Técnica de Clusterização não-hierárquica aplicada para a caracterização in silico de promotores associados a genes de choque térmico de Escherichia coli
Resumo
As técnicas computacionais têm importante papel na era pósgenômica, devido ao crescente número de dados biológicos gerados. Um campo bem desenvolvido envolve as questões relacionadas a regulação gênica. Com base no exposto, este trabalho dedica-se a uma análise in silico dos promotores relacionados aos fatores δ24 e δ32 da bactéria E. coli. Para isso, foi utilizada a técnica de Clusterização e a codificação dos dados nos valores de estabilidade. Os agrupamentos foram analisados com base na pureza obtida. A pureza média obtida foi de 63%, com destaque para os agrupamentos 7, 10 e 11, que obtiveram uma pureza média de 85%, 82% e 83% respectivamente. Foi possível observar diferentes graus de degeneração das sequências e as características que os grupos estudados apresentam. Deste modo, contribui-se para a compreensão dos diferentes perfis biológicos encontrados nos promotores bacterianos. Além disso, os resultados podem auxiliar na redução de falsos positivos em ferramentas de predição de promotores.
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.
Attwood, T. K. et al. (2011). Concepts, Historical Milestones and the Central Place of Bioinformatics in Modern Biology: A European Perspective, In Trends and Methodologies, Editado por Mahmood A. Mahdavi, InTech, Croácia.
Callebaut, W. (2012). Scientific perspectivism: A philosopher of science’s response to the challenge of big data biology. In Studies in History and Philosophy of Biological and Biomedical Sciences, páginas 69-80.
Crooks, G. E. et al. (2004). WebLogo: A Sequence Logo Generator. In Genome Research (14) (6), páginas 1188-1190.
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., et al. (2014). DNA duplex stability as discriminative characteristic for Escherichia coli 54- and 28- dependent promoter sequences. In Biologicals (42) (1), páginas 22-28.
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.
Gordon, L., et al. (2003). Sequence alignment for recognition of promoter regions. In Bioinformatics (19) (15), páginas 1964-1971.
Herbert., M. K. et al. (2006). Sequence-Resolved Detection of Pausing by Single RNA Polymerase Molecules. In Cell (125) (6), páginas 1083-1094.
Houten, B. V. e Kisker, C. (2014). Transcriptional pausing to scout ahead for DNA Damage. In Proceedings of the National Academy of Sciences (111) (11), páginas 3905-3906.
Jáuregui, R. et al. (2003). Conservation of DNA curvature signals in regulatory regions of prokaryotic genes. In Nucleic Acids Research, páginas 6770-6777.
Kanehisa, S. et al. (2014). Data, information, knowledge and principle: back to metabolism in KEGG. In Nucleic Acids Research (42), páginas D199-D205.
Kanhere, A. e Bansal, M (2005). A Novel method for prokaryotic promoter prediction based on DNA stability. In Bioinformatics (6) (1), páginas 1-10.
Kaushik, M. et al. (2016). A bouquet of DNA structures: Emerging diversity. In Biochemistry and Biophysics Reports (5), páginas 388-395.
Koo, B. M. et al. (2009). Dissection of recognition determinants of Escherichia coli δ32 suggets a composite -10 region with na ‘extended -10’ motif and a core -10 element. In Molecular Microbiology (72) (4), páginas 815-829.
Krebs, J.,Goldstein, S. e Kilpatrick, S. T. (2014) Genes XI, ed. Sudbury, Massachusetts: Jones and Bartlett, 930 p.
Lim, B. et al. (2013). Heat Shock Transcription Factor δ32 Co-opts the Signal Recognition Particle to Regulate Protein Homeostasis in E. coli. In PLOS Biology (11) (12), páginas 1-15.
Marx, V. (2013). The Big Challenges of Big Data. In Nature (498), páginas 255-260.
Ramprakash, J. e Schwarz, F. P. (2007) Identification and annotation of promoters regions in microbial genome sequences on the basis of DNA stability. In Journal of Biosciences (32), páginas 851-862.
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, 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), páginas D203-D213.
SantaLucia J and Hicks D (2004). The thermodynamics of DNA structural motifs. Annual review of biophysics and biomolecular structure (33), páginas 415-440.
Vvedenskaya, I. O. et al. (2014). Interactions between RNA polymerase and the “core recognition element” counteract pausing. In Science (344), páginas 1285-1289.
Witten, I. H. e Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. Ed. San Francisco: Morgan Kaufman, 560p.
Attwood, T. K. et al. (2011). Concepts, Historical Milestones and the Central Place of Bioinformatics in Modern Biology: A European Perspective, In Trends and Methodologies, Editado por Mahmood A. Mahdavi, InTech, Croácia.
Callebaut, W. (2012). Scientific perspectivism: A philosopher of science’s response to the challenge of big data biology. In Studies in History and Philosophy of Biological and Biomedical Sciences, páginas 69-80.
Crooks, G. E. et al. (2004). WebLogo: A Sequence Logo Generator. In Genome Research (14) (6), páginas 1188-1190.
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., et al. (2014). DNA duplex stability as discriminative characteristic for Escherichia coli 54- and 28- dependent promoter sequences. In Biologicals (42) (1), páginas 22-28.
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.
Gordon, L., et al. (2003). Sequence alignment for recognition of promoter regions. In Bioinformatics (19) (15), páginas 1964-1971.
Herbert., M. K. et al. (2006). Sequence-Resolved Detection of Pausing by Single RNA Polymerase Molecules. In Cell (125) (6), páginas 1083-1094.
Houten, B. V. e Kisker, C. (2014). Transcriptional pausing to scout ahead for DNA Damage. In Proceedings of the National Academy of Sciences (111) (11), páginas 3905-3906.
Jáuregui, R. et al. (2003). Conservation of DNA curvature signals in regulatory regions of prokaryotic genes. In Nucleic Acids Research, páginas 6770-6777.
Kanehisa, S. et al. (2014). Data, information, knowledge and principle: back to metabolism in KEGG. In Nucleic Acids Research (42), páginas D199-D205.
Kanhere, A. e Bansal, M (2005). A Novel method for prokaryotic promoter prediction based on DNA stability. In Bioinformatics (6) (1), páginas 1-10.
Kaushik, M. et al. (2016). A bouquet of DNA structures: Emerging diversity. In Biochemistry and Biophysics Reports (5), páginas 388-395.
Koo, B. M. et al. (2009). Dissection of recognition determinants of Escherichia coli δ32 suggets a composite -10 region with na ‘extended -10’ motif and a core -10 element. In Molecular Microbiology (72) (4), páginas 815-829.
Krebs, J.,Goldstein, S. e Kilpatrick, S. T. (2014) Genes XI, ed. Sudbury, Massachusetts: Jones and Bartlett, 930 p.
Lim, B. et al. (2013). Heat Shock Transcription Factor δ32 Co-opts the Signal Recognition Particle to Regulate Protein Homeostasis in E. coli. In PLOS Biology (11) (12), páginas 1-15.
Marx, V. (2013). The Big Challenges of Big Data. In Nature (498), páginas 255-260.
Ramprakash, J. e Schwarz, F. P. (2007) Identification and annotation of promoters regions in microbial genome sequences on the basis of DNA stability. In Journal of Biosciences (32), páginas 851-862.
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, 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), páginas D203-D213.
SantaLucia J and Hicks D (2004). The thermodynamics of DNA structural motifs. Annual review of biophysics and biomolecular structure (33), páginas 415-440.
Vvedenskaya, I. O. et al. (2014). Interactions between RNA polymerase and the “core recognition element” counteract pausing. In Science (344), páginas 1285-1289.
Witten, I. H. e Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. Ed. San Francisco: Morgan Kaufman, 560p.
Publicado
04/07/2016
Como Citar
DALL’ALBA, Gabriel; DE AVILA E SILVA, Scheila.
Técnica de Clusterização não-hierárquica aplicada para a caracterização in silico de promotores associados a genes de choque térmico de Escherichia coli. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 10. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 229-236.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2016.9970.
