Quantitative profiling of pseudouridylation dynamics in native RNAs with nanopore sequencing

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 .

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

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

Exploring pseudouridylation: dysregulation in disease and therapeutic potential

Pseudouridine (Ψ), the most abundant RNA modification, plays a role in pre-mRNA splicing, RNA stability, protein translation efficiency, and cellular responses to environmental stress. Dysregulation of pseudouridylation is linked to human diseases. This review explores recent insights into the role of RNA pseudouridylation alterations in human disorders and the therapeutic potential of Ψ. We discuss the impact of the reduction of Ψ levels in ribosomal, messenger, and transfer RNA in RNA processing, protein translation, and consequently its role in neurodevelopmental diseases and cancer. Furthermore, we review the success of N1-methyl-Ψ messenger RNA vaccines against COVID-19 and the development of RNA-guided pseudouridylation enzymes for treating genetic diseases caused by premature stop codons.

Quantitative mapping of the mammalian epitranscriptome

The epitranscriptome encompasses a diverse array of dynamic and reversible RNA modifications, affecting both coding and noncoding RNAs. Over 170 types of RNA chemical modifications have been identified, underscoring the need for innovative detection methods to deepen our understanding of RNA modification roles and mechanisms. In particular, the base resolution and quantitative information on RNA modifications are critical for understanding the regulation and functions of RNA modifications. Based on detection throughput and principles, existing quantitative RNA modification detection methods can be categorized into two groups, including next-generation sequencing and nanopore direct RNA sequencing. In this review, we focus on methodologies for elucidating the base resolution and stoichiometric information of RNA modifications. In addition, we further discuss the challenges and the potential prospects of the quantitative RNA modification detection methods.

Comparative analysis of 43 distinct RNA modifications by nanopore tRNA sequencing

Transfer RNAs are the fundamental adapter molecules of protein synthesis and the most abundant and heterogeneous class of noncoding RNA molecules in cells. The study of tRNA repertoires remains challenging, complicated by the presence of dozens of post transcriptional modifications. Nanopore sequencing is an emerging technology with promise for both tRNA sequencing and the detection of RNA modifications; however, such studies have been limited by the throughput and accuracy of direct RNA sequencing methods. Moreover, detection of the complete set of tRNA modifications by nanopore sequencing remains challenging. Here we show that recent updates to nanopore direct RNA sequencing chemistry (RNA004) combined with our own optimizations to tRNA sequencing protocols and analysis workflows enable high throughput coverage of tRNA molecules and characterization of nanopore signals produced by 43 distinct RNA modifications. We share best practices and protocols for nanopore sequencing of tRNA and further report successful detection of low abundance mitochondrial and viral tRNAs, providing proof of concept for use of nanopore sequencing to study tRNA populations in the context of infection and organelle biology. This work provides a roadmap to guide future efforts towards de novo detection of RNA modifications across multiple organisms using nanopore sequencing.

Nanopore guided annotation of transcriptome architectures

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

IMPORTANCE

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

The molecular basis of tRNA selectivity by human pseudouridine synthase 3

Pseudouridine (Ψ), the isomer of uridine, is ubiquitously found in RNA, including tRNA, rRNA, and mRNA. Human pseudouridine synthase 3 (PUS3) catalyzes pseudouridylation of position 38/39 in tRNAs. However, the molecular mechanisms by which it recognizes its RNA targets and achieves site specificity remain elusive. Here, we determine single-particle cryo-EM structures of PUS3 in its apo form and bound to three tRNAs, showing how the symmetric PUS3 homodimer recognizes tRNAs and positions the target uridine next to its active site. Structure-guided and patient-derived mutations validate our structural findings in complementary biochemical assays. Furthermore, we deleted PUS1 and PUS3 in HEK293 cells and mapped transcriptome-wide Ψ sites by Pseudo-seq. Although PUS1-dependent sites were detectable in tRNA and mRNA, we found no evidence that human PUS3 modifies mRNAs. Our work provides the molecular basis for PUS3-mediated tRNA modification in humans and explains how its tRNA modification activity is linked to intellectual disabilities.

Decoding the 'Fifth' Nucleotide: Impact of RNA Pseudouridylation on Gene Expression and Human Disease

Cellular RNAs, both coding and noncoding are adorned by > 100 chemical modifications, which impact various facets of RNA metabolism and gene expression. Very often derailments in these modifications are associated with a plethora of human diseases. One of the most oldest of such modification is pseudouridylation of RNA, wherein uridine is converted to a pseudouridine (Ψ) via an isomerization reaction. When discovered, Ψ was referred to as the 'fifth nucleotide' and is chemically distinct from uridine and any other known nucleotides. Experimental evidence accumulated over the past six decades, coupled together with the recent technological advances in pseudouridine detection, suggest the presence of pseudouridine on messenger RNA, as well as on diverse classes of non-coding RNA in human cells. RNA pseudouridylation has widespread effects on cellular RNA metabolism and gene expression, primarily via stabilizing RNA conformations and destabilizing interactions with RNA-binding proteins. However, much remains to be understood about the RNA targets and their recognition by the pseudouridylation machinery, the regulation of RNA pseudouridylation, and its crosstalk with other RNA modifications and gene regulatory processes. In this review, we summarize the mechanism and molecular machinery involved in depositing pseudouridine on target RNAs, molecular functions of RNA pseudouridylation, tools to detect pseudouridines, the role of RNA pseudouridylation in human diseases like cancer, and finally, the potential of pseudouridine to serve as a biomarker and as an attractive therapeutic target.

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.

Knockout of the longevity gene Klotho perturbs aging and Alzheimer's disease-linked brain microRNAs and tRNA fragments

Overexpression of the longevity gene Klotho prolongs lifespan, while its knockout shortens lifespan and impairs cognition via perturbation of myelination and synapse formation. However, comprehensive analysis of Klotho knockout effects on mammalian brain transcriptomics is lacking. Here, we report that Klotho knockout alters the levels of aging- and cognition related mRNAs, long non-coding RNAs, microRNAs and tRNA fragments. These include altered neuronal and glial regulators in murine models of aging and Alzheimer's disease and in human Alzheimer's disease post-mortem brains. We further demonstrate interaction of the knockout-elevated tRNA fragments with the spliceosome, possibly affecting RNA processing. Last, we present cell type-specific short RNA-seq datasets from FACS-sorted neurons and microglia of live human brain tissue demonstrating in-depth cell-type association of Klotho knockout-perturbed microRNAs. Together, our findings reveal multiple RNA transcripts in both neurons and glia from murine and human brain that are perturbed in Klotho deficiency and are aging- and neurodegeneration-related.

Long-read sequencing for brain tumors

Brain tumors and genomics have a long-standing history given that glioblastoma was the first cancer studied by the cancer genome atlas. The numerous and continuous advances through the decades in sequencing technologies have aided in the advanced molecular characterization of brain tumors for diagnosis, prognosis, and treatment. Since the implementation of molecular biomarkers by the WHO CNS in 2016, the genomics of brain tumors has been integrated into diagnostic criteria. Long-read sequencing, also known as third generation sequencing, is an emerging technique that allows for the sequencing of longer DNA segments leading to improved detection of structural variants and epigenetics. These capabilities are opening a way for better characterization of brain tumors. Here, we present a comprehensive summary of the state of the art of third-generation sequencing in the application for brain tumor diagnosis, prognosis, and treatment. We discuss the advantages and potential new implementations of long-read sequencing into clinical paradigms for neuro-oncology patients.

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.

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

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

Detection and Quantification of 5moU RNA Modification from Direct RNA Sequencing Data

Background

Chemically modified therapeutic mRNAs have gained momentum recently. In addition to commonly used modifications (e.g., pseudouridine), 5moU is considered a promising substitution for uridine in therapeutic mRNAs. Accurate identification of 5-methoxyuridine (5moU) would be crucial for the study and quality control of relevant in vitro-transcribed (IVT) mRNAs. However, current methods exhibit deficiencies in providing quantitative methodologies for detecting such modification. Utilizing the capabilities of Oxford nanopore direct RNA sequencing, in this study, we present NanoML-5moU, a machine-learning framework designed specifically for the read-level detection and quantification of 5moU modification for IVT data.

Materials and Methods

Nanopore direct RNA sequencing data from both 5moU-modified and unmodified control samples were collected. Subsequently, a comprehensive analysis and modeling of signal event characteristics (mean, median current intensities, standard deviations, and dwell times) were performed. Furthermore, classical machine learning algorithms, notably the Support Vector Machine (SVM), Random Forest (RF), and XGBoost were employed to discern 5moU modifications within NNUNN (where N represents A, C, U, or G) 5-mers.

Results

Notably, the signal event attributes pertaining to each constituent base of the NNUNN 5-mers, in conjunction with the utilization of the XGBoost algorithm, exhibited remarkable performance levels (with a maximum AUROC of 0.9567 in the "AGTTC" reference 5-mer dataset and a minimum AUROC of 0.8113 in the "TGTGC" reference 5-mer dataset). This accomplishment markedly exceeded the efficacy of the prevailing background error comparison model (ELIGOs AUC 0.751 for site-level prediction). The model's performance was further validated through a series of curated datasets, which featured customized modification ratios designed to emulate broader data patterns, demonstrating its general applicability in quality control of IVT mRNA vaccines. The NanoML-5moU framework is publicly available on GitHub (https://github.com/JiayiLi21/NanoML-5moU).

Conclusion

NanoML-5moU enables accurate read-level profiling of 5moU modification with nanopore direct RNA-sequencing, which is a powerful tool specialized in unveiling signal patterns in in vitro-transcribed (IVT) mRNAs.

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

Pseudouridine (psi) is one of the most abundant human mRNA modifications generated from the isomerization of uridine via psi synthases, including TRUB1 and PUS7. Nanopore direct RNA sequencing combined with our recent 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 changes in positional psi occupancies across cell types, which can inform our understanding of the impact 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, among a list of highly conserved sites across cell types, most are TRUB1 substrates and fall within the coding sequence. We find that these conserved psi positions correspond to higher levels of protein expression than expected, suggesting translation regulation. Interestingly, we identify cell type-specific sites of psi modification in ubiquitously expressed genes. We validate these sites by ruling out single-nucleotide variants, analyzing current traces, and performing enzymatic knockdowns of psi synthases. Finally, we characterize sites with multiple psi modifications on the same transcript (hypermodification type II) and found that these can be conserved or cell type specific. Among these, we discovered examples of multiple psi modifications within the same k-mer for the first time and analyzed the effect on current distribution. Our data support the hypothesis that motif sequence and the presence of psi synthase are insufficient to drive modifications, that psi modifications contribute to regulating translation and that cell type-specific trans-acting factors play a major role in driving pseudouridylation.

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

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

Sequencing accuracy and systematic errors of nanopore direct RNA sequencing

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

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.

Selective Capture and Manipulation of DNA through Double Charged Nanopores

In the past few decades, nanometer-scale pores have been employed as powerful tools for sensing biological molecules. Owing to its unique structure and properties, solid-state nanopores provide interesting opportunities for the development of DNA sequencing technology. Controlling DNA translocation in nanopores is an important means of improving the accuracy of sequencing. Here we present a proof of principle study of accelerating DNA captured across targeted graphene nanopores using surface charge density and find the intrinsic mechanism of the combination of electroosmotic flow induced by charges of nanopore and electrostatic attraction/repulsion between the nanopore and ssDNA. The theoretical study performed here provides a new means for controlling DNA transport dynamics and makes better and cheaper application of graphene in molecular sequencing.

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

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

Detection of ribonucleotides embedded in DNA by Nanopore sequencing

Ribonucleotides represent the most common non-canonical nucleotides found in eukaryotic genomes. The sources of chromosome-embedded ribonucleotides and the mechanisms by which unrepaired rNMPs trigger genome instability and human pathologies are not fully understood. The available sequencing technologies only allow to indirectly deduce the genomic location of rNMPs. Oxford Nanopore Technologies (ONT) may overcome such limitation, revealing the sites of rNMPs incorporation in genomic DNA directly from raw sequencing signals. We synthesized two types of DNA molecules containing rNMPs at known or random positions and we developed data analysis pipelines for DNA-embedded ribonucleotides detection by ONT. We report that ONT can identify all four ribonucleotides incorporated in DNA by capturing rNMPs-specific alterations in nucleotide alignment features, current intensity, and dwell time. We propose that ONT may be successfully employed to directly map rNMPs in genomic DNA and we suggest a strategy to build an ad hoc basecaller to analyse native genomes.

New directions for Ψ and m1A decoding in mRNA: deciphering the stoichiometry and function

Over the past decade, advancements in epitranscriptomics have significantly enhanced our understanding of mRNA metabolism and its role in human development and diseases. This period has witnessed breakthroughs in sequencing technologies and the identification of key proteins involved in RNA modification processes. Alongside the well-studied m6A, Ψ and m1A have emerged as key epitranscriptomic markers. Initially identified through transcriptome-wide profiling, these modifications are now recognized for their broad impact on RNA metabolism and gene expression. In this Perspective, we focus on the detections and functions of Ψ and m1A modifications in mRNA and discuss previous discrepancies and future challenges. We summarize recent advances and highlight the latest sequencing technologies for stoichiometric detection and their mechanistic investigations for functional unveiling in mRNA as the new research directions.

Recent developments, opportunities, and challenges in the study of mRNA pseudouridylation

Pseudouridine is an abundant mRNA modification found in diverse organisms ranging from bacteria and viruses to multicellular plants and humans. New developments in pseudouridine profiling provide quantitative tools to map mRNA pseudouridylation sites. Sparse biochemical studies establish the potential for mRNA pseudouridylation to affect most stages of the mRNA life cycle from birth to death. This recent progress sets the stage for deeper investigations into the molecular and cellular functions of specific mRNA pseudouridines, including in disease.

Comprehensive map of ribosomal 2'-O-methylation and C/D box snoRNAs in Drosophila melanogaster

During their maturation, ribosomal RNAs (rRNAs) are decorated by hundreds of chemical modifications that participate in proper folding of rRNA secondary structures and therefore in ribosomal function. Along with pseudouridine, methylation of the 2'-hydroxyl ribose moiety (Nm) is the most abundant modification of rRNAs. The majority of Nm modifications in eukaryotes are placed by Fibrillarin, a conserved methyltransferase belonging to a ribonucleoprotein complex guided by C/D box small nucleolar RNAs (C/D box snoRNAs). These modifications impact interactions between rRNAs, tRNAs and mRNAs, and some are known to fine tune translation rates and efficiency. In this study, we built the first comprehensive map of Nm sites in Drosophila melanogaster rRNAs using two complementary approaches (RiboMethSeq and Nanopore direct RNA sequencing) and identified their corresponding C/D box snoRNAs by whole-transcriptome sequencing. We de novo identified 61 Nm sites, from which 55 are supported by both sequencing methods, we validated the expression of 106 C/D box snoRNAs and we predicted new or alternative rRNA Nm targets for 31 of them. Comparison of methylation level upon different stresses show only slight but specific variations, indicating that this modification is relatively stable in D. melanogaster. This study paves the way to investigate the impact of snoRNA-mediated 2'-O-methylation on translation and proteostasis in a whole organism.

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

Chemical modifications in mRNAs such as pseudouridine (psi) can regulate gene expression, although our understanding of the functional impact of individual psi modifications, especially in neuronal cells, is limited. We apply nanopore direct RNA sequencing to investigate psi dynamics under cellular perturbations in SH-SY5Y cells. We assign sites to psi synthases using siRNA-based knockdown. A steady-state enzyme-substrate model reveals a strong correlation between psi synthase and mRNA substrate levels and psi modification frequencies. Next, we performed either differentiation or lead-exposure to SH-SY5Y cells and found that, upon lead exposure, not differentiation, the modification frequency is less dependent on enzyme levels suggesting translational control. Finally, we compared the plasticity of psi sites across cellular states and found that plastic sites can be condition-dependent or condition-independent; several of these sites fall within transcripts encoding proteins involved in neuronal processes. Our psi analysis and validation enable investigations into the dynamics and plasticity of RNA modifications.

Nanopore Guided Annotation of Transcriptome Architectures

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

Chemically Modified Platforms for Better RNA Therapeutics

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

Internal RNA 2'-O-methylation on the HIV-1 genome impairs reverse transcription

Viral RNA genomes are modified by epitranscriptomic marks, including 2'-O-methylation that is added by cellular or viral methyltransferases. 2'-O-Methylation modulates RNA structure, function and discrimination between self- and non-self-RNA by innate immune sensors such as RIG-I-like receptors. This is illustrated by human immunodeficiency virus type-1 (HIV-1) that decorates its RNA genome through hijacking the cellular FTSJ3 2'-O-methyltransferase, thereby limiting immune sensing and interferon production. However, the impact of such an RNA modification during viral genome replication is poorly understood. Here we show by performing endogenous reverse transcription on methylated or hypomethylated HIV-1 particles, that 2'-O-methylation negatively affects HIV-1 reverse transcriptase activity. Biochemical assays confirm that RNA 2'-O-methylation impedes reverse transcriptase activity, especially at low dNTP concentrations reflecting those in quiescent cells, by reducing nucleotide incorporation efficiency and impairing translocation. Mutagenesis highlights K70 as a critical amino acid for the reverse transcriptase to bypass 2'-O-methylation. Hence, the observed antiviral effect due to viral RNA 2'-O-methylation antagonizes the FTSJ3-mediated proviral effects, suggesting the fine-tuning of RNA methylation during viral replication.

Advances in the Structural and Functional Understanding of m1A RNA Modification

ConspectusRNA modification is a co- or post-transcriptional process by which specific nucleotides are chemically altered by enzymes after their initial incorporation into the RNA chain, expanding the chemical and functional diversity of RNAs. Our understanding of RNA modifications has changed dramatically in recent years. In the past decade, RNA methyltransferases (MTases) have been highlighted in numerous clinical studies and disease models, modifications have been found to be dynamically regulated by demodification enzymes, and significant technological advances have been made in the fields of RNA sequencing, mass spectrometry, and structural biology. Among RNAs, transfer RNAs (tRNAs) exhibit the greatest diversity and density of post-transcriptional modifications, which allow for potential cross-talks and regulation during their incorporation. N1-methyladenosine (m1A) modification is found in tRNAs at positions 9, 14, 16, 22, 57, and 58, depending on the tRNA and organism.Our laboratory has used and developed a large panel of tools to decipher the different mechanisms used by m1A tRNA MTases to recognize and methylate tRNA. We have solved the structures of TrmI from Thermus thermophilus (m1A58), TrmK from Bacillus subtilis (m1A22), and human TRMT10C (m1A9). These MTases do not share the same structure or organization to recognize tRNAs, but they all modify an adenosine, forming a non-Watson-Crick (WC) interaction. For TrmK, nuclear magnetic resonance (NMR) chemical shift mapping of the binding interface between TrmK and tRNASer was invaluable to build a TrmK/tRNA model, where both domains of TrmK participate in the binding of a full-length L-shaped tRNA and where the non-WC purine 13-A22 base pair positions the A22 N1-atom close to the methyl of the S-adenosyl-l-methionine (SAM) TrmK cofactor. For TRMT10C, cryoEM structures showed the MTase poised to N1-methylate A9 or G9 in tRNA and revealed different steps of tRNA maturation, where TRMT10C acts as a tRNA binding platform for sequential docking of each maturation enzyme. This work confers a role for TRMT10C in tRNA quality control and provides a framework to understand the link between mitochondrial tRNA maturation dysfunction and diseases.Methods to directly detect the incorporation of modifications during tRNA biosynthesis are rare and do not provide easy access to the temporality of their introduction. To this end, we have introduced time-resolved NMR to monitor tRNA maturation in the cellular environment. Combined with genetic and biochemical approaches involving the synthesis of specifically modified tRNAs, our methodology revealed that some modifications are incorporated in a defined sequential order, controlled by cross-talks between modification events. In particular, a strong modification circuit, namely Ψ55 → m5U54 → m1A58, controls the modification process in the T-arm of yeast elongator tRNAs. Conversely, we showed that m1A58 is efficiently introduced on unmodified initiator tRNAiMet without the need of any prior modification. Two distinct pathways are therefore followed for m1A58 incorporation in elongator and initiator tRNAs.We are undoubtedly entering an exciting period for the elucidation of the functions of RNA modifications and the intricate mechanisms by which modification enzymes identify and alter their RNA substrates. These are promising directions for the field of epitranscriptomics.

General Principles and Limitations for Detection of RNA Modifications by Sequencing

Among the many analytical methods applied to RNA modifications, a particularly pronounced surge has occurred in the past decade in the field of modification mapping. The occurrence of modifications such as m6A in mRNA, albeit known since the 1980s, became amenable to transcriptome-wide analyses through the advent of next-generation sequencing techniques in a rather sudden manner. The term "mapping" here refers to detection of RNA modifications in a sequence context, which has a dramatic impact on the interpretation of biological functions. As a consequence, an impressive number of mapping techniques were published, most in the perspective of what now has become known as "epitranscriptomics". While more and more different modifications were reported to occur in mRNA, conflicting reports and controversial results pointed to a number of technical and theoretical problems rooted in analytics, statistics, and reagents. Rather than finding the proverbial needle in a haystack, the tasks were to determine how many needles of what color in what size of a haystack one was looking at.As the authors of this Account, we think it important to outline the limitations of different mapping methods since many life scientists freshly entering the field confuse the accuracy and precision of modification mapping with that of normal sequencing, which already features numerous caveats by itself. Indeed, we propose here to qualify a specific mapping method by the size of the transcriptome that can be meaningfully analyzed with it.We here focus on high throughput sequencing by Illumina technology, referred to as RNA-Seq. We noted with interest the development of methods for modification detection by other high throughput sequencing platforms that act directly on RNA, e.g., PacBio SMRT and nanopore sequencing, but those are not considered here.In contrast to approaches relying on direct RNA sequencing, current Illumina RNA-Seq protocols require prior conversion of RNA into DNA. This conversion relies on reverse transcription (RT) to create cDNA; thereafter, the cDNA undergoes a sequencing-by-synthesis type of analysis. Thus, a particular behavior of RNA modified nucleotides during the RT-step is a prerequisite for their detection (and quantification) by deep sequencing, and RT properties have great influence on the detection efficiency and reliability. Moreover, the RT-step requires annealing of a synthetic primer, a prerequisite with a crucial impact on library preparation. Thus, all RNA-Seq protocols must feature steps for the introduction of primers, primer landing sites, or adapters on both the RNA 3'- and 5'-ends.

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.

Transcriptome-wide quantitative profiling of PUS7-dependent pseudouridylation by nanopore direct long read RNA sequencing

Pseudouridylation is a prevalent post-transcriptional RNA modification that impacts many aspects of RNA biology and function. The conversion of uridine to pseudouridine (Ψ) is catalyzed by the family of pseudouridine synthases (PUSs). Development of robust methods to determine PUS-dependent regulation of Ψ location and stoichiometry in low abundant mRNA is essential for biological and functional understanding of pseudouridylation. Here, we present a framework, NanoPsiPy, for identifying Ψ sites and quantify their levels in poly-A RNA at single-nucleotide resolution using direct RNA long-read Nanopore sequencing, based on the observation that Ψ can cause characteristic U-to-C basecalling errors in Nanopore direct RNA sequencing data. Our method was able to detect low and high stoichiometric Ψ sites in human mRNA. We validated our method by transcriptome-wide quantitative profiling of PUS7-dependent Ψ sites in poly-A RNA from a MYCN -amplified neuroblastoma cell line. We identified 8,625 PUS7-dependent Ψ sites in 1,246 mRNAs that encode proteins involved primarily in ribosome biogenesis, translation, and mitochondrial energy metabolism. Our work provides the first example of using direct RNA long-read Nanopore sequencing for transcriptome-wide quantitative profiling of mRNA pseudouridylation regulated by a PUS. We envision that our method will facilitate functional interrogation of PUSs in biological and pathological processes.

BID-seq for transcriptome-wide quantitative sequencing of mRNA pseudouridine at base resolution

Pseudouridine (Ψ) is an abundant RNA modification that is present in and affects the functions of diverse non-coding RNA species, including rRNA, tRNA and small nuclear RNA. Ψ also exists in mammalian mRNA and probably exhibits functional roles; however, functional investigations of mRNA Ψ modifications in mammals have been hampered by the lack of a quantitative method that detects Ψ at base precision. We have recently developed bisulfite-induced deletion sequencing (BID-seq), which provides the community with a quantitative method to map RNA Ψ distribution transcriptome-wide at single-base resolution. Here, we describe an optimized BID-seq protocol for mapping Ψ distribution across cellular mRNAs, which includes fast steps in both library preparation and data analysis. This protocol generates highly reproducible results by inducing high deletion ratios at Ψ modification within diverse sequence contexts, and meanwhile displayed almost zero background deletions at unmodified uridines. When used for transcriptome-wide Ψ profiling in mouse embryonic stem cells, the current protocol uncovered 8,407 Ψ sites from as little as 10 ng of polyA+ RNA input. This optimized BID-seq workflow takes 5 days to complete and includes four main sections: RNA preparation, library construction, next-generation sequencing (NGS) and data analysis. Library construction can be completed by researchers who have basic knowledge and skills in molecular biology and genetics. In addition to the experimental protocol, we provide BID-pipe ( https://github.com/y9c/pseudoU-BIDseq ), a user-friendly data analysis pipeline for Ψ site detection and modification stoichiometry quantification, requiring only basic bioinformatic and computational skills to uncover Ψ signatures from BID-seq data.

RNA modifications in physiology and disease: towards clinical applications

The ability of chemical modifications of single nucleotides to alter the electrostatic charge, hydrophobic surface and base pairing of RNA molecules is exploited for the clinical use of stable artificial RNAs such as mRNA vaccines and synthetic small RNA molecules - to increase or decrease the expression of therapeutic proteins. Furthermore, naturally occurring biochemical modifications of nucleotides regulate RNA metabolism and function to modulate crucial cellular processes. Studies showing the mechanisms by which RNA modifications regulate basic cell functions in higher organisms have led to greater understanding of how aberrant RNA modification profiles can cause disease in humans. Together, these basic science discoveries have unravelled the molecular and cellular functions of RNA modifications, have provided new prospects for therapeutic manipulation and have led to a range of innovative clinical approaches.

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

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

MODOMICS: a database of RNA modifications and related information. 2023 update

The MODOMICS database was updated with recent data and now includes new data types related to RNA modifications. Changes to the database include an expanded modification catalog, encompassing both natural and synthetic residues identified in RNA structures. This addition aids in representing RNA sequences from the RCSB PDB database more effectively. To manage the increased number of modifications, adjustments to the nomenclature system were made. Updates in the RNA sequences section include the addition of new sequences and the reintroduction of sequence alignments for tRNAs and rRNAs. The protein section was updated and connected to structures from the RCSB PDB database and predictions by AlphaFold. MODOMICS now includes a data annotation system, with 'Evidence' and 'Estimated Reliability' features, offering clarity on data support and accuracy. This system is open to all MODOMICS entries, enhancing the accuracy of RNA modification data representation. MODOMICS is available at https://iimcb.genesilico.pl/modomics/.

Why U matters: detection and functions of pseudouridine modifications in mRNAs

The uridine modifications pseudouridine (Ψ), dihydrouridine, and 5-methyluridine are present in eukaryotic mRNAs. Many uridine-modifying enzymes are associated with human disease, underscoring the importance of uncovering the functions of uridine modifications in mRNAs. These modified uridines have chemical properties distinct from those of canonical uridines, which impact RNA structure and RNA-protein interactions. Ψ, the most abundant of these uridine modifications, is present across (pre-)mRNAs. Recent work has shown that many Ψs are present at intermediate to high stoichiometries that are likely conducive to function and at locations that are poised to influence pre-/mRNA processing. Technological innovations and mechanistic investigations are unveiling the functions of uridine modifications in pre-mRNA splicing, translation, and mRNA stability, which are discussed in this review.

Quantitative analysis of tRNA abundance and modifications by nanopore RNA sequencing

Transfer RNAs (tRNAs) play a central role in protein translation. Studying them has been difficult in part because a simple method to simultaneously quantify their abundance and chemical modifications is lacking. Here we introduce Nano-tRNAseq, a nanopore-based approach to sequence native tRNA populations that provides quantitative estimates of both tRNA abundances and modification dynamics in a single experiment. We show that default nanopore sequencing settings discard the vast majority of tRNA reads, leading to poor sequencing yields and biased representations of tRNA abundances based on their transcript length. Re-processing of raw nanopore current intensity signals leads to a 12-fold increase in the number of recovered tRNA reads and enables recapitulation of accurate tRNA abundances. We then apply Nano-tRNAseq to Saccharomyces cerevisiae tRNA populations, revealing crosstalks and interdependencies between different tRNA modification types within the same molecule and changes in tRNA populations in response to oxidative stress.

Analyzing RNA posttranscriptional modifications to decipher the epitranscriptomic code

The discovery of RNA silencing has revealed that non-protein-coding sequences (ncRNAs) can cover essential roles in regulatory networks and their malfunction may result in severe consequences on human health. These findings have prompted a general reassessment of the significance of RNA as a key player in cellular processes. This reassessment, however, will not be complete without a greater understanding of the distribution and function of the over 170 variants of the canonical ribonucleotides, which contribute to the breathtaking structural diversity of natural RNA. This review surveys the analytical approaches employed for the identification, characterization, and detection of RNA posttranscriptional modifications (rPTMs). The merits of analyzing individual units after exhaustive hydrolysis of the initial biopolymer are outlined together with those of identifying their position in the sequence of parent strands. Approaches based on next generation sequencing and mass spectrometry technologies are covered in depth to provide a comprehensive view of their respective merits. Deciphering the epitranscriptomic code will require not only mapping the location of rPTMs in the various classes of RNAs, but also assessing the variations of expression levels under different experimental conditions. The fact that no individual platform is currently capable of meeting all such demands implies that it will be essential to capitalize on complementary approaches to obtain the desired information. For this reason, the review strived to cover the broadest possible range of techniques to provide readers with the fundamental elements necessary to make informed choices and design the most effective possible strategy to accomplish the task at hand.

Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets

2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping and exoribonuclease (DXO) protein, and the biogenesis and specificity of rRNA. Recent advancements in single-molecule sequencing techniques for long read RNA sequencing data offered by Oxford Nanopore technologies have enabled the direct detection of RNA modifications from sequencing data. In this study, we propose a bio-computational framework, Nm-Nano, for predicting the presence of Nm sites in direct RNA sequencing data generated from two human cell lines. The Nm-Nano framework integrates two supervised machine learning (ML) models for predicting Nm sites: Extreme Gradient Boosting (XGBoost) and Random Forest (RF) with K-mer embedding. Evaluation on benchmark datasets from direct RNA sequecing of HeLa and HEK293 cell lines, demonstrates high accuracy (99% with XGBoost and 92% with RF) in identifying Nm sites. Deploying Nm-Nano on HeLa and HEK293 cell lines reveals genes that are frequently modified with Nm. In HeLa cell lines, 125 genes are identified as frequently Nm-modified, showing enrichment in 30 ontologies related to immune response and cellular processes. In HEK293 cell lines, 61 genes are identified as frequently Nm-modified, with enrichment in processes like glycolysis and protein localization. These findings underscore the diverse regulatory roles of Nm modifications in metabolic pathways, protein degradation, and cellular processes. The source code of Nm-Nano can be freely accessed at https://github.com/Janga-Lab/Nm-Nano.

Unraveling C-to-U RNA editing events from direct RNA sequencing

In mammals, RNA editing events involve the conversion of adenosine (A) in inosine (I) by ADAR enzymes or the hydrolytic deamination of cytosine (C) in uracil (U) by the APOBEC family of enzymes, mostly APOBEC1. RNA editing has a plethora of biological functions, and its deregulation has been associated with various human disorders. While the large-scale detection of A-to-I is quite straightforward using the Illumina RNAseq technology, the identification of C-to-U events is a non-trivial task. This difficulty arises from the rarity of such events in eukaryotic genomes and the challenge of distinguishing them from background noise. Direct RNA sequencing by Oxford Nanopore Technology (ONT) permits the direct detection of Us on sequenced RNA reads. Surprisingly, using ONT reads from wild-type (WT) and APOBEC1-knock-out (KO) murine cell lines as well as in vitro synthesized RNA without any modification, we identified a systematic error affecting the accuracy of the Cs call, thereby leading to incorrect identifications of C-to-U events. To overcome this issue in direct RNA reads, here we introduce a novel machine learning strategy based on the isolation Forest (iForest) algorithm in which C-to-U editing events are considered as sequencing anomalies. Using in vitro synthesized and human ONT reads, our model optimizes the signal-to-noise ratio improving the detection of C-to-U editing sites with high accuracy, over 90% in all samples tested. Our results suggest that iForest, known for its rapid implementation and minimal memory requirements, is a promising tool to denoise ONT reads and reliably identify RNA modifications.

Adapting Nanopore Sequencing Basecalling Models for Modification Detection via Incremental Learning and Anomaly Detection

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

Genome-Wide Profiling of tRNA Using an Unexplored Reverse Transcriptase with High Processivity

Monitoring the dynamic changes of cellular tRNA pools is challenging, due to the extensive post-transcriptional modifications of individual species. The most critical component in tRNAseq is a processive reverse transcriptase (RT) that can read through each modification with high efficiency. Here we show that the recently developed group-II intron RT Induro has the processivity and efficiency necessary to profile tRNA dynamics. Using our Induro-tRNAseq, simpler and more comprehensive than the best methods to date, we show that Induro progressively increases readthrough of tRNA over time and that the mechanism of increase is selective removal of RT stops, without altering the misincorporation frequency. We provide a parallel dataset of the misincorporation profile of Induro relative to the related TGIRT RT to facilitate the prediction of non-annotated modifications. We report an unexpected modification profile among human proline isoacceptors, absent from mouse and lower eukaryotes, that indicates new biology of decoding proline codons.

Analysis approaches for the identification and prediction of N6-methyladenosine sites

The global dynamics in a variety of biological processes can be revealed by mapping transcriptional m⁶A sites, in particular full-transcriptome m⁶A. And individual m⁶A sites have contributed to biological function, which can be evaluated by stoichiometric information obtained from the single nucleotide resolution. Currently, the identification of m⁶A sites is mainly carried out by experiment and prediction methods, based on high-throughput sequencing and machine learning model respectively. This review summarizes the recent topics and progress made in bioinformatics methods of deciphering the m⁶A methylation, including the experimental detection of m⁶A methylation sites, techniques of data analysis, the way of predicting m⁶A methylation sites, m⁶A methylation databases, and detection of m⁶A modification in circRNA. At the end, the essay makes a brief discussion for the development perspective in this area.

RNA-Guided RNA Modifications: Biogenesis, Functions, and Applications

Post-transcriptional modifications are ubiquitous in both protein-coding and noncoding RNAs (ncRNAs), playing crucial functional roles in diverse biological processes across all kingdoms of life. These RNA modifications can be achieved through two distinct mechanisms: RNA-independent and RNA-guided (also known as RNA-dependent). In the RNA-independent mechanism, modifications are directly introduced onto RNA molecules by enzymes without the involvement of other RNA molecules, while the cellular RNA-guided RNA modification system exists in the form of RNA-protein complexes, wherein one guide RNA collaborates with a set of proteins, including the modifying enzyme. The primary function of guide RNAs lies in their ability to bind to complementary regions within the target RNAs, orchestrating the installation of specific modifications. Both mechanisms offer unique advantages and are critical to the diverse and dynamic landscape of RNA modifications. RNA-independent modifications provide rapid and direct modification of RNA molecules, while RNA-guided mechanisms offer precise and programmable means to introduce modifications at specific RNA sites. Recently, emerging evidence has shed light on RNA-guided RNA modifications as a captivating area of research, providing precise and programmable control over RNA sequences and functions.In this Account, we focus on RNA modifications synthesized in an RNA-guided manner, including 2'-O-methylated nucleotides (Nm), pseudouridine (Ψ), N4-acetylcytidine (ac4C), and inosine (I). This Account sheds light on the intricate processes of biogenesis and elucidates the regulatory roles of these modifications in RNA metabolism. These roles include pivotal functions such as RNA stability, translation, and splicing, where each modification contributes to the diverse and finely tuned regulatory landscape of RNA biology. In addition to elucidating the biogenesis and functions of these modifications, we also provide an overview of high-throughput methods and their underlying biochemical principles used for the transcriptome-wide investigation of these modifications and their fundamental interactions in RNA-guided systems. This includes exploring RNA-protein interactions and RNA-RNA interactions, which play crucial roles in the dynamic regulatory networks of RNA-guided modifications. The ever-advancing methodologies have greatly enhanced our understanding of the dynamic and widespread nature of RNA-guided RNA modifications and their regulatory functions. Furthermore, the applications of RNA-guided RNA modifications are discussed, illuminating their potential in diverse fields. From basic research to gene therapy, the programmable nature of RNA-guided modifications presents exciting opportunities for manipulating gene expression and developing innovative therapeutic strategies.

RNA pseudouridine modification in plants

Pseudouridine is one of the well-known chemical modifications in various RNA species. Current advances to detect pseudouridine show that the pseudouridine landscape is dynamic and affects multiple cellular processes. Although our understanding of this post-transcriptional modification mainly depends on yeast and human models, the recent findings provide strong evidence for the critical role of pseudouridine in plants. Here, we review the current knowledge of pseudouridine in plant RNAs, including its synthesis, degradation, regulatory mechanisms, and functions. Moreover, we propose future areas of research on pseudouridine modification in plants.

A single pseudouridine on rRNA regulates ribosome structure and function in the mammalian parasite Trypanosoma brucei

Trypanosomes are protozoan parasites that cycle between insect and mammalian hosts and are the causative agent of sleeping sickness. Here, we describe the changes of pseudouridine (Ψ) modification on rRNA in the two life stages of the parasite using four different genome-wide approaches. CRISPR-Cas9 knock-outs of all four snoRNAs guiding Ψ on helix 69 (H69) of the large rRNA subunit were lethal. A single knock-out of a snoRNA guiding Ψ530 on H69 altered the composition of the 80S monosome. These changes specifically affected the translation of only a subset of proteins. This study correlates a single site Ψ modification with changes in ribosomal protein stoichiometry, supported by a high-resolution cryo-EM structure. We propose that alteration in rRNA modifications could generate ribosomes preferentially translating state-beneficial proteins.

Deep learning and direct sequencing of labeled RNA captures transcriptome dynamics

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