Statistical modeling of immunoprecipitation efficiency of MeRIP-seq data enabled accurate detection and quantification of epitranscriptome.

Abstract

Background: Recent advancements in epitranscriptomics highlight reversible RNA modifications as crucial regulators, with N6-methyladenosine (m⁶A) being abundant in eukaryotic mRNAs. Immunoprecipitation (IP) with specific antibodies is one of the most prevalent methods for m⁶A profiling, enabling the isolation of modified RNA for downstream analysis of their functional roles, but no computational methods have been developed to explicitly report a specific variation value in IP efficiencies conveniently, which may hinder the identification of novel modified RNA sites, particularly those with low abundance or less well-characterized.

Results: We develop a comprehensive analytical tool, AEEIP, for estimating the IP efficiency and correcting antibody bias in epitranscriptomics directly, AEEIP employs a mixture model to estimate the proportion of modification-containing RNA fragments from the source of IP data. Validation with both simulated and real data shows that AEEIP successfully estimates antibody bias across different replicates and experimental conditions, and reveals that this bias may obscure the accurate identification of m⁶A sites, leading to false negatives in the quantification of m⁶A-seq data. The proposed method provides reproducible IP efficiency analysis and more robust results for quantifying epitranscriptomics, which is available at: https://github.com/whz991026/AEEIP.