Chen, Kunqi, Wei, Zhen, Zhang, Qing, Wu, Xiangyu, Rong, Rong, Lu, Zhiliang 
ORCID: 0000-0002-3442-1415, Su, Jionglong, de Magalhaes, Joao Pedro ORCID: 0000-0002-6363-2465, Rigden, Daniel J ORCID: 0000-0002-7565-8937 and Meng, Jia ORCID: 0000-0003-3455-205X
(2019)
WHISTLE: a high-accuracy map of the human N<SUP>6</SUP>-methyladenosine (m<SUP>6</SUP>A) epitranscriptome predicted using a machine learning approach.
NUCLEIC ACIDS RESEARCH, 47 (7).
e41-.
Abstract
N 6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotes, and plays a pivotal role in various biological processes, such as splicing, RNA degradation and RNA-protein interaction. We report here a prediction framework WHISTLE for transcriptome-wide m6A RNA-methylation site prediction. When tested on six independent datasets, our approach, which integrated 35 additional genomic features besides the conventional sequence features, achieved a major improvement in the accuracy of m6A site prediction (average AUC: 0.948 and 0.880 under the full transcript or mature messenger RNA models, respectively) compared to the state-of-the-art computational approaches MethyRNA (AUC: 0.790 and 0.732) and SRAMP (AUC: 0.761 and 0.706). It also out-performed the existing epitranscriptome databases MeT-DB (AUC: 0.798 and 0.744) and RMBase (AUC: 0.786 and 0.736), which were built upon hundreds of epitranscriptome high-throughput sequencing samples. To probe the putative biological processes impacted by changes in an individual m6A site, a network-based approach was implemented according to the 'guilt-by-association' principle by integrating RNA methylation profiles, gene expression profiles and protein-protein interaction data. Finally, the WHISTLE web server was built to facilitate the query of our high-accuracy map of the human m6A epitranscriptome, and the server is freely available at: www.xjtlu.edu.cn/biologicalsciences/whistle and http://whistle-epitranscriptome.com.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Humans, RNA, Adenosine, Sequence Analysis, RNA, Epigenesis, Genetic, Methylation, Internet, High-Throughput Nucleotide Sequencing, Transcriptome, Protein Interaction Maps, Machine Learning |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 15 Feb 2019 09:34 |
| Last Modified: | 14 Oct 2023 09:13 |
| DOI: | 10.1093/nar/gkz074 |
| Open Access URL: | https://academic.oup.com/nar/advance-article/doi/1... |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3032818 |
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