Comparação entre diferentes modelos de cálculo de curvatura do DNA como parâmetro de predição e reconhecimento in silico de promotores alternativos de Escherichia coli
Resumo
As tecnologias de inteligência artificial ganham aplicabilidade nas áreas científicas, como biologia, devido ao aumento de dados disponíveis em repositórios específicos. Considerando este contexto, é possível obter inferências relevantes com a aplicação destas técnicas em questões relacionadas à regulação gênica. Assim, este trabalho descreve a aplicação de redes neurais artificiais no reconhecimento e predição de promotores associados a fatores σ alternativos de E. coli. Além da separação por fator σ, este trabalho apresenta como diferencial, a exploração da curvatura da molécula de DNA como parâmetro de classificação. A exatidão obtida com a aplicação desta metodologia, para os diferentes fatores σ foi de: σ24: 72,36 %; σ28: 67,17%; σ32 : 72,67%; σ38 :75,45%. Estes resultados indicam o potencial da curvatura como uma característica a ser incorporada nas ferramentas de predição de promotores a fim de diminuir o número de falsos positivos.
Referências
Askary, A., Masoudi-Nejad, A., Sharafi, R., Mizbani, A., Parizi, S. N. e Purmasjedi, M. (2009). N4: A precise and highly sensitive promoter predictor using neural network fed by nearest neighbors. In Genes & Genetic Systems (84) (6), páginas 425-430.
Bolshoy, A., McNamara, P., Harrington, R. E. e Trifonov, E. N. (1991). Curved DNA without A–A: experimental estimation of all 16 DNA wedge angles. In Procedings of National Academic of Science of United States of America (88), páginas 2312– 2316.
Burden, S., Lin, Y.-X., e Zhang, R. (2005). Improving promoter prediction for the NNPP2.2 algorithm: a case study using Escherichia coli DNA sequences. In Bioinformatics (21) (5), páginas601-607.
de Avila e Silva, S., et al. (2014). DNA duplex stability as discriminative characteristic for Escherichia coli σ54- and σ28- dependent promoter sequences. In Biologicals (42) (1), páginas 22-28.
de Avila e Silva, S. e Echeverrigaray, S. (2012) “Bacterial Promoter Features Description and Their Application on E. coli in silico Prediction and Recognition Approaches, In Bioinformatics, Editado por Horácio Pérez-Sánchez, InTech, Croácia
de Avila e Silva, S., Echeverrigaray, S. e Gerhardt, G. J. L. (2011). BacPP: Bacterial promoter prediction - A tool for accurate sigma-factor specific assignment in enterobacteria. In Journal of Theoretical Biology (287), páginas 92-99.
Calladine, C. R., Drew, H. R. e McCall, M. J. (1988). The intrinsic curvature of DNA in solution. In Journal of Molecular Biology (201), páginas 127–137.
Gohlke, C. (2014) “Dnacurve.py”,http://www.lfd.uci.edu/~gohlke/code/dnacurve.py.html , Abril
Gordon, L., et al. (2003). Sequence alignment for recognition of promoter regions. In Bioinformatics (19) (15), páginas 1964-1971.
Howard, D., Benson, K. (2003). Evolutionary computation method for pattern recognition of cis-acting sites. In BioSystems (72) (1/2), páginas19-27.
Kanhere, A. e Bansal, M. (2005). Structural properties of promoters: similarities and differences between prokaryotes and eukaryotes. In Nucleic Acids Research (33) (10), páginas 3165-3175.
Kozobay-avraham, L., Hosid, S., Volkovich, Z. e Bolshoy, A. (2008). Prokaryote Clustering based on DNA curvature distributions. In Discrete Applied Mathematics (11), páginas 2378-2387.
Lewin, B. (2008), Genes IX, Artes Médicas, 9ª edição.
Olivares-Zavaleta, N., Jáuregui, R. e Merino, E. (2006). Genome analysis of Escherichia coli promoters evidences that DNA static curvature plays a more important role in gene transcription than has previously been antecipated. In Genomics (87), páginas 329-337.
Polate, K. e Günes, S. (2007). A novel approach to estimation of E. coli promoter gene sequences: Combining feature selection and least square support vector machine (FS_LSSVN). In Applied Mathematics and Computation (190), páginas 1574-1582.
R Development Core Team. (2008). “R: A language and environment for statistical computing”. URL: http://www.R-project.org, Abril.
Rangannan, V. e Bansal, M. (2007). Identification and annotation of promoter regions in microbial genome sequences on the basis of DNA stability. In Journal of Biosciences 32 (5), páginas 851-862.
Rani, T. S., Bhavani, S. D. e Bapi, R. S. (2007). Analysis of E. coli promoter recognition problem in dinucleotide feature space. In Bioinformatics (23), páginas 582-588.
Salgado, H. et al. (2013). RegulonDB v. 8: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and morre. In Nucleic Acids Research (41), páginasD203-D213.
Ulanovsky, L. E. e Trifonov, E. N. (1987). Estimation of wedge components in curved DNA. In Nature (326), páginas720–722.
Sonego, P., Kocsor, A. e Pongor, S. (2008). ROC analysis: applications to the classification of biological sequences and 3D structures. In Briefings in Bioinformatics (9) (3), páginas 198-209.
Song, K. (2012). Recognition of prokaryotic promoters based on a novel variablewindow Z-curve method, In Nucleic Acids Research (40) (3), páginas963-971.
Bolshoy, A., McNamara, P., Harrington, R. E. e Trifonov, E. N. (1991). Curved DNA without A–A: experimental estimation of all 16 DNA wedge angles. In Procedings of National Academic of Science of United States of America (88), páginas 2312– 2316.
Burden, S., Lin, Y.-X., e Zhang, R. (2005). Improving promoter prediction for the NNPP2.2 algorithm: a case study using Escherichia coli DNA sequences. In Bioinformatics (21) (5), páginas601-607.
de Avila e Silva, S., et al. (2014). DNA duplex stability as discriminative characteristic for Escherichia coli σ54- and σ28- dependent promoter sequences. In Biologicals (42) (1), páginas 22-28.
de Avila e Silva, S. e Echeverrigaray, S. (2012) “Bacterial Promoter Features Description and Their Application on E. coli in silico Prediction and Recognition Approaches, In Bioinformatics, Editado por Horácio Pérez-Sánchez, InTech, Croácia
de Avila e Silva, S., Echeverrigaray, S. e Gerhardt, G. J. L. (2011). BacPP: Bacterial promoter prediction - A tool for accurate sigma-factor specific assignment in enterobacteria. In Journal of Theoretical Biology (287), páginas 92-99.
Calladine, C. R., Drew, H. R. e McCall, M. J. (1988). The intrinsic curvature of DNA in solution. In Journal of Molecular Biology (201), páginas 127–137.
Gohlke, C. (2014) “Dnacurve.py”,http://www.lfd.uci.edu/~gohlke/code/dnacurve.py.html , Abril
Gordon, L., et al. (2003). Sequence alignment for recognition of promoter regions. In Bioinformatics (19) (15), páginas 1964-1971.
Howard, D., Benson, K. (2003). Evolutionary computation method for pattern recognition of cis-acting sites. In BioSystems (72) (1/2), páginas19-27.
Kanhere, A. e Bansal, M. (2005). Structural properties of promoters: similarities and differences between prokaryotes and eukaryotes. In Nucleic Acids Research (33) (10), páginas 3165-3175.
Kozobay-avraham, L., Hosid, S., Volkovich, Z. e Bolshoy, A. (2008). Prokaryote Clustering based on DNA curvature distributions. In Discrete Applied Mathematics (11), páginas 2378-2387.
Lewin, B. (2008), Genes IX, Artes Médicas, 9ª edição.
Olivares-Zavaleta, N., Jáuregui, R. e Merino, E. (2006). Genome analysis of Escherichia coli promoters evidences that DNA static curvature plays a more important role in gene transcription than has previously been antecipated. In Genomics (87), páginas 329-337.
Polate, K. e Günes, S. (2007). A novel approach to estimation of E. coli promoter gene sequences: Combining feature selection and least square support vector machine (FS_LSSVN). In Applied Mathematics and Computation (190), páginas 1574-1582.
R Development Core Team. (2008). “R: A language and environment for statistical computing”. URL: http://www.R-project.org, Abril.
Rangannan, V. e Bansal, M. (2007). Identification and annotation of promoter regions in microbial genome sequences on the basis of DNA stability. In Journal of Biosciences 32 (5), páginas 851-862.
Rani, T. S., Bhavani, S. D. e Bapi, R. S. (2007). Analysis of E. coli promoter recognition problem in dinucleotide feature space. In Bioinformatics (23), páginas 582-588.
Salgado, H. et al. (2013). RegulonDB v. 8: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and morre. In Nucleic Acids Research (41), páginasD203-D213.
Ulanovsky, L. E. e Trifonov, E. N. (1987). Estimation of wedge components in curved DNA. In Nature (326), páginas720–722.
Sonego, P., Kocsor, A. e Pongor, S. (2008). ROC analysis: applications to the classification of biological sequences and 3D structures. In Briefings in Bioinformatics (9) (3), páginas 198-209.
Song, K. (2012). Recognition of prokaryotic promoters based on a novel variablewindow Z-curve method, In Nucleic Acids Research (40) (3), páginas963-971.
Publicado
28/07/2014
Como Citar
E SILVA, Scheila de Avila; COELHO, Rafael; PORTELA, Priscila; PAZ, Jeane; ECHEVERRIGARAY, Sergio.
Comparação entre diferentes modelos de cálculo de curvatura do DNA como parâmetro de predição e reconhecimento in silico de promotores alternativos de Escherichia coli. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 8. , 2014, Brasília.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2014
.
p. 1-8.
ISSN 2763-8774.
