DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA

Comprehensive analysis of m6A methylome alterations after azacytidine plus venetoclax treatment for acute myeloid leukemia by nanopore sequencing

N6 adenosine methylation (m6A), one of the most prevalent internal modifications on mammalian RNAs, regulates RNA transcription, stabilization, and splicing. Growing evidence has focused on the functional role of m6A regulators on acute myeloid leukemia (AML). However, the global m6A levels after azacytidine (AZA) plus venetoclax (VEN) treatment in AML patients remain unclear. In our present study, bone marrow (BM) sample pairs (including pre-treatment [AML] and post-treatment [complete remission (CR)] samples) were harvested from three AML patients who had achieved CR after AZA plus VEN treatment for Nanopore direct RNA sequencing. Notably, the amount of m6A sites and the m6A levels in CR BMs was significantly lower than those in the AML BMs. Such a significant reduction in the m6A levels was also detected in AZA-treated HL-60 cells. Thirteen genes with decreased m6A and expression levels were identified, among which three genes (HPRT1, SNRPC, and ANP32B) were closely related to the prognosis of AML. Finally, we speculated the mechanism via which m6A modifications affected the mRNA stability of these three genes. In conclusion, we illustrated for the first time the global landscape of m6A levels in AZA plus VEN treated AML (CR) patients and revealed that AZA had a significant demethylation effect at the RNA level in AML patients. In addition, we identified new biomarkers for AZA plus VEN-treated AML via Nanopore sequencing technology in RNA epigenetics.

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.

Current progress in strategies to profile transcriptomic m6A modifications

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

2'-O-methylation at internal sites on mRNA promotes mRNA stability

2'-O-methylation (Nm) is a prominent RNA modification well known in noncoding RNAs and more recently also found at many mRNA internal sites. However, their function and base-resolution stoichiometry remain underexplored. Here, we investigate the transcriptome-wide effect of internal site Nm on mRNA stability. Combining nanopore sequencing with our developed machine learning method, NanoNm, we identify thousands of Nm sites on mRNAs with a single-base resolution. We observe a positive effect of FBL-mediated Nm modification on mRNA stability and expression level. Elevated FBL expression in cancer cells is associated with increased expression levels for 2'-O-methylated mRNAs of cancer pathways, implying the role of FBL in post-transcriptional regulation. Lastly, we find that FBL-mediated 2'-O-methylation connects to widespread 3' UTR shortening, a mechanism that globally increases RNA stability. Collectively, we demonstrate that FBL-mediated Nm modifications at mRNA internal sites regulate gene expression by enhancing mRNA stability.

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.

PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants

N 6-methyladenosine (m6A), which is the mostly prevalent modification in eukaryotic mRNAs, is involved in gene expression regulation and many RNA metabolism processes. Accurate prediction of m6A modification is important for understanding its molecular mechanisms in different biological contexts. However, most existing models have limited range of application and are species-centric. Here we present PEA-m6A, a unified, modularized and parameterized framework that can streamline m6A-Seq data analysis for predicting m6A-modified regions in plant genomes. The PEA-m6A framework builds ensemble learning-based m6A prediction models with statistic-based and deep learning-driven features, achieving superior performance with an improvement of 6.7% to 23.3% in the area under precision-recall curve compared with state-of-the-art regional-scale m6A predictor WeakRM in 12 plant species. Especially, PEA-m6A is capable of leveraging knowledge from pretrained models via transfer learning, representing an innovation in that it can improve prediction accuracy of m6A modifications under small-sample training tasks. PEA-m6A also has a strong capability for generalization, making it suitable for application in within- and cross-species m6A prediction. Overall, this study presents a promising m6A prediction tool, PEA-m6A, with outstanding performance in terms of its accuracy, flexibility, transferability, and generalization ability. PEA-m6A has been packaged using Galaxy and Docker technologies for ease of use and is publicly available at https://github.com/cma2015/PEA-m6A.

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.

Identification of Novel Regulators of Leaf Senescence Using a Deep Learning Model

Deep learning has emerged as a powerful tool for investigating intricate biological processes in plants by harnessing the potential of large-scale data. Gene regulation is a complex process that transcription factors (TFs), cooperating with their target genes, participate in through various aspects of biological processes. Despite its significance, the study of gene regulation has primarily focused on a limited number of notable instances, leaving numerous aspects and interactions yet to be explored comprehensively. Here, we developed DEGRN (Deep learning on Expression for Gene Regulatory Network), an innovative deep learning model designed to decipher gene interactions by leveraging high-dimensional expression data obtained from bulk RNA-Seq and scRNA-Seq data in the model plant Arabidopsis. DEGRN exhibited a compared level of predictive power when applied to various datasets. Through the utilization of DEGRN, we successfully identified an extensive set of 3,053,363 high-quality interactions, encompassing 1430 TFs and 13,739 non-TF genes. Notably, DEGRN's predictive capabilities allowed us to uncover novel regulators involved in a range of complex biological processes, including development, metabolism, and stress responses. Using leaf senescence as an example, we revealed a complex network underpinning this process composed of diverse TF families, including bHLH, ERF, and MYB. We also identified a novel TF, named MAF5, whose expression showed a strong linear regression relation during the progression of senescence. The mutant maf5 showed early leaf decay compared to the wild type, indicating a potential role in the regulation of leaf senescence. This hypothesis was further supported by the expression patterns observed across four stages of leaf development, as well as transcriptomics analysis. Overall, the comprehensive coverage provided by DEGRN expands our understanding of gene regulatory networks and paves the way for further investigations into their functional implications.

Comprehensive analysis of m6 A methylome and transcriptome by Nanopore sequencing in clear cell renal carcinoma

N6 -methyladenosine (m6 A) is the most prevalent epigenetic modification on eukaryotic messenger RNAs. Recent studies have focused on elucidating the key role of m6 A modification patterns in tumor progression. However, the relationship between m6 A and transcriptional regulation remains elusive. Nanopore technology enables the quantification of m6 A levels at each genomic site. In this study, a pair of tumor tissues and adjacent normal tissues from clear cell renal cell carcinoma (ccRCC) surgical samples were collected for Nanopore direct RNA sequencing. We identified 9644 genes displaying anomalous m6 A modifications, with 5343 genes upregulated and 4301 genes downregulated. Among these, 5224 genes were regarded as dysregulated genes, encompassing abnormal regulation of both m6 A modification and RNA expression. Gene Set Enrichment Analysis revealed an enrichment of these genes in pathways related to renal system progress and fatty acid metabolic progress. Furthermore, the χ2 test demonstrated a significant association between the levels of m6 A in dysregulated genes and their transcriptional expression levels. Additionally, we identified four obesity-associated genes (FTO, LEPR, ADIPOR2, and NPY5R) among the dysregulated genes. Further analyses using public databases revealed that these four genes were all related to the prognosis and diagnosis of ccRCC. This study introduced the novel approach of employing conjoint analysis of m6 A modification and RNA expression based on Nanopore sequencing to explore potential disease-related genes. Our work demonstrates the feasibility of the application of Nanopore sequencing technology in RNA epigenetic regulation research and identifies new potential therapeutic targets for ccRCC.

Arabidopsis as a model for translational research

Arabidopsis thaliana is currently the most-studied plant species on earth, with an unprecedented number of genetic, genomic, and molecular resources having been generated in this plant model. In the era of translating foundational discoveries to crops and beyond, we aimed to highlight the utility and challenges of using Arabidopsis as a reference for applied plant biology research, agricultural innovation, biotechnology, and medicine. We hope that this review will inspire the next generation of plant biologists to continue leveraging Arabidopsis as a robust and convenient experimental system to address fundamental and applied questions in biology. We aim to encourage lab and field scientists alike to take advantage of the vast Arabidopsis datasets, annotations, germplasm, constructs, methods, molecular and computational tools in our pursuit to advance understanding of plant biology and help feed the world's growing population. We envision that the power of Arabidopsis-inspired biotechnologies and foundational discoveries will continue to fuel the development of resilient, high-yielding, nutritious plants for the betterment of plant and animal health and greater environmental sustainability.

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.

NanoCon: contrastive learning-based deep hybrid network for nanopore methylation detection

MOTIVATION

5-Methylcytosine (5mC), a fundamental element of DNA methylation in eukaryotes, plays a vital role in gene expression regulation, embryonic development, and other biological processes. Although several computational methods have been proposed for detecting the base modifications in DNA like 5mC sites from Nanopore sequencing data, they face challenges including sensitivity to noise, and ignoring the imbalanced distribution of methylation sites in real-world scenarios.

RESULTS

Here, we develop NanoCon, a deep hybrid network coupled with contrastive learning strategy to detect 5mC methylation sites from Nanopore reads. In particular, we adopted a contrastive learning module to alleviate the issues caused by imbalanced data distribution in nanopore sequencing, offering a more accurate and robust detection of 5mC sites. Evaluation results demonstrate that NanoCon outperforms existing methods, highlighting its potential as a valuable tool in genomic sequencing and methylation prediction. In addition, we also verified the effectiveness of our representation learning ability on two datasets by visualizing the dimension reduction of the features of methylation and nonmethylation sites from our NanoCon. Furthermore, cross-species and cross-5mC methylation motifs experiments indicated the robustness and the ability to perform transfer learning of our model. We hope this work can contribute to the community by providing a powerful and reliable solution for 5mC site detection in genomic studies.

AVAILABILITY AND IMPLEMENTATION

The project code is available at https://github.com/Challis-yin/NanoCon.

Full-length transcriptome reveals the pivotal role of ABA and ethylene in the cold stress response of Tetrastigma hemsleyanum

Tetrastigma hemsleyanum is a valuable herb widely used in Chinese traditional and modern medicine. Winter cold severely limits the artificial cultivation of this plant, but the physiological and molecular mechanisms upon exposure to cold stress in T. hemsleyanum are unclear. T. hemsleyanum plants with different geographical origins exhibit large differences in response to cold stress. In this research study, using T. hemsleyanum ecotypes that exhibit frost tolerance (FR) and frost sensitivity (FS), we analyzed the response of cottage seedlings to a simulated frost treatment; plant hormones were induced with both short (2 h) and long (9 h) frost treatments, which were used to construct the full-length transcriptome and obtained 76,750 transcripts with all transcripts mapped to 28,805 genes, and 27,215 genes, respectively, annotated to databases. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed enrichment in plant hormone signaling pathways. Further analysis shows that differently expressed genes (DEGs) concentrated on calcium signaling, ABA biosynthesis and signal transduction, and ethylene in response to cold stress. We also found that endogenous ABA and ethylene content were increased after cold treatment, and exogenous ABA and ethylene significantly improved cold tolerance in both ecotypes. Our results elucidated the pivotal role of ABA and ethylene in response to cold stress in T. hemsleyanum and identified key genes.

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.

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.

The plant cytosolic m6A RNA methylome stabilizes photosynthesis in the cold

The sessile lifestyle of plants requires an immediate response to environmental stressors that affect photosynthesis, growth, and crop yield. Here, we showed that three abiotic perturbations-heat, cold, and high light-triggered considerable changes in the expression signatures of 42 epitranscriptomic factors (writers, erasers, and readers) with putative chloroplast-associated functions that formed clusters of commonly expressed genes in Arabidopsis. The expression changes under all conditions were reversible upon deacclimation, identifying epitranscriptomic players as modulators in acclimation processes. Chloroplast dysfunctions, particularly those induced by the oxidative stress-inducing norflurazon in a largely GENOME UNCOUPLED-independent manner, triggered retrograde signals to remodel chloroplast-associated epitranscriptomic expression patterns. N6-methyladenosine (m6A) is known as the most prevalent RNA modification and impacts numerous developmental and physiological functions in living organisms. During cold treatment, expression of components of the primary nuclear m6A methyltransferase complex was upregulated, accompanied by a significant increase in cellular m6A mRNA marks. In the cold, the presence of FIP37, a core component of the writer complex, played an important role in positive regulation of thylakoid structure, photosynthetic functions, and accumulation of photosystem I, the Cytb6f complex, cyclic electron transport proteins, and Curvature Thylakoid1 but not that of photosystem II components and the chloroplast ATP synthase. Downregulation of FIP37 affected abundance, polysomal loading, and translation of cytosolic transcripts related to photosynthesis in the cold, suggesting m6A-dependent translational regulation of chloroplast functions. In summary, we identified multifaceted roles of the cellular m6A RNA methylome in coping with cold; these were predominantly associated with chloroplasts and served to stabilize photosynthesis.

Epitranscriptomic subtyping, visualization, and denoising by global motif visualization

Advances in sequencing technologies have empowered epitranscriptomic profiling at the single-base resolution. Putative RNA modification sites identified from a single high-throughput experiment may contain one type of modification deposited by different writers or different types of modifications, along with false positive results because of the challenge of distinguishing signals from noise. However, current tools are insufficient for subtyping, visualization, and denoising these signals. Here, we present iMVP, which is an interactive framework for epitranscriptomic analysis with a nonlinear dimension reduction technique and density-based partition. As exemplified by the analysis of mRNA m5C and ModTect variant data, we show that iMVP allows the identification of previously unknown RNA modification motifs and writers and the discovery of false positives that are undetectable by traditional methods. Using putative m6A/m6Am sites called from 8 profiling approaches, we illustrate that iMVP enables comprehensive comparison of different approaches and advances our understanding of the difference and pattern of true positives and artifacts in these methods. Finally, we demonstrate the ability of iMVP to analyze an extremely large human A-to-I editing dataset that was previously unmanageable. Our work provides a general framework for the visualization and interpretation of epitranscriptomic data.

Navigating the pitfalls of mapping DNA and RNA modifications

Chemical modifications to nucleic acids occur across the kingdoms of life and carry important regulatory information. Reliable high-resolution mapping of these modifications is the foundation of functional and mechanistic studies, and recent methodological advances based on next-generation sequencing and long-read sequencing platforms are critical to achieving this aim. However, mapping technologies may have limitations that sometimes lead to inconsistent results. Some of these limitations are technical in nature and specific to certain types of technology. Here, however, we focus on common (yet not always widely recognized) pitfalls that are shared among frequently used mapping technologies and discuss strategies to help technology developers and users mitigate their effects. Although the emphasis is primarily on DNA modifications, RNA modifications are also discussed.

The covalent nucleotide modifications within plant mRNAs: What we know, how we find them, and what should be done in the future

Although covalent nucleotide modifications were first identified on the bases of transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs), a number of these epitranscriptome marks have also been found to occur on the bases of messenger RNAs (mRNAs). These covalent mRNA features have been demonstrated to have various and significant effects on the processing (e.g. splicing, polyadenylation, etc.) and functionality (e.g. translation, transport, etc.) of these protein-encoding molecules. Here, we focus our attention on the current understanding of the collection of covalent nucleotide modifications known to occur on mRNAs in plants, how they are detected and studied, and the most outstanding future questions of each of these important epitranscriptomic regulatory signals.

Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning

The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. In recent years, the development of new high-throughput experimental and computational techniques has been a key driving force in discovering the properties of RNA modifications. Machine learning applications, such as for classification, clustering or de novo identification, have been critical in these advances. Nonetheless, various challenges remain before the full potential of machine learning for epitranscriptomics can be leveraged. In this review, we provide a comprehensive survey of machine learning methods to detect RNA modifications using diverse input data sources. We describe strategies to train and test machine learning methods and to encode and interpret features that are relevant for epitranscriptomics. Finally, we identify some of the current challenges and open questions about RNA modification analysis, including the ambiguity in predicting RNA modifications in transcript isoforms or in single nucleotides, or the lack of complete ground truth sets to test RNA modifications. We believe this review will inspire and benefit the rapidly developing field of epitranscriptomics in addressing the current limitations through the effective use of machine learning.

DeepEdit: single-molecule detection and phasing of A-to-I RNA editing events using nanopore direct RNA sequencing

Single-molecule detection and phasing of A-to-I RNA editing events remain an unresolved problem. Long-read and PCR-free nanopore native RNA sequencing offers a great opportunity for direct RNA editing detection. Here, we develop a neural network model, DeepEdit, that not only recognizes A-to-I editing events in single reads of Oxford Nanopore direct RNA sequencing, but also resolves the phasing of RNA editing events on transcripts. We illustrate the robustness of DeepEdit by applying it to Schizosaccharomyces pombe and Homo sapiens transcriptome data. We anticipate DeepEdit to be a powerful tool for the study of RNA editing from a new perspective.

Shaping the landscape of N6-methyladenosine RNA methylation in Arabidopsis

N 6-methyladenosine (m6A) modification on messenger RNAs (mRNAs) is deposited by evolutionarily conserved methyltransferases (writers). How individual m6A writers sculpt the overall landscape of the m6A methylome and the resulting biological impact in multicellular organisms remains unknown. Here, we systematically surveyed the quantitative m6A methylomes at single-nucleotide resolution and their corresponding transcriptomes in Arabidopsis (Arabidopsis thaliana) bearing respective impaired m6A writers. The m6A sites associated with the five Arabidopsis writers were located mostly within 3' untranslated regions with peaks at around 100 bp downstream of stop codons. m6A predominantly promoted the usage of distal poly(A) sites but had little effect on RNA splicing. Notably, impaired m6A writers resulted in hypomethylation and downregulation of transcripts encoding ribosomal proteins, indicating a possible correlation between m6A and protein translation. Besides the common effects on mRNA metabolism and biological functions uniquely exerted by different Arabidopsis m6A writers compared with their counterparts in human cell lines, our analyses also revealed the functional specificity of individual Arabidopsis m6A writers in plant development and response to stresses. Our findings thus reveal insights into the biological roles of various Arabidopsis m6A writers and their cognate counterparts in other multicellular m6A methyltransferase complexes.

Plant synthetic epigenomic engineering for crop improvement

Efforts have been directed to redesign crops with increased yield, stress adaptability, and nutritional value through synthetic biology-the application of engineering principles to biology. A recent expansion in our understanding of how epigenetic mechanisms regulate plant development and stress responses has unveiled a new set of resources that can be harnessed to develop improved crops, thus heralding the promise of "synthetic epigenetics." In this review, we summarize the latest advances in epigenetic regulation and highlight how innovative sequencing techniques, epigenetic editing, and deep learning-driven predictive tools can rapidly extend these insights. We also proposed the future directions of synthetic epigenetics for the development of engineered smart crops that can actively monitor and respond to internal and external cues throughout their life cycles.

Detecting RNA modification using direct RNA sequencing: A systematic review

Post-transcriptional RNA modifications are involved in a range of important cellular processes, including the regulation of gene expression and fine-tuning of the functions of RNA molecules. To decipher the context-specific functions of these post-transcriptional modifications, it is crucial to accurately determine their transcriptomic locations and modification levels under a given cellular condition. With the newly emerged sequencing technology, especially nanopore direct RNA sequencing, different RNA modifications can be detected simultaneously with a single molecular level resolution. Here we provide a systematic review of 15 published RNA modification prediction tools based on direct RNA sequencing data, including their computational models, input-output formats, supported modification types, and reported performances. Finally, we also discussed the potential challenges and future improvements of nanopore sequencing-based methods for RNA modification detection.

Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation

As the most pervasive epigenetic mark present on mRNA and lncRNA, N⁶-methyladenosine (m⁶A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification have achieved great success in recent years; nevertheless, their potential remains underexploited. One reason for this is that existing models usually consider only the sequence of transcripts, ignoring the various regions (or geography) of transcripts such as 3′UTR and intron, where the epigenetic mark forms and functions. Here, we developed three simple yet powerful encoding schemes for transcripts to capture the submolecular geographic information of RNA, which is largely independent from sequences. We show that m⁶A prediction models based on geographic information alone can achieve comparable performances to classic sequence-based methods. Importantly, geographic information substantially enhances the accuracy of sequence-based models, enables isoform- and tissue-specific prediction of m⁶A sites, and improves m⁶A signal detection from direct RNA sequencing data. The geographic encoding schemes we developed have exhibited strong interpretability, and are applicable to not only m⁶A but also N¹-methyladenosine (m¹A), and can serve as a general and effective complement to the widely used sequence encoding schemes in deep learning applications concerning RNA transcripts.

Advances in detecting N6-methyladenosine modification in circRNAs

Circular RNAs (circRNAs) are a class of noncoding RNAs with covalently single-stranded closed loop structures derived from back-splicing event of linear precursor mRNAs (pre-mRNAs). N6-methyladenosine (m⁶A), the most abundant epigenetic modification in eukaryotic RNAs, has been shown to play a crucial role in regulating the fate and biological function of circRNAs, and thus affecting various physiological and pathological processes. Accurate identification of m⁶A modification in circRNAs is an essential step to fully elucidate the crosstalk between m⁶A and circRNAs. In recent years, the rapid development of high-throughput sequencing technology and bioinformatic methodology has propelled the establishment of a multitude of approaches to detect circRNAs and m⁶A modification, including in vitro-based and in silico methods. Based on this, the research community has started on a new journey to develop methods for identification of m⁶A modification in circRNAs. In this review, we provide a comprehensive review and evaluation of the existing methods responsible for detecting circRNAs, m⁶A modification, and especially, m⁶A modification in circRNAs, which mainly focused on those developed based on high-throughput technologies and methodology of bioinformatics. This handy reference can help researchers figure out towards which direction this field will go.

Exploring epitranscriptomics for crop improvement and environmental stress tolerance

Climate change and stressful environmental conditions severely hamper crop growth, development and yield. Plants respond to environmental perturbations, through their plasticity provided by key-genes, governed at post-/transcriptional levels. Gene-regulation in plants is a multilevel process controlled by diverse cellular entities that includes transcription factors (TF), epigenetic regulators and non-coding RNAs beside others. There are successful studies confirming the role of epigenetic modifications (DNA-methylation/histone-modifications) in gene expression. Recent years have witnessed emergence of a highly specialized field the "Epitranscriptomics". Epitranscriptomics deals with investigating post-transcriptional RNA chemical-modifications present across the life forms that change structural, functional and biological characters of RNA. However, deeper insights on of epitranscriptomic modifications, with >140 types known so far, are to be understood fully. Researchers have identified epitranscriptome marks (writers, erasers and readers) and mapped the site-specific RNA modifications (m6A, m⁵C, 3' uridylation, etc.) responsible for fine-tuning gene expression in plants. Simultaneous advancement in sequencing platforms, upgraded bioinformatic tools and pipelines along with conventional labelled techniques have further given a statistical picture of these epitranscriptomic modifications leading to their potential applicability in crop improvement and developing climate-smart crops. We present herein the insights on epitranscriptomic machinery in plants and how epitranscriptome and epitranscriptomic modifications underlying plant growth, development and environmental stress responses/adaptations. Third-generation sequencing technology, advanced bioinformatics tools and databases being used in plant epitranscriptomics are also discussed. Emphasis is given on potential exploration of epitranscriptome engineering for crop-improvement and developing environmental stress tolerant plants covering current status, challenges and future directions.

Nanopore-Based Detection of Viral RNA Modifications

The chemical modification of ribonucleotides plays an integral role in the biology of diverse viruses and their eukaryotic host cells. Mapping the precise identity, location, and abundance of modified ribonucleotides remains a key goal of many studies aimed at characterizing the function and importance of a given modification. While mapping of specific RNA modifications through short-read sequencing approaches has powered a wealth of new discoveries in the past decade, this approach is limited by inherent biases and an absence of linkage information. Moreover, in viral contexts, the challenge is increased due to the compact nature of viral genomes giving rise to many overlapping transcript isoforms that cannot be adequately resolved using short-read sequencing approaches. The recent emergence of nanopore sequencing, specifically the ability to directly sequence native RNAs from virus-infected host cells, provides not just a new methodology for mapping modified ribonucleotides but also a new conceptual framework for what can be derived from the resulting sequencing data. In this minireview, we provide a detailed overview of how nanopore direct RNA sequencing works, the computational approaches applied to identify modified ribonucleotides, and the core concepts underlying both. We further highlight recent studies that have applied this approach to interrogating viral biology and finish by discussing key experimental considerations and how we predict that these methodologies will continue to evolve.