying adaptation mechanisms in amphibious plants.

Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) has emerged as a powerful tool for RNA modification identification. However, concurrently detecting multiple types of modifications in a single DRS sample remains a challenge. Here, we develop TandemMod, a transferable deep learning framework capable of detecting multiple types of RNA modifications in single DRS data. To train high-performance TandemMod models, we generate in vitro epitranscriptome datasets from cDNA libraries, containing thousands of transcripts labeled with various types of RNA modifications. We validate the performance of TandemMod on both in vitro transcripts and in vivo human cell lines, confirming its high accuracy for profiling m6A and m5C modification sites. Furthermore, we perform transfer learning for identifying other modifications such as m7G, Ψ, and inosine, significantly reducing training data size and running time without compromising performance. Finally, we apply TandemMod to identify 3 types of RNA modifications in rice grown in different environments, demonstrating its applicability across species and conditions. In summary, we provide a resource with ground-truth labels that can serve as benchmark datasets for nanopore-based modification identification methods, and TandemMod for identifying diverse RNA modifications using a single DRS sample.

Prediction of m6A and m5C at single-molecule resolution reveals a transcriptome-wide co-occurrence of RNA modifications

The epitranscriptome embodies many new and largely unexplored functions of RNA. A significant roadblock hindering progress in epitranscriptomics is the identification of more than one modification in individual transcript molecules. We address this with CHEUI (CH3 (methylation) Estimation Using Ionic current). CHEUI predicts N6-methyladenosine (m6A) and 5-methylcytosine (m5C) in individual molecules from the same sample, the stoichiometry at transcript reference sites, and differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals to achieve high single-molecule, transcript-site, and stoichiometry accuracies in multiple tests using synthetic RNA standards and cell line data. CHEUI's capability to identify two modification types in the same sample reveals a co-occurrence of m6A and m5C in individual mRNAs in cell line and tissue transcriptomes. CHEUI provides new avenues to discover and study the function of the epitranscriptome.

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.

Studying m6A in the brain: a perspective on current methods, challenges, and future directions

A major mechanism of post-transcriptional RNA regulation in cells is the addition of chemical modifications to RNA nucleosides, which contributes to nearly every aspect of the RNA life cycle. N6-methyladenosine (m6A) is a highly prevalent modification in cellular mRNAs and non-coding RNAs, and it plays important roles in the control of gene expression and cellular function. Within the brain, proper regulation of m6A is critical for neurodevelopment, learning and memory, and the response to injury, and m6A dysregulation has been implicated in a variety of neurological disorders. Thus, understanding m6A and how it is regulated in the brain is important for uncovering its roles in brain function and potentially identifying novel therapeutic pathways for human disease. Much of our knowledge of m6A has been driven by technical advances in the ability to map and quantify m6A sites. Here, we review current technologies for characterizing m6A and highlight emerging methods. We discuss the advantages and limitations of current tools as well as major challenges going forward, and we provide our perspective on how continued developments in this area can propel our understanding of m6A in the brain and its role in brain disease.

Detecting m6A at single-molecular resolution via direct RNA sequencing and realistic training data

Direct RNA sequencing offers the possibility to simultaneously identify canonical bases and epi-transcriptomic modifications in each single RNA molecule. Thus far, the development of computational methods has been hampered by the lack of biologically realistic training data that carries modification labels at molecular resolution. Here, we report on the synthesis of such samples and the development of a bespoke algorithm, mAFiA (m6A Finding Algorithm), that accurately detects single m6A nucleotides in both synthetic RNAs and natural mRNA on single read level. Our approach uncovers distinct modification patterns in single molecules that would appear identical at the ensemble level. Compared to existing methods, mAFiA also demonstrates improved accuracy in measuring site-level m6A stoichiometry in biological samples.

Nanopore Guided Annotation of Transcriptome Architectures

High-resolution annotations of transcriptomes from all domains of life are essential for many sequencing-based RNA analyses, including Nanopore direct RNA sequencing (DRS), which would otherwise be hindered by misalignments and other analysis artefacts. DRS allows the capture and full-length sequencing of native RNAs, without recoding or amplification bias, and resulting data may be interrogated to define the identity and location of chemically modified ribonucleotides, as well as the length of poly(A) tails on individual RNA molecules. Existing software solutions for generating high-resolution transcriptome annotations are poorly suited to small gene dense organisms such as viruses due to the challenge of identifying distinct transcript isoforms where alternative splicing and overlapping RNAs are prevalent. To resolve this, we identified key characteristics of DRS datasets and developed a novel approach to transcriptome. We demonstrate, using a combination of synthetic and original datasets, that our novel approach yields a high level of precision and recall when reconstructing both gene sparse and gene dense transcriptomes from DRS datasets. We further apply this approach to generate a new high resolution transcriptome annotation of the neglected pathogen human adenovirus type F 41 for which we identify 77 distinct transcripts encoding at least 23 different proteins.

GLORI for absolute quantification of transcriptome-wide m6A at single-base resolution

N6-methyladenosine (m6A) is the most abundant posttranscriptional chemical modification in mRNA, involved in regulating various physiological and pathological processes throughout mRNA metabolism. Recently, we developed GLORI, a sequencing method that enables the production of a globally absolute-quantitative m6A map at single-base resolution. Our technique utilizes the glyoxal- and nitrite-based chemical reaction, which selectively deaminates unmethylated adenosines while leaving m6A intact. The RNA library can then be prepared using a modified library construction protocol from enhanced UV crosslinking and immunoprecipitation (eCLIP) or commercial kits. Here we provide a detailed protocol for proper RNA sample handling and provide further guidelines for the use of a tailored bioinformatics pipeline (GLORI-tools) for subsequent data analysis. Compared with current methods, this new method is both exceptionally sensitive and robust, capable of identifying ~80,000 m6A sites with 50 Gb sequencing data in mammalian cells. It also provides a quantitative readout for m6A sites at single-base resolution. We hope the technique will provide a precise and unbiased tool for investigating m6A biology across various fields. Basic expertise in molecular biology and knowledge of bioinformatics are required for the protocol. The entire procedure can be completed within a week, with the library construction process taking ~4 d.

Decoding epitranscriptomic regulation of viral infection: mapping of RNA N6-methyladenosine by advanced sequencing technologies

Elucidating the intricate interactions between viral pathogens and host cellular machinery during infection is paramount for understanding pathogenic mechanisms and identifying potential therapeutic targets. The RNA modification N6-methyladenosine (m6A) has emerged as a significant factor influencing the trajectory of viral infections. Hence, the precise and quantitative mapping of m6A modifications in both host and viral RNA is pivotal to understanding its role during viral infection. With the rapid advancement of sequencing technologies, scientists are able to detect m6A modifications with various quantitative, high-resolution, transcriptome approaches. These technological strides have reignited research interest in m6A, underscoring its significance and prompting a deeper investigation into its dynamics during viral infections. This review provides a comprehensive overview of the historical evolution of m6A epitranscriptome sequencing technologies, highlights the latest developments in transcriptome-wide m6A mapping, and emphasizes the innovative technologies for detecting m6A modification. We further discuss the implications of these technologies for future research into the role of m6A in viral infections.

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.

Nanopore DNA sequencing technologies and their applications towards single-molecule proteomics

Sequencing of nucleic acids with nanopores has emerged as a powerful tool offering rapid readout, high accuracy, low cost and portability. This label-free method for sequencing at the single-molecule level is an achievement on its own. However, nanopores also show promise for the technologically even more challenging sequencing of polypeptides, something that could considerably benefit biological discovery, clinical diagnostics and homeland security, as current techniques lack portability and speed. Here we survey the biochemical innovations underpinning commercial and academic nanopore DNA/RNA sequencing techniques, and explore how these advances can fuel developments in future protein sequencing with nanopores.

Utilization of nanopore direct RNA sequencing to analyze viral RNA modifications

Modifications on viral RNAs (vRNAs), either genomic RNAs or RNA transcripts, have complex effects on the viral life cycle and cellular responses to viral infection. The advent of Oxford Nanopore Technologies Direct RNA Sequencing provides a new strategy for studying RNA modifications. To this end, multiple computational tools have been developed, but a systemic evaluation of their performance in mapping vRNA modifications is lacking. Here, 10 computational tools were tested using the Sindbis virus (SINV) RNAs isolated from infected mammalian (BHK-21) or mosquito (C6/36) cells, with in vitro-transcribed RNAs serving as modification-free control. Three single-mode approaches were shown to be inapplicable in the viral context, and three out of seven comparative methods required cutoff adjustments to reduce false-positive predictions. Utilizing optimized cutoffs, an integrated analysis of comparative tools suggested that the intersected predictions of Tombo_com and xPore were significantly enriched compared with the background. Consequently, a pipeline integrating Tombo_com and xPore was proposed for vRNA modification detection; the performance of which was supported by N6-methyladenosine prediction in severe acute respiratory syndrome coronavirus 2 RNAs using publicly available data. When applied to SINV RNAs, this pipeline revealed more intensive modifications in subgenomic RNAs than in genomic RNAs. Modified uridines were frequently identified, exhibiting substantive overlapping between vRNAs generated in different cell lines. On the other hand, the interpretation of other modifications remained unclear, underlining the limitations of the current computational tools despite their notable potential.IMPORTANCEComputational approaches utilizing Oxford Nanopore Technologies Direct RNA Sequencing data were almost exclusively designed to map eukaryotic epitranscriptomes. Therefore, extra caution must be exercised when using these tools to detect vRNA modifications, as in most cases, vRNA modification profiles should be regarded as unknown epitranscriptomes without prior knowledge. Here, we comprehensively evaluated the performance of 10 computational tools in detecting vRNA modification sites. All tested single-mode methods failed to differentiate native and in vitro-transcribed samples. Using optimized cutoff values, seven tested comparative tools generated very different predictions. An integrated analysis showed significant enrichment of Tombo_com and xPore predictions against the background. A pipeline for vRNA modification detection was proposed accordingly and applied to Sindbis virus RNAs. In conclusion, our study underscores the need for the careful application of computational tools to analyze viral epitranscriptomics. It also offers insights into alphaviral RNA modifications, although further validation is required.

Chemically Modified Platforms for Better RNA Therapeutics

RNA-based therapies have catalyzed a revolutionary transformation in the biomedical landscape, offering unprecedented potential in disease prevention and treatment. However, despite their remarkable achievements, these therapies encounter substantial challenges including low stability, susceptibility to degradation by nucleases, and a prominent negative charge, thereby hindering further development. Chemically modified platforms have emerged as a strategic innovation, focusing on precise alterations either on the RNA moieties or their associated delivery vectors. This comprehensive review delves into these platforms, underscoring their significance in augmenting the performance and translational prospects of RNA-based therapeutics. It encompasses an in-depth analysis of various chemically modified delivery platforms that have been instrumental in propelling RNA therapeutics toward clinical utility. Moreover, the review scrutinizes the rationale behind diverse chemical modification techniques aiming at optimizing the therapeutic efficacy of RNA molecules, thereby facilitating robust disease management. Recent empirical studies corroborating the efficacy enhancement of RNA therapeutics through chemical modifications are highlighted. Conclusively, we offer profound insights into the transformative impact of chemical modifications on RNA drugs and delineates prospective trajectories for their future development and clinical integration.

Heterogeneity of chemical modifications on RNA

The chemical modifications of RNAs broadly impact almost all cellular events and influence various diseases. The rapid advance of sequencing and other technologies opened the door to global methods for profiling all RNA modifications, namely the "epitranscriptome." The mapping of epitranscriptomes in different cells and tissues unveiled that RNA modifications exhibit extensive heterogeneity, in type, amount, and in location. In this mini review, we first introduce the current understanding of modifications on major types of RNAs and the methods that enabled their discovery. We next discuss the tissue and cell heterogeneity of RNA modifications and briefly address the limitations of current technologies. With much still remaining unknown, the development of the epitranscriptomic field lies in the further developments of novel technologies.

Exploring N6-methyladenosine (m6A) modification in tree species: opportunities and challenges

N 6-methyladenosine (m6A) in eukaryotes is the most common and widespread internal modification in mRNA. The modification regulates mRNA stability, translation efficiency, and splicing, thereby fine-tuning gene regulation. In plants, m6A is dynamic and critical for various growth stages, embryonic development, morphogenesis, flowering, stress response, crop yield, and biomass. Although recent high-throughput sequencing approaches have enabled the rapid identification of m6A modification sites, the site-specific mechanism of this modification remains unclear in trees. In this review, we discuss the functional significance of m6A in trees under different stress conditions and discuss recent advancements in the quantification of m6A. Quantitative and functional insights into the dynamic aspect of m6A modification could assist researchers in engineering tree crops for better productivity and resistance to various stress conditions.

In silico λ-dynamics predicts protein binding specificities to modified RNAs

RNA modifications shape gene expression through a smorgasbord of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.

Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing

N6-methyladenosine (m6A) is the most abundant internal eukaryotic mRNA modification, and is involved in the regulation of various biological processes. Direct Nanopore sequencing of native RNA (dRNA-seq) emerged as a leading approach for its identification. Several software were published for m6A detection and there is a strong need for independent studies benchmarking their performance on data from different species, and against various reference datasets. Moreover, a computational workflow is needed to streamline the execution of tools whose installation and execution remains complicated. We developed NanOlympicsMod, a Nextflow pipeline exploiting containerized technology for comparing 14 tools for m6A detection on dRNA-seq data. NanOlympicsMod was tested on dRNA-seq data generated from in vitro (un)modified synthetic oligos. The m6A hits returned by each tool were compared to the m6A position known by design of the oligos. In addition, NanOlympicsMod was used on dRNA-seq datasets from wild-type and m6A-depleted yeast, mouse and human, and each tool's hits were compared to reference m6A sets generated by leading orthogonal methods. The performance of the tools markedly differed across datasets, and methods adopting different approaches showed different preferences in terms of precision and recall. Changing the stringency cut-offs allowed for tuning the precision-recall trade-off towards user preferences. Finally, we determined that precision and recall of tools are markedly influenced by sequencing depth, and that additional sequencing would likely reveal additional m6A sites. Thanks to the possibility of including novel tools, NanOlympicsMod will streamline the benchmarking of m6A detection tools on dRNA-seq data, improving future RNA modification characterization.

Mapping m6A Sites on HIV-1 RNA Using Oligonucleotide LC-MS/MS

The biological significance of chemical modifications to the ribonucleic acid (RNA) of human immunodeficiency virus type-1 (HIV-1) has been recognized. However, our understanding of the site-specific and context-dependent roles of these chemical modifications remains limited, primarily due to the absence of nucleotide-resolution mapping of modification sites. In this study, we present a method for achieving nucleotide-resolution mapping of chemical modification sites on HIV-1 RNA using liquid chromatography and tandem mass spectrometry (LC-MS/MS). LC-MS/MS, a powerful tool capable of directly analyzing native RNAs, has proven effective for mapping RNA modifications in small RNA molecules, including ribosomal RNA and transfer RNA. However, longer RNAs have posed challenges, such as the 9 Kb HIV-1 virion RNA, due to the complexity of and ambiguity in mass differences among RNase T1-cleaved RNA fragments in LC-MS/MS data. Here, we introduce a new target RNA enrichment method to isolate small local RNA fragments of HIV-1 RNA that potentially harbor site-specific N6-methyladenosine (m6A) modifications. In our initial trial, we used target-specific DNA probes only and encountered insufficient RNA fragmentation due to inefficient S1 digestion near the target site. Recognizing that inefficient S1 digestion by HIV-1 RNA is likely due to the formation of secondary structures in proximity to the target site, we designed multiple DNA probes annealing to various sites of HIV-1 RNA to better control the structures of RNA substrates for S1 digestion. The use of these non-target DNA probes significantly improved the isolation of more homogeneous target RNA fragments of approximately 50 bases in length. Oligonucleotide LC-MS/MS analysis of these isolated target RNA fragments successfully separated and detected both m6A-methylated and non-methylated oligomers at the two m6A-predicted sites. The principle of this new target enrichment strategy holds promise and should be broadly applicable to the analysis of any lengthy RNA that was previously deemed infeasible for investigation using oligonucleotide LC-MS/MS.

Detecting m6A RNA modification from nanopore sequencing using a semi-supervised learning framework

Direct nanopore-based RNA sequencing can be used to detect post-transcriptional base modifications, such as m6A methylation, based on the electric current signals produced by the distinct chemical structures of modified bases. A key challenge is the scarcity of adequate training data with known methylation modifications. We present Xron, a hybrid encoder-decoder framework that delivers a direct methylation-distinguishing basecaller by training on synthetic RNA data and immunoprecipitation-based experimental data in two steps. First, we generate data with more diverse modification combinations through in silico cross-linking. Second, we use this dataset to train an end-to-end neural network basecaller followed by fine-tuning on immunoprecipitation-based experimental data with label-smoothing. The trained neural network basecaller outperforms existing methylation detection methods on both read-level and site-level prediction scores. Xron is a standalone, end-to-end m6A-distinguishing basecaller capable of detecting methylated bases directly from raw sequencing signals, enabling de novo methylome assembly.

m7GHub V2.0: an updated database for decoding the N7-methylguanosine (m7G) epitranscriptome

With recent progress in mapping N7-methylguanosine (m7G) RNA methylation sites, tens of thousands of experimentally validated m7G sites have been discovered in various species, shedding light on the significant role of m7G modification in regulating numerous biological processes including disease pathogenesis. An integrated resource that enables the sharing, annotation and customized analysis of m7G data will greatly facilitate m7G studies under various physiological contexts. We previously developed the m7GHub database to host mRNA m7G sites identified in the human transcriptome. Here, we present m7GHub v.2.0, an updated resource for a comprehensive collection of m7G modifications in various types of RNA across multiple species: an m7GDB database containing 430 898 putative m7G sites identified in 23 species, collected from both widely applied next-generation sequencing (NGS) and the emerging Oxford Nanopore direct RNA sequencing (ONT) techniques; an m7GDiseaseDB hosting 156 206 m7G-associated variants (involving addition or removal of an m7G site), including 3238 disease-relevant m7G-SNPs that may function through epitranscriptome disturbance; and two enhanced analysis modules to perform interactive analyses on the collections of m7G sites (m7GFinder) and functional variants (m7GSNPer). We expect that m7Ghub v.2.0 should serve as a valuable centralized resource for studying m7G modification. It is freely accessible at: www.rnamd.org/m7GHub2.

Streamlining remote nanopore data access with slow5curl

BACKGROUND

As adoption of nanopore sequencing technology continues to advance, the need to maintain large volumes of raw current signal data for reanalysis with updated algorithms is a growing challenge. Here we introduce slow5curl, a software package designed to streamline nanopore data sharing, accessibility, and reanalysis.

RESULTS

Slow5curl allows a user to fetch a specified read or group of reads from a raw nanopore dataset stored on a remote server, such as a public data repository, without downloading the entire file. Slow5curl uses an index to quickly fetch specific reads from a large dataset in SLOW5/BLOW5 format and highly parallelized data access requests to maximize download speeds. Using all public nanopore data from the Human Pangenome Reference Consortium (>22 TB), we demonstrate how slow5curl can be used to quickly fetch and reanalyze raw signal reads corresponding to a set of target genes from each individual in large cohort dataset (n = 91), minimizing the time, egress costs, and local storage requirements for their reanalysis.

CONCLUSIONS

We provide slow5curl as a free, open-source package that will reduce frictions in data sharing for the nanopore community: https://github.com/BonsonW/slow5curl.

Gapless genome assembly and epigenetic profiles reveal gene regulation of whole-genome triplication in lettuce

BACKGROUND

Lettuce, an important member of the Asteraceae family, is a globally cultivated cash vegetable crop. With a highly complex genome (∼2.5 Gb; 2n = 18) rich in repeat sequences, current lettuce reference genomes exhibit thousands of gaps, impeding a comprehensive understanding of the lettuce genome.

FINDINGS

Here, we present a near-complete gapless reference genome for cutting lettuce with high transformability, using long-read PacBio HiFi and Nanopore sequencing data. In comparison to stem lettuce genome, we identify 127,681 structural variations (SVs, present in 0.41 Gb of sequence), reflecting the divergence of leafy and stem lettuce. Interestingly, these SVs are related to transposons and DNA methylation states. Furthermore, we identify 4,612 whole-genome triplication genes exhibiting high expression levels associated with low DNA methylation levels and high N6-methyladenosine RNA modifications. DNA methylation changes are also associated with activation of genes involved in callus formation.

CONCLUSIONS

Our gapless lettuce genome assembly, an unprecedented achievement in the Asteraceae family, establishes a solid foundation for functional genomics, epigenomics, and crop breeding and sheds new light on understanding the complexity of gene regulation associated with the dynamics of DNA and RNA epigenetics in genome evolution.

Direct Analysis of HIV mRNA m6A Methylation by Nanopore Sequencing

The post-transcriptional processing and chemical modification of HIV RNA are understudied aspects of HIV virology, primarily due to the limited ability to accurately map and quantify RNA modifications. Modification-specific antibodies or modification-sensitive endonucleases coupled with short-read RNA sequencing technologies have allowed for low-resolution or limited mapping of important regulatory modifications of HIV RNA such as N6-methyladenosine (m6A). However, a high-resolution map of where these sites occur on HIV transcripts is needed for detailed mechanistic understanding. This has recently become possible with new sequencing technologies. Here, we describe the direct RNA sequencing of HIV transcripts using an Oxford Nanopore Technologies sequencer and the use of this technique to map m6A at near single nucleotide resolution. This technology also provides the ability to identify splice variants with long RNA reads and thus, can provide high-resolution RNA modification maps that distinguish between overlapping splice variants. The protocols outlined here for m6A also provide a powerful paradigm for studying any other RNA modifications that can be detected on the nanopore platform.

The rise of epitranscriptomics: recent developments and future directions

The epitranscriptomics field has undergone tremendous growth since the discovery that the RNA N6-methyladenosine (m6A) modification is reversible and is distributed throughout the transcriptome. Efforts to map RNA modifications transcriptome-wide and reshape the epitranscriptome in disease settings have facilitated mechanistic understanding and drug discovery in the field. In this review we discuss recent advancements in RNA modification detection methods and consider how these developments can be applied to gain novel insights into the epitranscriptome. We also highlight drug discovery efforts aimed at developing epitranscriptomic therapeutics for cancer and other diseases. Finally, we consider engineering of the epitranscriptome as an emerging direction to investigate RNA modifications and their causal effects on RNA processing at high specificity.

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 technique to improve the basecalling of modification-rich sequences, which are usually of high biological interests. With sequence backbones resolved, we further run anomaly detection 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.

Adaptive sampling for nanopore direct RNA-sequencing

Nanopore long-read sequencing enables real-time monitoring and controlling of individual nanopores. This allows us to enrich or deplete specific sequences in DNA sequencing in a process called "adaptive sampling." So far, adaptive sampling (AS) was not applicable to the direct sequencing of RNA. Here, we show that AS is feasible and useful for direct RNA sequencing (DRS), which has its specific technical and biological challenges. Using a well-controlled in vitro transcript-based model system, we identify essential characteristics and parameter settings for AS in DRS, as the superior performance of depletion over enrichment. Here, the efficiency of depletion is close to the theoretical maximum. Additionally, we demonstrate that AS efficiently depletes specific transcripts in transcriptome-wide sequencing applications. Specifically, we applied our AS approach to poly(A)-enriched RNA samples from human-induced pluripotent stem cell-derived cardiomyocytes and mouse whole heart tissue and show efficient 2.5- to 2.8-fold depletion of highly abundant mitochondrial-encoded transcripts. Finally, we characterize depletion and enrichment performance for complex transcriptome subsets, that is, at the level of the entire Chromosome 11, proving the general applicability of direct RNA AS. Our analyses provide evidence that AS is especially useful to enable the detection of lowly expressed transcripts and reduce the sequencing of highly abundant disturbing transcripts.

Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

Identifying N6-Methyladenosine Sites in HepG2 Cell Lines Using Oxford Nanopore Technology

RNA modifications, particularly N6-methyladenosine (m6A), are pivotal regulators of RNA functionality and cellular processes. We analyzed m6A modifications by employing Oxford Nanopore technology and the m6Anet algorithm, focusing on the HepG2 cell line. We identified 3968 potential m6A modification sites in 2851 transcripts, corresponding to 1396 genes. A gene functional analysis revealed the active involvement of m6A-modified genes in ubiquitination, transcription regulation, and protein folding processes, aligning with the known role of m6A modifications in histone ubiquitination in cancer. To ensure data robustness, we assessed reproducibility across technical replicates. This study underscores the importance of evaluating algorithmic reproducibility, especially in supervised learning. Furthermore, we examined correlations between transcriptomic, translatomic, and proteomic levels. A strong transcriptomic-translatomic correlation was observed. In conclusion, our study deepens our understanding of m6A modifications' multifaceted impacts on cellular processes and underscores the importance of addressing reproducibility concerns in analytical approaches.

Deep learning and direct sequencing of labeled RNA captures transcriptome dynamics

Quantification of the dynamics of RNA metabolism is essential for understanding gene regulation in health and disease. Existing methods rely on metabolic labeling of nascent RNAs and physical separation or inference of labeling through PCR-generated mutations, followed by short-read sequencing. However, these methods are limited in their ability to identify transient decay intermediates or co-analyze RNA decay with cis-regulatory elements of RNA stability such as poly(A) tail length and modification status, at single molecule resolution. Here we use 5-ethynyl uridine (5EU) to label nascent RNA followed by direct RNA sequencing with nanopores. We developed RNAkinet, a deep convolutional and recurrent neural network that processes the electrical signal produced by nanopore sequencing to identify 5EU-labeled nascent RNA molecules. RNAkinet demonstrates generalizability to distinct cell types and organisms and reproducibly quantifies RNA kinetic parameters allowing the combined interrogation of RNA metabolism and cis-acting RNA regulatory elements.

Nanopore-based direct sequencing of RNA transcripts with 10 different modified nucleotides reveals gaps in existing technology

RNA undergoes complex posttranscriptional processing including chemical modifications of the nucleotides. The resultant-modified nucleotides are an integral part of RNA sequences that must be considered in studying the biology of RNA and in the design of RNA therapeutics. However, the current "RNA-sequencing" methods primarily sequence complementary DNA rather than RNA itself, which means that the modifications present in RNA are not captured in the sequencing results. Emerging direct RNA-sequencing technologies, such as those offered by Oxford Nanopore, aim to address this limitation. In this study, we synthesized and used Nanopore technology to sequence RNA transcripts consisting of canonical nucleotides and 10 different modifications in various concentrations. The results show that direct RNA sequencing still has a baseline error rate of >10%, and although some modifications can be detected, many remain unidentified. Thus, there is a need to develop sequencing technologies and analysis methods that can comprehensively capture the total complexity of RNA. The RNA sequences obtained through this project are made available for benchmarking analysis methods.

RNA N6-methyladenosine modification-based biomarkers for absorbed ionizing radiation dose estimation

Radiation triage and biological dosimetry are critical for the medical management of massive potentially exposed individuals following radiological accidents. Here, we performed a genome-wide screening of radiation-responding mRNAs, whose N6-methyladenosine (m6A) levels showed significant alteration after acute irradiation. The m6A levels of three genes, Ncoa4, Ate1 and Fgf22, in peripheral blood mononuclear cells (PBMCs) of mice showed excellent dose-response relationships and could serve as biomarkers of radiation exposure. Especially, the RNA m6A of Ncoa4 maintained a high level as long as 28 days after irradiation. We demonstrated its responsive specificity to radiation, conservation across the mice, monkeys and humans, and the dose-response relationship in PBMCs from cancer patients receiving radiation therapy. Finally, NOCA4 m6A-based biodosimetric models were constructed for estimating absorbed radiation doses in mice or humans. Collectively, this study demonstrated the potential feasibility of RNA m6A in radiation accidents management and clinical applications.

New insights into transcriptome variation during cattle adipocyte adipogenesis by direct RNA sequencing

We performed direct RNA sequencing (DRS) together with PCR-amplified cDNA long and short read sequencing for cattle adipocyte at different stages. We proved that the DRS was with advantages to avoid artificial transcripts and questionable exitrons. Totally, we obtained 68,124 transcripts with information of alternative splicing, poly (A) length and mRNA modification. The number of transcripts for adipogenesis was expanded by alternative splicing, which lead regulation mechanisms far more complex than ever known. We detected 891 differentially expressed genes (DEGs). However, 62.78% transcripts of DEGs were not significantly differentially expressed, and 248 transcripts showed opposite changing directions with their genes. The poly (A) tail became globally shorter in differentiated adipocyte than in primary adipocyte, and had a weak negative correlation with gene/transcript expression. Moreover, the study of different mRNA modifications implied their potential roles in gene expression and alternative splicing. Overall, our study promoted better understanding of adipogenesis mechanisms in cattle adipocytes.