The Statistical Power of Phylogenetic Motif Models

  • John Hawkins
  • Timothy L. Bailey
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)


One component of the genomic program controlling the transcriptional regulation of genes are the locations and arrangement of transcription factors bound to the promoter and enhancer regions of a gene. Because the genomic locations of the functional binding sites of most transcription factors is not yet known, predicting them is of great importance. Unfortunately, it is well known that the low specificity of the binding of transcription factors to DNA makes such prediction, using position-specific probability matrices (motifs) alone, subject to huge numbers of false positives. One approach to alleviating this problem has been to use phylogenetic “shadowing” or “footprinting” to remove unconserved regions of the genome from consideration. Another approach has been to combine a phylogenetic model and the site-specificity model into a single, predictive model of conserved binding sites. Both of these approaches are based on alignments of orthologous genomic regions from two or more species. In this work, we use a simplified, theoretical model to study the statistical power of the later approach to the prediction of features such as transcription factor binding sites. We investigate the question of the number of genomes required at varying evolutionary distances to achieve specified levels of accuracy (false positive and false negative prediction rates). We show that this depends strongly on the information content of the position-specific probability matrix and on the evolutionary model. We explore the effects of modifying the structure of the phylogenetic model, and conclude that placing the target genome at the root of the tree has a negligible effect on the power predicted by the model. Hence, as it is much easier to calculate, we can use this as an approximation to phylogenetic motif scanning using real trees. Finally we perform an empirical study and demonstrate that the performance of current phylogenetic motif scanning programs is far from the theoretical limit of their power, leaving ample room for improvement.


Transcription Factor Binding Site Evolutionary Distance Motif Model Target Genome Motif Search 
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 2008

Authors and Affiliations

  • John Hawkins
    • 1
  • Timothy L. Bailey
    • 1
  1. 1.Institute for Molecular Bioscience, QLD 4072The University of QueenslandAustralia

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