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Abstract:

One of the big challenges of the post-genomic era is identifying regulatory systems and integrating them into genetic networks. Gene expression is determined by protein-protein interactions among regulatory proteins and with RNA polymerase(s), and protein-DNA interactions of these trans-acting factors with cis-acting DNA sequences in the promoter regions of those regulated genes. Therefore, identifying these protein-DNA interactions, by means of the DNA motifs that characterize the regulatory factors operating in the transcription of a gene, becomes crucial for determining which genes participate in a regulation process, how they behave and how they are connected to build genetic networks. In this paper, we propose a hybrid promoter analysis methodology (HPAM) to discover complex promoter motifs that combines: the neural network efficiency and ability of representing imprecise and incomplete patterns; the flexibility and interpretability of fuzzy models; and the multi-objective evolutionary algorithms capability to identify optimal instances of a model by searching according to multiple criteria. We test our methodology by learning and predicting the RNA polymerase motif in prokaryotic genomes. This constitutes a special challenge due to the multiplicity of the RNA polymerase targets and its connectivity with other transcription factors, which sometimes require multiple functional binding sites even in close located regulatory regions; and the uncertainty of its motif, which allows sites with low specificity (i.e., differing from the best alignment or consensus) to still be functional. HPAM is available for public use in http://soar-tools.wustl.edu. © 2004 Elsevier B.V. All rights reserved.

Registro:

Documento: Artículo
Título:A hybrid promoter analysis methodology for prokaryotic genomes
Autor:Cotik, V.; Romero Zaliz, R.; Zwir, I.
Filiación:Departamento De Computación, Fac. De Ciencias Exactas Y Naturales, Universidad De Buenos Aires, Argentina
Department of Molecular Microbiology, Howard Hughes Medical Institute, Washington Univ. School of Medicine, United States
Depto. De Cie. De La Comp. E I., ETS De Ing. Informática, Universidad De Granada, Spain
Palabras clave:Fuzzy sets; Gene regulation; Multi-objective evolutionary algorithms; Pattern recognition; Prokaryotic promoters; RNA polymerase; Time delay neural networks; Computational methods; DNA; Evolutionary algorithms; Fuzzy sets; Interpolation; Neural networks; Pattern recognition; Proteins; RNA; Gene regulation; Multi-objective evolutionary algorithms; Prokaryotic promoters; RNA polymerase; Time delay neural networks; Genes
Año:2005
Volumen:152
Número:1
Página de inicio:83
Página de fin:102
DOI: http://dx.doi.org/10.1016/j.fss.2004.10.016
Título revista:Fuzzy Sets and Systems
Título revista abreviado:Fuzzy Sets Syst
ISSN:01650114
CODEN:FSSYD
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650114_v152_n1_p83_Cotik

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Citas:

---------- APA ----------
Cotik, V., Romero Zaliz, R. & Zwir, I. (2005) . A hybrid promoter analysis methodology for prokaryotic genomes. Fuzzy Sets and Systems, 152(1), 83-102.
http://dx.doi.org/10.1016/j.fss.2004.10.016
---------- CHICAGO ----------
Cotik, V., Romero Zaliz, R., Zwir, I. "A hybrid promoter analysis methodology for prokaryotic genomes" . Fuzzy Sets and Systems 152, no. 1 (2005) : 83-102.
http://dx.doi.org/10.1016/j.fss.2004.10.016
---------- MLA ----------
Cotik, V., Romero Zaliz, R., Zwir, I. "A hybrid promoter analysis methodology for prokaryotic genomes" . Fuzzy Sets and Systems, vol. 152, no. 1, 2005, pp. 83-102.
http://dx.doi.org/10.1016/j.fss.2004.10.016
---------- VANCOUVER ----------
Cotik, V., Romero Zaliz, R., Zwir, I. A hybrid promoter analysis methodology for prokaryotic genomes. Fuzzy Sets Syst. 2005;152(1):83-102.
http://dx.doi.org/10.1016/j.fss.2004.10.016