m6Aexpress-Reader: Prediction of m6A regulated expression genes by integrating m6A sites and reader binding information in specific- context

被引:1
作者
Zhang, Teng [1 ]
Zhang, Shao-Wu [1 ]
Zhang, Song-Yao [1 ]
Ma, Qian-qian [1 ]
机构
[1] Northwestern Polytech Univ, Sch Automat, Key Lab Informat Fus Technol, Minist Educ, Xi'an 710027, Shaanxi, Peoples R China
基金
中国博士后科学基金;
关键词
M(6)A reader; M(6)A regulated gene expression; Methylation level; Bayesian hierarchical model; RNA METHYLATION; N-6-METHYLADENOSINE; WRITERS;
D O I
10.1016/j.ymeth.2022.03.008
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:167 / 178
页数:12
相关论文
共 76 条
  • [61] m6A mRNA Methylation Is Essential for Oligodendrocyte Maturation and CNS Myelination
    Xu, Huan
    Dzhashiashvili, Yulia
    Shah, Ankeeta
    Kunjamma, Rejani B.
    Weng, Yi-lan
    Elbaz, Benayahu
    Fei, Qili
    Jones, Joshua S.
    Li, Yang I.
    Zhuang, Xiaoxi
    Ming, Guo-Li
    He, Chuan
    Popko, Brian
    [J]. NEURON, 2020, 105 (02) : 293 - +
  • [62] Up-regulation of IGF2BP2 by multiple mechanisms in pancreatic cancer promotes cancer proliferation by activating the PI3K/Akt signaling pathway
    Xu, Xiaodong
    Yu, Yan
    Zong, Ke
    Lv, Pengwei
    Gu, Yuantin
    [J]. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH, 2019, 38 (01)
  • [63] A Review in Research Progress Concerning m6A Methylation and Immunoregulation
    Zhang, Caiyan
    Fu, Jinrong
    Zhou, Yufeng
    [J]. FRONTIERS IN IMMUNOLOGY, 2019, 10
  • [64] Hypoxia induces the breast cancer stem cell phenotype by HIF-dependent and ALKBH5-mediated m6A-demethylation of NANOG mRNA
    Zhang, Chuanzhao
    Samanta, Debangshu
    Lu, Haiquan
    Bullen, John W.
    Zhang, Huimin
    Chen, Ivan
    He, Xiaoshun
    Semenza, Gregg L.
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2016, 113 (14) : E2047 - E2056
  • [65] Recent advances in functional annotation and prediction of the epitranscriptome
    Zhang, Song-Yao
    Zhang, Shao-Wu
    Zhang, Teng
    Fan, Xiao-Nan
    Meng, Jia
    [J]. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2021, 19 : 3015 - 3026
  • [66] FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes
    Zhang, Song-Yao
    Zhang, Shao-Wu
    Fan, Xiao-Nan
    Zhang, Teng
    Meng, Jia
    Huang, Yufei
    [J]. BIOINFORMATICS, 2019, 35 (14) : I90 - I98
  • [67] Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods
    Zhang, Song-Yao
    Zhang, Shao-Wu
    Fan, Xiao-Nan
    Meng, Jia
    Chen, Yidong
    Gao, Shou-Jiang
    Huang, Yufei
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2019, 15 (01)
  • [68] m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks
    Zhang, Song-Yao
    Zhang, Shao-Wu
    Liu, Lian
    Meng, Jia
    Huang, Yufei
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2016, 12 (12)
  • [69] Zhang T., 2021, NUCLEIC ACIDS RES
  • [70] trumpet: transcriptome-guided quality assessment of m6A-seq data
    Zhang, Teng
    Zhang, Shao-Wu
    Zhang, Lin
    Meng, Jia
    [J]. BMC BIOINFORMATICS, 2018, 19