A Genetic Algorithm for Motif Finding Based on Statistical Significance

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• 作者：Josep Basha Gutierrez (20)
  Martin Frith (21)
  Kenta Nakai (22)
  
  20. Department of Medical Bioinformatics ; Graduate School of Frontier Sciences ; The University of Tokyo ; 5-1-5 Kashiwanoha ; Kashiwa-shi ; Chiba-ken ; 277-8561 ; Japan
  21. Computational Biology Research Center ; AIST Tokyo Waterfront Bio-IT Research Building ; 2-4-7 Aomi ; Koto-ku ; Tokyo ; 135-0064 ; Japan
  22. Human Genome Center ; The Institute of Medical Science ; The University of Tokyo ; 4-6-1 Shirokane-dai ; Minato-ku ; Tokyo ; 108-8639 ; Japan
  
• 关键词：Motif finding ; Genetic Algorithm ; Transcription Factor Binding Site ; Statistical significance
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9043
• 期：1
• 页码：438-449
• 全文大小：1,354 KB
• 参考文献：1. Vijayvargiya, S., Shukla, P.: Identification of Transcription Factor Binding Sites in Biological Sequences Using Genetic Algorithm. International Journal of Research & Reviews in Computer Science 2(2) (2011)
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  4. Shu, J.J., Li, Y. (2013) A statistical thin-tail test of predicting regulatory regions in the Drosophila genome. Theoretical Biology and Medical Modelling 10: pp. 11 CrossRef
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  6. Pevzner, P.A., Sze, S.H. (2000) Combinatorial approaches to finding subtle signals in DNA sequences. ISMB 8: pp. 269-278
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  10. Lenhard, B., Wasserman, W.W. (2002) TFBS: Computational framework for transcription factor binding site analysis. Bioinformatics 18: pp. 1135-1136 135" target="_blank" title="It opens in new window">CrossRef
  
• 作者单位：Bioinformatics and Biomedical Engineering
• 丛书名：978-3-319-16482-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Understanding of transcriptional regulation through the discovery of transcription factor binding sites (TFBS) is a fundamental problem in molecular biology research. Here we propose a new computational method for motif discovery by mixing a genetic algorithm structure with several statistical coefficients. The algorithm was tested with 56 data sets from four different species. The motifs obtained were compared to the known motifs for each one of the data sets, and the accuracy in this prediction compared to 14 other methods both at nucleotide and site level. The results, though did not stand out in detection of false positives, showed a remarkable performance in most of the cases in sensitivity and in overall performance at site level, generally outperforming the other methods in these statistics, and suggesting that the algorithm can be a useful tool to successfully predict motifs in different kinds of sets of DNA sequences.