RNA Methylation Sites Research Articles

SUPREM: an engineered non-site-specific m6A RNA methyltransferase with highly improved efficiency.

N 6-Methyladenine(m6A) RNA methylation plays a key role in RNA processing and translational regulation, influencing both normal physiological and pathological processes. Yet, current techniques for studying RNA methylation struggle to isolate the effects of individual m6A modifications. Engineering of RNA methyltransferases (RNA MTases) could enable development of improved synthetic biology tools to manipulate RNA methylation, but it is challenging due to limited understanding of structure-function relationships in RNA MTases. Herein, using ancestral sequence reconstruction, we explore the sequence space of the bacterial DNA methyltransferase EcoGII (M.EcoGII), a promising target for protein engineering due to its lack of sequence specificity and its residual activity on RNA. We thereby created an efficient non-specific RNA MTase termed SUPer RNA EcoGII Methyltransferase (SUPREM), which exhibits 8-fold higher expression levels, 7°C higher thermostability and 12-fold greater m6A RNA methylation activity compared with M.EcoGII. Immunofluorescent staining and quantitativeliquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis confirmed SUPREM's higher RNA methylation activity compared with M.EcoGII in mammalian cells. Additionally, Nanopore direct RNA sequencing highlighted that SUPREM is capable of methylating a larger number of RNA methylation sites than M.EcoGII. Through phylogenetic and mutational analysis, we identified a critical residue for the enhanced RNA methylation activity of SUPREM. Collectively, our findings indicate that SUPREM holds promise as a versatile tool for in vivo RNA methylation and labeling.

Cellular Metabolic Labeling of Nucleic Acids and Its Applications

AbstractNucleic acids are considered as fundamental molecules of living systems, which serve as universal genetic information messengers and repositories. To uncover the multifaceted aspects of nucleic acid function and metabolism within cells, labeling has become indispensable. This labeling technique enables the visualization, isolation, characterization, and even quantification of specific nucleic acid species. This review delves into cellular metabolic approaches for nucleic acid labeling, wherein enzymatic steps are employed to introduce nucleic acid modifications before conjugation with a label for detection or isolation. The discussion begins with metabolic labeling for DNA, RNA with various reactive groups and post‐transcriptional RNA labeling for RNA methylation and acetylation sites, emphasizing recent advancements in the field and then, we spotlighted pertinent applications for cellular imaging and sequencing. of labeling.

MSCAN: multi-scale self- and cross-attention network for RNA methylation site prediction

BackgroundEpi-transcriptome regulation through post-transcriptional RNA modifications is essential for all RNA types. Precise recognition of RNA modifications is critical for understanding their functions and regulatory mechanisms. However, wet experimental methods are often costly and time-consuming, limiting their wide range of applications. Therefore, recent research has focused on developing computational methods, particularly deep learning (DL). Bidirectional long short-term memory (BiLSTM), convolutional neural network (CNN), and the transformer have demonstrated achievements in modification site prediction. However, BiLSTM cannot achieve parallel computation, leading to a long training time, CNN cannot learn the dependencies of the long distance of the sequence, and the Transformer lacks information interaction with sequences at different scales. This insight underscores the necessity for continued research and development in natural language processing (NLP) and DL to devise an enhanced prediction framework that can effectively address the challenges presented.ResultsThis study presents a multi-scale self- and cross-attention network (MSCAN) to identify the RNA methylation site using an NLP and DL way. Experiment results on twelve RNA modification sites (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um) reveal that the area under the receiver operating characteristic of MSCAN obtains respectively 98.34%, 85.41%, 97.29%, 96.74%, 99.04%, 79.94%, 76.22%, 65.69%, 92.92%, 92.03%, 95.77%, 89.66%, which is better than the state-of-the-art prediction model. This indicates that the model has strong generalization capabilities. Furthermore, MSCAN reveals a strong association among different types of RNA modifications from an experimental perspective. A user-friendly web server for predicting twelve widely occurring human RNA modification sites (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um) is available at http://47.242.23.141/MSCAN/index.php.ConclusionsA predictor framework has been developed through binary classification to predict RNA methylation sites.

Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC. Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups. Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients. Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.

M7GHub V2.0: an updated database for decoding the N7-methylguanosine (m7G) epitranscriptome.

With recent progress in mapping N7-methylguanosine (m7G) RNA methylation sites, tens of thousands of experimentally validated m7G sites have been discovered in various species, shedding light on the significant role of m7G modification in regulating numerous biological processes including disease pathogenesis. An integrated resource that enables the sharing, annotation and customized analysis of m7G data will greatly facilitate m7G studies under various physiological contexts. We previously developed the m7GHub database to host mRNA m7G sites identified in the human transcriptome. Here, we present m7GHub v.2.0, an updated resource for a comprehensive collection of m7G modifications in various types of RNAacross multiple species: an m7GDB database containing 430898 putative m7G sites identified in 23 species, collected from both widely applied next-generation sequencing (NGS) and the emerging Oxford Nanopore direct RNA sequencing (ONT) techniques; an m7GDiseaseDB hosting 156 206 m7G-associated variants (involving addition or removal of an m7G site), including 3238 disease-relevant m7G-SNPs that may function through epitranscriptome disturbance; and two enhanced analysis modules to perform interactive analyses on the collections of m7G sites (m7GFinder) and functional variants (m7GSNPer). We expect that m7Ghub v.2.0 should serve as a valuable centralized resource for studying m7G modification. It is freely accessible at: www.rnamd.org/m7GHub2.

CUCUME: An RNA methylation database integrating systemic mRNAs signals, GWAS and QTL genetic regulation and epigenetics in different tissues of Cucurbitaceae

As an internal modification of transcripts, RNA methylation determines RNA fate by changing RNA–protein binding affinity. In plants, RNA methylation is ubiquitous and is involved in all aspects of RNA post-transcriptional regulation. For instance, long-distance mobile RNAs, strongly influenced by their methylation status, play important roles in plant growth, development and environmental adaptation. Cucumber/pumpkin heterografts are widely used to improve stress tolerance of cucumber and to study mobile RNA signals due to their strong developed vasculature system. Here, we developed the Cucume (Cucurbit RNA methylation, http://cucume.cn/) database for these two important vegetables, cucumber (Cucumis sativus L.) and pumpkin (Cucurbita moschata) with high productivity worldwide. We identified mRNAs harboring 5-methylcytosine (m5C) and N6-methyladenosine (m6A) sites in pumpkin and cucumber at the whole genome level via Methylated RNA Immunoprecipitation sequencing (MeRIP-seq) of different tissues and the vascular exudates. In addition to RNA methylation sites, the Cucume database includes graft-transmissible systemic mRNAs identified in previous studies using cucumber/pumpkin heterografts. The further integration of cucumber genome-wide association analysis (GWAS) and quantitative trait loci (QTL) allows the study of RNA methylation-related genetic and epigenetic regulation in cucurbits. Therefore, the here developed Cucume database will promote understanding the role of cucurbit RNA methylation in RNA mobility and QTL, ultimately benefitting future breeding of agronomic crop germplasms.

Primary sequence-assisted prediction of m6A RNA methylation sites from Oxford nanopore direct RNA sequencing data.

Traditional epitranscriptome profiling approach relies on specific antibodies or chemical treatments to capture modified RNA molecules and then applies high throughput sequencing to identify their transcriptomic locations. However, due to the lack of suitable or high-quality antibodies, only a small proportion of the 170 documented RNA modifications were profiled with those approaches. Direct sequencing of native RNA molecules using Oxford Nanopore Technologies (ONT) enabled straight inspection of RNA modifications and offered a promising alternative solution. N6-methyladenosine (m6A) is known to cause characteristic changes and increased base call errors of ONT signals compared with non-modified adenosines, based on which, the m6A sites can be identified directly from ONT signals. Meanwhile, a number of studies have shown that it is possible to predict m6A sites from RNA primary sequences. Using the m6A sites revealed by Illumina technology as benchmark, we showed that, the accuracy of ONT-based m6A site prediction can be further increased by integrating additional information from the primary sequences of RNA (AUROC of 0.918), compared with using ONT signals only (AUROC 0.878 using Base call error features, and 0.804 using Tombo features), providing a new perspective for more reliable mining of the relatively noisy ONT signals.

EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction.

BackgroundRecent research recommends that epi-transcriptome regulation through post-transcriptional RNA modifications is essential for all sorts of RNA. Exact identification of RNA modification is vital for understanding their purposes and regulatory mechanisms. However, traditional experimental methods of identifying RNA modification sites are relatively complicated, time-consuming, and laborious.Machine learning approaches have been applied in the procedures of RNA sequence features extraction and classification in a computational way, which may supplement experimental approaches more efficiently. Recently, convolutional neural network (CNN) and long short-term memory (LSTM) have been demonstrated achievements in modification site prediction on account of their powerful functions in representation learning. However, CNN can learn the local response from the spatial data but cannot learn sequential correlations. And LSTM is specialized for sequential modeling and can access both the contextual representation but lacks spatial data extraction compared with CNN. There is strong motivation to construct a prediction framework using natural language processing (NLP), deep learning (DL) for these reasons.ResultsThis study presents an ensemble multiscale deep learning predictor (EMDLP) to identify RNA methylation sites in an NLP and DL way. It organically combines the dilated convolution and Bidirectional LSTM (BiLSTM), which helps to take better advantage of the local and global information for site prediction.The first step of EMDLP is to represent the RNA sequences in an NLP way. Thus, three encodings, e.g., RNA word embedding, One-hot encoding, and RGloVe, which is an improved learning method of word vector representation based on GloVe, are adopted to decipher sites from the viewpoints of the local and global information. Then, a dilated convolutional Bidirectional LSTM network (DCB) model is constructed with the dilated convolutional neural network (DCNN) followed by BiLSTM to extract potential contributing features for methylation site prediction. Finally, these three encoding methods are integrated by a soft vote to obtain better predictive performance. Experiment results on m1A and m6A reveal that the area under the receiver operating characteristic(AUROC) of EMDLP obtains respectively 95.56%, 85.24%, and outperforms the state-of-the-art models. To maximize user convenience, a user-friendly webserver for EMDLP was publicly available at http://www.labiip.net/EMDLP/index.php (http://47.104.130.81/EMDLP/index.php).ConclusionsWe developed a predictor for m1A and m6A methylation sites.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12859-022-04756-1.

PSP-PJMI: An innovative feature representation algorithm for identifying DNA N4-methylcytosine sites

Identifying DNA N4-methylcytosine (4mC) sites is an essential step to study the biological functional mechanism. Feature representation is the primary step to identify 4mC sites due to its influencing the performance of the downstream 4mC site predictive model. Extracting numerical features having strong categorical information from DNA sequences is the key issue to build a 4mC predictive model having good performance. Therefore, a feature representation algorithm referred to as PSP-PJMI is proposed in this paper. It first proposes Pointwise Joint Mutual Information (PJMI), then the bidirectional k-nucleotide Position-Specific Propensities (PSP), so that the PSP-PJMI feature representation algorithm is developed. The parameter ξ is used to indicate the interval from the current nucleotide to the forward or backward dinucleotide in the bidirectional trinucleotide PSP, so that the position information of nucleotides is extracted from a DNA sequence as far as possible. The features corresponding to various ξ are concatenated to comprise the high dimensional feature vector having rich categorical information. The 4mC-BiNP model for identifying DNA 4mC sites is constructed using SVM and the extracted features. The experimental results of 10-fold cross validation test, cross-species validation test, and independent test on 6 species datasets show that the proposed PSP-PJMI algorithm can extract features having richer categorical information than the available feature representation algorithms can do. The 4mC-BiNP model is superior to the state-of-the-art predictive models for identifying DNA 4mC sites. Furthermore, the PSP-PJMI algorithm can be used to extract features for identifying other DNA methylation sites, and also be used for RNA sequences to predict RNA methylation sites.

Exploring the sequence space of unknown oligomers and polymers

The characterization of the chemistry of life on earth has been facilitated by developments in analysis and sequencing of bio-oligomers using tandem mass spectrometry (MS/MS). Bio-oligomers can be identified with sequence-level resolution in analytes more complex than any synthetic mixture, enabled by well-established knowledge of fragmentation properties and extensive MS/MS databases built up over decades. However, unknown oligomer systems remain difficult to characterize, as no comparable databases exist, partly because of the vast chemical diversity and fragmentation pathways. Here, we present oligomer-soup-sequencing (OLIGOSS), a new approach to the sequencing of unknown oligomer systems. Using a novel set of backbone-agnostic abstract properties to define fragmentation, OLIGOSS is capable of sequencing any linear oligomer class amenable to MS/MS, regardless of backbone chemistry. We validated OLIGOSS by sequencing synthetic peptides, polyesters, polyimines and depsipeptide oligomers, mapped RNA methylation sites, and a ribosomally synthesized peptide, thioholgamide, directly from a cell lysate without purification. New approach to the sequencing of unknown oligomer systems Sequencing for any linear oligomer classes amenable to MS/MS Validation using synthetic peptides, polyesters, polyimines, and depsipeptide oligomers Used to map RNA methylation sites and sequence a ribosomally synthesized thioholgamide Doran et al. present oligomer-soup-sequencing (OLIGOSS), which offers an approach to the sequencing of unknown oligomer systems. Using a set of backbone-agnostic abstract properties to define fragmentation, OLIGOSS is capable of sequencing any linear oligomer class amenable to MS/MS, regardless of backbone chemistry.

ConsRM: collection and large-scale prediction of the evolutionarily conserved RNA methylation sites, with implications for the functional epitranscriptome.

Motivation N6-methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. Evidence increasingly demonstrates its crucial importance in essential molecular mechanisms and various diseases. With recent advances in sequencing techniques, tens of thousands of m6A sites are identified in a typical high-throughput experiment, posing a key challenge to distinguish the functional m6A sites from the remaining 'passenger' (or 'silent') sites. Results: We performed a comparative conservation analysis of the human and mouse m6A epitranscriptomes at single site resolution. A novel scoring framework, ConsRM, was devised to quantitatively measure the degree of conservation of individual m6A sites. ConsRM integrates multiple information sources and a positive-unlabeled learning framework, which integrated genomic and sequence features to trace subtle hints of epitranscriptome layer conservation. With a series validation experiments in mouse, fly and zebrafish, we showed that ConsRM outperformed well-adopted conservation scores (phastCons and phyloP) in distinguishing the conserved and unconserved m6A sites. Additionally, the m6A sites with a higher ConsRM score are more likely to be functionally important. An online database was developed containing the conservation metrics of 177998 distinct human m6A sites to support conservation analysis and functional prioritization of individual m6A sites. And it is freely accessible at: https://www.xjtlu.edu.cn/biologicalsciences/con.

WHISTLE: A Functionally Annotated High-Accuracy Map of Human m6A Epitranscriptome.

N6-Methyladenosine (m6A) is the most prevalent posttranscriptional modification in eukaryotes and plays a pivotal role in various biological processes, such as splicing, RNA degradation, and RNA-protein interaction. Accurately identification of the location of m6A is essential for related downstream studies. In this chapter, we introduce a prediction framework WHISTLE, which enables us to acquire so far the most accurate map of the transcriptome-wide human m6A RNA-methylation sites (with an average AUC: 0.948 and 0.880 under the full transcript or mature messenger RNA models, respectively, when tested on independent datasets). Besides, each individual m6A site was also functionally annotated according to the "guilt-by-association" principle by integrating RNA methylation data, gene expression data and protein-protein interaction data. A web server was constructed for conveniently querying the predicted RNA methylation sites and their putative biological functions. The website supports the query by genes, by GO function, table view, and the download of all the functionally annotated map of predicted map of human m6A epitranscriptome. The WHISTLE web server is freely available at: www.xjtlu.edu.cn/biologicalsciences/whistle and http://whistle-epitranscriptome.com .

Decoding the epitranscriptional landscape from native RNA sequences.

Traditional epitranscriptomics relies on capturing a single RNA modification by antibody or chemical treatment, combined with short-read sequencing to identify its transcriptomic location. This approach is labor-intensive and may introduce experimental artifacts. Direct sequencing of native RNA using Oxford Nanopore Technologies (ONT) can allow for directly detecting the RNA base modifications, although these modifications might appear as sequencing errors. The percent Error of Specific Bases (%ESB) was higher for native RNA than unmodified RNA, which enabled the detection of ribonucleotide modification sites. Based on the %ESB differences, we developed a bioinformatic tool, epitranscriptional landscape inferring from glitches of ONT signals (ELIGOS), that is based on various types of synthetic modified RNA and applied to rRNA and mRNA. ELIGOS is able to accurately predict known classes of RNA methylation sites (AUC > 0.93) in rRNAs from Escherichiacoli, yeast, and human cells, using either unmodified in vitro transcription RNA or a background error model, which mimics the systematic error of direct RNA sequencing as the reference. The well-known DRACH/RRACH motif was localized and identified, consistent with previous studies, using differential analysis of ELIGOS to study the impact of RNA m6A methyltransferase by comparing wild type and knockouts in yeast and mouse cells. Lastly, the DRACH motif could also be identified in the mRNA of three human cell lines. The mRNA modification identified by ELIGOS is at the level of individual base resolution. In summary, we have developed a bioinformatic software package to uncover native RNA modifications.

LITHOPHONE: Improving lncRNA Methylation Site Prediction Using an Ensemble Predictor.

N6-methyladenosine (m6A) is one of the most widely studied epigenetic modifications, which plays an important role in many biological processes, such as splicing, RNA localization, and degradation. Studies have shown that m6A on lncRNA has important functions, including regulating the expression and functions of lncRNA, regulating the synthesis of pre-mRNA, promoting the proliferation of cancer cells, and affecting cell differentiation and many others. Although a number of methods have been proposed to predict m6A RNA methylation sites, most of these methods aimed at general m6A sites prediction without noticing the uniqueness of the lncRNA methylation prediction problem. Since many lncRNAs do not have a polyA tail and cannot be captured in the polyA selection step of the most widely adopted RNA-seq library preparation protocol, lncRNA methylation sites cannot be effectively captured and are thus likely to be significantly underrepresented in existing experimental data affecting the accuracy of existing predictors. In this paper, we propose a new computational framework, LITHOPHONE, which stands for long noncoding RNA methylation sites prediction from sequence characteristics and genomic information with an ensemble predictor. We show that the methylation sites of lncRNA and mRNA have different patterns exhibited in the extracted features and should be differently handled when making predictions. Due to the used experiment protocols, the number of known lncRNA m6A sites is limited, and insufficient to train a reliable predictor; thus, the performance can be improved by combining both lncRNA and mRNA data using an ensemble predictor. We show that the newly developed LITHOPHONE approach achieved a reasonably good performance when tested on independent datasets (AUC: 0.966 and 0.835 under full transcript and mature mRNA modes, respectively), marking a substantial improvement compared with existing methods. Additionally, LITHOPHONE was applied to scan the entire human lncRNAome for all possible lncRNA m6A sites, and the results are freely accessible at: http://180.208.58.19/lith/.

WITMSG: Large-scale Prediction of Human Intronic m6A RNA Methylation Sites from Sequence and Genomic Features.

IntroductionN6-methyladenosine (m6A) is one of the most widely studied epigenetic modifications. It plays important roles in various biological processes, such as splicing, RNA localization and degradation, many of which are related to the functions of introns. Although a number of computational approaches have been proposed to predict the m6A sites in different species, none of them were optimized for intronic m6A sites. As existing experimental data overwhelmingly relied on polyA selection in sample preparation and the intronic RNAs are usually underrepresented in the captured RNA library, the accuracy of general m6A sites prediction approaches is limited for intronic m6A sites prediction task.Methodology A computational framework, WITMSG, dedicated to the large-scale prediction of intronic m6A RNA methylation sites in humans has been proposed here for the first time. Based on the random forest algorithm and using only known intronic m6A sites as the training data, WITMSG takes advantage of both conventional sequence features and a variety of genomic characteristics for improved prediction performance of intron-specific m6A sites.Results and Conclusion It has been observed that WITMSG outperformed competing approaches (trained with all the m6A sites or intronic m6A sites only) in 10-fold cross-validation (AUC: 0.940) and when tested on independent datasets (AUC: 0.946). WITMSG was also applied intronome-wide in humans to predict all possible intronic m6A sites, and the prediction results are freely accessible at http://rnamd.com/intron/.

ISGm1A: Integration of Sequence Features and Genomic Features to Improve the Prediction of Human m1A RNA Methylation Sites

As a new epitranscriptomic modification, N1-methyladenosine (m 1 A) plays an important role in the gene expression regulation. Although some computational methods were proposed to predict m 1 A modification sites, all of these methods apply machine learning predictions based on the nucleotide sequence features, and they missed the layer of information in transcript topology and RNA secondary structures. To enhance the prediction model of m 1 A RNA methylation, we proposed a computational framework, ISGm1A, which stands for integration sequence features and genomic features to improve the prediction of human m 1 A RNA methylation sites. Based on the random forest algorithm, ISGm1A takes advantage of both conventional sequence features and 75 genomic characteristics to improve the prediction performance of m 1 A sites in human. The results of five-fold cross validation and independent test show that ISGm1A outperforms other prediction algorithms (AUC = 0.903 and 0.909). In addition, through analyzing the importance of features, we found that the genomic features play a more important role in site prediction than the sequence features. Furthermore, with ISGm1A, we generated a high accuracy map of m 1 A by predicting all adenines sites in the transcriptome. The data and the results of the study are freely accessible at: https://github.com/lianliu09/m1a_prediction.git.

M6Acomet: large-scale functional prediction of individual m6A RNA methylation sites from an RNA co-methylation network

BackgroundOver one hundred different types of post-transcriptional RNA modifications have been identified in human. Researchers discovered that RNA modifications can regulate various biological processes, and RNA methylation, especially N6-methyladenosine, has become one of the most researched topics in epigenetics.ResultsTo date, the study of epitranscriptome layer gene regulation is mostly focused on the function of mediator proteins of RNA methylation, i.e., the readers, writers and erasers. There is limited investigation of the functional relevance of individual m6A RNA methylation site. To address this, we annotated human m6A sites in large-scale based on the guilt-by-association principle from an RNA co-methylation network. It is constructed based on public human MeRIP-Seq datasets profiling the m6A epitranscriptome under 32 independent experimental conditions. By systematically examining the network characteristics obtained from the RNA methylation profiles, a total of 339,158 putative gene ontology functions associated with 1446 human m6A sites were identified. These are biological functions that may be regulated at epitranscriptome layer via reversible m6A RNA methylation. The results were further validated on a soft benchmark by comparing to a random predictor.ConclusionsAn online web server m6Acomet was constructed to support direct query for the predicted biological functions of m6A sites as well as the sites exhibiting co-methylated patterns at the epitranscriptome layer. The m6Acomet web server is freely available at: www.xjtlu.edu.cn/biologicalsciences/m6acomet.

DRUM: Inference of Disease-Associated m6A RNA Methylation Sites From a Multi-Layer Heterogeneous Network.

Recent studies have revealed that the RNA N6-methyladenosine (m6A) modification plays a critical role in a variety of biological processes and associated with multiple diseases including cancers. Till this day, transcriptome-wide m6A RNA methylation sites have been identified by high-throughput sequencing technique combined with computational methods, and the information is publicly available in a few bioinformatics databases; however, the association between individual m6A sites and various diseases are still largely unknown. There are yet computational approaches developed for investigating potential association between individual m6A sites and diseases, which represents a major challenge in the epitranscriptome analysis. Thus, to infer the disease-related m6A sites, we implemented a novel multi-layer heterogeneous network-based approach, which incorporates the associations among diseases, genes and m6A RNA methylation sites from gene expression, RNA methylation and disease similarities data with the Random Walk with Restart (RWR) algorithm. To evaluate the performance of the proposed approach, a ten-fold cross validation is performed, in which our approach achieved a reasonable good performance (overall AUC: 0.827, average AUC 0.867), higher than a hypergeometric test-based approach (overall AUC: 0.7333 and average AUC: 0.723) and a random predictor (overall AUC: 0.550 and average AUC: 0.486). Additionally, we show that a number of predicted cancer-associated m6A sites are supported by existing literatures, suggesting that the proposed approach can effectively uncover the underlying epitranscriptome circuits of disease mechanisms. An online database DRUM, which stands for disease-associated ribonucleic acid methylation, was built to support the query of disease-associated RNA m6A methylation sites, and is freely available at: www.xjtlu.edu.cn/biologicalsciences/drum.

WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach.

N 6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotes, and plays a pivotal role in various biological processes, such as splicing, RNA degradation and RNA–protein interaction. We report here a prediction framework WHISTLE for transcriptome-wide m6A RNA-methylation site prediction. When tested on six independent datasets, our approach, which integrated 35 additional genomic features besides the conventional sequence features, achieved a major improvement in the accuracy of m6A site prediction (average AUC: 0.948 and 0.880 under the full transcript or mature messenger RNA models, respectively) compared to the state-of-the-art computational approaches MethyRNA (AUC: 0.790 and 0.732) and SRAMP (AUC: 0.761 and 0.706). It also out-performed the existing epitranscriptome databases MeT-DB (AUC: 0.798 and 0.744) and RMBase (AUC: 0.786 and 0.736), which were built upon hundreds of epitranscriptome high-throughput sequencing samples. To probe the putative biological processes impacted by changes in an individual m6A site, a network-based approach was implemented according to the ‘guilt-by-association’ principle by integrating RNA methylation profiles, gene expression profiles and protein–protein interaction data. Finally, the WHISTLE web server was built to facilitate the query of our high-accuracy map of the human m6A epitranscriptome, and the server is freely available at: www.xjtlu.edu.cn/biologicalsciences/whistle and http://whistle-epitranscriptome.com.

Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. <P><P> Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. <P><P> Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. <P><P> Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. <P><P> Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. <P><P> Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.