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

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 m<sup>6</sup>A sites, leading to false negatives in the quantification of m<sup>6</sup>A-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.

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Publication Details

Journal
Computational and Structural Biotechnology Journal
Published
2025-01-01
DOI
https://doi.org/10.1016/j.csbj.2025.08.030
Citations
1
Primary Topic
RNA modifications and cancer
Type
article
Field-Weighted Citation Impact
0.81

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article

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

Bowen Song, Manli Zhu, H Wang, Zhen Wei et al.
1 citations
Computational and Structural Biotechnology Journal
RNA modifications and cancer
0.81
article

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

Bowen Song, Manli Zhu, H Wang, Zhen Wei, Jia Meng, Jionglong Su, Anh Nguyen, Kunqi Chen, Yue Wang
article en
1 citations

Abstract

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 m<sup>6</sup>A sites, leading to false negatives in the quantification of m<sup>6</sup>A-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.

Computational and Structural Biotechnology JournalVol. 27
Fujian Medical University (CN), Nanjing University of Chinese Medicine (CN), University of Liverpool (GB), Soochow University (CN), Microsoft Research Asia (China) (CN), Xi’an Jiaotong-Liverpool University (CN)
National Natural Science Foundation of China, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Xi’an Jiaotong-Liverpool University, Nanjing University of Chinese Medicine, Nanjing University
Openalex Percentile: Top 24%
RNA modifications and cancer
0.81
DOIOpen Access PDFOpenAlex
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