RADAR: Differential analysis of MeRIP-seq data with a random effect model

Zijie Scott Zhang; Qi Zhang; Mark A. Eckert; Allen C. Zhu; Agnieszka Chryplewicz; Dario F. De Jesus; Decheng Ren; Rohit Kulkarni; Ernst R. Lengyel; Chuan He; Mengjie Chen

doi:10.1186/s13059-019-1915-9

BioinformaticsGenome biologyDec 1, 2019

Peer-reviewed journal article

RADAR: Differential analysis of MeRIP-seq data with a random effect model

Zijie Scott Zhang iD, Qi Zhang iD, Mark A. Eckert iD, Allen C. Zhu iD, Agnieszka Chryplewicz iD, Dario F. De Jesus iD, Decheng Ren iD, Rohit Kulkarni iD, Ernst R. Lengyel iD, Chuan He iD, Mengjie Chen iD

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Abstract

Epitranscriptome profiling using MeRIP-seq is a powerful technique for in vivo functional studies of reversible RNA modifications. We develop RADAR, a comprehensive analytical tool for detecting differentially methylated loci in MeRIP-seq data. RADAR enables accurate identification of altered methylation sites by accommodating variability of pre-immunoprecipitation expression level and post-immunoprecipitation count using different strategies. In addition, it is compatible with complex study design when covariates need to be incorporated in the analysis. Through simulation and real datasets analyses, we show that RADAR leads to more accurate and reproducible differential methylation analysis results than alternatives, which is available at https://github.com/scottzijiezhang/RADAR.