MoAIMS: efficient software for detection of enriched regions of MeRIP-Seq
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Yiqian Zhang1,2, Michiaki Hamada3,4,5,6,7
1Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1 Okubo Shinjuku-ku, Tokyo, 169-8555, Japan.
Background:
Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) is a popular sequencing method for studying RNA modifications and, in particular, for N6-methyladenosine (m6A), the most abundant RNA methylation modification found in various species. The detection of enriched regions is a main challenge of MeRIP-Seq analysis, however current tools either require a long time or do not fully utilize features of RNA sequencing such as strand information which could cause ambiguous calling. On the other hand, with more attention on the treatment experiments of MeRIP-Seq, biologists need intuitive evaluation on the treatment effect from comparison. Therefore, efficient and user-friendly software that can solve these tasks must be developed.
Results:
We developed a software named "model-based analysis and inference of MeRIP-Seq (MoAIMS)" to detect enriched regions of MeRIP-Seq and infer signal proportion based on a mixture negative-binomial model. MoAIMS is designed for transcriptome immunoprecipitation sequencing experiments; therefore, it is compatible with different RNA sequencing protocols. MoAIMS offers excellent processing speed and competitive performance when compared with other tools. When MoAIMS is applied to studies of m6A, the detected enriched regions contain known biological features of m6A. Furthermore, signal proportion inferred from MoAIMS for m6A treatment datasets (perturbation of m6A methyltransferases) showed a decreasing trend that is consistent with experimental observations, suggesting that the signal proportion can be used as an intuitive indicator of treatment effect.
Conclusions:
MoAIMS is efficient and easy-to-use software implemented in R. MoAIMS can not only detect enriched regions of MeRIP-Seq efficiently but also provide intuitive evaluation on treatment effect for MeRIP-Seq treatment datasets.
Insights
We developed MoAIMS, a fast and user-friendly software for analyzing methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data. MoAIMS accurately detects RNA modification sites and provides intuitive evaluation of treatment effects in MeRIP-Seq experiments.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) is crucial for studying RNA modifications like N6-methyladenosine (m6A).
- Current MeRIP-Seq analysis tools face challenges in accurately detecting enriched regions and evaluating treatment effects.
- Existing methods are often time-consuming and may not fully leverage RNA sequencing features like strand information.
Purpose of the Study:
- To develop efficient and user-friendly software for MeRIP-Seq data analysis.
- To improve the detection of enriched regions in MeRIP-Seq data.
- To provide an intuitive method for evaluating treatment effects in MeRIP-Seq experiments.
Main Methods:
- Developed "model-based analysis and inference of MeRIP-Seq (MoAIMS)" software.
- Utilized a mixture negative-binomial model for detecting enriched regions and inferring signal proportion.
- Ensured compatibility with various RNA sequencing protocols for transcriptome immunoprecipitation sequencing.
Main Results:
- MoAIMS demonstrates excellent processing speed and competitive performance compared to existing tools.
- Detected enriched regions using MoAIMS align with known biological features of m6A.
- Inferred signal proportion from MoAIMS for m6A treatment datasets showed a decreasing trend consistent with experimental observations.
Conclusions:
- MoAIMS is an efficient and user-friendly R-implemented software for MeRIP-Seq analysis.
- The software accurately detects enriched regions and offers intuitive evaluation of treatment effects.
- Signal proportion inferred by MoAIMS serves as a valuable indicator for treatment effects in MeRIP-Seq studies.
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