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Enhancing Motif Refinement by Incorporating Comparative Genomics Data

  • Erliang Zeng
  • Giri Narasimhan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4463)

Abstract

Transcription factor binding sites (TFBS) are often located in the upstream regions of genes and transcription factors (TFs) cause transcription regulation by binding at these locations. Predicting these binding sites is a difficult problem, and traditional methods have a high degree of false positives in their predictions. Comparative genomics data can help to improve motif predictions. In this paper, a new strategy is presented, which refines motif by taking the comparative genomics data into account. Tested with the help of both simulation data and biological data, we show that our method makes improved predictions. We also propose a new metric to score a motif profile. This score is biologically motivated and helps the algorithm in its predictions.

Keywords

Transcription Factor Binding Site Upstream Sequence Conservation Score Orthologous Sequence Position Weight Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Erliang Zeng
    • 1
  • Giri Narasimhan
    • 1
  1. 1.Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, Florida, 33199USA

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