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Updated: Nov 12, 2025
Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
Published on: September 28, 2017
Identification of m6A-Associated RNA Binding Proteins Using an Integrative Computational Framework
Yiqian Zhang1, Michiaki Hamada1,2,3,4
1Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, Tokyo, Japan.
Abstract:
N6-methyladenosine (m6A) is an abundant modification on mRNA that plays an important role in regulating essential RNA activities. Several wet lab studies have identified some RNA binding proteins (RBPs) that are related to m6A's regulation. The objective of this study was to identify potential m6A-associated RBPs using an integrative computational framework. The framework was composed of an enrichment analysis and a classification model. Utilizing RBPs' binding data, we analyzed reproducible m6A regions from independent studies using this framework. The enrichment analysis identified known m6A-associated RBPs including YTH domain-containing proteins; it also identified RBM3 as a potential m6A-associated RBP for mouse. Furthermore, a significant correlation for the identified m6A-associated RBPs is observed at the protein expression level rather than the gene expression level. On the other hand, a Random Forest classification model was built for the reproducible m6A regions using RBPs' binding data. The RBP-based predictor demonstrated not only competitive performance when compared with sequence-based predictions but also reflected m6A's action of repelling against RBPs, which suggested that our framework can infer interaction between m6A and m6A-associated RBPs beyond sequence level when utilizing RBPs' binding data. In conclusion, we designed an integrative computational framework for the identification of known and potential m6A-associated RBPs. We hope the analysis will provide more insights on the studies of m6A and RNA modifications.
Insights
This study introduces a computational framework to identify RNA binding proteins (RBPs) associated with N6-methyladenosine (m6A) modifications. The method successfully identified known and potential m6A-associated RBPs, offering new insights into RNA regulation.
Area of Science:
- Molecular Biology
- Bioinformatics
- Epigenetics
Background:
- N6-methyladenosine (m6A) is a prevalent mRNA modification crucial for regulating RNA functions.
- Previous research has identified several RNA binding proteins (RBPs) involved in m6A regulation through experimental methods.
Purpose of the Study:
- To develop and apply an integrative computational framework for identifying m6A-associated RBPs.
- To analyze reproducible m6A regions using RBPs' binding data and assess the framework's predictive power.
Main Methods:
- An integrative computational framework combining enrichment analysis and a Random Forest classification model was developed.
- The framework utilized RNA binding protein (RBP) binding data to analyze reproducible m6A regions from independent studies.
- Performance was compared against sequence-based prediction methods.
Main Results:
- Enrichment analysis identified known m6A-associated RBPs (e.g., YTH domain proteins) and proposed RBM3 as a potential m6A-associated RBP in mice.
- A significant correlation was observed between m6A-associated RBPs and protein expression levels, rather than gene expression.
- The Random Forest model demonstrated competitive performance and revealed m6A's role in repelling RBPs, suggesting inference of interactions beyond sequence level.
Conclusions:
- An effective integrative computational framework was designed for identifying known and potential m6A-associated RBPs.
- The study provides a computational approach to gain deeper insights into m6A modifications and their associated regulatory proteins.
