Detecting M6A Methylation Regions from Methylated RNA Immunoprecipitation Sequencing

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2021 Mar 16

PMID 33724304

Citations 5

Authors

Zhenxing Guo

Andrew M Shafik

Peng Jin

Zhijin Wu

Hao Wu

Affiliations

Soon will be listed here.

Abstract

Motivation: The post-transcriptional epigenetic modification on mRNA is an emerging field to study the gene regulatory mechanism and their association with diseases. Recently developed high-throughput sequencing technology named Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) enables one to profile mRNA epigenetic modification transcriptome wide. A few computational methods are available to identify transcriptome-wide mRNA modification, but they are either limited by over-simplified model ignoring the biological variance across replicates or suffer from low accuracy and efficiency.

Results: In this work, we develop a novel statistical method, based on an empirical Bayesian hierarchical model, to identify mRNA epigenetic modification regions from MeRIP-seq data. Our method accounts for various sources of variations in the data through rigorous modeling and applies shrinkage estimation by borrowing information from transcriptome-wide data to stabilize the parameter estimation. Simulation and real data analyses demonstrate that our method is more accurate, robust and efficient than the existing peak calling methods.

Availability And Implementation: Our method TRES is implemented as an R package and is freely available on Github at https://github.com/ZhenxingGuo0015/TRES.

Supplementary Information: Supplementary data are available at Bioinformatics online.

Citing Articles

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Variable calling of m6A and associated features in databases: a guide for end-users.

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RgnTX: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity.

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Analyzing mRNA Epigenetic Sequencing Data with TRESS.

Guo Z, Shafik A, Jin P, Wu Z, Wu H Methods Mol Biol. 2023; 2624:163-183.

PMID: 36723816 DOI: 10.1007/978-1-0716-2962-8_12.

Analysis approaches for the identification and prediction of -methyladenosine sites.

Yang Y, Liu Z, Lu J, Sun Y, Fu Y, Pan M Epigenetics. 2022; 18(1):2158284.

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