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Identification of co-regulated genes through Bayesian clustering of predicted regulatory binding sites

Nature Biotechnology volume 21, pages 435439 (2003) | Download Citation



The identification of co-regulated genes and their transcription-factor binding sites (TFBS) are key steps toward understanding transcription regulation. In addition to effective laboratory assays, various computational approaches for the detection of TFBS in promoter regions of coexpressed genes have been developed. The availability of complete genome sequences combined with the likelihood that transcription factors and their cognate sites are often conserved during evolution has led to the development of phylogenetic footprinting1,2. The modus operandi of this technique is to search for conserved motifs upstream of orthologous genes from closely related species1,2. The method can identify hundreds of TFBS without prior knowledge of co-regulation or coexpression. Because many of these predicted sites are likely to be bound by the same transcription factor, motifs with similar patterns can be put into clusters so as to infer the sets of co-regulated genes, that is, the regulons. This strategy utilizes only genome sequence information and is complementary to and confirmative of gene expression data generated by microarray experiments. However, the limited data available to characterize individual binding patterns, the variation in motif alignment, motif width, and base conservation, and the lack of knowledge of the number and sizes of regulons make this inference problem difficult. We have developed a Gibbs sampling-based3 Bayesian motif clustering (BMC) algorithm to address these challenges. Tests on simulated data sets show that BMC produces many fewer errors than hierarchical and K-means clustering methods4. The application of BMC to hundreds of predicted γ-proteobacterial motifs2 correctly identified many experimentally reported regulons, inferred the existence of previously unreported members of these regulons, and suggested novel regulons.

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This research was partly supported by US National Institutes of Health (NIH) grant R01HG02518-01 and National Science Foundation grants DMS-0104129 and DMS-0204674 to J.S.L., NIH grant R01HG01257 to C.E.L., and Department of Energy grant DEFG0201ER63204 to C.E.L. and L.A.M.

Author information


  1. Department of Statistics, Harvard University, Cambridge, MA 02138.

    • Zhaohui S. Qin
    •  & Jun S. Liu
  2. The Wadsworth Center, New York State Department of Health, Albany, NY 12201.

    • Lee Ann McCue
    • , William Thompson
    • , Linda Mayerhofer
    •  & Charles E. Lawrence
  3. Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY 12180.

    • Charles E. Lawrence


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The authors declare no competing financial interests.

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Correspondence to Jun S. Liu.

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