PFP: A Computational Framework for Phylogenetic Footprinting in Prokaryotic Genomes

  • Dongsheng Che
  • Guojun Li
  • Shane T. Jensen
  • Jun S. Liu
  • Ying Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4983)


Phylogenetic footprinting is a widely used approach for the prediction of transcription factor binding sites (TFBSs) through identification of conserved motifs in the upstream sequences of orthologous genes in eukaryotic genomes. However, this popular strategy may not be directly applicable to prokaryotic genomes, where typically about half of the genes in a genome form multiple-gene transcription units or operons. The promoter sequences for these operons are located in the inter-operonic rather than inter-genic regions, which require prediction of TFBSs at the transcriptional unit instead of individual gene level. We have formulated as a bipartite graph matching problem the identification of conserved operons (including both single-gene and multi-gene operons) whose individual gene members are orthologous between two genomes and present a graph-theoretic solution. By applying this method to Escherichia coli K12 and 11 of its phylogeneticly neighboring species, we have predicted 2,478 sets of conserved operons, and discovered potential binding motifs for each of these operons. By comparing the prediction results of our approach and other prediction approaches, we conclude that it is advantageous to use our approach for prediction of cis regulatory binding sites in prokaryotes. The prediction software package PFP is available at .


Reference Genome Orthologous Gene Motif Discovery Target Genome Prokaryotic Genome 
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

  • Dongsheng Che
    • 1
    • 2
  • Guojun Li
    • 1
  • Shane T. Jensen
    • 3
  • Jun S. Liu
    • 4
  • Ying Xu
    • 1
  1. 1.Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensUSA
  2. 2.Department of Computer ScienceUniversity of GeorgiaAthensUSA
  3. 3.Department of Statistics, The Wharton SchoolUniversity of PennsylvaniaPhiladelphiaUSA
  4. 4.Department of StatisticsHarvard UniversityCambridgeUSA

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