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MotifMark: Finding Regulatory Motifs in DNA Sequences

Hamid Reza Hassanzadeh, Pushkar Kolhe, Charles L. Isbell, May D. Wang
doi: https://doi.org/10.1101/134296
Hamid Reza Hassanzadeh
1Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA. (Email: )
Roles: Student Member, IEEE
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  • For correspondence: hassanzadeh@gatech.edu
Pushkar Kolhe
3College of Computing, Georgia Institute of Technology, Atlanta, GA 30332 USA. (Email: )
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  • For correspondence: pushkar@cc.gatech.edu
Charles L. Isbell
2College of Computing, Georgia Institute of Technology, Atlanta, GA 30332 USA. (Email: )
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  • For correspondence: isbell@cc.gatech.edu
May D. Wang
4Department of Biomedical Engineering, Georgia Institute of Technology and Emory University and the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA
Roles: Student Member, IEEE
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Abstract

The interaction between proteins and DNA is a key driving force in a significant number of biological processes such as transcriptional regulation, repair, recombination, splicing, and DNA modification. The identification of DNA-binding sites and the specificity of target proteins in binding to these regions are two important steps in understanding the mechanisms of these biological activities. A number of high-throughput technologies have recently emerged that try to quantify the affinity between proteins and DNA motifs. Despite their success, these technologies have their own limitations and fall short in precise characterization of motifs, and as a result, require further downstream analysis to extract useful and interpretable information from a haystack of noisy and inaccurate data. Here we propose MotifMark, a new algorithm based on graph theory and machine learning, that can find binding sites on candidate probes and rank their specificity in regard to the underlying transcription factor. We developed a pipeline to analyze experimental data derived from compact universal protein binding microarrays and benchmarked it against two of the most accurate motif search methods. Our results indicate that MotifMark can be a viable alternative technique for prediction of motif from protein binding microarrays and possibly other related high-throughput techniques.

Footnotes

  • * This work was supported by the grants from National Institutes of Health (NCI Transformative R01 CA163256, and National Center for Advancing Translational Sciences UL1TR000454), Microsoft Research and Hewlett Packard. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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 May 05, 2017.
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MotifMark: Finding Regulatory Motifs in DNA Sequences
Hamid Reza Hassanzadeh, Pushkar Kolhe, Charles L. Isbell, May D. Wang
bioRxiv 134296; doi: https://doi.org/10.1101/134296
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MotifMark: Finding Regulatory Motifs in DNA Sequences
Hamid Reza Hassanzadeh, Pushkar Kolhe, Charles L. Isbell, May D. Wang
bioRxiv 134296; doi: https://doi.org/10.1101/134296

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