A Linear Regression Predictor for Identifying N6-Methyladenosine Sites Using Frequent Gapped K-mer Pattern

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.

A Deep Ensemble Predictor for Identifying Anti-Hypertensive Peptides Using Pretrained Protein Embedding

Hypertension (HT), or high blood pressure is one of the most common and main causes in cardiovascular diseases, which is also related to a series of detrimental diseases in humans. Deficiencies in effective treatment in HT are often associated with a series of diseases including multi-infarct dementia, amputation, and renal failure. Therefore, identifying anti-hypertension peptides has the vital realistic significance. Although many bioactive peptides have been developed to reduce blood pressure, they are time-consuming and laborious. In views of the obstacles of the intrinsic methods in antihypertensive peptide (AHTP) classification, computational methods are suggested as a supplement to identify AHTPs. In this study, we develop a comprehensive feature representation algorithm based on pretrained model and convolutional neural network and apply the deep ensemble model to construct the prediction model. The new predictor is used to identify AHTPs in benchmark and independent datasets. It has been shown in the independent test set that the performance is better than the recent methods. Comparative results indicate that our model can shed some light on hypertension therapy and gains more insights of classifying AHTPs. The implements and codes can be found in https://github.com/yuanying566/AHPred-DE.

Arhgef2 regulates neural differentiation in the cerebral cortex through mRNA m6A-methylation of Npdc1 and Cend1

N⁶-methyladenosine (m⁶A) is emerging as a vital factor regulating neural differentiation. Here, we report that deficiency of Arhgef2, a novel cause of a neurodevelopmental disorder we identified recently, impairs neurogenesis, neurite outgrowth, and synaptic formation by regulating m⁶A methylation. Arhgef2 knockout decreases expression of Mettl14 and total m⁶A level significantly in the cerebral cortex. m⁶A sequencing reveals that loss of Arhgef2 reduces m⁶A methylation of 1,622 mRNAs, including Npdc1 and Cend1, which are both strongly associated with cell cycle exit and terminal neural differentiation. Arhgef2 deficiency decreases m⁶A methylations of the Npdc1 and Cend1 mRNAs via down-regulation of Mettl14, and thereby inhibits the translation of Npdc1 and nuclear export of Cend1 mRNAs. Overexpression of Mettl14, Npdc1, and Cend1 rescue the abnormal phenotypes in Arhgef2 knockout mice, respectively. Our study provides a critical insight into a mechanism by which defective Arhgef2 mediates m⁶A-tagged target mRNAs to impair neural differentiation.

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Arhgef2 mediates total m⁶A level via Mettl14

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Arhgef2 affects m⁶A methylations of the Npdc1 and Cend1 mRNAs

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Decreased m⁶A methylations inhibits translation of Npdc1 and nuclear export of Cend1

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Reduced protein expression of Npdc1 and Cend1 hinders neural differentiation

Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors

RNA modification is an essential step towards generation of new RNA structures. Such modification is potentially able to modify RNA function or its stability. Among different modifications, 5-Hydroxymethylcytosine (5hmC) modification of RNA exhibit significant potential for a series of biological processes. Understanding the distribution of 5hmC in RNA is essential to determine its biological functionality. Although conventional sequencing techniques allow broad identification of 5hmC, they are both time-consuming and resource-intensive. In this study, we propose a new computational tool called iRNA5hmC-PS to tackle this problem. To build iRNA5hmC-PS we extract a set of novel sequence-based features called Position-Specific Gapped k-mer (PSG k-mer) to obtain maximum sequential information. Our feature analysis shows that our proposed PSG k-mer features contain vital information for the identification of 5hmC sites. We also use a group-wise feature importance calculation strategy to select a small subset of features containing maximum discriminative information. Our experimental results demonstrate that iRNA5hmC-PS is able to enhance the prediction performance, dramatically. iRNA5hmC-PS achieves 78.3% prediction performance, which is 12.8% better than those reported in the previous studies. iRNA5hmC-PS is publicly available as an online tool at http://103.109.52.8:81/iRNA5hmC-PS. Its benchmark dataset, source codes, and documentation are available at https://github.com/zahid6454/iRNA5hmC-PS.

The m6A Methyltransferase METTL3 Is Functionally Implicated in DLBCL Development by Regulating m6A Modification in PEDF

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of lymphoma, whose treatment still has a major challenge of achieving a satisfactory curative effect. The underlying mechanisms also have not been fully illustrated. N⁶-Methyladenosine (m6A) has been identified as the most prevalent internal modification of mRNAs present in eukaryotes, which is involved in the pathogenesis of cancers. It remains unclear how m6A mRNA methylation is functionally linked to the pathogenesis of DLBCL. In this study, we sought to explore the roles of METTL3 on DLBCL development. The results showed that m6A level for RNA methylation and the expression level of METTL3 were upregulated in DLBCL tissues and cell lines. Functionally, downregulated METTL3 expression in DLBCL cells inhibited the cell proliferation ability. Further mechanism analysis indicated that METTL3 knockdown abates the m6A methylation and total mRNA level of pigment epithelium-derived factor (PEDF). However, Wnt/β-catenin signaling was not thus activated. Overexpressed PEDF abrogates the inhibition of cell proliferation in DLBCL cells that is caused by METTL3 silence. In summary, the above-mentioned results demonstrated that the METTL3 promotes DLBCL progression by regulating the m6A level of PEDF.

Bioinformatics approaches for deciphering the epitranscriptome: Recent progress and emerging topics

Post-transcriptional RNA modification occurs on all types of RNA and plays a vital role in regulating every aspect of RNA function. Thanks to the development of high-throughput sequencing technologies, transcriptome-wide profiling of RNA modifications has been made possible. With the accumulation of a large number of high-throughput datasets, bioinformatics approaches have become increasing critical for unraveling the epitranscriptome. We review here the recent progress in bioinformatics approaches for deciphering the epitranscriptomes, including epitranscriptome data analysis techniques, RNA modification databases, disease-association inference, general functional annotation, and studies on RNA modification site prediction. We also discuss the limitations of existing approaches and offer some future perspectives.

Extremely-randomized-tree-based Prediction of N6-Methyladenosine Sites in Saccharomyces cerevisiae

Introduction

N⁶-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved.

Methodology

In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set.

Results

Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors.

Conclusion

In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.