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Modelling the transcription factor DNA-binding affinity using genome-wide ChIP-based data

Monther Alhamdoosh, Dianhui Wang
doi: https://doi.org/10.1101/061978
Monther Alhamdoosh
1Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3083, Australia
2Bio21 Institute, The University of Melbourne, 30 Flemington Road, Parkville, Victoria 3010, Australia
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Dianhui Wang
1Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria 3083, Australia
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Abstract

Understanding protein-DNA binding affinity is still a mystery for many transcription factors (TFs). Although several approaches have been proposed in the literature to model the DNA-binding specificity of TFs, they still have some limitations. Most of the methods require a cut-off threshold in order to classify a K-mer as a binding site (BS) and finding such a threshold is usually done by handcraft rather than a science. Some other approaches use a prior knowledge on the biological context of regulatory elements in the genome along with machine learning algorithms to build classifier models for TFBSs. Noticeably, these methods deliberately select the training and testing datasets so that they are very separable. Hence, the current methods do not actually capture the TF-DNA binding relationship. In this paper, we present a threshold-free framework based on a novel ensemble learning algorithm in order to locate TFBSs in DNA sequences. Our proposed approach creates TF-specific classifier models using genome-wide DNA-binding experiments and a prior biological knowledge on DNA sequences and TF binding preferences. Systematic background filtering algorithms are utilized to remove non-functional K-mers from training and testing datasets. To reduce the complexity of classifier models, a fast feature selection algorithm is employed. Finally, the created classifier models are used to scan new DNA sequences and identify potential binding sites. The analysis results show that our proposed approach is able to identify novel binding sites in the Saccharomyces cerevisiae genome.

Contact monther.alhamdoosh{at}unimelb.edu.au, dh.wang{at}latrobe.edu.au

Availability http://homepage.cs.latrobe.edu.au/dwang/DNNESCANweb

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 07, 2016.
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Modelling the transcription factor DNA-binding affinity using genome-wide ChIP-based data
Monther Alhamdoosh, Dianhui Wang
bioRxiv 061978; doi: https://doi.org/10.1101/061978
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Modelling the transcription factor DNA-binding affinity using genome-wide ChIP-based data
Monther Alhamdoosh, Dianhui Wang
bioRxiv 061978; doi: https://doi.org/10.1101/061978

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