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Nucleic Acids Research Advance Access originally published online on November 6, 2006
Nucleic Acids Research 2007 35(Database issue):D132-D136; doi:10.1093/nar/gkl800
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Nucleic Acids Research, 2007, Vol. 35, Database issue D132-D136
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Articles

Tractor_DB (version 2.0): a database of regulatory interactions in gamma-proteobacterial genomes

Abel González Pérez, Vladimir Espinosa Angarica, Ana Tereza R. Vasconcelos1,* and Julio Collado-Vides2

National Bioinformatics Center Cuba 1 National Laboratory for Scientific Computing Brazil 2 Center of Genomics Mexico

*To whom correspondence should be addressed at Bioinformatics Laboratory-LABINFO National Laboratory of Scientfic Computation Av. Getulio Vargas, 333, Quitandinha ZC: 25651-075 Petrópolis Rio de Janeiro, Brazil. Tel: +55 24 2233 6065; Fax: +55 24 2231 5595; Email: atrv{at}lncc.br

Received August 16, 2006. Revised September 20, 2006. Accepted October 2, 2006.

The version 2.0 of Tractor_DB is now accessible at its three international mirrors: www.bioinfo.cu/Tractor_DB, www.tractor.lncc.br and http://www.ccg.unam.mx/tractorDB. This database contains a collection of computationally predicted Transcription Factors' binding sites in gamma-proteobacterial genomes. These data should aid researchers in the design of microarray experiments and the interpretation of their results. They should also facilitate studies of Comparative Genomics of the regulatory networks of this group of organisms. In this paper we describe the main improvements incorporated to the database in the past year and a half which include incorporating information on the regulatory networks of 13—increasing to 30—new gamma-proteobacteria and developing a new computational strategy to complement the putative sites identified by the original weight matrix-based approach. We have also added dynamically generated navigation tabs to the navigation interfaces. Moreover, we developed a new interface that allows users to directly retrieve information on the conservation of regulatory interactions in the 30 genomes included in the database by navigating a map that represents a core of the known Escherichia coli regulatory network.


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