Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features.

Zhao, Z; Zhang, X; Chen, F; Fang, L; Li, J

Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features.

Zhao, Z Zhang, X

Chen, F Fang, L Li, J

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Publisher:: BMC
Publication Type:: Journal Article
Citation:: BMC genomics, 2020, 21, (1), pp. 627
Issue Date:: 2020-09-11

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Field	Value	Language
dc.contributor.author	Zhao, Z
dc.contributor.author	Zhang, X https://orcid.org/0000-0002-3783-6560
dc.contributor.author	Chen, F
dc.contributor.author	Fang, L
dc.contributor.author	Li, J
dc.date.accessioned	2020-10-26T19:58:43Z
dc.date.available	2020-08-27
dc.date.available	2020-10-26T19:58:43Z
dc.date.issued	2020-09-11
dc.identifier.citation	BMC genomics, 2020, 21, (1), pp. 627
dc.identifier.issn	1471-2164
dc.identifier.issn	1471-2164
dc.identifier.uri	http://hdl.handle.net/10453/143530
dc.description.abstract	BACKGROUND:DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem. RESULTS:The feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene. CONCLUSIONS:The results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.
dc.format	Electronic
dc.language	eng
dc.publisher	BMC
dc.relation.ispartof	BMC genomics
dc.relation.isbasedon	10.1186/s12864-020-07033-8
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	06 Biological Sciences, 08 Information and Computing Sciences, 11 Medical and Health Sciences
dc.subject.classification	Bioinformatics
dc.title	Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features.
dc.type	Journal Article
utslib.citation.volume	21
utslib.location.activity	England
utslib.for	06 Biological Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	11 Medical and Health Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-10-26T19:58:35Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	1

Abstract:

BACKGROUND:DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem. RESULTS:The feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene. CONCLUSIONS:The results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.

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