Detecting m6A methylation regions from Methylated RNA Immunoprecipitation Sequencing

Variable calling of m6A and associated features in databases: a guide for end-users

N6-methyladenosine (m$^{6}$A) is a widely-studied methylation to messenger RNAs, which has been linked to diverse cellular processes and human diseases. Numerous databases that collate m$^{6}$A profiles of distinct cell types have been created to facilitate quick and easy mining of m$^{6}$A signatures associated with cell-specific phenotypes. However, these databases contain inherent complexities that have not been explicitly reported, which may lead to inaccurate identification and interpretation of m$^{6}$A-associated biology by end-users who are unaware of them. Here, we review various m$^{6}$A-related databases, and highlight several critical matters. In particular, differences in peak-calling pipelines across databases drive substantial variability in both peak number and coordinates with only moderate reproducibility, and the inclusion of peak calls from early m$^{6}$A sequencing protocols may lead to the reporting of false positives or negatives. The awareness of these matters will help end-users avoid the inclusion of potentially unreliable data in their studies and better utilize m$^{6}$A databases to derive biologically meaningful results.

m6Aexpress-enet: Predicting the regulatory expression m6A sites by an enet-regularization negative binomial regression model

As the most abundant mRNA modification, m6A controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific m6A sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple m6A sites in each gene could influence the prediction performance. To address the above challenges, we propose an elastic-net regularized negative binomial regression model (called m6Aexpress-enet) to predict which m6A site could potentially regulate its gene expression. Comprehensive evaluations on simulated datasets demonstrate that m6Aexpress-enet could achieve the top prediction performance. Applying m6Aexpress-enet on real MeRIP-seq data from human lymphoblastoid cell lines, we have uncovered the complex regulatory pattern of predicted m6A sites and their unique enrichment pathway of the constructed co-methylation modules. m6Aexpress-enet proves itself as a powerful tool to enable biologists to discover the mechanism of m6A regulatory gene expression. Furthermore, the source code and the step-by-step implementation of m6Aexpress-enet is freely accessed at https://github.com/tengzhangs/m6Aexpress-enet.

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

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

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.

RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity

Colocalization analysis of genomic region sets has been widely adopted to unveil potential functional interactions between corresponding biological attributes, which often serves as the basis for further investigation. A number of methods have been developed for colocalization analysis of genomic elements. However, none of them explicitly considered the transcriptome heterogeneity and isoform ambiguity, making them less appropriate for analyzing transcriptome elements. Here, we developed RgnTX, an R/Bioconductor tool for the colocalization analysis of transcriptome elements with permutation tests. Different from existing approaches, RgnTX directly takes advantage of transcriptome annotation, and offers high flexibility in the null model to simulate realistic transcriptome-wide background, such as the complex alternative splicing patterns. Importantly, it supports the testing of transcriptome elements without clear isoform association, which is often the real scenario due to technical limitations. Proposed package offers a wide selection of pre-defined functions, easy to be utilized by users for visualizing permutation results, calculating shifted z-scores and conducting multiple hypothesis testing under Benjamini-Hochberg correction. Moreover, with synthetic and real datasets, we show that RgnTX novel testing modes return distinct and more significant results compared to existing genome-based methods. We believe RgnTX should make a useful tool to characterize the randomness of the transcriptome, and for conducting statistical association analysis for genomic region sets within the heterogeneous transcriptome. The package now has been accepted by Bioconductor and is freely available at: https://bioconductor.org/packages/RgnTX.

Analyzing mRNA Epigenetic Sequencing Data with TRESS

RNA epigenetics has emerged as an active topic to study gene regulation mechanisms. In this regard, the MeRIP-seq technology allows profiling transcriptome-wide mRNA modifications, in particular m⁶A. The primary goals for the analysis of MeRIP-seq data are the identification of m⁶A-methylated regions under each condition and across different biological conditions. Here we describe detailed procedures to guide researchers in MeRIP-seq data analyses by providing step-by-step instructions of the dedicated bioconductor package TRESS.

Differential RNA methylation analysis for MeRIP-seq data under general experimental design

MOTIVATION

RNA epigenetics is an emerging field to study the post-transcriptional gene regulation. The dynamics of RNA epigenetic modification have been reported to associate with many human diseases. Recently developed high-throughput technology named Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) enables the transcriptome-wide profiling of N6-methyladenosine (m6A) modification and comparison of RNA epigenetic modifications. There are a few computational methods for the comparison of mRNA modifications under different conditions but they all suffer from serious limitations.

RESULTS

In this work, we develop a novel statistical method to detect differentially methylated mRNA regions from MeRIP-seq data. We model the sequence count data by a hierarchical negative binomial model that accounts for various sources of variations and derive parameter estimation and statistical testing procedures for flexible statistical inferences under general experimental designs. Extensive benchmark evaluations in simulation and real data analyses demonstrate that our method is more accurate, robust and flexible compared to existing methods.

AVAILABILITY AND IMPLEMENTATION

Our method TRESS is implemented as an R/Bioconductor package and is available at https://bioconductor.org/packages/devel/TRESS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Epitranscriptomic dynamics in brain development and disease

Distinct cell types are generated at specific times during brain development and are regulated by epigenetic, transcriptional, and newly emerging epitranscriptomic mechanisms. RNA modifications are known to affect many aspects of RNA metabolism and have been implicated in the regulation of various biological processes and in disease. Recent studies imply that dysregulation of the epitranscriptome may be significantly associated with neuropsychiatric, neurodevelopmental, and neurodegenerative disorders. Here we review the current knowledge surrounding the role of the RNA modifications N6-methyladenosine, 5-methylcytidine, pseudouridine, A-to-I RNA editing, 2'O-methylation, and their associated machinery, in brain development and human diseases. We also highlight the need for the development of new technologies in the pursuit of directly mapping RNA modifications in both genome- and single-molecule-level approach.

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.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.