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As the most abundant RNA modification, N6-methyladenosine (m6A) plays an important role in various RNA activities including gene expression and translation. With the rapid application of MeRIP-seq technology, samples of multiple groups, such as the involved multiple viral/ bacterial infection or distinct cell differentiation stages, are extracted from same experimental unit. However, our current knowledge about how the dynamic m6A regulating gene expression and the role in certain biological processes (e.g. immune response in this complex context) is largely elusive due to lack of effective tools. To address this issue, we proposed a Bayesian hierarchical mixture model (called m6Aexpress-BHM) to predict m6A regulation of gene expression (m6A-reg-exp) in multiple groups of MeRIP-seq experiment with limited samples. Comprehensive evaluations of m6Aexpress-BHM on the simulated data demonstrate its high predicting precision and robustness. Applying m6Aexpress-BHM on three real-world datasets (i.e. Flaviviridae infection, infected time-points of bacteria and differentiation stages of dendritic cells), we predicted more m6A-reg-exp genes with positive regulatory mode that significantly participate in innate immune or adaptive immune pathways, revealing the underlying mechanism of the regulatory function of m6A during immune response. In addition, we also found that m6A may influence the expression of PD-1/PD-L1 via regulating its interacted genes. These results demonstrate the power of m6Aexpress-BHM , helping us understand the m6A regulatory function in immune system. [ABSTRACT FROM AUTHOR]

Motivation The driver genes play a key role in the evolutionary process of cancer. Effectively identifying these driver genes is crucial to cancer diagnosis and treatment. However, due to the high heterogeneity of cancers, it remains challenging to identify the driver genes for individual patients. Although some computational methods have been proposed to tackle this problem, they seldom consider the fact that the genes functionally similar to the well-established driver genes may likely play similar roles in cancer process, which potentially promotes the driver gene identification. Thus, here we developed a novel approach of IMCDriver to promote the driver gene identification both for cohorts and individual patients. Results IMCDriver first considers the well-established driver genes as prior information, and adopts the using multi-omics data (e.g. somatic mutation, gene expression and protein–protein interaction) to compute the similarity between patients/genes. Then, IMCDriver prioritizes the personalized mutated genes according to their functional similarity to the well-established driver genes via Inductive Matrix Completion. Finally, IMCDriver identifies the highly rank-ordered genes as the personalized driver genes. The results on five cancer datasets from the Cancer Genome Consortium show that our IMCDriver outperforms other existing state-of-the-art methods both in the cohort and patient-specific driver gene identification. IMCDriver also reveals some novel driver genes that potentially drive cancer development. In addition, even for the driver genes rarely mutated among a population, IMCDriver can still identify them and prioritize them with high priorities. Availability and implementation Code available at https://github.com/NWPU-903PR/IMCDriver. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Considerable evidence suggests that during the progression of cancer initiation, the state transition from wellness to disease is not necessarily smooth but manifests switch-like nonlinear behaviors, preventing the cancer prediction and early interventional therapy for patients. Understanding the mechanism of such wellness-to-disease transitions is a fundamental and challenging task. Despite the advances in flux theory of nonequilibrium dynamics and 'critical slowing down'-based system resilience theory, a system-level approach still lacks to fully describe this state transition. Here, we present a novel framework (called bioRFR) to quantify such wellness-to-disease transition during cancer initiation through uncovering the biological system's resilience function from gene expression data. We used bioRFR to reconstruct the biologically and dynamically significant resilience functions for cancer initiation processes (e.g. BRCA, LUSC and LUAD). The resilience functions display the similar resilience pattern with hysteresis feature but different numbers of tipping points, which implies that once the cell become cancerous, it is very difficult or even impossible to reverse to the normal state. More importantly, bioRFR can measure the severe degree of cancer patients and identify the personalized key genes that are associated with the individual system's state transition from normal to tumor in resilience perspective, indicating that bioRFR can contribute to personalized medicine and targeted cancer therapy. [ABSTRACT FROM AUTHOR]

Motivation N 6-methyladenosine (m6A) is the most abundant mammalian mRNA methylation with versatile functions. To date, although a number of bioinformatics tools have been developed for location discovery of m6A modification, functional understanding is still quite limited. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, there is an urgent need for user-friendly tools to identify and explore the functional relevance of context-specific m6A methylation to gain insights into the epitranscriptome layer of gene expression regulation. Results We introduced here Funm6AViewer, a novel platform to identify, prioritize and visualize the functional gene interaction networks mediated by dynamic m6A RNA methylation unveiled from a case control study. By taking the differential RNA methylation data and differential gene expression data, both of which can be inferred from the widely used MeRIP-seq data, as the inputs, Funm6AViewer enables a series of analysis, including: (i) examining the distribution of differential m6A sites, (ii) prioritizing the genes mediated by dynamic m6A methylation and (iii) characterizing functionally the gene regulatory networks mediated by condition-specific m6A RNA methylation. Funm6AViewer should effectively facilitate the understanding of the epitranscriptome circuitry mediated by this reversible RNA modification. Availability and implementation Funm6AViewer is available both as a convenient web server (https://www.xjtlu.edu.cn/biologicalsciences/funm6aviewer) with graphical interface and as an independent R package (https://github.com/NWPU-903PR/Funm6AViewer) for local usage. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

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.)