Abstract
As a new epitranscriptomic modification, N1-methyladenosine (m 1 A) plays an important role in the gene expression regulation. Although some computational methods were proposed to predict m 1 A modification sites, all of these methods apply machine learning predictions based on the nucleotide sequence features, and they missed the layer of information in transcript topology and RNA secondary structures. To enhance the prediction model of m 1 A RNA methylation, we proposed a computational framework, ISGm1A, which stands for integration sequence features and genomic features to improve the prediction of human m 1 A RNA methylation sites. Based on the random forest algorithm, ISGm1A takes advantage of both conventional sequence features and 75 genomic characteristics to improve the prediction performance of m 1 A sites in human. The results of five-fold cross validation and independent test show that ISGm1A outperforms other prediction algorithms (AUC = 0.903 and 0.909). In addition, through analyzing the importance of features, we found that the genomic features play a more important role in site prediction than the sequence features. Furthermore, with ISGm1A, we generated a high accuracy map of m 1 A by predicting all adenines sites in the transcriptome. The data and the results of the study are freely accessible at: https://github.com/lianliu09/m1a_prediction.git.
Highlights
RNA epigenetic modification plays an important role in various stages of RNA life cycle, which occurs in all types of RNA
support vector machine (SVM) is another machine learning method widely applied in the many prediction models of computational biology, base on which, iRNA-3typeA, RAMPred and iRNA-PseColl realized the prediction of m1A sites in human, mouse and yeast
In order to compare the performance of five classifiers, a five-fold cross validation was applied to the training data, and the best classifier would be employed in the m1A prediction
Summary
RNA epigenetic modification plays an important role in various stages of RNA life cycle, which occurs in all types of RNA. Observed to be evolutionarily conserved and prevalent in humans, rodents and yeast, and its topological selectivity and biological mechanism on RNA expression suggest the existence of a novel layer of epigenetic regulation on RNA These findings could provide a new perspective for the RNA biology [2]. Using techniques of m1A-MAP, some research identified the methylated sites of m1A in nucleus and mitochondria RNA Their results demonstrated that most of the methylation modification sites of m1A were concentrated in 5’UTR of mRNA transcript, and a small portion of m1A sites in accordance with the ‘‘GUUCRA’’ sequence motif was created by a known methylase complex TRMT6 / 61A.
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