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

Citation
Zhang, Z., et al. “Radar: Differential Analysis Of Merip-Seq Data With A Random Effect Model.”. Genome Biology, p. 294.
Center University of Chicago
Author Zijie Zhang, Qi Zhan, Mark Eckert, Allen Zhu, Agnieszka Chryplewicz, Dario F De Jesus, Decheng Ren, Rohit N Kulkarni, Ernst Lengyel, Chuan He, Mengjie Chen
Keywords Differential methylation, MeRIP-seq, N6-adenosine methylation (m6A)
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 dataset 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.

Year of Publication
2019
Journal
Genome biology
Volume
20
Issue
1
Number of Pages
294
Date Published
12/2019
ISSN Number
1474-760X
DOI
10.1186/s13059-019-1915-9
Alternate Journal
Genome Biol.
PMID
31870409
PMCID
PMC6927177
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