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

Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues

N6-methyladenosine (m6A) is the most prevalent, abundant, and conserved internal modification in the eukaryotic messenger RNA (mRNAs) and plays a crucial role in the cellular process. Although more than ten methods were developed for m6A detection over the past decades, there were rooms left to improve the predictive accuracy and the efficiency. In this paper, we proposed an improved method for predicting m6A modification sites, which was based on bi-directional gated recurrent unit (Bi-GRU) and convolutional neural networks (CNN), called Deepm6A-MT. The Deepm6A-MT has two input channels. One is to use an embedding layer followed by the Bi-GRU and then by the CNN, and another is to use one-hot encoding, dinucleotide one-hot encoding, and nucleotide chemical property codes. We trained and evaluated the Deepm6A-MT both by the 5-fold cross-validation and the independent test. The empirical tests showed that the Deepm6A-MT achieved the state of the art performance. In addition, we also conducted the cross-species and the cross-tissues tests to further verify the Deepm6A-MT for effectiveness and efficiency. Finally, for the convenience of academic research, we deployed the Deepm6A-MT to the web server, which is accessed at the URL http://www.biolscience.cn/Deepm6A-MT/.

Insights into N6-methyladenosine (m6A) modification of noncoding RNA in tumor microenvironment

N6-methyladenosine (m6A) is the most abundant RNA modification in eukaryotes, and it participates in the regulation of pathophysiological processes in various diseases, including malignant tumors, by regulating the expression and function of both coding and non-coding RNAs (ncRNAs). More and more studies demonstrated that m6A modification regulates the production, stability, and degradation of ncRNAs and that ncRNAs also regulate the expression of m6A-related proteins. Tumor microenvironment (TME) refers to the internal and external environment of tumor cells, which is composed of numerous tumor stromal cells, immune cells, immune factors, and inflammatory factors that are closely related to tumors occurrence and development. Recent studies have suggested that crosstalk between m6A modifications and ncRNAs plays an important role in the biological regulation of TME. In this review, we summarized and analyzed the effects of m6A modification-associated ncRNAs on TME from various perspectives, including tumor proliferation, angiogenesis, invasion and metastasis, and immune escape. Herein, we showed that m6A-related ncRNAs can not only be expected to become detection markers of tumor tissue samples, but can also be wrapped into exosomes and secreted into body fluids, thus exhibiting potential as markers for liquid biopsy. This review provides a deeper understanding of the relationship between m6A-related ncRNAs and TME, which is of great significance to the development of a new strategy for precise tumor therapy.

Random Forest model reveals the interaction between N6-methyladenosine modifications and RNA-binding proteins

RNA-binding proteins (RBPs) have critical roles in N6-methyladenosine (m6A) modification process. We designed a Random Forest (RF) model to systematically analyze the interaction among RBPs and m6A modifications by integrating the binding signals from hundreds of RBPs. Accurate prediction of m6A sites demonstrated significant connections between RBP bindings and m6A modifications. The relative importance of different RBPs from the model provided a quantitative metric to evaluate their interactions with m6A modifications. Redundancy analysis showed that several RBPs may have similar binding patterns with m6A sites. The RF model exhibited fairly high prediction accuracy across cell lines, suggesting a conservative RBP interaction network regulates m6A occupancy. Specific RBPs can engage to the corresponding regional m6A sites and deploy distinct regulatory processes, such as cleavage site selection of the alternative polyadenylation (APA). We also integrated histone modifications into our RF model, which demonstrated H3K36me3 and H3K27me3 as determining features for m6A distribution.

Crosstalk between N6-methyladenosine (m6A) modification and noncoding RNA in tumor microenvironment

N6-methyladenosine (m6A) is the most abundant RNA modification in eukaryotes, and it participates in the regulation of pathophysiological processes in various diseases, including malignant tumors, by regulating the expression and function of both coding and non-coding RNAs (ncRNAs). More and more studies demonstrated that m6A modification regulates the production, stability, and degradation of ncRNAs and that ncRNAs also regulate the expression of m6A-related proteins. Tumor microenvironment (TME) refers to the internal and external environment of tumor cells, which is composed of numerous tumor stromal cells, immune cells, immune factors, and inflammatory factors that are closely related to tumors occurrence and development. Recent studies have suggested that crosstalk between m6A modifications and ncRNAs plays an important role in the biological regulation of TME. In this review, we summarized and analyzed the effects of m6A modification-associated ncRNAs on TME from various perspectives, including tumor proliferation, angiogenesis, invasion and metastasis, and immune escape. Herein, we showed that m6A-related ncRNAs can not only be expected to become detection markers of tumor tissue samples, but can also be wrapped into exosomes and secreted into body fluids, thus exhibiting potential as markers for liquid biopsy. This review provides a deeper understanding of the relationship between m6A-related ncRNAs and TME, which is of great significance to the development of a new strategy for precise tumor therapy.

Dynamic regulation and key roles of ribonucleic acid methylation

Ribonucleic acid (RNA) methylation is the most abundant modification in biological systems, accounting for 60% of all RNA modifications, and affects multiple aspects of RNA (including mRNAs, tRNAs, rRNAs, microRNAs, and long non-coding RNAs). Dysregulation of RNA methylation causes many developmental diseases through various mechanisms mediated by N ⁶-methyladenosine (m⁶A), 5-methylcytosine (m⁵C), N ¹-methyladenosine (m¹A), 5-hydroxymethylcytosine (hm⁵C), and pseudouridine (Ψ). The emerging tools of RNA methylation can be used as diagnostic, preventive, and therapeutic markers. Here, we review the accumulated discoveries to date regarding the biological function and dynamic regulation of RNA methylation/modification, as well as the most popularly used techniques applied for profiling RNA epitranscriptome, to provide new ideas for growth and development.

MTDeepM6A-2S: A two-stage multi-task deep learning method for predicting RNA N6-methyladenosine sites of Saccharomyces cerevisiae

N6-methyladenosine (m6A) is one of the most important RNA modifications, which is involved in many biological activities. Computational methods have been developed to detect m6A sites due to their high efficiency and low costs. As one of the most widely utilized model organisms, many methods have been developed for predicting m6A sites of Saccharomyces cerevisiae. However, the generalization of these methods was hampered by the limited size of the benchmark datasets. On the other hand, over 60,000 low resolution m6A sites and more than 10,000 base resolution m6A sites of Saccharomyces cerevisiae are recorded in RMBase and m6A-Atlas, respectively. The base resolution m6A sites are often obtained from low resolution results by post calibration. In view of these, we proposed a two-stage deep learning method, named MTDeepM6A-2S, to predict RNA m6A sites of Saccharomyces cerevisiae based on RNA sequence information. In the first stage, a multi-task model with convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) deep framework was built to not only detect the low resolution m6A sites but also assign a reasonable probability for the predicted site. In the second stage, a transfer-learning strategy was used to build the model to predict the base resolution m6A sites from those low resolution m6A sites. The effectiveness of our model was validated on both training and independent test sets. The results show that our model outperforms other state-of-the-art models on the independent test set, which indicates that our model holds high potential to become a useful tool for epitranscriptomics analysis.

Advances in detecting N6-methyladenosine modification in circRNAs

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

Promoter-Bound Full-Length Intronic Circular RNAs-RNA Polymerase II Complexes Regulate Gene Expression in the Human Parasite Entamoeba histolytica

Ubiquitous eukaryotic non-coding circular RNAs are involved in numerous co- and post-transcriptional regulatory mechanisms. Recently, we reported full-length intronic circular RNAs (flicRNAs) in Entamoeba histolytica, with 3′ss–5′ss ligation points and 5′ss GU-rich elements essential for their biogenesis and their suggested role in transcription regulation. Here, we explored how flicRNAs impact gene expression regulation. Using CLIP assays, followed by qRT-PCR, we identified that the RabX13 control flicRNA and virulence-associated flicRNAs were bound to the HA-tagged RNA Pol II C-terminus domain in E. histolytica transformants. The U2 snRNA was also present in such complexes, indicating that they belonged to transcription initiation/elongation complexes. Correspondingly, inhibition of the second step of splicing using boric acid reduced flicRNA formation and modified the expression of their parental genes and non-related genes. flicRNAs were also recovered from chromatin immunoprecipitation eluates, indicating that the flicRNA-Pol II complex was formed in the promoter of their cognate genes. Finally, two flicRNAs were found to be cytosolic, whose functions remain to be uncovered. Here, we provide novel evidence of the role of flicRNAs in gene expression regulation in cis, apparently in a widespread fashion, as an element bound to the RNA polymerase II transcription initiation complex, in E. histolytica.

The functional roles, cross-talk and clinical implications of m6A modification and circRNA in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. HCC has high rates of death and recurrence, as well as very low survival rates. N6-methyladenosine (m6A) is the most abundant modification in eukaryotic RNAs, and circRNAs are a class of circular noncoding RNAs that are generated by back-splicing and they modulate multiple functions in a variety of cellular processes. Although the carcinogenesis of HCC is complex, emerging evidence has indicated that m6A modification and circRNA play vital roles in HCC development and progression. However, the underlying mechanisms governing HCC, their cross-talk, and clinical implications have not been fully elucidated. Therefore, in this paper, we elucidated the biological functions and molecular mechanisms of m6A modification in the carcinogenesis of HCC by illustrating three different regulatory factors ("writer", "eraser", and "reader") of the m6A modification process. Additionally, we dissected the functional roles of circRNAs in various malignant behaviors of HCC, thereby contributing to HCC initiation, progression and relapse. Furthermore, we demonstrated the cross-talk and interplay between m6A modification and circRNA by revealing the effects of the collaboration of circRNA and m6A modification on HCC progression. Finally, we proposed the clinical potential and implications of m6A modifiers and circRNAs as diagnostic biomarkers and therapeutic targets for HCC diagnosis, treatment and prognosis evaluation.

N6-methyladenosine RNA modification: A promising regulator in central nervous system injury

In addition to DNA methylation, reversible epigenetic modification occurring in RNA has been discovered recently. The most abundant type of RNA methylation is N6-methyladenosine (m6A) modification, which is dynamically regulated by methylases ("writers"), demethylases ("erasers") and m6A-binding proteins ("readers"). As an essential posttranscriptional regulator, m6A can control mRNA splicing, processing, stability, export and translation. Recent studies have revealed that m6A modification has the strongest tissue specificity for brain tissue and plays crucial roles in central nervous system (CNS) injures by affecting its downstream target genes or non-coding RNAs. This review focuses on the expression and function of m6A regulatory proteins in CNS trauma in vitro and in vivo. We also highlight the latest insights into the molecular mechanisms of pathological damage in the CNS. Understanding m6A dynamics, functions, and machinery will yield an opportunity for designing and developing novel therapeutic agents for CNS injuries.

WHISTLE server: A high-accuracy genomic coordinate-based machine learning platform for RNA modification prediction

The primary sequences of DNA, RNA and protein have been used as the dominant information source of existing machine learning tools, especially for contexts not fully explored by wet-experimental approaches. Since molecular markers are profoundly orchestrated in the living organisms, those markers that cannot be unambiguously recovered from the primary sequence often help to predict other biological events. To the best of our knowledge, there is no current tool to build and deploy machine learning models that consider genomic evidence. We therefore developed the WHISTLE server, the first machine learning platform based on genomic coordinates. It features convenient covariate extraction and model web deployment with 46 distinct genomic features integrated along with the conventional sequence features. We showed that, when predicting m⁶A sites from SRAMP project, the model integrating genomic features substantially outperformed those based on only sequence features. The WHISTLE server should be a useful tool for studying biological attributes specifically associated with genomic coordinates, and is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/whi2.

Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of RNAs. Here, we present MultiRM, a method for the integrated prediction and interpretation of post-transcriptional RNA modifications from RNA sequences. Built upon an attention-based multi-label deep learning framework, MultiRM not only simultaneously predicts the putative sites of twelve widely occurring transcriptome modifications (m⁶A, m¹A, m⁵C, m⁵U, m⁶Am, m⁷G, Ψ, I, Am, Cm, Gm, and Um), but also returns the key sequence contents that contribute most to the positive predictions. Importantly, our model revealed a strong association among different types of RNA modifications from the perspective of their associated sequence contexts. Our work provides a solution for detecting multiple RNA modifications, enabling an integrated analysis of these RNA modifications, and gaining a better understanding of sequence-based RNA modification mechanisms.

RNA modifications appear to play a role in determining RNA structure and function. Here, the authors develop a deep learning model that predicts the location of 12 RNA modifications using primary sequence, and show that several modifications are associated, which suggests dependencies between them.

Recent advances in functional annotation and prediction of the epitranscriptome

RNA modifications, in particular N⁶-methyladenosine (m⁶A), participate in every stages of RNA metabolism and play diverse roles in essential biological processes and disease pathogenesis. Thanks to the advances in sequencing technology, tens of thousands of RNA modification sites can be identified in a typical high-throughput experiment; however, it remains a major challenge to decipher the functional relevance of these sites, such as, affecting alternative splicing, regulation circuit in essential biological processes or association to diseases. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, we review here recent progress in functional annotation and prediction of RNA modification sites from a bioinformatics perspective. The review covers naïve annotation with associated biological events, e.g., single nucleotide polymorphism (SNP), RNA binding protein (RBP) and alternative splicing, prediction of key sites and their regulatory functions, inference of disease association, and mining the diagnosis and prognosis value of RNA modification regulators. We further discussed the limitations of existing approaches and some future perspectives.

RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis

Deciphering the biological impacts of millions of single nucleotide variants remains a major challenge. Recent studies suggest that RNA modifications play versatile roles in essential biological mechanisms, and are closely related to the progression of various diseases including multiple cancers. To comprehensively unveil the association between disease-associated variants and their epitranscriptome disturbance, we built RMDisease, a database of genetic variants that can affect RNA modifications. By integrating the prediction results of 18 different RNA modification prediction tools and also 303,426 experimentally-validated RNA modification sites, RMDisease identified a total of 202,307 human SNPs that may affect (add or remove) sites of eight types of RNA modifications (m⁶A, m⁵C, m¹A, m⁵U, Ψ, m⁶Am, m⁷G and Nm). These include 4,289 disease-associated variants that may imply disease pathogenesis functioning at the epitranscriptome layer. These SNPs were further annotated with essential information such as post-transcriptional regulations (sites for miRNA binding, interaction with RNA-binding proteins and alternative splicing) revealing putative regulatory circuits. A convenient graphical user interface was constructed to support the query, exploration and download of the relevant information. RMDisease should make a useful resource for studying the epitranscriptome impact of genetic variants via multiple RNA modifications with emphasis on their potential disease relevance. RMDisease is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/rmd.

The m6A methylation landscape stratifies hepatocellular carcinoma into 3 subtypes with distinct metabolic characteristics

Objective

Epigenetic aberration plays an important role in the development and progression of hepatocellular carcinoma (HCC). However, the alteration of RNA N6-methyladenosine (m6A) modifications and its role in HCC progression remain unclear. We therefore aimed to provide evidence using bioinformatics analysis.

Methods

We comprehensively analyzed the m6A regulator modification patterns of 605 HCC samples and correlated them with metabolic alteration characteristics. We elucidated 390 gene-based m6A-related signatures and defined an m6Ascore to quantify m6A modifications. We then assessed their values for predicting prognoses and therapeutic responses in HCC patients.

Results

We identified 3 distinct m6A modification patterns in HCC, and each pattern had distinct metabolic characteristics. The evaluation of m6A modification patterns using m6Ascores could predict the prognoses, tumor stages, and responses to sorafenib treatments of HCC patients. A nomogram based on m6Ascores showed high accuracy in predicting the overall survival of patients. The area under the receiver operating characteristic curve of predictions of 1, 3, and 5-year overall survivals were 0.71, 0.69, and 0.70 in the training cohort, and in the test cohort it was 0.74, 0.75, and 0.71, respectively. M6Acluster C1, which corresponded to hypoactive mRNA methylation, lower expression of m6A regulators, and a lower m6Ascore, was characterized by metabolic hyperactivity, lower tumor stage, better prognosis, and lower response to sorafenib treatment. In contrast, m6Acluster C3 was distinct in its hyperactive mRNA methylations, higher expression of m6A regulators, and higher m6Ascores, and was characterized by hypoactive metabolism, advanced tumor stage, poorer prognosis, and a better response to sorafenib. The m6Acluster, C2, was intermediate between C1 and C3.

Conclusions

HCCs harbored distinct m6A regulator modification patterns that contributed to the metabolic heterogeneity and diversity of HCC. Development of m6A gene signatures and the m6Ascore provides a more comprehensive understanding of m6A modifications in HCC, and helps predict the prognosis and treatment response.

Advances in the profiling of N6-methyladenosine (m6A) modifications

Over 160 RNA modifications have been identified, including N⁷-methylguanine (m⁷G), N⁶-methyladenosine (m⁶A), and 5-methylcytosine (m⁵C). These modifications play key roles in regulating the fate of RNA. In eukaryotes, m⁶A is the most abundant mRNA modification, accounting for over 80% of all RNA methylation modifications. Highly dynamic m⁶A modification may exert important effects on organismal reproduction and development. Significant advances in understanding the mechanism of m⁶A modification have been made using immunoprecipitation, chemical labeling, and site-directed mutagenesis, combined with next-generation sequencing. Single-molecule real-time and nanopore direct RNA sequencing (DRS) approaches provide additional ways to study RNA modifications at the cellular level. In this review, we explore the technical history of identifying m⁶A RNA modifications, emphasizing technological advances in detecting m⁶A modification. In particular, we discuss the challenge of generating accurate dynamic single-base resolution m⁶A maps and also strategies for improving detection specificity. Finally, we outline a roadmap for future research in this area, focusing on the application of RNA epigenetic modification, represented by m⁶A modification.

Prediction of RNA Methylation Status From Gene Expression Data Using Classification and Regression Methods

RNA N⁶-methyladenosine (m⁶A) has emerged as an important epigenetic modification for its role in regulating the stability, structure, processing, and translation of RNA. Instability of m⁶A homeostasis may result in flaws in stem cell regulation, decrease in fertility, and risk of cancer. To this day, experimental detection and quantification of RNA m⁶A modification are still time-consuming and labor-intensive. There is only a limited number of epitranscriptome samples in existing databases, and a matched RNA methylation profile is not often available for a biological problem of interests. As gene expression data are usually readily available for most biological problems, it could be appealing if we can estimate the RNA methylation status from gene expression data using in silico methods. In this study, we explored the possibility of computational prediction of RNA methylation status from gene expression data using classification and regression methods based on mouse RNA methylation data collected from 73 experimental conditions. Elastic Net-regularized Logistic Regression (ENLR), Support Vector Machine (SVM), and Random Forests (RF) were constructed for classification. Both SVM and RF achieved the best performance with the mean area under the curve (AUC) = 0.84 across samples; SVM had a narrower AUC spread. Gene Site Enrichment Analysis was conducted on those sites selected by ENLR as predictors to access the biological significance of the model. Three functional annotation terms were found statistically significant: phosphoprotein, SRC Homology 3 (SH3) domain, and endoplasmic reticulum. All 3 terms were found to be closely related to m⁶A pathway. For regression analysis, Elastic Net was implemented, which yielded a mean Pearson correlation coefficient = 0.68 and a mean Spearman correlation coefficient = 0.64. Our exploratory study suggested that gene expression data could be used to construct predictors for m⁶A methylation status with adequate accuracy. Our work showed for the first time that RNA methylation status may be predicted from the matched gene expression data. This finding may facilitate RNA modification research in various biological contexts when a matched RNA methylation profile is not available, especially in the very early stage of the study.

PSI-MOUSE: Predicting Mouse Pseudouridine Sites From Sequence and Genome-Derived Features

Pseudouridine (Ψ) is the first discovered and the most prevalent posttranscriptional modification, which has been widely studied during the past decades. Pseudouridine was observed in almost all kinds of RNAs and shown to have important biological functions. Currently, the time-consuming and high-cost procedures of experimental approaches limit its uses in real-life Ψ site detection. Alternatively, by taking advantage of the explosive growth of Ψ sequencing data, the computational methods may provide a more cost-effective avenue. To date, the existing mouse Ψ site predictors were all developed based on sequence-derived features, and their performance can be further improved by adding the domain knowledge derived feature. Therefore, it is highly desirable to propose a genomic feature-based computational method to increase the accuracy and efficiency of the identification of Ψ RNA modification in the mouse transcriptome. In our study, a predictive framework PSI-MOUSE was built. Besides the conventional sequence-based features, PSI-MOUSE first introduced 38 additional genomic features derived from the mouse genome, which achieved a satisfactory improvement in the prediction performance, compared with other existing models. Moreover, PSI-MOUSE also features in automatically annotating the putative Ψ sites with diverse types of posttranscriptional regulations (RNA-binding protein [RBP]-binding regions, miRNA-RNA interactions, and splicing sites), which can serve as a useful research tool for the study of Ψ RNA modification in the mouse genome. Finally, 3282 experimentally validated mouse Ψ sites were also collected in a database with customized query functions. For the convenience of academic users, a website was built to provide a user-friendly interface for the query and analysis on the database. The website is freely accessible at www.xjtlu.edu.cn/biologicalsciences/psimouse and http://psimouse.rnamd.com. We introduced the genome-derived features to mouse for the first time, and we achieved a good performance in mouse Ψ site prediction. Compared with the existing state-of-art methods, our newly developed approach PSI-MOUSE obtained a substantial improvement in prediction accuracy, marking the reliable contributions of genomic features for the prediction of RNA modifications in a species other than human.

LITHOPHONE: Improving lncRNA Methylation Site Prediction Using an Ensemble Predictor

N 6-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/.

PIANO: A Web Server for Pseudouridine-Site (Ψ) Identification and Functional Annotation

Known as the "fifth RNA nucleotide", pseudouridine (Ψ or psi) is the first-discovered and most abundant RNA modification occurring at the Uridine site, and it plays a prominent role in a number of biological processes. Thousands of Ψ sites have been identified within different biological contexts thanks to the advancement in high-throughput sequencing technology; nevertheless, the transcriptome-wide distribution, biomolecular functions, regulatory mechanisms, and disease relevance of pseudouridylation are largely elusive. We report here a web server-PIANO-for pseudouridine site (Ψ) identification and functional annotation. PIANO was built upon a high-accuracy predictor that takes advantage of both conventional sequence features and 42 additional genomic features. When tested on six independent datasets generated from four independent Ψ-profiling technologies (Ψ-seq, RBS-seq, Pseudo-seq, and CeU-seq) as benchmarks, PIANO achieved an average AUC of 0.955 and 0.838 under the full transcript and mature mRNA models, respectively, marking a substantial improvement in accuracy compared to the existing in silico Ψ-site prediction methods, i.e., PPUS (0.713 and 0.707), iRNA-PseU (0.713 and 0.712), and PseUI (0.634 and 0.652). Besides, PIANO web server systematically annotates the predicted Ψ sites with post-transcriptional regulatory mechanisms (miRNA-targets, RBP-binding regions, and splicing sites) in its prediction report to help the users explore potential machinery of Ψ. Moreover, a concise query interface was also built for 4,303 known Ψ sites, which is currently the largest collection of experimentally validated human Ψ sites. The PIANO website is freely accessible at: http://piano.rnamd.com.