Genome Biology (Dec 2019)

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

  • 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

DOI
https://doi.org/10.1186/s13059-019-1915-9
Journal volume & issue
Vol. 20, no. 1
pp. 1 – 17

Abstract

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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 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.

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