[Arabidopsis] Two new methods/software for indentifying transcription factors involved in biolgical processes

[hairong wei]

If you are facing challenges in your research for identifying TFs involved
in a biological process, we may help you with two of our newly developed
methods/software, TF-finder and TF-cluster.



TF-finder has published in BMC Bioinformatics



http://www.biomedcentral.com/1471-2105/11/425



TF-cluster has been developed but not published.  We applied TF-cluster to
the microarray data from human stem cells and Arabidopsis roots.  The
software can easily identify TFs controlling human stem cell pluripotency
renewal, neurel development, muscle development, Arabidopsis root growth
with an increditably high efficiency, with 50-80% TFs identified being
supported by existing literature.



The principle underlying TF-cluster pipeline is a brand-new network
construction method followed by a state of the art decomposition algorithm
to produce functionally coordinated TFs (controling a biological
process/trait).   The other reason It works so efficiently is because we
considered "some biological models" that are often ingored.



Requirement:



TF-finder requires gene expression data and some knowledgebase of the
biological processes studied.  TF-cluster requires gene expression data
only.  In both cases,  the biological processes you want to study should be
active in the data.  Although we think date set should ideally contain 100
or more chips/measurement, we can try small data sets with 50 chips.



We are interested in applying them widely so that we can continue to improve
them and make them the best software in discovering TFs.



If interested, please contact Dr. Wei for more infomation.



Hairong Wei, Ph.D.

Assistant Professor of Plant Bioinformatics, Molecular Biology, and Genetics

Biotechnology Research Center

School of Forest Resources and Environmental Science

Michigan Technological University

1400 Townsend Drive, Houghton, MI 49931

Tel: (906)487-1473; Fax: (906)487-2915


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