TREEMM: identification of bipartite motifs in promoter sequences


TREEMM is a program for unsupervised clustering of promoter sequence based on modeling of distinct classes of bipartite motifs designed to represent binding sites of different Sigma factors. It allows to account for the non-random distribution of such motifs across a tree aimed at summarizing the correlation between the activities of the promoters.

The publication that describes TREEMM and should be referenced is

  • P. Nicolas, U. Mäder, E. Dervyn, T. Rochat, A. Leduc, N. Pigeonneau, E. Bidnenko, E. Marchadier, M. Hoebeke, (41 authors), and P. Noirot. (2012) Condition-Dependent Transcriptome Reveals High-Level Regulatory Architecture in Bacillus subtilis. Science. 335. 1099-1103 (PubMed)

    Information about how to install the software can be found in the INSTALL file. Usage of the program is described in the README file. You can freely use, modify and distribute this program under the terms of th GPL.

    Linux x86 64-bit Binaries, C++ source code and data sets are included in the zipped archive