Ensemble Framework for Motif Discovery Based on Data Partitioning

Computational DNA motif prediction is a challenging problem because motifs are short, degenerated, and are associated with ill-defined features. With the advances of genome-wide ChIP analysis technology, computational motif discovery tools are necessary to effectively tackle the large-scale datasets...

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Main Author:	Choong, Allen Chieng Hoon
Format:	Thesis
Language:	English English
Published:	2020
Subjects:	Q Science (General)
Online Access:	http://ir.unimas.my/id/eprint/31761/1/Ensemble%20Framework%20for%20Motif%20Discovery%20Based%20on%20Data%20Partitioning%20-%2024%20pgs.pdf http://ir.unimas.my/id/eprint/31761/4/Allen%20Choong%20Chieng%20Hoon%20ft.pdf
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spelling	my-unimas-ir.317612023-04-18T02:30:16Z Ensemble Framework for Motif Discovery Based on Data Partitioning 2020-09-11 Choong, Allen Chieng Hoon Q Science (General) Computational DNA motif prediction is a challenging problem because motifs are short, degenerated, and are associated with ill-defined features. With the advances of genome-wide ChIP analysis technology, computational motif discovery tools are necessary to effectively tackle the large-scale datasets for motifs search. Ensemble of DNA motif discovery methods is one of the most successful approaches for motif discovery. Nevertheless, most of the existing works cannot perform motif searches in ChIP datasets because of the limited input sizes of the classical tools employed in the ensemble. Ensemble approach not only uses the results from the classical motif discovery tools, it also combines the discovered results to produce better results. The merging algorithm contributes to the prediction accuracy of the discovered motifs. The primary contribution of this thesis work is the development of an ensemble method called ENSPART with the novelty of using data partitioning technique on ChIP dataset for DNA motif prediction. The idea is to reduce the search space by portioning the input datasets into subsets and tackle by ensemble of classical motif discovery tools separately. Then, using a proposed merging algorithm, the candidate motifs are merged regardless the different lengths. Three experiments are conducted. ChIP datasets have been downloaded to evaluate the performances of the ENSPART with Receiver Operative Curves and Area Under Curve performance metrics. ENSPART was compared with the genome-wide motif discovery tools MEME-ChIP, ChIPMunk, and RSAT peak-motifs using partitioning technique. The results demonstrate that ENSPART performed significantly better than MEME-ChIP and RSAT peak-motifs in terms of the two performance metrics. Another set of datasets are gathered and sampled without partitioning. ENSPART is compared to its employed classifiers: AMD, BioProspector, MDscan, MEME-ChIP, MotifSampler, and Weeder 2. ENSPART is also compared to MEME-ChIP, ChIPMunk, and RSAT peak-motifs without partitioning. The results show that ENSPART produces significantly better results than its individual classifiers and also MEME-ChIP, ChIPMunk, and RSAT peak-motifs. Finally, an experiment on the simulated datasets is conducted. ENSPART is compared to GimmeMotifs and MotifVoter which both are also ensemble-based tools. The results show that ENSPART produce significantly higher precision and recall rates than GimmeMotifs and MotifVoter. In conclusion, the ensemble technique is effective for DNA motif prediction, while the ChIP dataset can be tackled effectively using data partitioning techniques. The developed merging technique in ENSPART allows effective merging of same motifs from different data partitions. Such methods are generally applicable to any ensemble techniques that utilised classical motif discovery tools, or more recently, ChIP analysis tools. Universiti Malaysia Sarawak (UNIMAS) 2020-09 Thesis http://ir.unimas.my/id/eprint/31761/ http://ir.unimas.my/id/eprint/31761/1/Ensemble%20Framework%20for%20Motif%20Discovery%20Based%20on%20Data%20Partitioning%20-%2024%20pgs.pdf text en public http://ir.unimas.my/id/eprint/31761/4/Allen%20Choong%20Chieng%20Hoon%20ft.pdf text en validuser phd doctoral Universiti Malaysia Sarawak (UNIMAS) Faculty of Cognitive Sciences and Human Development
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English English
topic	Q Science (General)
spellingShingle	Q Science (General) Choong, Allen Chieng Hoon Ensemble Framework for Motif Discovery Based on Data Partitioning
description	Computational DNA motif prediction is a challenging problem because motifs are short, degenerated, and are associated with ill-defined features. With the advances of genome-wide ChIP analysis technology, computational motif discovery tools are necessary to effectively tackle the large-scale datasets for motifs search. Ensemble of DNA motif discovery methods is one of the most successful approaches for motif discovery. Nevertheless, most of the existing works cannot perform motif searches in ChIP datasets because of the limited input sizes of the classical tools employed in the ensemble. Ensemble approach not only uses the results from the classical motif discovery tools, it also combines the discovered results to produce better results. The merging algorithm contributes to the prediction accuracy of the discovered motifs. The primary contribution of this thesis work is the development of an ensemble method called ENSPART with the novelty of using data partitioning technique on ChIP dataset for DNA motif prediction. The idea is to reduce the search space by portioning the input datasets into subsets and tackle by ensemble of classical motif discovery tools separately. Then, using a proposed merging algorithm, the candidate motifs are merged regardless the different lengths. Three experiments are conducted. ChIP datasets have been downloaded to evaluate the performances of the ENSPART with Receiver Operative Curves and Area Under Curve performance metrics. ENSPART was compared with the genome-wide motif discovery tools MEME-ChIP, ChIPMunk, and RSAT peak-motifs using partitioning technique. The results demonstrate that ENSPART performed significantly better than MEME-ChIP and RSAT peak-motifs in terms of the two performance metrics. Another set of datasets are gathered and sampled without partitioning. ENSPART is compared to its employed classifiers: AMD, BioProspector, MDscan, MEME-ChIP, MotifSampler, and Weeder 2. ENSPART is also compared to MEME-ChIP, ChIPMunk, and RSAT peak-motifs without partitioning. The results show that ENSPART produces significantly better results than its individual classifiers and also MEME-ChIP, ChIPMunk, and RSAT peak-motifs. Finally, an experiment on the simulated datasets is conducted. ENSPART is compared to GimmeMotifs and MotifVoter which both are also ensemble-based tools. The results show that ENSPART produce significantly higher precision and recall rates than GimmeMotifs and MotifVoter. In conclusion, the ensemble technique is effective for DNA motif prediction, while the ChIP dataset can be tackled effectively using data partitioning techniques. The developed merging technique in ENSPART allows effective merging of same motifs from different data partitions. Such methods are generally applicable to any ensemble techniques that utilised classical motif discovery tools, or more recently, ChIP analysis tools.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Choong, Allen Chieng Hoon
author_facet	Choong, Allen Chieng Hoon
author_sort	Choong, Allen Chieng Hoon
title	Ensemble Framework for Motif Discovery Based on Data Partitioning
title_short	Ensemble Framework for Motif Discovery Based on Data Partitioning
title_full	Ensemble Framework for Motif Discovery Based on Data Partitioning
title_fullStr	Ensemble Framework for Motif Discovery Based on Data Partitioning
title_full_unstemmed	Ensemble Framework for Motif Discovery Based on Data Partitioning
title_sort	ensemble framework for motif discovery based on data partitioning
granting_institution	Universiti Malaysia Sarawak (UNIMAS)
granting_department	Faculty of Cognitive Sciences and Human Development
publishDate	2020
url	http://ir.unimas.my/id/eprint/31761/1/Ensemble%20Framework%20for%20Motif%20Discovery%20Based%20on%20Data%20Partitioning%20-%2024%20pgs.pdf http://ir.unimas.my/id/eprint/31761/4/Allen%20Choong%20Chieng%20Hoon%20ft.pdf
_version_	1783728408173215744

Ensemble Framework for Motif Discovery Based on Data Partitioning

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