ISGm1A: Integration of Sequence Features and Genomic Features to Improve the Prediction of Human m<sub>1</sub>A RNA Methylation Sites
As a new epitranscriptomic modification, N1-methyladenosine (m<sup>1</sup>A) plays an important role in the gene expression regulation. Although some computational methods were proposed to predict m<sup>1</sup>A modification sites, all of these methods apply machine learning...
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| Format: | Article |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9079809/ |
| _version_ | 1818619430544867328 |
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| author | Lian Liu Xiujuan Lei Jia Meng Zhen Wei |
| author_facet | Lian Liu Xiujuan Lei Jia Meng Zhen Wei |
| author_sort | Lian Liu |
| collection | DOAJ |
| description | As a new epitranscriptomic modification, N1-methyladenosine (m<sup>1</sup>A) plays an important role in the gene expression regulation. Although some computational methods were proposed to predict m<sup>1</sup>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<sup>1</sup>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<sup>1</sup>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<sup>1</sup>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<sup>1</sup>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. |
| first_indexed | 2024-12-16T17:37:22Z |
| format | Article |
| id | doaj.art-a135bd2ac0064f318e0aa442faadf090 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:37:22Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a135bd2ac0064f318e0aa442faadf0902022-12-21T22:22:42ZengIEEEIEEE Access2169-35362020-01-018819718197710.1109/ACCESS.2020.29910709079809ISGm1A: Integration of Sequence Features and Genomic Features to Improve the Prediction of Human m<sub>1</sub>A RNA Methylation SitesLian Liu0https://orcid.org/0000-0001-5778-5230Xiujuan Lei1https://orcid.org/0000-0002-9901-1732Jia Meng2Zhen Wei3School of Computer Science, Shaanxi Normal University, Xi’an, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an, ChinaDepartment of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaDepartment of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaAs a new epitranscriptomic modification, N1-methyladenosine (m<sup>1</sup>A) plays an important role in the gene expression regulation. Although some computational methods were proposed to predict m<sup>1</sup>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<sup>1</sup>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<sup>1</sup>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<sup>1</sup>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<sup>1</sup>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.https://ieeexplore.ieee.org/document/9079809/Epitranscriptomem¹Asite predictionsequence featuresgenomic features |
| spellingShingle | Lian Liu Xiujuan Lei Jia Meng Zhen Wei ISGm1A: Integration of Sequence Features and Genomic Features to Improve the Prediction of Human m<sub>1</sub>A RNA Methylation Sites IEEE Access Epitranscriptome m¹A site prediction sequence features genomic features |
| title | ISGm1A: Integration of Sequence Features and Genomic Features to Improve the Prediction of Human m<sub>1</sub>A RNA Methylation Sites |
| title_full | ISGm1A: Integration of Sequence Features and Genomic Features to Improve the Prediction of Human m<sub>1</sub>A RNA Methylation Sites |
| title_fullStr | ISGm1A: Integration of Sequence Features and Genomic Features to Improve the Prediction of Human m<sub>1</sub>A RNA Methylation Sites |
| title_full_unstemmed | ISGm1A: Integration of Sequence Features and Genomic Features to Improve the Prediction of Human m<sub>1</sub>A RNA Methylation Sites |
| title_short | ISGm1A: Integration of Sequence Features and Genomic Features to Improve the Prediction of Human m<sub>1</sub>A RNA Methylation Sites |
| title_sort | isgm1a integration of sequence features and genomic features to improve the prediction of human m sub 1 sub a rna methylation sites |
| topic | Epitranscriptome m¹A site prediction sequence features genomic features |
| url | https://ieeexplore.ieee.org/document/9079809/ |
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