Use of DNA-Barcoding in the alignment and location of Primers for the recognition of cyanobacteria
Abstract
Bioinformatics is an area that has great prominence and has been gaining even more visibility for prediction of diseases through DNA. Since it appeared, bioinformatics has always been directly associated with molecular biology, the field of biology responsible for studying the structure and functions of genetic material, as well as proteins, which are the results obtained in a DNA synthesis. The techniques of DNA-Barcoding and Machine Learning tend to help even more researchers in the area, seeking to bring quick and intelligent solutions. And it is with this aim that the objective of this work is based, which seeks to unite the techniques of Barcoding and Machine Learning for the sequencing and identification of Cyanobacteria.
Keywords:
DNA-Barcoding, Bioinformatics, Applied Computing
References
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BECKERS, B., BEECK, M. O., THIJS, S., TRUYENS, S., WEYENS, N., BOERJAN, W., VANGRONSVELD, J. Performance of 16s rDNA Primer Pairs in the Study of Rhizosphere and Endosphere Bacterial Microbiomes in MetaBarcoding Studies. Frontiers in Microbiology, 2016, v. 7, p. 1-15.
BLAST. Disponível em: https://blast.ncbi.nlm.nih.gov/Blast.cgi. Acessado em: 14/10/2020.
CLUSTAL OMEGA. Disponível em: https://www.ebi.ac.uk/Tools/msa/clustalo/. Acessado em: 14/10/2020.
CORDIER, T., FORSTER, D., DUFRESNE, Y., MARTINS, C. I. M., STOECK, T., PAWLOWSKI, J. Supervised machine learning outperforms taxonomy-based environmental DNA metaBarcoding applied to biomonitoring. Molecular Ecology Resources. 2018, v. 18, p. 1381-1391.
DELONG, R. K., ZHOU, Q. Polymerase Chain Reaction (PCR). Introductory Experiments on Biomolecules and Their Interactions, 2015, 59–66.
GENEBIO, BLAST. 2020. Disponível em http://www.genebio.ufba.br/BLAST/. Acessado em: 16/06/2020.
GERHARD, W. A., GUNSCH, C. K. MetaBarcoding and machine learning analysis of environmental DNA in ballast water arriving to hub ports. Environment International, 2019, v. 124, p. 312-319.
GIEGERICH, R.; MEYER, F; SCHLEIIERMACHER, C. GeneFisher: software support for the detection of postulated genes. In: INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS FOR MOLECULAR BIOLOGY, 4.; 1996. BethesdaMD: NCBI. p. 68-77.
HEBERT, P. D. N.; CYWINSKA, A.; BALL, S. L.; DEWAARD, J. R. Biological identifications through DNA barcodes. Proceedings. Royal Society Biological Sciences Meeting. 2003, v. 270, 313-321.
JAIN, Anil K.; DUBES, Richard C. Algorithms for Clustering Data. Prentice Hall. New Jersey. 1988.
KRESS, W. J., ERICKSON, D. L. DNA barcodes: genes, genomics, and bioinformatics. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105, 2761–2762.
LIBBRECHT, Maxwell W., NOBLE, William S. Machine learning in genetics and genomics. HHS Public Access, 2015, v. 16, p. 321-332.
LUSCOMBE, N. M., GREENBAUM, D., GERSTEIN, M. What is bioinformatics? A proposed definition and overview of the field. Methods Inf. Med. 2001, 40, 346-358.
MITCHELL, Tom. Machine Learning, McGraw-Hill; 1997.
REZENDE, Solange Oliveira. Sistemas Inteligentes – Fundamentos e Aplicações. São Paulo: Manole, 2003.
VINCENT, W. F. Cyanobacteria. Encyclopedia of Inland Waters, 2009. 226–232.
Published
2020-11-04
How to Cite
SANMARTIN, Victor; FROZZA, Rejane.
Use of DNA-Barcoding in the alignment and location of Primers for the recognition of cyanobacteria. In: REGIONAL SCHOOL ON COMPUTING OF RIO GRANDE DO SUL (ERCOMP-RS), 1. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 68-73.
DOI: https://doi.org/10.5753/ercomprs.2020.14298.
