Recent advances in functional annotation and prediction of the epitranscriptome

RNA modifications, in particular N(6)-methyladenosine (m(6)A), participate in every stages of RNA metabolism and play diverse roles in essential biological processes and disease pathogenesis. Thanks to the advances in sequencing technology, tens of thousands of RNA modification sites can be identified in a typical high-throughput experiment; however, it remains a major challenge to decipher the functional relevance of these sites, such as, affecting alternative splicing, regulation circuit in essential biological processes or association to diseases. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, we review here recent progress in functional annotation and prediction of RNA modification sites from a bioinformatics perspective. The review covers naïve annotation with associated biological events, e.g., single nucleotide polymorphism (SNP), RNA binding protein (RBP) and alternative splicing, prediction of key sites and their regulatory functions, inference of disease association, and mining the diagnosis and prognosis value of RNA modification regulators. We further discussed the limitations of existing approaches and some future perspectives

m7GHub: deciphering the location, regulation and pathogenesis of internal mRNA N7-methylguanosine (m7G) sites in human

Motivation Recent progress in N7-methylguanosine (m7G) RNA methylation studies has focused on its internal (rather than capped) presence within mRNAs. Tens of thousands of internal mRNA m7G sites have been identified within mammalian transcriptomes, and a single resource to best share, annotate and analyze the massive m7G data generated recently are sorely needed. Results We report here m7GHub, a comprehensive online platform for deciphering the location, regulation and pathogenesis of internal mRNA m7G. The m7GHub consists of four main components, including: the first internal mRNA m7G database containing 44 058 experimentally validated internal mRNA m7G sites, a sequence-based high-accuracy predictor, the first web server for assessing the impact of mutations on m7G status, and the first database recording 1218 disease-associated genetic mutations that may function through regulation of m7G methylation. Together, m7GHub will serve as a useful resource for research on internal mRNA m7G modification

Transcriptome-Wide Analysis of RNA m6A Methylation and Gene Expression Changes Among Two Arabidopsis Ecotypes and Their Reciprocal Hybrids

The remodeling of transcriptome, epigenome, proteome, and metabolome in hybrids plays an important role in heterosis. N(6)-methyladenosine (m6A) methylation is the most abundant type of post-transcriptional modification for mRNAs, but the pattern of inheritance from parents to hybrids and potential impact on heterosis are largely unknown. We constructed transcriptome-wide mRNA m6A methylation maps of Arabidopsis thaliana Col-0 and Landsberg erecta (Ler) and their reciprocal F1 hybrids. Generally, the transcriptome-wide pattern of m6A methylation tends to be conserved between accessions. Approximately 74% of m6A methylation peaks are consistent between the parents and hybrids, indicating that a majority of the m6A methylation is maintained after hybridization. We found a significant association between differential expression and differential m6A modification, and between non-additive expression and non-additive methylation on the same gene. The overall RNA m6A level between Col-0 and Ler is clearly different but tended to disappear at the allelic sites in the hybrids. Interestingly, many enriched biological functions of genes with differential m6A modification between parents and hybrids are also conserved, including many heterosis-related genes involved in biosynthetic processes of starch. Collectively, our study revealed the overall pattern of inheritance of mRNA m6A modifications from parents to hybrids and a potential new layer of regulatory mechanisms related to heterosis formation

Novel Algorithm Development for ‘NextGeneration’ Sequencing Data Analysis

In recent years, the decreasing cost of ‘Next generation’ sequencing has spawned numerous applications for interrogating whole genomes and transcriptomes in research, diagnostic and forensic settings. While the innovations in sequencing have been explosive, the development of scalable and robust bioinformatics software and algorithms for the analysis of new types of data generated by these technologies have struggled to keep up. As a result, large volumes of NGS data available in public repositories are severely underutilised, despite providing a rich resource for data mining applications. Indeed, the bottleneck in genome and transcriptome sequencing experiments has shifted from data generation to bioinformatics analysis and interpretation. This thesis focuses on development of novel bioinformatics software to bridge the gap between data availability and interpretation. The work is split between two core topics – computational prioritisation/identification of disease gene variants and identification of RNA N6 -adenosine Methylation from sequencing data. The first chapter briefly discusses the emergence and establishment of NGS technology as a core tool in biology and its current applications and perspectives. Chapter 2 introduces the problem of variant prioritisation in the context of Mendelian disease, where tens of thousands of potential candidates are generated by a typical sequencing experiment. Novel software developed for candidate gene prioritisation is described that utilises data mining of tissue-specific gene expression profiles (Chapter 3). The second part of chapter investigates an alternative approach to candidate variant prioritisation by leveraging functional and phenotypic descriptions of genes and diseases from multiple biomedical domain ontologies (Chapter 4). Chapter 5 discusses N6 AdenosineMethylation, a recently re-discovered posttranscriptional modification of RNA. The core of the chapter describes novel software developed for transcriptome-wide detection of this epitranscriptomic mark from sequencing data. Chapter 6 presents a case study application of the software, reporting the previously uncharacterised RNA methylome of Kaposi’s Sarcoma Herpes Virus. The chapter further discusses a putative novel N6-methyl-adenosine -RNA binding protein and its possible roles in the progression of viral infection