SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features
By Yuan Zhou, Pan Zeng, Yan-Hui Li, Ziding Zhang, Qinghua Cui. Nucleic Acids Research, 2016
859 citations recorded by the source corpus.
Abstract N6-methyladenosine (m6A) 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 m6A consensus motifs in mammalian transcriptomes are modified. Therefore, accurate identification of RNA m6A sites becomes emergently important. For the above purpose, here a computational predictor of mammalian m6A site named SRAMP is established. To depict the sequence context around m6A 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/.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/788d54bc80471d408d627159c67cb4950663f34b
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In Molecular Biology
Citations in the corpus
- Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA (2018, methodology): “With the same unbalanced independent test set, neural networks based on all four predictors achieved better prediction than SRAMP, with the Gene2vec method achieving the best results (Table 1).”