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

The complex roles of m 6 A modifications in neural stem cell proliferation, differentiation, and self-renewal and implications for memory and neurodegenerative diseases

N6-methyladenosine (m 6 A), the most prevalent and conserved RNA modification in eukaryotic cells, profoundly influences virtually all aspects of mRNA metabolism. mRNA plays crucial roles in neural stem cell genesis and neural regeneration, where it is highly concentrated and actively involved in these processes. Changes in m 6 A modification levels and the expression levels of related enzymatic proteins can lead to neurological dysfunction and contribute to the development of neurological diseases. Furthermore, the proliferation and differentiation of neural stem cells, as well as nerve regeneration, are intimately linked to memory function and neurodegenerative diseases. This paper presents a comprehensive review of the roles of m 6 A in neural stem cell proliferation, differentiation, and self-renewal, as well as its implications in memory and neurodegenerative diseases. m 6 A has demonstrated divergent effects on the proliferation and differentiation of neural stem cells. These observed contradictions may arise from the time-specific nature of m 6 A and its differential impact on neural stem cells across various stages of development. Similarly, the diverse effects of m 6 A on distinct types of memory could be attributed to the involvement of specific brain regions in memory formation and recall. Inconsistencies in m 6 A levels across different models of neurodegenerative disease, particularly Alzheimer's disease and Parkinson's disease, suggest that these disparities are linked to variations in the affected brain regions. Notably, the opposing changes in m 6 A levels observed in Parkinson's disease models exposed to manganese compared to normal Parkinson's disease models further underscore the complexity of m 6 A's role in neurodegenerative processes. The roles of m 6 A in neural stem cell proliferation, differentiation, and self-renewal, and its implications in memory and neurodegenerative diseases, appear contradictory. These inconsistencies may be attributed to the time-specific nature of m 6 A and its varying effects on distinct brain regions and in different environments.

Nanopore direct RNA sequencing of human transcriptomes reveals the complexity of mRNA modifications and crosstalk between regulatory features

The identification and functional characterization of chemical modifications on an mRNA molecule, in particular N6-methyladenosine (m6A) modification, significantly broadened our understanding of RNA function and regulation. While interactions between RNA modifications and other RNA features have been proposed, direct evidence showing correlation is limited. Here, using Oxford Nanopore long-read direct RNA sequencing (dRNA-seq), we simultaneously interrogate the transcriptome and epitranscriptome of a human leukemia cell line to investigate the correlation between m6A modifications, mRNA abundance, mRNA stability, polyadenylation (poly(A)) tail length, and alternative splicing. High-quality dRNA-seq is important for unbiased and large-scale correlative analyses. Global assessments indicated a negative association between poly(A) tail length and mRNA abundance while uncovering pathway-specific responses upon depletion of the m6A-forming enzyme METTL3. Overall, our study presented a rich dRNA-seq data resource that has been validated and can be further exploited to inquire into the complexity of RNA modifications and potential interplays between RNA regulatory elements.

Quantitative and Multiplexing Analysis of MicroRNAs by Direct Full-Length Sequencing in Nanopores

MicroRNAs (miRNAs) play important regulatory roles in biology. Direct sequencing of miRNAs in full-length can reveal comprehensive information on their sequences, abundance, and modifications, which, however, has yet to be achieved due to their extremely short length (∼22 nt). Herein, we developed Direct-miR-seq, a nanopore-based direct RNA sequencing (DRS) method that elongates miRNAs at both the 5' and 3' ends by ligating with custom nucleic acid adaptors to ensure full-length sequencing of miRNAs with high yield and accuracy. Compared to standard DRS, Direct-miR-seq enabled sequencing of the whole sequence of miRNAs, achieved a 26-fold sequencing yield, and exhibited reduced bias across miRNA species along with low sequencing error rates. We applied Direct-miR-seq to native RNA populations from cells and human serum to demonstrate its capability to selectively capture miRNAs of known sequences in complex RNA environments for revealing quantitative information in abundance and m6A modification at single-molecule and single-base resolution of ∼100 miRNA species in a single sequencing event. We envision that Direct-miR-seq may be translated toward a variety of biological and medical applications by sequencing miRNAs and other small RNAs.

A tale of two parasites: a glimpse into the RNA methylome of patient-derived Plasmodium falciparum and Plasmodium vivax isolates

BACKGROUND

Understanding the molecular mechanisms of the malarial parasites in hosts is crucial for developing effective treatments. Epitranscriptomic research on pathogens has unveiled the significance of RNA methylation in gene regulation and pathogenesis. This is the first report investigating methylation signatures and alternative splicing events using Nanopore Direct RNA Sequencing to single-base resolution in Plasmodium falciparum and Plasmodium vivax clinical isolates with hepatic dysfunction complications.

METHODS

Direct RNA Sequencing using Nanopore from clinical isolates of P. falciparum and P. vivax showing hepatic dysfunction manifestation was performed. Subsequently, transcriptome reconstruction using FLAIR and transcript classification using SQANTI3, followed by methylation detection using CHEUI and m6Anet to identify N6-methyladenosine (m6A) and 5-methylcytosine (m5C) methylation signatures, was done. The alternative splicing events from both the datasets were documented.

RESULTS

The reference genome of Plasmodium reports > 5000 genes out of which ~ 50% was identified as expressed in the two sequenced isolates, including novel isoforms and intergenic transcripts, highlighting extensive transcriptome diversity. The distinct RNA methylation profiles of m6A and m5C from the expressed transcripts were observed in sense, Natural Antisense Transcripts (NATs) and intergenic categories hinting at species-specific regulatory mechanisms. Dual modification events were observed in a significant number of transcripts in both the parasites. Modified transcripts originating from apicoplast and mitochondrial genomes have also been detected. These modifications are unevenly present in the annotated regions of the mRNA, potentially influencing mRNA export and translation. Several splicing events were observed, with alternative 3' and 5' end splicing predominating in the datasets suggesting differences in translational kinetics and possible protein characteristics in these disease conditions.

CONCLUSION

The data shows the presence of modified sense, NATs and alternatively spliced transcripts. These phenomena together suggest the presence of multiple regulatory layers which decides the post-translational proteome of the parasites in particular disease conditions. Studies like these will help to decipher the post-translational environments of malaria parasites in vivo and elucidate their inherent proteome plasticity, thus allowing the conceptualization of novel strategies for interventions.

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.

The motor neuron m6A repertoire governs neuronal homeostasis and FTO inhibition mitigates ALS symptom manifestation

Amyotrophic lateral sclerosis (ALS) is a swiftly progressive and fatal neurodegenerative ailment marked by the degenerative motor neurons (MNs). Why MNs are specifically susceptible in predominantly sporadic cases remains enigmatic. Here, we demonstrated N6-methyladenosine (m6A), an RNA modification catalyzed by the METTL3/METTL14 methyltransferase complex, as a pivotal contributor to ALS pathogenesis. By conditional knockout Mettl14 in murine MNs, we recapitulate almost the full spectrum of ALS disease characteristics. Mechanistically, pervasive m6A hypomethylation triggers dysregulated expression of high-risk genes associated with ALS and an unforeseen reduction of chromatin accessibility in MNs. Additionally, we observed diminished m6A levels in induced pluripotent stem cell derived MNs (iPSC~MNs) from familial and sporadic ALS patients. Restoring m6A equilibrium via a small molecule or gene therapy significantly preserves MNs from degeneration and mitigates motor impairments in ALS iPSC~MNs and murine models. Our study presents a substantial stride towards identifying pioneering efficacious ALS therapies via RNA modifications.

Long-read RNA sequencing reveals allele-specific N 6-methyladenosine modifications

Long-read sequencing technology enables highly accurate detection of allele-specific RNA expression, providing insights into the effects of genetic variation on splicing and RNA abundance. Furthermore, the ability to directly sequence RNA enables the detection of RNA modifications in tandem with ascertaining the allelic origin of each molecule. Here, we leverage these advantages to determine allele-biased patterns of N 6-methyladenosine (m6A) modifications in native mRNA. We used human and mouse cells with known genetic variants to assign the allelic origin of each mRNA molecule combined with a supervised machine learning model to detect read-level m6A modification ratios. Our analyses reveal the importance of sequences adjacent to the DRACH motif in determining m6A deposition, in addition to allelic differences that directly alter the motif. Moreover, we discover allele-specific m6A modification events with no genetic variants in close proximity to the differentially modified nucleotide, demonstrating the unique advantage of using long-reads and surpassing the capabilities of antibody-based short-read approaches. This technological advance will further our understanding of the role of genetics in determining mRNA modifications.

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.

The microbiota-m6A-metabolism axis: Implications for therapeutic strategies in gastrointestinal cancers

Gastrointestinal (GI) cancers remain a leading cause of cancer-related mortality worldwide, with metabolic reprogramming recognized as a central driver of tumor progression and therapeutic resistance. Among the key regulatory layers, N6-methyladenosine (m6A) RNA modification-mediated by methyltransferases (writers such as METTL3/14), RNA-binding proteins (readers like YTHDFs and IGF2BPs), and demethylases (erasers including FTO and ALKBH5), plays a pivotal role in controlling gene expression and metabolic flux in the tumor context. Concurrently, the gut microbiota profoundly influences GI tumorigenesis and immune evasion by modulating metabolite availability and remodeling the tumor microenvironment. Recent evidence has uncovered a bidirectional crosstalk between microbial metabolites and m6A methylation: microbiota-derived signals dynamically regulate m6A deposition on metabolic and immune transcripts, while m6A modifications, in turn, regulate the stability and translation of key mRNAs such as PD-L1 and FOXP3. This reciprocal interaction forms self-reinforcing epigenetic circuits that drive tumor plasticity, immune escape, and metabolic adaptation. In this review, we dissect the molecular underpinnings of the microbiota-m6A-metabolism axis in GI cancers and explore its potential to inform novel strategies in immunotherapy, metabolic intervention, and microbiome-guided precision oncology.

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

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

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

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

Leveraging basecaller's move table to generate a lightweight k-mer model for nanopore sequencing analysis

MOTIVATION

Nanopore sequencing by Oxford Nanopore Technologies (ONT) enables direct analysis of DNA and RNA by capturing raw electrical signals. Different nanopore chemistries have varied k-mer lengths, current levels, and standard deviations, which are stored in "k-mer models." In cases where official models are lacking or unsuitable for specific sequencing conditions, tailored k-mer models are crucial to ensure precise signal-to-sequence alignment, analysis and interpretation. The process of transforming raw signal data into nucleotide sequences, known as basecalling, is a fundamental step in nanopore sequencing.

RESULTS

In this study, we leverage the move table produced by ONT's basecalling software to create a lightweight de novo k-mer model for RNA004 chemistry. We demonstrate the validity of our custom k-mer model by using it to guide signal-to-sequence alignment analysis, achieving high alignment rates (97.48%) compared to larger default models. Additionally, our 5-mer model exhibits similar performance as the default 9-mer models another analysis, such as detection of m6A RNA modifications. We provide our method, termed Poregen, as a generalizable approach for creation of custom, de novo k-mer models for nanopore signal data analysis.

AVAILABILITY AND IMPLEMENTATION

Poregen is an open source package under an MIT license: https://github.com/hiruna72/poregen.

Transcriptomics in the era of long-read sequencing

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

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

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

Wolbachia elevates host methyltransferase expression and alters the m6A methylation landscape in Aedes aegypti mosquito cells

Wolbachia pipientis is an intracellular endosymbiotic bacterium that blocks the replication of several arboviruses in transinfected Aedes aegypti mosquitoes, yet its antiviral mechanism remains unknown. For the first time, we employed Nanopore direct RNA sequencing technology to investigate the impact of wAlbB strain of Wolbachia on the host's N6-methyladenosine (m6A) machinery and post-transcriptional modification landscape. Our study revealed that Wolbachia infection elevates the expression of genes involved in the mosquito's m6A methyltransferase complex. However, knocking down these m6A-related genes did not affect Wolbachia density. Nanopore sequencing identified 1,392 differentially modified m6A DRACH motifs on mosquito transcripts, with 776 showing increased and 616 showing decreased m6A levels due to Wolbachia. These m6A sites were predominantly enriched in coding sequences and 3'-untranslated regions. Gene Ontology analysis revealed that genes with reduced m6A levels were over-represented in functional GO terms associated with purine nucleotide binding functions critical in the post-transcriptional modification process of m6A. Differential gene expression analysis of the Nanopore data uncovered that a total of 643 protein-coding genes were significantly differentially expressed, 427 were downregulated, and 216 were upregulated. Several classical and non-classical immune-related genes were amongst the downregulated DEGs. Notably, it revealed a critical host factor, transmembrane protein 41B (TMEM41B), which is required for flavivirus infection, was upregulated and methylated in the presence of Wolbachia. Indeed, there is a strong correlation between gene expression being upregulated in genes with both increased and decreased levels of m6A modification, respectively. Our findings underscore Wolbachia's ability to modulate many intracellular aspects of its mosquito host by influencing post-transcriptional m6A modifications and gene expression, and it unveils a potential link behind its antiviral properties.

Single-molecule m6A detection empowered by endogenous labeling unveils complexities across RNA isoforms

The landscape of N6-methyadenosine (m6A) on different RNA isoforms is still incompletely understood. Here, in HEK293T cells, we endogenously label the methylated m6A sites on single Oxford Nanopore Technology (ONT) direct RNA sequencing (DRS) reads by APOBEC1-YTH-induced C-to-U mutations 10-100 nt away, obtaining 1,020,237 5-mer single-read m6A signals. We then trained m6Aiso, a deep residual neural network model that accurately identifies and quantifies m6A at single-read resolution. Analyzing m6Aiso-determined m6A on single reads and isoforms uncovers distance-dependent linkages of m6A sites along single molecules. It also uncovers specific methylation of identical m6A sites on intron-retained isoforms, partly due to their differential distances to exon junctions and isoform-specific binding of TARBP2. Moreover, we find that transcription factor SMAD3 promotes m6A deposition on its transcribed RNA isoforms during epithelial-mesenchymal transition, resulting in isoform-specific regulation of m6A on isoforms with alternative promoters. Our study underscores the effectiveness of m6Aiso in elucidating the intricate dynamics and complexities of m6A across RNA isoforms.

Direct RNA sequencing of the Escherichia coli epitranscriptome uncovers alterations under heat stress

Modifications of RNA, known as the epitranscriptome, affect gene expression, translation, and splicing in eukaryotes, with implications for developmental processes, cancer, and viral infections. In prokaryotes, regulation at the level of the epitranscriptome is still poorly understood. Here, we used nanopore direct RNA sequencing of Escherichia coli to study RNA modifications and their changes under heat stress. With a single sequencing reaction, we detected most known modification types in ribosomal RNA (rRNA), transfer RNA (tRNA), and messenger RNA (mRNA). RNA sequencing was complemented by a multifaceted approach that included mass spectrometry, deletion mutants, single-nucleotide polymerase chain reaction, and in vitro methylation. Known 5-methylcytidine (m5C) and N6-methyladenosine (m6A) sites in the rRNA were confirmed, but these types of modifications could not be localized in the mRNA. In response to heat stress, levels of m5C, m6A, and N6,N6-dimethyladenosine increased in the 16S rRNA. Sequencing and mass spectrometry data demonstrated a decrease in tRNA modification abundance in the anticodon loop at 45°C. In general, mRNA modifications at 37°C were enriched in the coding regions of genes associated with general metabolism and RNA processing, which shifted to genes involved in cell wall synthesis and membrane transport under heat stress. This study provides new insights into the complexity of post-transcriptional regulation in bacteria.

Defining expansions and perturbations to the RNA polymerase III transcriptome and epitranscriptome by modified direct RNA nanopore sequencing

RNA polymerase III (Pol III) transcribes cytosolic transfer RNAs (tRNAs) and other non-coding RNAs (ncRNAs) essential to cellular function. However, many aspects of Pol III transcription and processing, including RNA modifications, remain poorly understood, mainly due to a lack of available sensitive and systematic methods for their analysis. Here, we present DRAP3R (Direct Read and Analysis of Polymerase III transcribed RNAs), a modified nanopore direct RNA sequencing approach and analysis framework that enables the specific and sensitive capture of nascent Pol III transcribed RNAs. Applying DRAP3R to distinct cell types, we identify previously unconfirmed tRNA genes and other novel Pol III transcribed RNAs, thus expanding the known Pol III transcriptome. Critically, DRAP3R also enables discrimination between co- and post-transcriptional RNA modifications such as pseudouridine (Ψ) and N 6-methyladenosine (m6A) at single-nucleotide resolution across all examined transcript types and reveals differential Ψ installation patterns across tRNA isodecoders and other ncRNAs. Finally, applying DRAP3R to epithelial cells infected with Herpes Simplex Virus Type 1 reveals an extensive remodelling of both the Pol III transcriptome and epitranscriptome. Our findings thus establish DRAP3R as a powerful tool for systematically studying Pol III transcribed RNAs and their modifications in diverse cellular contexts.

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.

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

RNA modifications shape gene expression through a variety 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.

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.

A Comparative Evaluation of Computational Models for RNA modification detection using Nanopore sequencing with RNA004 Chemistry

Direct RNA sequencing from Oxford Nanopore Technologies (ONT) has become a valuable method for studying RNA modifications such as N6-methyladenosine (m6A), pseudouridine (ψ), and 5-methylcytosine (m5C). Recent advancements in the RNA004 chemistry substantially reduce sequencing errors compared to previous chemistries (e.g., RNA002), thereby promising enhanced accuracy for epitranscriptomic analysis. In this study, we benchmark the performance of two state-of-the-art RNA modification detection models capable of handling RNA004 data - ONT's Dorado and m6Anet - using two wild-type (WT) cell lines, HEK293T and HeLa, with respective ground truths from GLORI and eTAM-seq, and their paired in vitro transcribed (IVT) RNA as negative controls. We found that under default settings and considering sites with ≥10% modification ratio and ≥10X coverage, Dorado has higher recall (~0.92) than m6Anet (~0.51) for m6A detection. Among the overlapping methylated sites between ground truth and computational predictions, there are high correlations of site-specific m6A modification stoichiometry, with correlation coefficient of ~0.89 for Dorado-truth comparison and ~0.72 for m6Anet-truth comparison. However, combined assessment of WT and IVT datasets show that while the per-site false positive rate (FPR) can be lower (~8% for Dorado and ~33% for m6Anet), both computational tools can have high per-site false discovery rate (FDR) of m6A (~40% for Dorado and ~80% for m6Anet) due to the low prevalence of m6A in transcriptome, with a similar trend observed for pseudouridine (~95% FDR for Dorado). Additional motif analysis reveals that both Dorado and m6Anet exhibit high heterogeneity of false positive calls across sequence contexts, suggesting that sequence contexts help determine accuracy of specific modification calls. There is also a substantial overlap of false positive calls between the two IVT samples, suggesting a post-filtering strategy to improve modification calling by compiling a set of low-confidence sites with a probabilistic model from several IVT samples across diverse cells/tissues. Our analysis highlights key strengths and limitations of the current generation of m6A detection algorithms and offers insights into optimizing thresholds and interpretability. The IVT datasets generated by the RNA004 chemistry provides a publicly available benchmark resource for further development and refinement of computational methods.

Exploring the methyl-verse: Dynamic interplay of epigenome and m6A epitranscriptome

The orchestration of dynamic epigenetic and epitranscriptomic modifications is pivotal for the fine-tuning of gene expression. However, these modifications are traditionally examined independently. Recent compelling studies have disclosed an interesting communication and interplay between m6A RNA methylation (m6A epitranscriptome) and epigenetic modifications, enabling the formation of feedback circuits and cooperative networks. Intriguingly, the interaction between m6A and DNA methylation machinery, coupled with the crosstalk between m6A RNA and histone modifications shape the transcriptional profile and translational efficiency. Moreover, m6A modifications interact also with non-coding RNAs, modulating their stability, abundance, and regulatory functions. In the light of these findings, m6A imprinting acts as a versatile checkpoint, linking epigenetic and epitranscriptomic layers toward a multilayer and time-dependent control of gene expression and cellular homeostasis. The scope of the present review is to decipher the m6A-coordinated circuits with DNA imprinting, chromatin architecture, and non-coding RNAs networks in normal physiology and carcinogenesis. Ultimately, we summarize the development of innovative CRISPR-dCas engineering platforms fused with m6A catalytic components (m6A writers or erasers) to achieve transcript-specific editing of m6A epitranscriptomes that can create new insights in modern RNA therapeutics.

N6-methyladenosine in inflammatory diseases: Important actors and regulatory targets

N6-methyladenosine (m6A) is one of the most prevalent epigenetic modifications in eukaryotic cells. It regulates RNA function and stability by modifying RNA methylation through writers, erasers, and readers. As a result, m6A plays a critical role in a wide range of biological processes. Inflammation is a common and fundamental pathological process. Numerous studies have investigated the role of m6A modifications in inflammatory diseases. This review highlights the mechanisms by which m6A contributes to inflammation, focusing on pathogen-induced infectious diseases, autoimmune disorders, allergic conditions, and metabolic disorder-related inflammatory diseases.

Epigenetic regulation in female reproduction: the impact of m6A on maternal-fetal health

With the development of public health, female diseases have become the focus of current concern. The unique reproductive anatomy of women leads to the development of gynecological diseases gradually become an important part of the socio-economic burden. Epigenetics plays an irreplaceable role in gynecologic diseases. As an important mRNA modification, m6A is involved in the maturation of ovum cells and maternal-fetal microenvironment. At present, researchers have found that m6A is involved in the regulation of gestational diabetes and other reproductive system diseases, but the specific mechanism is not clear. In this manuscript, we summarize the components of m6A, the biological function of m6A, the progression of m6A in the maternal-fetal microenvironment and a variety of gynecological diseases as well as the progression of targeted m6A treatment-related diseases, providing a new perspective for clinical treatment-related diseases.

Decoding the m6A epitranscriptomic landscape for biotechnological applications using a direct RNA sequencing approach

Epitranscriptomic modifications, particularly N6-methyladenosine (m6A), are crucial regulators of gene expression, influencing processes such as RNA stability, splicing, and translation. Traditional computational methods for detecting m6A from Nanopore direct RNA sequencing (DRS) data are constrained by their reliance on experimentally validated labels, often resulting in the underestimation of modification sites. Here, we introduce pum6a, an innovative attention-based framework that integrates positive and unlabeled multi-instance learning (MIL) to address the challenges of incomplete labeling and missing read-level annotations. By combining electrical signal features with base alignment data and employing a weighted Noisy-OR probability mechanism, pum6a achieves enhanced sensitivity and accuracy in m6A detection, particularly in low-coverage loci. Pum6a outperforms existing methods in identifying m6A sites across various cell lines and species, without requiring extensive parameter tuning. We further apply pum6a to study the dynamic regulation of m6A demethylases in gastric cancer under hypoxia, revealing distinct roles for FTO and ALKBH5 in modulating m6A modifications and uncovering key insights into m6A -mediated transcript stability. Our findings highlight the potential of pum6a as a powerful tool for advancing the understanding of epitranscriptomic regulation in health and disease, paving the way for biotechnological and therapeutic applications.

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.

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.

Altered m6A RNA methylation profiles in depression implicate the dysregulation of discrete cellular functions in males and females

Adverse environmental stress represents a significant risk factor for major depressive disorder (MDD), often resulting in disrupted synaptic connectivity which is known to be partly regulated by epigenetic mechanisms. N6-methyladenosine (m6A), an epitranscriptomic modification, has emerged as a crucial regulator of activity-dependent gene regulation. In this study, we characterized m6A profiles in the ventromedial prefrontal cortex (vmPFC) of individuals with MDD. Using m6A sequencing, we identified a total of 30,279 high-confidence m6A peaks, exhibiting significant enrichment in genes related to neuronal and synaptic function. The m6A peaks between males and females with MDD that passed the significance threshold showed opposite m6A patterns, while the threshold-free m6A patterns were concordant. Distinct m6A profiles were found in MDD for each sex, with dysregulation associated with microtubule movement in males and neuronal projection in females. Our results suggest the potential roles of m6A as part of the dysregulated molecular network in MDD.

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.

m6A sites in the coding region trigger translation-dependent mRNA decay

N6-Methyladenosine (m6A) is the predominant internal RNA modification in eukaryotic messenger RNAs (mRNAs) and plays a crucial role in mRNA stability. Here, using human cells, we reveal that m6A sites in the coding sequence (CDS) trigger CDS-m6A decay (CMD), a pathway that is distinct from previously reported m6A-dependent degradation mechanisms. Importantly, CDS m6A sites act considerably faster and more efficiently than those in the 3' untranslated region, which to date have been considered the main effectors. Mechanistically, CMD depends on translation, whereby m6A deposition in the CDS triggers ribosome pausing and transcript destabilization. The subsequent decay involves the translocation of the CMD target transcripts to processing bodies (P-bodies) and recruitment of the m6A reader protein YT521-B homology domain family protein 2 (YTHDF2). Our findings highlight CMD as a previously unknown pathway, which is particularly important for controlling the expression of developmental regulators and retrogenes.

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.

Nanopore direct RNA sequencing reveals N6-methyladenosine and polyadenylation landscapes on long non-coding RNAs in Arabidopsis thaliana

BACKGROUND

Long non-coding RNAs (lncRNAs) play important roles in various biological processes, including stage development in plants. N6-methyladenosine (m6A) modification and polyadenylation are noteworthy regulatory processes that impact transcript functions by modulating their abundance. However, the specific landscapes of m6A modification and polyadenylation on lncRNAs remain largely unexplored. The advent of nanopore direct RNA sequencing (DRS) provides unprecedented opportunities for directly detecting m6A modifications and estimating polyadenine (poly[A]) tail lengths on individual RNA molecules.

RESULTS

Here we utilized nanopore DRS to identify lncRNAs and map the transcriptome-wide m6A modification and polyadenylation landscapes in the model plant Arabidopsis thaliana. Leveraging the Low-abundance Aware Full-length Isoform clusTEr (LAFITE) assembly pipeline, we identified 1149 novel lncRNAs in seventeen nanopore DRS datasets from the wild-type Columbia-0. Through the precise detection of 2381 m6A modification sites on lncRNAs at single-base resolution, we observed that lncRNAs exhibited lower methylation levels compared to protein-coding RNAs, and m6A modification facilitated lncRNA abundance. Additionally, we estimated the poly(A) tail lengths of individual lncRNAs and found that poly(A) tails contributed to lncRNA stability, while their effect was not length-dependent. Furthermore, by comparing lncRNA abundance between 2-week seedlings and 5-week floral buds, we revealed the dynamic expression patterns of lncRNAs during the transition from the vegetative stage to the reproductive stage. These observations provided insights into their potential roles in specific tissues or stages in Arabidopsis, including regulating stage development. Moreover, by integrating information on m6A modification, we unveiled a positive correlation between methylation variances and differential expressions of lncRNAs during stage development.

CONCLUSIONS

These findings highlighted the significance of epigenetic modification and post-transcriptional processing in shaping lncRNA expression and their functions during Arabidopsis stage development, contributing to the growing field of lncRNA research in plants.

CLINICAL TRIAL NUMBER

Not applicable.

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.

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.

Detecting m6A RNA modification from nanopore sequencing using a semisupervised learning framework

Direct nanopore-based RNA sequencing can be used to detect posttranscriptional base modifications, such as N6-methyladenosine (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 (IP)-based experimental data in two steps. First, we generate data with more diverse modification combinations through in silico cross-linking. Second, we use this data set to train an end-to-end neural network basecaller followed by fine-tuning on IP-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.

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.

N6-methyladenosine and its epitranscriptomic effects on hematopoietic stem cell regulation and leukemogenesis

N6-methyladenosine (m6A) RNA modification orchestrates cellular epitranscriptome through tuning the homeostasis of transcript stability, translation efficiency, and the transcript affinity toward RNA-binding proteins (RBPs). An aberrant m6A deposition on RNA can lead toward oncogenic expression profile (mRNA), impaired mitochondrial metabolism (mtRNA), and translational suppression (rRNA) of tumor suppressor genes. In addition, non-coding RNAs (ncRNAs), such as X-inactive specific transcript (XIST), miRNAs, and α-ketoglutarate-centric metabolic transcripts are also regulated by the m6A epitranscriptome. Notably, recent studies had uncovered a myriad of m6A-modified transcripts the center of hematopoietic stem cell (HSC) regulation, in which m6A modification act as a context dependent switch to the on and off of hematopoietic stem cell (HSC) maintenance, lineage commitment and terminal differentiation. In this review, we sequentially unfold the m6A mediated epithelial-to-hematopoietic transition in progenitor blood cell production, lymphocytic lineage expansion (T cells, B cells, NK cells, and non-NK ILCs), and the m6A crosstalk with the onco-metabolic prospects of leukemogenesis. Together, an encompassing body of evidence highlighted the emerging m6A significance in the regulation of HSC biology and leukemogenesis.

Characterizing transcripts of HIV-1 different substrains using direct RNA sequencing

Post-transcriptional processing and modification of viral RNA, including alternative splicing, polyadenylation, and methylation, play crucial roles in regulating viral gene expression, enhancing genomic stability, and increasing replication efficiency. These processes have significant implications for viral biology and antiviral therapies. In this study, using Oxford Nanopore Technology (ONT) direct RNA sequencing (DRS), we provided a comprehensive analysis of the transcriptome and epitranscriptome features of the HIV-1 B (NL4-3) subtype strain and, for the first time, characterized these features in the CRF01_AE (GX2005002) subtype strain. We identified 11 novel splicing sites among the 61 RNA isoforms in NL4-3 and defined the splicing sites for GX2005002 based on its 63 RNA isoforms. Furthermore, we identified 74 and 79 chemically modified sites in the transcripts of NL4-3 and GX2005002, respectively. Although differences in poly(A) tail length were observed between the two HIV-1 strains, no specific correlation was detected between poly(A) tail length and the number of modification sites. Additionally, three distinct N6-methyladenosine (m6A) modification sites were identified in both NL4-3 and GX2005002 transcripts. This study provides a detailed analysis of post-transcriptional processing modifications in HIV-1 and suggests promising avenues for future research that could potentially be applied as new therapeutic targets in HIV treatment.

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.

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.

Dynamic landscape of m6A modifications and related post-transcriptional events in muscle-invasive bladder cancer

BACKGROUND

Muscle-invasive bladder carcinoma (MIBC) is a serious and more advanced stage of bladder carcinoma. N6-Methyladenosine (m6A) is a dynamic and reversible modifications that primarily affects RNA stability and alternative splicing. The dysregulation of m6A in MIBC can be potential target for clinical interventions, but there have been limited studies on m6A modifications in MIBC and their associations with post-transcriptional regulatory processes.

METHODS

Paired tumor and adjacent-normal tissues were obtained from three patients with MIBC following radical cystectomy. The additional paired tissues for validation were obtained from patients underwent transurethral resection. Utilizing Nanopore direct-RNA sequencing, we characterized the m6A RNA methylation landscape in MIBC, with a focus on identifying post-transcriptional events potentially affected by changes in m6A sites. This included an examination of differential transcript usage, polyadenylation signal sites, and variations in poly(A) tail length, providing insights into the broader impact of m6A alterations on RNA processing in MIBC.

RESULTS

The prognostic-related m6A genes and m6A-risk model constructed by machine learning enables the stratification of high and low-risk patients with precision. A novel m6A modification site in the 3' untranslated region (3'UTR) of IGLL5 gene were identified, characterized by a lower m6A methylation ratio, elongated poly(A) tails, and a notable bias in transcript usage. Furthermore, we discovered two particular transcripts, VWA1-203 and CEBPB-201. VWA1-203 displayed diminished m6A methylation levels, a truncated 3'UTR, and an elongated poly(A) tail, whereas CEBPB-201 showed opposite trends, highlighting the complex interplay between m6A modifications and RNA processing. Source code was provided on GitHub ( https://github.com/lelelililele/Nanopore-m6A-analysis ).

CONCLUSIONS

The state-of-the-art Nanopore direct-RNA sequencing and machine learning techniques enables comprehensive identification of m6A modification and provided insights into the potential post-transcriptional regulation mechanisms on the development and progression in MIBC.

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.

Machine learning and related approaches in transcriptomics

Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.

Long-read RNA sequencing reveals allele-specific N6-methyladenosine modifications

Long-read sequencing technology enables highly accurate detection of allele-specific RNA expression, providing insights into the effects of genetic variation on splicing and RNA abundance. Furthermore, the ability to directly sequence RNA promises the detection of RNA modifications in tandem with ascertaining the allelic origin of each molecule. Here, we leverage these advantages to determine allele-biased patterns of N6-methyladenosine (m6A) modifications in native mRNA. We utilized human and mouse cells with known genetic variants to assign allelic origin of each mRNA molecule combined with a supervised machine learning model to detect read-level m6A modification ratios. Our analyses revealed the importance of sequences adjacent to the DRACH-motif in determining m6A deposition, in addition to allelic differences that directly alter the motif. Moreover, we discovered allele-specific m6A modification (ASM) events with no genetic variants in close proximity to the differentially modified nucleotide, demonstrating the unique advantage of using long reads and surpassing the capabilities of antibody-based short-read approaches. This technological advancement promises to advance our understanding of the role of genetics in determining mRNA modifications.

Role of m6A RNA Methylation in Ischemic Stroke

Ischemic stroke is a prominent contributor to global morbidity and mortality rates. The intricate and diverse mechanisms underlying ischemia-reperfusion injury remain poorly comprehended. RNA methylation, an emerging epigenetic modification, plays a crucial role in regulating numerous biological processes, including immunity, DNA damage response, tumorigenesis, metastasis, stem cell renewal, adipocyte differentiation, circadian rhythms, cellular development and differentiation, and cell division. Among the various RNA modifications, N6-methyladenosine (m6A) modification stands as the most prevalent in mammalian mRNA. Recent studies have demonstrated the crucial involvement of m6A modification in the pathophysiological progression of ischemic stroke. This review aims to elucidate the advancements in ischemic stroke-specific investigations pertaining to m6A modification, consolidate the underlying mechanisms implicated in the participation of m6A modification during the onset of ischemic stroke, and deliberate on the potential of m6A modification as a viable therapeutic target for ischemic stroke.

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.

Analysis of bacterial transcriptome and epitranscriptome using nanopore direct RNA sequencing

Bacterial gene expression is a complex process involving extensive regulatory mechanisms. Along with growing interests in this field, Nanopore Direct RNA Sequencing (DRS) provides a promising platform for rapid and comprehensive characterization of bacterial RNA biology. However, the DRS of bacterial RNA is currently deficient in the yield of mRNA-mapping reads and has yet to be exploited for transcriptome-wide RNA modification mapping. Here, we showed that pre-processing of bacterial total RNA (size selection followed by ribosomal RNA depletion and polyadenylation) guaranteed high throughputs of sequencing data and considerably increased the amount of mRNA reads. This way, complex transcriptome architectures were reconstructed for Escherichia coli and Staphylococcus aureus and extended the boundaries of 225 known E. coli operons and 89 defined S. aureus operons. Utilizing unmodified in vitro-transcribed (IVT) RNA libraries as a negative control, several Nanopore-based computational tools globally detected putative modification sites in the E. coli and S. aureus transcriptomes. Combined with Next-Generation Sequencing-based N6-methyladenosine (m6A) detection methods, 75 high-confidence m6A candidates were identified in the E. coli protein-coding transcripts, while none were detected in S. aureus. Altogether, we demonstrated the potential of Nanopore DRS in systematic and convenient transcriptome and epitranscriptome analysis.

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 .