RNA methylation is beginning to emerge as an universal epigenetic mark that may play a critical role in gene regulation. However, technologies aimed at identifying and characterizing transcriptome-wide RNA methylation (methyltranscriptome) are still at their early stages. This is largely because unlike DNA methylation, RNA methylation has to take into consideration transcript abundance, variations in gene expression levels, mRNA degradation, and most importantly positional bias caused by transcript isoforms. Furthermore, differences in RNA methylation in two different cellular contexts (e.g. normal vs stress) or different disease states (e.g. benign vs. cancer) pose yet another computational challenge for characterizing methyltranscriptome. The overall goal of this proposal is to develop, for the first time, computational graphical models to enable 1) accurate and reproducible detection of global mRNA methylations, and 2) context-specific differential RNA methylations in normal and disease states. To achieve these goals, we propose three specific aims:
in Aim 1, we will develop graphical models for detecting mRNA methylation that accounts for biological variations and read biases. We will also develop graphical model for detecting splicing-specific methylation sites.
In Aim 2, we will develop graphical models for detecting context-specific differential methylation.
In Aim 3, we will characterize and experimentally validate the transcriptome-wide, cell type-specific m5C and m6A methylation in normal and disease states. Successful completion of these aims will not only create a collection of comprehensive tools that enable the identification of global and context-specific mRNA methylations, but will also shed lights on the role of mRNA methylation in regulating gene expression, splicing, RNA editing, and RNA stability. This project leverages our expertise in epigenetics, computational modeling, high performance computing, bioinformatics and high throughput sequencing to add a new dimension to the emerging field of RNA methylaton and greatly contribute to the advances of computational modeling and learning.
This study adds a new paradigm to epigenetics research by proposing a critical role for mRNA methylation in gene regulation in normal and disease states. By identifying changes in mRNA methylation, our study promises mRNA methylation as potential biomarkers in human diseases. Reversibility of mRNA methylation can be also exploited for therapeutic interventions that involve reactivation or re-silencing of specific genes
|Chen, Hung-I Harry; Jin, Yufang; Huang, Yufei et al. (2016) Detection of high variability in gene expression from single-cell RNA-seq profiling. BMC Genomics 17 Suppl 7:508|
|Cui, Xiaodong; Meng, Jia; Zhang, Shaowu et al. (2016) A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data. Bioinformatics 32:i378-i385|
|Liu, Yuanhang; Wilson, Desiree; Leach, Robin J et al. (2016) MBDDiff: an R package designed specifically for processing MBDcap-seq datasets. BMC Genomics 17 Suppl 4:432|
|Cui, Xiaodong; Meng, Jia; Zhang, Shaowu et al. (2016) A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data. BMC Genomics 17 Suppl 7:520|
|Cui, Xiaodong; Wei, Zhen; Zhang, Lin et al. (2016) Guitar: An R/Bioconductor Package for Gene Annotation Guided Transcriptomic Analysis of RNA-Related Genomic Features. Biomed Res Int 2016:8367534|
|Chen, Hung-I; Liu, Yuanhang; Zou, Yi et al. (2015) Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads. BMC Genomics 16 Suppl 7:S14|
|Liu, Hui; Flores, Mario A; Meng, Jia et al. (2015) MeT-DB: a database of transcriptome methylation in mammalian cells. Nucleic Acids Res 43:D197-203|
|Cui, Xiaodong; Meng, Jia; Rao, Manjeet K et al. (2015) HEPeak: an HMM-based exome peak-finding package for RNA epigenome sequencing data. BMC Genomics 16 Suppl 4:S2|
|Liu, Lian; Zhang, Shao-Wu; Zhang, Yu-Chen et al. (2015) Decomposition of RNA methylome reveals co-methylation patterns induced by latent enzymatic regulators of the epitranscriptome. Mol Biosyst 11:262-74|
|Meng, Jia; Lu, Zhiliang; Liu, Hui et al. (2014) A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. Methods 69:274-81|