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NiC17H14N5O5, triclinic, P1̄ (no. 2), a = 7.8199(8) Å, b = 8.6969(8) Å, c = 13.1934(13) Å, α = 93.955(1)°, β = 99.951(1)°, γ = 109.946(1)°, V = 822.79(14) Å3, Z = 2, Rgt(F) = 0.0316, wRref(F2) = 0.0688, T = 150 K.

Motivation As the most abundant mammalian mRNA methylation, N 6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive. Results To address this, we propose in this paper FunDMDeep-m6A a novel pipeline for identifying context-specific (e.g. disease versus normal, differentiated cells versus stem cells or gene knockdown cells versus wild-type cells) m6A-mediated functional genes. FunDMDeep-m6A includes, at the first step, DMDeep-m6A a novel method based on a deep learning model and a statistical test for identifying differential m6A methylation (DmM) sites from MeRIP-Seq data at a single-base resolution. FunDMDeep-m6A then identifies and prioritizes functional DmM genes (FDmMGenes) by combing the DmM genes (DmMGenes) with differential expression analysis using a network-based method. This proposed network method includes a novel m6A-signaling bridge (MSB) score to quantify the functional significance of DmMGenes by assessing functional interaction of DmMGenes with their signaling pathways using a heat diffusion process in protein-protein interaction (PPI) networks. The test results on 4 context-specific MeRIP-Seq datasets showed that FunDMDeep-m6A can identify more context-specific and functionally significant FDmMGenes than m6A-Driver. The functional enrichment analysis of these genes revealed that m6A targets key genes of many important context-related biological processes including embryonic development, stem cell differentiation, transcription, translation, cell death, cell proliferation and cancer-related pathways. These results demonstrate the power of FunDMDeep-m6A for elucidating m6A regulatory functions and its roles in biological processes and diseases. Availability and implementation The R-package for DMDeep-m6A is freely available from https://github.com/NWPU-903PR/DMDeepm6A1.0. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

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N 6 -methyladenosine (m 6 A) is the most abundant eukaryotic modification internal mRNA, which plays the crucial roles in the occurrence and development of cancer. However, current knowledge about m 6 A-mediated functional circuit and key genes targeted by m 6 A methylation in cancer is mostly elusive. Thus, here we proposed a novel network-based approach (called m 6 Acancer-Net) to identify m 6 A-mediated driver genes and their associated network in specific type of cancer, such as acute myeloid leukemia. m 6 A-mediated cancer driver genes are defined as genes mediated by m 6 A methylation, significantly mutated, and functionally interacted in cancer. m 6 Acancer-Net identified the m 6 A-mediated cancer driver genes by combining gene functional interaction network with RNA methylation, gene expression and mutation information. A cancer-specific gene-site heterogeneous network was firstly constructed by connecting the m 6 A site co-methylation network with the functional interaction pruned gene co-expression network generated from large scale gene expression profile of specific cancer. Then, the functional m 6 A-mediated genes were identified by selecting the m 6 A regulators as seed genes to perform the random walk with restart algorithm on the gene-site heterogeneous network. Finally, m 6 A-mediated cancer driver gene subnetworks were constructed by performing the heat diffusion of mutation frequency for functional m 6 A-mediated genes in protein-protein interaction networks. The experimental results of m 6 Acancer-Net on the acute myeloid leukemia (AML) and glioblastoma multiforme (GBM) data from TCGA project show that the m 6 A-mediated caner driver genes identified by m 6 Acancer-Net are targeted by m 6 A regulators, and mediate significant cancer-related pathways. They play crucial roles in development and prognostic stratification of cancer. Moreover, 15 m 6 A-mediated cancer driver genes identified in AML are validated by literatures to mediate AML progress, and 14 m 6 A-mediated cancer driver genes identified in GBM are validated by literatures to participate in development of GBM. m 6 Acancer-Net is reliable to identify the functionally significant m 6 A-mediated driver genes in specific cancer, and it can effectively facilitate the understanding of regulatory and therapeutic mechanism of cancer driver genes in epitranscriptome layer., (Copyright © 2022 Elsevier Inc. All rights reserved.)

N 6 -methyladenosine (m 6 A) is the most abundant form of mRNA modification and plays an important role in regulating gene expression. However, the mechanisms of m 6 A regulated gene expression in cell or condition specific, are still poorly understood. Even though, some methods are able to predict m 6 A regulated expression (m 6 A-reg-exp) genes in specific context, they don't introduce the m 6 A reader binding information, while this information can help to predict m 6 A-reg-exp genes and more clearly to explain the mechanisms of m 6 A-mediated gene expression process. Thus, by integrating m 6 A sites and reader binding information, we proposed a novel method (called m 6 Aexpress-Reader) to predict m 6 A-reg-exp genes from limited MeRIP-seq data in specific context. m 6 Aexpress-Reader adopts the reader binding signal strength to weight the posterior distribution of the estimated regulatory coefficients for enhancing the prediction power. By using m 6 Aexpress-Reader, we found the complex characteristic of m 6 A on gene expression regulation and the distinct regulated pattern of m 6 A-reg-exp genes with different reader binding. m 6 A readers, YTHDF2 or IGF2BP1/3 all play an important role in various cancers and the key cancer pathways. In addition, m 6 Aexpress-Reader reveals the distinct m 6 A regulated mode of reader targeted genes in cancer. m 6 Aexpress-Reader could be a useful tool for studying the m 6 A regulation on reader target genes in specific context and it can be freely accessible at: https://github.com/NWPU-903PR/m6AexpressReader., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Recent studies have revealed that the RNA N 6 -methyladenosine (m 6 A) modification plays a critical role in a variety of biological processes and associated with multiple diseases including cancers. Till this day, transcriptome-wide m 6 A 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 m 6 A sites and various diseases are still largely unknown. There are yet computational approaches developed for investigating potential association between individual m 6 A sites and diseases, which represents a major challenge in the epitranscriptome analysis. Thus, to infer the disease-related m 6 A sites, we implemented a novel multi-layer heterogeneous network-based approach, which incorporates the associations among diseases, genes and m 6 A 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 m 6 A 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 d isease-associated r ibon u cleic acid m ethylation, was built to support the query of disease-associated RNA m 6 A methylation sites, and is freely available at: www.xjtlu.edu.cn/biologicalsciences/drum.