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Updated: Mar 25, 2026
A Method for Measuring RNA N6-methyladenosine Modifications in Cells and Tissues
Published on: December 5, 2016
SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features
Yuan Zhou1, Pan Zeng2, Yan-Hui Li2
1Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China MOE Key Lab of Molecular Cardiovascular Sciences, Peking University, Beijing 100191, China Center for Noncoding RNA Medicine, Peking University Health Science Center, Beijing 100191, China soontide6825@163.com.
Abstract:
N(6)-methyladenosine (m(6)A) is a prevalent RNA methylation modification involved in the regulation of degradation, subcellular localization, splicing and local conformation changes of RNA transcripts. High-throughput experiments have demonstrated that only a small fraction of the m(6)A consensus motifs in mammalian transcriptomes are modified. Therefore, accurate identification of RNA m(6)A sites becomes emergently important. For the above purpose, here a computational predictor of mammalian m(6)A site named SRAMP is established. To depict the sequence context around m(6)A sites, SRAMP combines three random forest classifiers that exploit the positional nucleotide sequence pattern, the K-nearest neighbor information and the position-independent nucleotide pair spectrum features, respectively. SRAMP uses either genomic sequences or cDNA sequences as its input. With either kind of input sequence, SRAMP achieves competitive performance in both cross-validation tests and rigorous independent benchmarking tests. Analyses of the informative features and overrepresented rules extracted from the random forest classifiers demonstrate that nucleotide usage preferences at the distal positions, in addition to those at the proximal positions, contribute to the classification. As a public prediction server, SRAMP is freely available at http://www.cuilab.cn/sramp/.
Insights
Accurately identifying N(6)-methyladenosine (m(6)A) sites in mammals is crucial. The new computational tool SRAMP effectively predicts m(6)A sites using sequence patterns, improving RNA modification analysis.
Area of Science:
- Molecular Biology
- Bioinformatics
- Genomics
Background:
- N(6)-methyladenosine (m(6)A) is a key RNA modification regulating transcript fate.
- Only a subset of m(6)A consensus motifs are actually modified in mammalian transcriptomes.
- Precise identification of m(6)A sites is essential for understanding RNA regulation.
Purpose of the Study:
- To develop a computational predictor for identifying mammalian m(6)A sites.
- To establish a tool that aids in the accurate mapping of m(6)A modifications.
- To provide a publicly accessible resource for m(6)A site prediction.
Main Methods:
- Development of SRAMP, a computational predictor for m(6)A sites.
- Integration of three random forest classifiers utilizing sequence patterns, K-nearest neighbor information, and nucleotide pair spectra.
- Input flexibility using either genomic or cDNA sequences.
Main Results:
- SRAMP demonstrates competitive performance in cross-validation and independent benchmarking tests.
- Analysis reveals that nucleotide preferences at both proximal and distal positions influence m(6)A site classification.
- The predictor effectively captures sequence context crucial for m(6)A site identification.
Conclusions:
- SRAMP provides a reliable method for predicting mammalian m(6)A sites.
- Understanding sequence preferences aids in deciphering m(6)A modification patterns.
- The freely available SRAMP server facilitates research in RNA epigenetics.
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