Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer

Dynamic RNA Modifications in Gene Expression Regulation

The recent discovery of reversible mRNA methylation has opened a new realm of post-transcriptional gene regulation in eukaryotes. The identification and functional characterization of proteins that specifically recognize RNA N6-methyladenosine (m6A) unveiled it as a modification that cells utilize to accelerate mRNA metabolism and translation. N6-adenosine methylation directs mRNAs to distinct fates by grouping them for differential processing, translation and decay in processes such as cell differentiation, embryonic development and stress responses. Other mRNA modifications, including N1-methyladenosine (m1A), 5-methylcytosine (m5C) and pseudouridine, together with m6A form the epitranscriptome and collectively code a new layer of information that controls protein synthesis.

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Post-transcriptional gene regulation by mRNA modifications

Gene expression can be regulated post-transcriptionally through dynamic and reversible RNA modifications. A recent noteworthy example is N(6)-methyladenosine (m(6)A), which affects messenger RNA (mRNA) localization, stability, translation and splicing. Here we report on a new mRNA modification, N(1)-methyladenosine (m(1)A), that occurs on thousands of different gene transcripts in eukaryotic cells, from yeast to mammals, at an estimated average transcript stoichiometry of 20% in humans. Employing newly developed sequencing approaches, we show that m(1)A is enriched around the start codon upstream of the first splice site: it preferentially decorates more structured regions around canonical and alternative translation initiation sites, is dynamic in response to physiological conditions, and correlates positively with protein production. These unique features are highly conserved in mouse and human cells, strongly indicating a functional role for m(1)A in promoting translation of methylated mRNA.

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The dynamic N 1 -methyladenosine methylome in eukaryotic messenger RNA

Summary The availability of cancer genomic data makes it possible to analyze genes related to cancer. Cancer is usually the result of a set of genes and the signal of a single gene could be covered by background noise. Here, we present a web server named Gene Set Cancer Analysis (GSCALite) to analyze a set of genes in cancers with the following functional modules. (i) Differential expression in tumor versus normal, and the survival analysis; (ii) Genomic variations and their survival analysis; (iii) Gene expression associated cancer pathway activity; (iv) miRNA regulatory network for genes; (v) Drug sensitivity for genes; (vi) Normal tissue expression and eQTL for genes. GSCALite is a user-friendly web server for dynamic analysis and visualization of gene set in cancer and drug sensitivity correlation, which will be of broad utilities to cancer researchers. Availability and implementation GSCALite is available on http://bioinfo.life.hust.edu.cn/web/GSCALite/. Supplementary information Supplementary data are available at Bioinformatics online.

GSCALite: a web server for gene set cancer analysis.

N(1)-Methyladenosine (m(1)A) is a prevalent post-transcriptional RNA modification, yet little is known about its abundance, topology and dynamics in mRNA. Here, we show that m(1)A is prevalent in Homo sapiens mRNA, which shows an m(1)A/A ratio of ∼0.02%. We develop the m(1)A-ID-seq technique, based on m(1)A immunoprecipitation and the inherent ability of m(1)A to stall reverse transcription, as a means for transcriptome-wide m(1)A profiling. m(1)A-ID-seq identifies 901 m(1)A peaks (from 600 genes) in mRNA and noncoding RNA and reveals a prominent feature, enrichment in the 5' untranslated region of mRNA transcripts, that is distinct from the pattern for N(6)-methyladenosine, the most abundant internal mammalian mRNA modification. Moreover, m(1)A in mRNA is reversible by ALKBH3, a known DNA/RNA demethylase. Lastly, we show that m(1)A methylation responds dynamically to stimuli, and we identify hundreds of stress-induced m(1)A sites. Collectively, our approaches allow comprehensive analysis of m(1)A modification and provide tools for functional studies of potential epigenetic regulation via the reversible and dynamic m(1)A methylation.

Transcriptome-wide mapping reveals reversible and dynamic N1-methyladenosine methylome

In recent years, major breakthroughs in RNA-modification-mediated regulation of gene expression have been made, leading to the emerging field of epitranscriptomics.Our understanding of the distribution, regulation and function of these dynamic RNA modifications is based on sequencing technologies. In this Review, we focus on the major mRNA modifications in the transcriptome of eukaryotic cells: N6-methyladenosine, N6, 2'-O-dimethyladenosine, 5-methylcytidine, 5-hydroxylmethylcytidine, inosine, pseudouridine and N1-methyladenosine. We discuss the sequencing technologies used to profile these epitranscriptomic marks, including scale, resolution, quantitative feature, pre-enrichment capability and the corresponding bioinformatics tools. We also discuss the challenges of epitranscriptome profiling and highlight the prospect of future detection tools. We aim to guide the choice of different detection methods and inspire new ideas in RNA biology.

Epitranscriptome sequencing technologies: decoding RNA modifications

Base-Resolution Mapping Reveals Distinct m1A Methylome in Nuclear- and Mitochondrial-Encoded Transcripts

Transcription factors (TFs) and microRNAs (miRNAs) can jointly regulate target gene expression in the forms of feed-forward loops (FFLs) or feedback loops (FBLs). These regulatory loops serve as important motifs in gene regulatory networks and play critical roles in multiple biological processes and different diseases. Major progress has been made in bioinformatics and experimental study for the TF and miRNA co-regulation in recent years. To further speed up its identification and functional study, it is indispensable to make a comprehensive review. In this article, we summarize the types of FFLs and FBLs and their identified methods. Then, we review the behaviors and functions for the experimentally identified loops according to biological processes and diseases. Future improvements and challenges are also discussed, which includes more powerful bioinformatics approaches and high-throughput technologies in TF and miRNA target prediction, and the integration of networks of multiple levels.

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Xushen Xiong

Papers

Transcriptome-wide mapping reveals reversible and dynamic N1-methyladenosine methylome

Epitranscriptome sequencing technologies: decoding RNA modifications

Base-Resolution Mapping Reveals Distinct m1A Methylome in Nuclear- and Mitochondrial-Encoded Transcripts

Transcription factor and microRNA co-regulatory loops: important regulatory motifs in biological processes and diseases

Landscape and Regulation of m6A and m6Am Methylome across Human and Mouse Tissues