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. 2019 Jul 15;35(14):i90-i98.
doi: 10.1093/bioinformatics/btz316.

FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes

Song-Yao Zhang  1 , Shao-Wu Zhang  1 , Xiao-Nan Fan  1 , Teng Zhang  1 , Jia Meng  2 , Yufei Huang  3   4
Affiliations

FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes

Song-Yao Zhang et al. Bioinformatics. .

Abstract

Motivation: As the most abundant mammalian mRNA methylation, N6-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.

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Figures

Fig. 1.
Fig. 1.
Flowchart of FunDMDeep-m6A. (A) Single-base differential m6A methylation (DmM) site identification (DMDeep-m6A). (B) Differential expression analysis. A deeper grey color means the gene has a greater degree of DE. (C) Functional DmMGene identification and prioritization by integrating the DE and DmM using a network-based method. DmMGenes are mapped to 4 PPI networks. For each PPI network, MSB score for each DmMGene is calculated by integrating the weighted DE scores of its MSB neighbors and itself. An MSB is a fully connected triangle or square motif in the network, which is denoted using bold black edges. Orange nodes denote DmM genes, green nodes denote signaling genes, and grey nodes denote genes that are neither DmM and nor signaling genes. The ranks of DmMGenes in the 4 networks are integrated using the α-RRA method. The deeper the color of DmMGenes or FDmMGenes, the bigger the MSB scores and hence the more significant functions
Fig. 2.
Fig. 2.
Characteristics of DmM sites and FDmMGenes. (A) DmM site distribution on mRNA. (B) The number of DmMGenes, FDmMGenes and their harbored DmM sites in the 3’UTR, CDS and 5’UTR. For the MOLM13, Hela and A549 dataset, only METTL3-dependent genes and m6A sites are calculated. (C) Comparison of MSB scores with DE scores of DmMGenes and FDmMGenes. The MSB score of a gene is the mean of normalized MSB scores of 4 PPI networks
Fig. 3.
Fig. 3.
Comparison of FDmMGenes with mDrGenes. (A) The number of m6A-Driven genes (mDrGenes) and FDmMGenes. (B) Enrichment of top 5 functional enriched GO biological processes for top 100 FDmMGenes and mDrGenes. The FDmMGenes are ranked based on their MSB scores and the mDrGenes are ranked based on their DE degree. The enrichment analysis was done using DAVID
Fig. 4.
Fig. 4.
DmMgenes’ MSB score along with DE score. Each dot in the plot denotes a DmMGene and the shape of the dot denotes if the gene is either DmM (circle) or FDmM (triangle). The color is used to emphasize the functional DmMGenes. The red dots are known context-specific functional genes from (Batista et al., 2014) and (Ianniello and Fatica, 2018), which are genes involved in the maintenance of stem cell state and key regulators of endodermal differentiation for (A) hESCs dataset and m6A regulated genes relevant for AML proliferation for (B) MOLM13 dataset. The blue dots are prioritized FDmMGenes with relatively high MSB scores compared to its DE scores
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