Syntactic recognition of regulatory regions in Escherichia coli

D.A. Rosenblueth1, D. Thieffry2, A.M. Huerta2, H. Salgado2 and J. Collado-Vides

1Instituto de Investigaciones en Matematicas Aplicadas y en Sistemas, Universidad Nacional Autonoma de Mexico, Ciudad Universitaria, Mexico D.F., 04510 Mexico,
2Centro de Investigacion sobre Fijacion de Nitrogeno, Universidad Nacional Autonoma de Mexico, Cuernavaca A.P. 565-A, Morelos 62100, Mexico and Corresponding author

Computer Applications in the Biosciences 12(5), 415-422 (Oct, 1996)

Abstract

Motivation. One of the most common methodologies to identify cis-regulatory sites in regulatory regions in the DNA is that of weight matrices, as testified by several articles in this issue. An alternative to strengthen the computational predictions in regulatory regions is to develop methods that incorporate more biological properties present in such DNA regions. The grammatical implementation presented in this paper provides a concrete example in this direction.

Results. On the basis of the analysis of an exhaustive collection of regulatory regions in Escherichia coli, a grammatical model for the regulatory regions of [sigma]70 promoters has been developed. The terminal symbols of the grammar represent individual sites for the binding of activator and repressor proteins, and include the precise position of sites in relation to transcription initiation. Combining these symbols, the grammar generates a large number of different sentences, each of which can be searched for matching against a collection of regulatory regions by means of weight matrices specific for each set of sites for individual proteins. On the basis of this grammatical model, a Prolog parser is presented here. Specific subgrammars for ArgR, LexA and TyR were implemented. When parsing a collection of 128 [sigma]70 promoter regions, the syntactic recognizer produces a much lower number of false-positive sites than the standard search using weight matrices.

Availability. A WWW interface is under development and will be freely accessible at the url: http://www.cifn.unam.mx/ Computational_Biology/index.htm.

Contact. E-mail: collado@cifn.unam.mx