Identify m6A Sites Research Articles

I6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome.

DNA N6-methyladenine (6mA) is associated with a wide range of biological processes. Since the distribution of 6mA site in the genome is non-random, accurate identification of 6mA sites is crucial for understanding its biological functions. Although experimental methods have been proposed for this regard, they are still cost-ineffective for detecting 6mA site in genome-wide scope. Therefore, it is desirable to develop computational methods to facilitate the identification of 6mA site. In this study, a computational method called i6mA-Pred was developed to identify 6mA sites in the rice genome, in which the optimal nucleotide chemical properties obtained by the using feature selection technique were used to encode the DNA sequences. It was observed that the i6mA-Pred yielded an accuracy of 83.13% in the jackknife test. Meanwhile, the performance of i6mA-Pred was also superior to other methods. A user-friendly web-server, i6mA-Pred is freely accessible at http://lin-group.cn/server/i6mA-Pred.

IRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition

As a prevalent post-transcriptional modification, N6-methyladenosine (m6A) plays key roles in a series of biological processes. Although experimental technologies have been developed and applied to identify m6A sites, they are still cost-ineffective for transcriptome-wide detections of m6A. As good complements to the experimental techniques, some computational methods have been proposed to identify m6A sites. However, their performance remains unsatisfactory. In this study, we firstly proposed an Euclidean distance based method to construct a high quality benchmark dataset. By encoding the RNA sequences using pseudo nucleotide composition, a new predictor called iRNA(m6A)-PseDNC was developed to identify m6A sites in the Saccharomyces cerevisiae genome. It has been demonstrated by the 10-fold cross validation test that the performance of iRNA(m6A)-PseDNC is superior to the existing methods. Meanwhile, for the convenience of most experimental scientists, established at the site http://lin-group.cn/server/iRNA(m6A)-PseDNC.php is its web-server, by which users can easily get their desired results without need to go through the detailed mathematics. It is anticipated that iRNA(m6A)-PseDNC will become a useful high throughput tool for identifying m6A sites in the S. cerevisiae genome.

Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites

Abstract N6-methyladenosine (m6A), as one of the most well-studied RNA modifications, has been found to be involved with a wide range of biological processes. Recently, diverse computational methods have been developed for automated identification of m6A sites within RNAs. To identify m6A sites accurately, one of the major challenges is to extract informative features to describe characteristics of m6A sites. However, existing feature representation methods are usually hand-crafted based, and cannot capture discriminative information of m6A sites. In this paper, we develop a m6A site predictor, named DeepM6APred. In this predictor, we propose to use a deep learning based feature descriptor with deep belief network (DBN) to extract high-level latent features. By integrating the deep features with traditional handcrafted features, we train a classification model based on support vector machine and successfully improve the predictive ability of m6A sites. Experimental results on a benchmark dataset show that our proposed method outperforms the state-of-the-art predictors, at least 2% higher in terms of Matthew's correlation coefficient (MCC). Moreover, a webserver that implements the DeepM6APred is established, which is currently available at the website: http://server.malab.cn/DeepM6APred . It is expected to be a useful tool to assist biologists to reveal the functional mechanisms of m6A sites.

Identifying RNA N6-Methyladenosine Sites in Escherichia coli Genome.

N6-methyladenosine (m6A) plays important roles in a branch of biological and physiological processes. Accurate identification of m6A sites is especially helpful for understanding their biological functions. Since the wet-lab techniques are still expensive and time-consuming, it's urgent to develop computational methods to identify m6A sites from primary RNA sequences. Although there are some computational methods for identifying m6A sites, no methods whatsoever are available for detecting m6A sites in microbial genomes. In this study, we developed a computational method for identifying m6A sites in Escherichia coli genome. The accuracies obtained by the proposed method are >90% in both 10-fold cross-validation test and independent dataset test, indicating that the proposed method holds the high potential to become a useful tool for the identification of m6A sites in microbial genomes.

BERMP: a cross-species classifier for predicting m6A sites by integrating a deep learning algorithm and a random forest approach.

N6-methyladenosine (m6A) is a prevalent RNA methylation modification involved in several biological processes. Hundreds or thousands of m6A sites identified from different species using high-throughput experiments provides a rich resource to construct in-silico approaches for identifying m6A sites. The existing m6A predictors are developed using conventional machine-learning (ML) algorithms and most are species-centric. In this paper, we develop a novel cross-species deep-learning classifier based on bidirectional Gated Recurrent Unit (BGRU) for the prediction of m6A sites. In comparison with conventional ML approaches, BGRU achieves outstanding performance for the Mammalia dataset that contains over fifty thousand m6A sites but inferior for the Saccharomyces cerevisiae dataset that covers around a thousand positives. The accuracy of BGRU is sensitive to the data size and the sensitivity is compensated by the integration of a random forest classifier with a novel encoding of enhanced nucleic acid content. The integrated approach dubbed as BGRU-based Ensemble RNA Methylation site Predictor (BERMP) has competitive performance in both cross-validation test and independent test. BERMP also outperforms existing m6A predictors for different species. Therefore, BERMP is a novel multi-species tool for identifying m6A sites with high confidence. This classifier is freely available at http://www.bioinfogo.org/bermp.

Regulatory Role of N6 -methyladenosine (m6 A) Methylation in RNA Processing and Human Diseases.

N6 -methyladenosine (m6 A) modification is an abundant and conservative RNA modification in bacterial and eukaryotic cells. m6 A modification mainly occurs in the 3' untranslated regions (UTRs) and near the stop codons of mRNA. Diverse strategies have been developed for identifying m6 A sites in single nucleotide resolution. Dynamic regulation of m6 A is found in metabolism, embryogenesis, and developmental processes, indicating a possible epigenetic regulation role along RNA processing and exerting biological functions. It has been known that m6 A editing involves in nuclear RNA export, mRNA degradation, protein translation, and RNA splicing. Deficiency of m6 A modification will lead to kinds of diseases, such as obesity, cancer, type 2 diabetes mellitus (T2DM), infertility, and developmental arrest. Some specific inhibitors against methyltransferase and demethylase have been developed to selectively regulate m6 A modification, which may be advantageous for treatment of m6 A related diseases. J. Cell. Biochem. 118: 2534-2543, 2017. © 2017 Wiley Periodicals, Inc.

Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine

N6-methyladenosine (m6A) refers to methylation of the adenosine nucleotide acid at the nitrogen-6 position. It plays an important role in a series of biological processes, such as splicing events, mRNA exporting, nascent mRNA synthesis, nuclear translocation and translation process. Numerous experiments have been done to successfully characterize m6A sites within sequences since high-resolution mapping of m6A sites was established. However, as the explosive growth of genomic sequences, using experimental methods to identify m6A sites are time-consuming and expensive. Thus, it is highly desirable to develop fast and accurate computational identification methods. In this study, we propose a sequence-based predictor called RAM-NPPS for identifying m6A sites within RNA sequences, in which we present a novel feature representation algorithm based on multi-interval nucleotide pair position specificity, and use support vector machine classifier to construct the prediction model. Comparison results show that our proposed method outperforms the state-of-the-art predictors on three benchmark datasets across the three species, indicating the effectiveness and robustness of our method. Moreover, an online webserver implementing the proposed predictor has been established at http://server.malab.cn/RAM-NPPS/. It is anticipated to be a useful prediction tool to assist biologists to reveal the mechanisms of m6A site functions.

Identifying N 6-methyladenosine sites in the Arabidopsis thaliana transcriptome

N 6-Methyladenosine (m6A) plays important roles in many biological processes. The knowledge of the distribution of m6A is helpful for understanding its regulatory roles. Although the experimental methods have been proposed to detect m6A, the resolutions of these methods are still unsatisfying especially for Arabidopsis thaliana. Benefitting from the experimental data, in the current work, a support vector machine-based method was proposed to identify m6A sites in A. thaliana transcriptome. The proposed method was validated on a benchmark dataset using jackknife test and was also validated by identifying strain-specific m6A sites in A. thaliana. The obtained predictive results indicate that the proposed method is quite promising. For the convenience of experimental biologists, an online webserver for the proposed method was built, which is freely available at http://lin.uestc.edu.cn/server/M6ATH . These results indicate that the proposed method holds a potential to become an elegant tool in identifying m6A site in A. thaliana.

Genome-wide detection of high abundance N6-methyladenosine sites by microarray.

N6-methyladenosine (m6A), the most abundant internal RNA modification, functions in diverse biological processes, including regulation of embryonic stem cell self-renewal and differentiation. As yet, methods to detect m6A in the transcriptome rely on the availability and quality of an m6A antibody and are often associated with a high rate of false positives. Here, based on our observation that m6A interferes with A–T/U pairing, we report a microarray-based technology to map m6A sites in mouse embryonic stem cells. We identified 72 unbiased sites exhibiting high m6A levels from 66 PolyA RNAs. Bioinformatics analyses suggest identified sites are enriched on developmental regulators and may in some contexts modulate microRNA/mRNA interactions. Overall, we have developed microarray-based technology to capture highly enriched m6A sites in the mammalian transcriptome. This method provides an alternative means to identify m6A sites for certain applications.