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. 2020 May 9;11(5):529.
doi: 10.3390/genes11050529.

iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm

Omid Mahmoudi  1 , Abdul Wahab  1 , Kil To Chong  2
Affiliations

iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm

Omid Mahmoudi et al. Genes (Basel). .

Abstract

One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequences through the high-throughput laboratory techniques but still, these lab processes are time consuming and costly. Diverse computational methods have been proposed to identify m6A sites accurately. In this paper, we proposed a computational model named iMethyl-deep to identify m6A Saccharomyces Cerevisiae on two benchmark datasets M6A2614 and M6A6540 by using single nucleotide resolution to convert RNA sequence into a high quality feature representation. The iMethyl-deep obtained 89.19% and 87.44% of accuracy on M6A2614 and M6A6540 respectively which show that our proposed method outperforms the state-of-the-art predictors, at least 8.44%, 8.96%, 8.69% and 0.173 on M6A2614 and 15.47%, 28.52%, 25.54 and 0.5 on M6A6540 higher in terms of four metrics Sp, Sn, ACC and MCC respectively. Meanwhile, M6A6540 dataset never used to train a model.

Keywords: RNA N6-methyladenosine site; bioinformatics; computational biology; deep learning; methylation; yeast genome.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
A graphical illustration of iMethyl-deep. Inputted RNA sequence converted into one-hot encoded, then fed into the Convolution Neural Network (CNN) layers for training the datasets.
Figure 2
Figure 2
Performance evaluation illustration of iMethyl-deep on M6A2146 dataset.
Figure 3
Figure 3
The receiver operating characteristics (ROC) curve of iMethyl-deep on M6A2614 dataset.
Figure 4
Figure 4
Graphical illustration of confusion matrix of iMethyl-deep on M6A2614 dataset.
Figure 5
Figure 5
Performance evaluation illustration of iMethyl-deep on M6A6540 dataset.
Figure 6
Figure 6
The receiver operating characteristics (ROC) curve of iMethyl-deep on M6A6540 dataset.
Figure 7
Figure 7
Graphical illustration of confusion matrix of iMethyl-deep on M6A6540 dataset.
See this image and copyright information in PMC

References

    1. Desrosiers R., Friderici K., Rottman F. Identification of methylated nucleosides in messenger RNA from Novikoff hepatoma cells. Proc. Natl. Acad. Sci. USA. 1974;71:3971–3975. doi: 10.1073/pnas.71.10.3971. - DOI - PMC - PubMed
    1. Meyer K.D., Saletore Y., Zumbo P., Elemento O., Mason C.E., Jaffrey S.R. Comprehensive analysis of mRNA methylation reveals enrichment in 3 UTRs and near stop codons. Cell. 2012;149:1635–1646. doi: 10.1016/j.cell.2012.05.003. - DOI - PMC - PubMed
    1. Nilsen T.W. Internal mRNA methylation finally finds functions. Science. 2014;343:1207–1208. doi: 10.1126/science.1249340. - DOI - PubMed
    1. Meyer K.D., Jaffrey S.R. The dynamic epitranscriptome: N 6-methyladenosine and gene expression control. Nat. Rev. Mol. Cell Biol. 2014;15:313–326. doi: 10.1038/nrm3785. - DOI - PMC - PubMed
    1. Alarcón C.R., Lee H., Goodarzi H., Halberg N., Tavazoie S.F. N 6-methyladenosine marks primary microRNAs for processing. Nature. 2015;519:482–485. doi: 10.1038/nature14281. - DOI - PMC - PubMed

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