An efficient algorithm for improving structure-based prediction of transcription factor binding sites

BMC Bioinformatics

Research Article

Alvin Farrel¹  Jun-tao Guo¹ 

[1] Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, 28223, Charlotte, NC, USA;

关键词: Transcription factor binding site;    Structure-based prediction;    Binding motif;    Integrative energy function;    Fragment-based method;    Pentamer;   

DOI  :  10.1186/s12859-017-1755-0

 received in 2017-03-11, accepted in 2017-07-12,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundGene expression is regulated by transcription factors binding to specific target DNA sites. Understanding how and where transcription factors bind at genome scale represents an essential step toward our understanding of gene regulation networks. Previously we developed a structure-based method for prediction of transcription factor binding sites using an integrative energy function that combines a knowledge-based multibody potential and two atomic energy terms. While the method performs well, it is not computationally efficient due to the exponential increase in the number of binding sequences to be evaluated for longer binding sites. In this paper, we present an efficient pentamer algorithm by splitting DNA binding sequences into overlapping fragments along with a simplified integrative energy function for transcription factor binding site prediction.ResultsA DNA binding sequence is split into overlapping pentamers (5 base pairs) for calculating transcription factor-pentamer interaction energy. To combine the results from overlapping pentamer scores, we developed two methods, Kmer-Sum and PWM (Position Weight Matrix) stacking, for full-length binding motif prediction. Our results show that both Kmer-Sum and PWM stacking in the new pentamer approach along with a simplified integrative energy function improved transcription factor binding site prediction accuracy and dramatically reduced computation time, especially for longer binding sites.ConclusionOur new fragment-based pentamer algorithm and simplified energy function improve both efficiency and accuracy. To our knowledge, this is the first fragment-based method for structure-based transcription factor binding sites prediction.

【 授权许可】

CC BY   
© The Author(s). 2017

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