ALGORITHMS FOR STRUCTURE BASED-PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES

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Farrel, A. (2016). ALGORITHMS FOR STRUCTURE BASED-PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Transcription factors (TFs) regulate gene expression through binding to specific target DNA sites. Accurate annotation of transcription factor binding sites (TFBSs) at genome scale represents an essential step toward our understanding of gene regulation networks. In this dissertation, we present a structure-based method for computational prediction of TFBSs using a novel, integrative energy (IE) function and an efficient pentamer algorithm. The integrative energy function combines a multibody (MB) knowledge-based potential and atomic energy terms (hydrogen bond and π-interaction) that might not be accurately captured by the knowledge-based potential owing to the mean force nature and low count problem. A pentamer algorithm is developed to address the computational complexity issue due to the exponential increase of the number of DNA sequences for longer binding sites that need to be evaluated. Test results show that the new energy function improves the prediction accuracy over the knowledge-based, statistical potentials based on a non-redundant dataset that consists of TF-DNA complexes from 12 different families. The pentamer algorithm improves TFBS prediction accuracy while greatly reducing the time complexity for long binding sites.

Details
Author

Farrel, Alvin

Title

ALGORITHMS FOR STRUCTURE BASED-PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES

Physical Description

1 online resource (93 pages) : PDF

Date

2016

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Transcription factors (TFs) regulate gene expression through binding to specific target DNA sites. Accurate annotation of transcription factor binding sites (TFBSs) at genome scale represents an essential step toward our understanding of gene regulation networks. In this dissertation, we present a structure-based method for computational prediction of TFBSs using a novel, integrative energy (IE) function and an efficient pentamer algorithm. The integrative energy function combines a multibody (MB) knowledge-based potential and atomic energy terms (hydrogen bond and π-interaction) that might not be accurately captured by the knowledge-based potential owing to the mean force nature and low count problem. A pentamer algorithm is developed to address the computational complexity issue due to the exponential increase of the number of DNA sequences for longer binding sites that need to be evaluated. Test results show that the new energy function improves the prediction accuracy over the knowledge-based, statistical potentials based on a non-redundant dataset that consists of TF-DNA complexes from 12 different families. The pentamer algorithm improves TFBS prediction accuracy while greatly reducing the time complexity for long binding sites.

Genre

doctoral dissertations

Subjects--Topics

Computer science
Biology
Bioinformatics

Degree

Ph.D.

Keywords

Binding Site Prediction
Energy Function
Homology Model
Knowledge-Based
Pentamer
Transcription Factor

Subject Area

Bioinformatics

Advisor(s)

Guo, Jun-tao

Committee Members

Fodor, Anthony
Jacobs, Donald
Shi, Xinghua
Song, Bao-Hua

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2016.

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Rights Holder Information

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Identifier

Farrel_uncc_0694D_11263

Permalink

http://hdl.handle.net/20.500.13093/etd:599