The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Jia Meng - One of the best experts on this subject based on the ideXlab platform.
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WITMSG: Large-scale Prediction of Human Intronic m6A RNA Methylation Sites from Sequence and Genomic Features
Current Genomics, 2020Co-Authors: Jia MengAbstract:Introduction: N6-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/.
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ISGm1A: Integration of Sequence Features and Genomic Features to Improve the Prediction of Human m1A RNA Methylation Sites
IEEE Access, 2020Co-Authors: Jia MengAbstract:As a new epitranscriptomic modification, N1-methyladenosine (m1A) plays an important role in the gene expression regulation. Although some computational methods were proposed to predict m1A 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 m1A RNA Methylation, we proposed a computational framework, ISGm1A, which stands for integration sequence features and genomic features to improve the prediction of human m1A 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 m1A 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 m1A 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.
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Predict disease-related RNA Methylation sites from the Methylation-expression association by using hypergeometric test
BIBE 2019; The Third International Conference on Biological Information and Biomedical Engineering, 2019Co-Authors: Yujiao Tang, Kunqi Chen, Xiangyu Wu, Jia MengAbstract:N6-methyladenosine (m(exp 6)A) is the most abundant RNA modification on mRNA and lncRNA in human. Recent studies have shown that it is implicated in various critical biological processes, such as translation and alteRNAtive splicing, and involves in multiple human diseases, including cancer and obesity. However, only a small number of RNA Methylation sites have been explicitly associated to disease conditions with experimental approaches, since RNA m(exp 6)A Methylation sites may potentially play a pivotal regulatory role in a wide range of human pathogenesis and should not be ignored, an efficient predictor for disease-associated m(exp 6)A RNA Methylation sites becomes a major challenge. In order to obtain a comprehensive understanding of disease-associated m(exp 6)A RNA Methylation site, we purpose here a computational framework to integrate the three different layers of a network structure, including the expression profiles of genes, the Methylation profiles of m(exp 6)A RNA Methylation sites and gene-disease associations, and utilize subsequently the Hypergenomic test to predict integrally the potential disease-associated m(exp 6)A sites. We show with a rigorous cross-validation that the AUROC of the proposed approach is 0.73; and a number of predictions are supported by existing literatures, suggesting our prediction is helpful for identifying the novel m(exp 6)A sites associated to human disease. Ultimately, the predicted results are freely available online at: http://180.208.58.19/DRRMSDB/, which supports the queries of diseaserelated m(exp 6)A RNA Methylation sites. We presented a very first attempt for computational prediction of diseaseassociated RNA Methylation sites, helping researchers of the field to understand the roles of m(exp 6)A RNA Methylation in human diseases and facilitating the development of the treatments.
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m6acomet large scale functional prediction of individual m 6 a RNA Methylation sites from an RNA co Methylation network
BMC Bioinformatics, 2019Co-Authors: Lin Zhang, Jia Meng, Kunqi Chen, Xiangyu Wu, Qing Zhang, Jionglong SuAbstract:Over 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. To 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. An 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 .
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DRUM: Inference of Disease-Associated m6A RNA Methylation Sites From a Multi-Layer Heterogeneous Network.
Frontiers in Genetics, 2019Co-Authors: Yujiao Tang, Shao-wu Zhang, Yufei Huang, Kunqi Chen, Xiangyu Wu, Song-yao Zhang, Bowen Song, Jia MengAbstract: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.
Frank Lyko - One of the best experts on this subject based on the ideXlab platform.
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RNA Methylation and Its Role in the Hematopoietic System
Blood, 2017Co-Authors: Frank LykoAbstract:RNA Methylation represents a novel expansion of traditional epigenetic concepts. RNAs can be methylated at adenine and at cytosine residues, and both modifications have distinct regulatory potential. Our work focuses on the DNMT2 enzyme, which is a member of the animal (cytosine-5) DNA methyltransferase family and has long been considered to function as a DNA methyltransferase. However, a DNA methyltransferase activity could not be confirmed conclusively and more recent work clearly demonstrates that DNMT2 is a tRNA methyltransferase. This unexpected substrate is interpreted to reflect an evolutionary ancient substrate switch from DNA to tRNA that expanded the epigenetic regulatory capacity of the DNMT family to also include RNA. To analyze the function of DNMT2, we performed a detailed analysis of knockout mice. These mice are viable and fertile, but also show a reduction of hematopoietic stem and progenitor cell populations and a cell-autonomous defect in their differentiation.1 RNA bisulfite sequencing revealed that Dnmt2 methylates C38 of tRNA Asp(GTC), Gly(GCC), and Val(AAC). Proteomic analyses from primary bone marrow cells uncovered systematic differences in protein expression that are due to specific codon mistranslation by tRNAs lacking DNMT2-dependent Methylation. Together, these results illustrate the regulatory capacity of DNMT2-mediated tRNA Methylation in genome recoding.2 Our current work addresses additional mechanistic aspects that link tRNA Methylation to translational fidelity and investigates the relevance of DNMT2-mediated tRNA Methylation for leukemogenesis. 1. Tuorto F, Herbst F, Alerasool N, et al. The tRNA methyltransferase Dnmt2 is required for accurate polypeptide synthesis during haematopoiesis. EMBO J. 2015;34(18):2350-2362. 2. Tuorto F, Lyko F. Genome recoding by tRNA modifications. Open Biol. 2016;6(12):160287. Disclosures No relevant conflicts of interest to declare.
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RNA Methylation by dnmt2 protects transfer RNAs against stress induced cleavage
Genes & Development, 2010Co-Authors: Matthias Schaefer, Katharina Hanna, Tim Pollex, Francesca Tuorto, Madeleine Meusburger, Mark Helm, Frank LykoAbstract:The covalent modification of nucleic acids plays an important role in regulating the functions of DNA and RNA. DNA modifications have been analyzed in considerable detail, and the characterization of (cytosine-5) DNA Methylation has been crucial for understanding the molecular basis of epigenetic gene regulation (Klose and Bird 2006). (Cytosine-5) Methylation has also been documented in various RNA species, including tRNA, but the function of RNA Methylation has not been firmly established yet (Motorin et al. 2010). Dnmt2 proteins were originally assigned to the DNA methyltransferase family, because of their strong sequence conservation of catalytic DNA methyltransferase motifs (Okano et al. 1998; Yoder and Bestor 1998). A recent study has suggested that Dnmt2-mediated DNA Methylation is important for transposon silencing in Drosophila (Phalke et al. 2009). However, only a weak and distributive DNA Methylation activity has been reported in various systems (Jeltsch et al. 2006). The ambiguities associated with the DNA methyltransferase activity of Dnmt2 have also prompted the search for alteRNAtive enzyme substrates, and resulted in the discovery of a tRNA methyltransferase activity of Dnmt2 (Goll et al. 2006). Purified recombinant human Dnmt2 methylated RNA preparations from Dnmt2 mutant mice, flies, and plants. Further experiments identified C38 in the anti-codon loop of tRNAAsp as the Methylation target site of Dnmt2 (Goll et al. 2006). However, the functional relevance of the tRNA methyltransferase activity of Dnmt2 remains to be established. Dnmt2 mutant mice, flies, and plants were reported to be viable and fertile (Goll et al. 2006) under standard laboratory conditions. A distinct Dnmt2 mutant phenotype, caused by morpholino knockdown experiments, has so far been reported only in zebrafish, leading to lethal differentiation defects in the retina, liver, and brain (Rai et al. 2007). In addition, two studies have indicated increased stress tolerance in Dnmt2-overexpressing flies and amoebas (Lin et al. 2005; Fisher et al. 2006). However, the underlying molecular mechanisms have not been investigated yet.
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Azacytidine Inhibits RNA Methylation at DNMT2 Target Sites in Human Cancer Cell Lines
Cancer Research, 2009Co-Authors: Matthias Schaefer, Sabine Hagemann, Katharina Hanna, Frank LykoAbstract:The cytosine analogues azacytidine and decitabine are currently being developed as drugs for epigenetic cancer therapy. Although various studies have shown that both drugs are effective in inhibiting DNA Methylation, it has also become clear that their mode of action is not limited to DNA deMethylation. Because azacytidine is a ribonucleoside, the primary target of this drug may be cellular RNA rather than DNA. We have now analyzed the possibility that azacytidine inhibits the RNA methyltransferase DNMT2. We found that DNMT2 is variably expressed in human cancer cell lines. RNA bisulfite sequencing showed that azacytidine, but not decitabine, inhibits cytosine 38 Methylation of tRNA Asp ,a major substrate of DNMT2. Azacytidine caused a substantially stronger effect than decitabine on the metabolic rate of all the cancer cell lines tested, consistent with an effect of this drug on RNA metabolism. Of note, drug-induced loss of RNA Methylation seemed specific for DNMT2 target sites because we did not observe any significant deMethylation at sites known to be methylated by other RNA methyltransferases. Our results uncover a novel and quantifiable drug activity of azacytidine and raise the possibility that tRNA hypoMethylation might contribute to patient responses. [Cancer Res 2009;69(20):8127–32]
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RNA cytosine Methylation analysis by bisulfite sequencing
Nucleic Acids Research, 2008Co-Authors: Matthias Schaefer, Katharina Hanna, Tim Pollex, Frank LykoAbstract:Covalent modifications of nucleic acids play an important role in regulating their functions. Among these modifications, (cytosine-5) DNA Methylation is best known for its role in the epigenetic regulation of gene expression. Post-transcriptional RNA modification is a characteristic feature of noncoding RNAs, and has been described for rRNAs, tRNAs and miRNAs. (Cytosine-5) RNA Methylation has been detected in stable and long-lived RNA molecules, but its function is still unclear, mainly due to technical limitations. In order to facilitate the analysis of RNA Methylation patterns we have established a protocol for the chemical deamination of cytosines in RNA, followed by PCR-based amplification of cDNA and DNA sequencing. Using tRNAs and rRNAs as examples we show that cytosine Methylation can be reproducibly and quantitatively detected by bisulfite sequencing. The combination of this method with deep sequencing allowed the analysis of a large number of RNA molecules. These results establish a versatile method for the identification and characterization of RNA Methylation patterns, which will be useful for defining the biological function of RNA Methylation.
Matthias Schaefer - One of the best experts on this subject based on the ideXlab platform.
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RNA Methylation by dnmt2 protects transfer RNAs against stress induced cleavage
Genes & Development, 2010Co-Authors: Matthias Schaefer, Katharina Hanna, Tim Pollex, Francesca Tuorto, Madeleine Meusburger, Mark Helm, Frank LykoAbstract:The covalent modification of nucleic acids plays an important role in regulating the functions of DNA and RNA. DNA modifications have been analyzed in considerable detail, and the characterization of (cytosine-5) DNA Methylation has been crucial for understanding the molecular basis of epigenetic gene regulation (Klose and Bird 2006). (Cytosine-5) Methylation has also been documented in various RNA species, including tRNA, but the function of RNA Methylation has not been firmly established yet (Motorin et al. 2010). Dnmt2 proteins were originally assigned to the DNA methyltransferase family, because of their strong sequence conservation of catalytic DNA methyltransferase motifs (Okano et al. 1998; Yoder and Bestor 1998). A recent study has suggested that Dnmt2-mediated DNA Methylation is important for transposon silencing in Drosophila (Phalke et al. 2009). However, only a weak and distributive DNA Methylation activity has been reported in various systems (Jeltsch et al. 2006). The ambiguities associated with the DNA methyltransferase activity of Dnmt2 have also prompted the search for alteRNAtive enzyme substrates, and resulted in the discovery of a tRNA methyltransferase activity of Dnmt2 (Goll et al. 2006). Purified recombinant human Dnmt2 methylated RNA preparations from Dnmt2 mutant mice, flies, and plants. Further experiments identified C38 in the anti-codon loop of tRNAAsp as the Methylation target site of Dnmt2 (Goll et al. 2006). However, the functional relevance of the tRNA methyltransferase activity of Dnmt2 remains to be established. Dnmt2 mutant mice, flies, and plants were reported to be viable and fertile (Goll et al. 2006) under standard laboratory conditions. A distinct Dnmt2 mutant phenotype, caused by morpholino knockdown experiments, has so far been reported only in zebrafish, leading to lethal differentiation defects in the retina, liver, and brain (Rai et al. 2007). In addition, two studies have indicated increased stress tolerance in Dnmt2-overexpressing flies and amoebas (Lin et al. 2005; Fisher et al. 2006). However, the underlying molecular mechanisms have not been investigated yet.
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Azacytidine Inhibits RNA Methylation at DNMT2 Target Sites in Human Cancer Cell Lines
Cancer Research, 2009Co-Authors: Matthias Schaefer, Sabine Hagemann, Katharina Hanna, Frank LykoAbstract:The cytosine analogues azacytidine and decitabine are currently being developed as drugs for epigenetic cancer therapy. Although various studies have shown that both drugs are effective in inhibiting DNA Methylation, it has also become clear that their mode of action is not limited to DNA deMethylation. Because azacytidine is a ribonucleoside, the primary target of this drug may be cellular RNA rather than DNA. We have now analyzed the possibility that azacytidine inhibits the RNA methyltransferase DNMT2. We found that DNMT2 is variably expressed in human cancer cell lines. RNA bisulfite sequencing showed that azacytidine, but not decitabine, inhibits cytosine 38 Methylation of tRNA Asp ,a major substrate of DNMT2. Azacytidine caused a substantially stronger effect than decitabine on the metabolic rate of all the cancer cell lines tested, consistent with an effect of this drug on RNA metabolism. Of note, drug-induced loss of RNA Methylation seemed specific for DNMT2 target sites because we did not observe any significant deMethylation at sites known to be methylated by other RNA methyltransferases. Our results uncover a novel and quantifiable drug activity of azacytidine and raise the possibility that tRNA hypoMethylation might contribute to patient responses. [Cancer Res 2009;69(20):8127–32]
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RNA cytosine Methylation analysis by bisulfite sequencing
Nucleic Acids Research, 2008Co-Authors: Matthias Schaefer, Katharina Hanna, Tim Pollex, Frank LykoAbstract:Covalent modifications of nucleic acids play an important role in regulating their functions. Among these modifications, (cytosine-5) DNA Methylation is best known for its role in the epigenetic regulation of gene expression. Post-transcriptional RNA modification is a characteristic feature of noncoding RNAs, and has been described for rRNAs, tRNAs and miRNAs. (Cytosine-5) RNA Methylation has been detected in stable and long-lived RNA molecules, but its function is still unclear, mainly due to technical limitations. In order to facilitate the analysis of RNA Methylation patterns we have established a protocol for the chemical deamination of cytosines in RNA, followed by PCR-based amplification of cDNA and DNA sequencing. Using tRNAs and rRNAs as examples we show that cytosine Methylation can be reproducibly and quantitatively detected by bisulfite sequencing. The combination of this method with deep sequencing allowed the analysis of a large number of RNA molecules. These results establish a versatile method for the identification and characterization of RNA Methylation patterns, which will be useful for defining the biological function of RNA Methylation.
Shao-wu Zhang - One of the best experts on this subject based on the ideXlab platform.
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DRUM: Inference of Disease-Associated m6A RNA Methylation Sites From a Multi-Layer Heterogeneous Network.
Frontiers in Genetics, 2019Co-Authors: Yujiao Tang, Shao-wu Zhang, Yufei Huang, Kunqi Chen, Xiangyu Wu, Song-yao Zhang, Bowen Song, Jia MengAbstract: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.
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qnb differential RNA Methylation analysis for count based small sample sequencing data with a quad negative binomial model
BMC Bioinformatics, 2017Co-Authors: Shao-wu Zhang, Jia Meng, Yufei HuangAbstract:As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA Methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA Methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA Methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose Methylation level cannot be accurately estimated due to their low expression level, making differential RNA Methylation analysis a difficult task. We present QNB, a statistical approach for differential RNA Methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m1A-Seq, Par-CLIP, RIP-Seq, etc.
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DRME: Count-based differential RNA Methylation analysis at small sample size scenario.
Analytical Biochemistry, 2016Co-Authors: Shao-wu Zhang, Yixin Zhang, Yufei Huang, Runsheng Chen, Jia MengAbstract:Abstract Differential Methylation, which concerns difference in the degree of epigenetic regulation via Methylation between two conditions, has been formulated as a beta or beta-binomial distribution to address the within-group biological variability in sequencing data. However, a beta or beta-binomial model is usually difficult to infer at small sample size scenario with discrete reads count in sequencing data. On the other hand, as an emerging research field, RNA Methylation has drawn more and more attention recently, and the differential analysis of RNA Methylation is significantly different from that of DNA Methylation due to the impact of transcriptional regulation. We developed DRME to better address the differential RNA Methylation problem. The proposed model can effectively describe within-group biological variability at small sample size scenario and handles the impact of transcriptional regulation on RNA Methylation. We tested the newly developed DRME algorithm on simulated and 4 MeRIP-Seq case–control studies and compared it with Fisher's exact test. It is in principle widely applicable to several other RNA-related data types as well, including RNA Bisulfite sequencing and PAR–CLIP. The code together with an MeRIP-Seq dataset is available online ( https://github.com/lzcyzm/DRME ) for evaluation and reproduction of the figures shown in this article.
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Spatially Enhanced Differential RNA Methylation Analysis from Affinity-Based Sequencing Data with Hidden Markov Model
BioMed Research International, 2015Co-Authors: Yu Chen Zhang, Lin Zhang, Shao-wu Zhang, Yufei Huang, Jia MengAbstract:With the development of new sequencing technology, the entire N6-methyl-adenosine (m6A) RNA methylome can now be unbiased profiled with methylated RNA immune-precipitation sequencing technique (MeRIP-Seq), making it possible to detect differential Methylation states of RNA between two conditions, for example, between normal and cancerous tissue. However, as an affinity-based method, MeRIP-Seq has yet provided base-pair resolution; that is, a single Methylation site determined from MeRIP-Seq data can in practice contain multiple RNA Methylation residuals, some of which can be regulated by different enzymes and thus differentially methylated between two conditions. Since existing peak-based methods could not effectively differentiate multiple Methylation residuals located within a single Methylation site, we propose a hidden Markov model (HMM) based approach to address this issue. Specifically, the detected RNA Methylation site is further divided into multiple adjacent small bins and then scanned with higher resolution using a hidden Markov model to model the dependency between spatially adjacent bins for improved accuracy. We tested the proposed algorithm on both simulated data and real data. Result suggests that the proposed algorithm clearly outperforms existing peak-based approach on simulated systems and detects differential Methylation regions with higher statistical significance on real dataset.
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a protocol for RNA Methylation differential analysis with merip seq data and exomepeak r bioconductor package
Methods, 2014Co-Authors: Jia Meng, Lin Zhang, Shao-wu Zhang, Zhiliang Lu, Yidong Chen, Yufei HuangAbstract:Despite the prevalent studies of DNA/Chromatin related epigenetics, such as, histone modifications and DNA Methylation, RNA epigenetics has not drawn deserved attention until a new affinity-based sequencing approach MeRIP-Seq was developed and applied to survey the global mRNA N6-methyladenosine (m6A) in mammalian cells. As a marriage of ChIP-Seq and RNA-Seq, MeRIP-Seq has the potential to study the transcriptome-wide distribution of various post-transcriptional RNA modifications. We have previously developed an R/Bioconductor package ‘exomePeak’ for detecting RNA Methylation sites under a specific experimental condition or the identifying the differential RNA Methylation sites in a case control study from MeRIP-Seq data. Compared with other relatively well studied data types such as ChIP-Seq and RNA-Seq, the study of MeRIP-Seq data is still at very early stage, and existing protocols are not optimized for dealing with the intrinsic characteristic of MeRIP-Seq data. We therein provide here a detailed and easy-to-use protocol of using exomePeak R/Bioconductor package along with other software programs for analysis of MeRIP-Seq data, which covers raw reads alignment, RNA Methylation site detection, motif discovery, differential RNA Methylation analysis, and functional analysis. Particularly, the rationales behind each processing step as well as the specific method used, the best practice, and possible alteRNAtive strategies are briefly discussed. The exomePeak R/Bioconductor package is freely available from Bioconductor: http://www.bioconductor.org/packages/release/bioc/html/exomePeak.html
Yufei Huang - One of the best experts on this subject based on the ideXlab platform.
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DRUM: Inference of Disease-Associated m6A RNA Methylation Sites From a Multi-Layer Heterogeneous Network.
Frontiers in Genetics, 2019Co-Authors: Yujiao Tang, Shao-wu Zhang, Yufei Huang, Kunqi Chen, Xiangyu Wu, Song-yao Zhang, Bowen Song, Jia MengAbstract: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.
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Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model
Current Bioinformatics, 2018Co-Authors: Lin Zhang, Yufei Huang, Yanling He, Huaizhi Wang, Xuesong Wang, Jia MengAbstract: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/.
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qnb differential RNA Methylation analysis for count based small sample sequencing data with a quad negative binomial model
BMC Bioinformatics, 2017Co-Authors: Shao-wu Zhang, Jia Meng, Yufei HuangAbstract:As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA Methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA Methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA Methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose Methylation level cannot be accurately estimated due to their low expression level, making differential RNA Methylation analysis a difficult task. We present QNB, a statistical approach for differential RNA Methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m1A-Seq, Par-CLIP, RIP-Seq, etc.
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DRME: Count-based differential RNA Methylation analysis at small sample size scenario.
Analytical Biochemistry, 2016Co-Authors: Shao-wu Zhang, Yixin Zhang, Yufei Huang, Runsheng Chen, Jia MengAbstract:Abstract Differential Methylation, which concerns difference in the degree of epigenetic regulation via Methylation between two conditions, has been formulated as a beta or beta-binomial distribution to address the within-group biological variability in sequencing data. However, a beta or beta-binomial model is usually difficult to infer at small sample size scenario with discrete reads count in sequencing data. On the other hand, as an emerging research field, RNA Methylation has drawn more and more attention recently, and the differential analysis of RNA Methylation is significantly different from that of DNA Methylation due to the impact of transcriptional regulation. We developed DRME to better address the differential RNA Methylation problem. The proposed model can effectively describe within-group biological variability at small sample size scenario and handles the impact of transcriptional regulation on RNA Methylation. We tested the newly developed DRME algorithm on simulated and 4 MeRIP-Seq case–control studies and compared it with Fisher's exact test. It is in principle widely applicable to several other RNA-related data types as well, including RNA Bisulfite sequencing and PAR–CLIP. The code together with an MeRIP-Seq dataset is available online ( https://github.com/lzcyzm/DRME ) for evaluation and reproduction of the figures shown in this article.
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Spatially Enhanced Differential RNA Methylation Analysis from Affinity-Based Sequencing Data with Hidden Markov Model
BioMed Research International, 2015Co-Authors: Yu Chen Zhang, Lin Zhang, Shao-wu Zhang, Yufei Huang, Jia MengAbstract:With the development of new sequencing technology, the entire N6-methyl-adenosine (m6A) RNA methylome can now be unbiased profiled with methylated RNA immune-precipitation sequencing technique (MeRIP-Seq), making it possible to detect differential Methylation states of RNA between two conditions, for example, between normal and cancerous tissue. However, as an affinity-based method, MeRIP-Seq has yet provided base-pair resolution; that is, a single Methylation site determined from MeRIP-Seq data can in practice contain multiple RNA Methylation residuals, some of which can be regulated by different enzymes and thus differentially methylated between two conditions. Since existing peak-based methods could not effectively differentiate multiple Methylation residuals located within a single Methylation site, we propose a hidden Markov model (HMM) based approach to address this issue. Specifically, the detected RNA Methylation site is further divided into multiple adjacent small bins and then scanned with higher resolution using a hidden Markov model to model the dependency between spatially adjacent bins for improved accuracy. We tested the proposed algorithm on both simulated data and real data. Result suggests that the proposed algorithm clearly outperforms existing peak-based approach on simulated systems and detects differential Methylation regions with higher statistical significance on real dataset.
