DeepFinder: An integration of feature-based and deep learning approach for DNA motif discovery

Nung, Kion Lee and Farah Liyana, binti Azizan and Yu, Shiong Wong and Norshafarina, binti Omar (2018) DeepFinder: An integration of feature-based and deep learning approach for DNA motif discovery. Biotechnology and Biotechnological Equipment, 32 (3). pp. 1-10. ISSN 1310-2818

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Official URL: https://www.tandfonline.com/doi/full/10.1080/13102...

Abstract

We propose an improved solution to the three-stage DNA motif prediction approach. The three-stage approach uses only a subset of input sequences for initial motif prediction, and the initial motifs obtained are employed for site detection in the remaining input subset of non-overlaps. The currently available solution is not robust because motifs obtained from the initial subset are represented as a position weight matrices, which results in high false positives. Our approach, called DeepFinder, employs deep learning neural networks with features associated with binding sites to construct a motif model. Furthermore, multiple prediction tools are used in the initial motif prediction process to obtain a higher number of positive hits. Our features are engineered from the context of binding sites, which are assumed to be enriched with specificity information of sites recognized by transcription factor proteins. DeepFinder is evaluated using several performance metrics on ten chromatin immunoprecipitation (ChIP) datasets. The results show marked improvement of our solution in comparison with the existing solution. This indicates the effectiveness and potential of our proposed DeepFinder for large-scale motif analysis. © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Item Type: E-Article
Uncontrolled Keywords: Deep learning neural network, motif discovery, DNA sequence feature, chromatin immunoprecipiation, sequencing analysis, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, , research, Universiti Malaysia Sarawak.
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QH Natural history > QH426 Genetics
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Ibrahim
Date Deposited: 26 Mar 2018 01:44
Last Modified: 09 Jul 2019 07:10
URI: http://ir.unimas.my/id/eprint/19914

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