Dataset Information

Genome-wide detection of high abundance N6-methyladenosine sites by microarray.

ABSTRACT: N(6)-methyladenosine (m(6)A), the most abundant internal RNA modification, functions in diverse biological processes, including regulation of embryonic stem cell self-renewal and differentiation. As yet, methods to detect m(6)A in the transcriptome rely on the availability and quality of an m(6)A antibody and are often associated with a high rate of false positives. Here, based on our observation that m(6)A interferes with A-T/U pairing, we report a microarray-based technology to map m(6)A sites in mouse embryonic stem cells. We identified 72 unbiased sites exhibiting high m(6)A levels from 66 PolyA RNAs. Bioinformatics analyses suggest identified sites are enriched on developmental regulators and may in some contexts modulate microRNA/mRNA interactions. Overall, we have developed microarray-based technology to capture highly enriched m(6)A sites in the mammalian transcriptome. This method provides an alternative means to identify m(6)A sites for certain applications.

SUBMITTER: Li Y

PROVIDER: S-EPMC4509940 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Genome-wide detection of high abundance N6-methyladenosine sites by microarray.

Li Yue Y Wang Yang Y Zhang Zhaolei Z Zamudio Alicia Viridiana AV Zhao Jing Crystal JC

RNA (New York, N.Y.) 20150619 8

N(6)-methyladenosine (m(6)A), the most abundant internal RNA modification, functions in diverse biological processes, including regulation of embryonic stem cell self-renewal and differentiation. As yet, methods to detect m(6)A in the transcriptome rely on the availability and quality of an m(6)A antibody and are often associated with a high rate of false positives. Here, based on our observation that m(6)A interferes with A-T/U pairing, we report a microarray-based technology to map m(6)A sites ...[more]

PMID: 26092943

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Project description:N6-methyladenosine (m6A) is the most abundant within eukaryotic messenger RNA modification, which plays an essential regulatory role in the control of cellular functions and gene expression. However, it remains an outstanding challenge to detect mRNA m6A transcriptome-wide at base resolution via experimental approaches, which are generally time-consuming and expensive. Developing computational methods is a good strategy for accurate in silico detection of m6A modification sites from the large amount of RNA sequence data. Unfortunately, the existing computational models are usually only for m6A site prediction in a single species, without considering the tissue level of species, while most of them are constructed based on low-confidence level data generated by an m6A antibody immunoprecipitation (IP)-based sequencing method, thereby restricting reliability and generalizability of proposed models. Here, we review recent advances in computational prediction of m6A sites and construct a new computational approach named im6APred using ensemble deep learning to accurately identify m6A sites based on high-confidence level data in multiple tissues of mammals. Our model im6APred builds upon a comprehensive evaluation of multiple classification methods, including four traditional classification algorithms and three deep learning methods and their ensembles. The optimal base-classifier combinations are then chosen by five-fold cross-validation test to achieve an effective stacked model. Our model im6APred can produce the area under the receiver operating characteristic curve (AUROC) in the range of 0.82-0.91 on independent tests, indicating that our model has the ability to learn general methylation rules on RNA bases and generalize to m6A transcriptome-wide identification. Moreover, AUROCs in the range of 0.77-0.96 were achieved using cross-species/tissues validation on the benchmark dataset, demonstrating differences in predictive performance at the tissue level and the need for constructing tissue-specific models for m6A site prediction.

Dataset Information

Genome-wide detection of high abundance N6-methyladenosine sites by microarray.

Publications

Genome-wide detection of high abundance N6-methyladenosine sites by microarray.

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