iGENE: Application for filamentous and yeast genomic identification

Authors

DOI:

https://doi.org/10.33448/rsd-v11i2.25103

Keywords:

Biotechnology; Computer vision; Image processing; Identification of microorganisms; Library.

Abstract

Innovations in genomic and proteomic methodologies for identification of microrganisms associated with digital technologies are in alignment with Industry 4.0 vision and are increasing. The objective of this work was to develop a prototype of an application (App) for the identification of filamentous fungi and yeasts at the species level. The construction of the prototype was carried out in order to present a web application with a responsive interface. The App was developed using a cloud computing process with a cascade model. As part of the App’s requirements, a Cloud Firestore database was built with image processing through a skImage library. For this purpose, agarose gels with filamentous fungi and yeasts restriction profiles previously identified at the species level by genomic (PCR/RFLP) and proteomic (mass spectrometry) methodologies were selected. The App identified as iGENE was able to perform the recognition of restriction profiles of agarose gels, comparing it to the filamentous fungi and yeasts registered in its library. The result at the species level was possible for profiles with more than 90% similarity. Although the analyzed images presented this profile, the App was built in order to also consider identifications at the genus level for similarities between 89 and 70%, as well as “unidentified microorganism” below this score. The inclusion of new filamentous fungi and yeasts species in the App library will allow for greater robustness in the generation of the identification result at the species level.

References

A. Rockenbach, D., Anderle, N., Griebler, D., & Souza, S. (2018). Estudo Comparativo de Bancos de Dados NoSQL. Revista Eletrônica Argentina-Brasil ne Tecnologias na Informação e da Comunicação, 1(8), http://dx.doi.org/10.5281/zenodo.1228503.

Barbosa, Kevin Haley et al. Impacto do MALDI-TOF no diagnóstico da Sepse: uma revisão integrativa / Impact of MALDI-TOF in the diagnosis of Sepsis: an integrative review. Brazilian Journal of Development, 7(6), 58556-58574, https://doi.org/10.34117/bjdv7n6-313.

Cole, L., Austin, D., & Cole, L. (2004). Visual object recognition using template matching. In Australian conference on robotics and automation.

Chaves Moreira, T., & Rene S. M. Souza, M. (2020). O uso da espectrometria de massa maldi tof na identificação de microrganismos para diagnóstico laboratorial. Revista Eletrônica Biociências, Biotecnologia e Saúde, 12(24), 53–59. https://interin.utp.br/index.php/GR1/article/view/2445/2047

Ericsson de Oliveira Xavier, A. R., Cardoso, L., Brito, R. V. J., Nobre, S. A. M., De Almeida, A. C., Ericsson de Oliveira, A. M., & De Sousa Xavier, M. A. (2019). Detection and identification of medically important microorganisms isolated from pigeon excreta collected in a university in a newly industrialized country. Biotemas, 32(1), 11–20. https://doi.org/10.5007/2175-7925.2019v32n1p11

Elmasri, R., & Navathe, S. B. (2011). Sistemas de Banco de Dados (6th ed.). Person Addison Wesley.

Feliciano, F. F., Souza, I. L. d., & Leta, F. R. (2010). Visão computacional aplicacada à metrologia dimensional automatizada: considerações sobre sua exatidão. Engevista, 7(2). https://doi.org/10.22409/engevista.v7i2.164

Fernandes, L. F., Souza, G. Á. A. D., Almeida, A. C. d., Cardoso, L., Xavier, M. A. d. S., Pinheiro, T. P. P., Cruz, G. H. S. d., Dourado, H. F. S., Silva, W. S., & Xavier, A. R. E. d. O. (2020). Identification and characterization of methicillin-resistant Staphylococcus spp. isolated from surfaces near patients in an intensive care unit of a hospital in southeastern Brazil. Revista da Sociedade Brasileira de Medicina Tropical, 53. https://doi.org/10.1590/0037-8682-0244-2020

Google. (2021, July 16). Como exibir sites. Centro de arquitetura do Cloud. https://cloud.google.com/architecture/web-serving-overview?hl=pt_br#app-engine

Kordalewska, M., Kalita, J., Bakuła, Z., Brillowska-Dąbrowska, A., & Jagielski, T. (2018). PCR-RFLP assays for species-specific identification of fungi belonging to Scopulariopsis and related genera. Medical Mycology, 57(5), 643–648. https://doi.org/10.1093/mmy/myy106

Lima, F. R., & Gomes, R. (2020). Conceitos e tecnologias da indústria 4.0. Revista Brasileira de Inovação, 19, Artigo e0200023. https://doi.org/10.20396/rbi.v19i0.8658766

Luciana Nobre, L., Felipe José Nobre, L., Mauro Aparecido, d. S. X., Josiane, d. S., Leia, C., Frederico Santos, B., Rosimar Fonseca, d. S., Soraia Aparecida Maia, D., & Alessandra Rejane Ericsson, d. O. X. (2020). Molecular identification and characterization of filamentous fungi and yeasts isolated in a pharmaceutical industry environment. Journal of Applied Pharmaceutical Science. https://doi.org/10.7324/japs.2020.10704

Oliveira, M. A. L., Lago, C. L. d., Tavares, M. F. M., & Silva, J. A. F. d. (2003). Análise de ácidos graxos por eletroforese capilar utilizando detecção condutométrica sem contato. Química Nova, 26(6), 821–824.

Robledo-Leal, E., Rivera-Morales, L. G., Sangorrín, M. P., González, G. M., Ramos-Alfano, G., Adame-Rodriguez, J. M., Alcocer-Gonzalez, J. M., Arechiga-Carvajal, E. T., & Rodriguez-Padilla, C. (2018). Identification and susceptibility of clinical isolates of Candida spp. to killer toxins. Brazilian Journal of Biology, 78(4), 742–749. https://doi.org/10.1590/1519-6984.175635

Ronaldo Albertin, M., Luiza Bufalari Elienesio, M., dos Santos Aires, A., Lopes Jaguaribe Pontes, H., & Pinheiro Aragão, D. (2017). Principais inovações tecnológicas da Indústria 4.0 e suas aplicações e implicações na manufatura. In XXIV Simpósio de Engenharia se Produção.

Rosa, M. A. da, Brun, A. L., & Kiel, G. (2011). Ferramenta Multiplataforma para Construção Automática de Dendogramas a partir de Imagens de Eletroforese. Revista de Exatas e TECnológicas, 2(1), 08-17.

Sayuri Tahara Amaral, C., de Souza, O., Hilkner de Souza, L., José da Silva, G., & Noboru Fatori Trevizan, L. (2020). Novos caminhos da biotecnologia: As inovações da indústria 4.0 na saúde humana. Revista Brasileira Multidisciplinar, 23(3), 203–231. https://doi.org/0.25061/2527-2675/ReBraM/2020.v23i3.889

Santos, J., Xavier, M. A. S., Cardoso, L., Nobre, S. A. M., Bacchi, R. R., Cangussu, C. H. C., Almeida, A. C., Leite, L. N., Barreto, N. A. P., & Xavier, A. R. E. O. (2020). Research Article Identification and molecular analysis of yeasts found in domestic pigeon droppings in Montes Claros, MG, Brazil. Genetics and Molecular Research, 19(1). https://doi.org/10.4238/gmr18521

Soares, L. F. S.; Stein, L. H.; Tieppo, E.; Moro, J. M. S.; Coutinho, M. A., Raittz, R. T.; Marchaukosk, J. N., Iris Hass, Picheth, G. (2010). Análise Eletroforética em Géis Unidimensionais: Nova Abordagem Focada em Inteligência Artificial e Estudo Comparativo de Soluções. In: VI WORKSHOP DE VISÃO COMPUTACIONAL WVC 2010.

Sommerville, I. (2011). Engenharia de Software (9th ed.). Person Prentice Hall.

Telles, E. S., Barone, D. A. C., & Da Silva, A. M. (2020). Inteligência Artificial no Contexto da Indústria 4.0. In Workshop sobre as Implicações da Computação na Sociedad. Sociedade Brasileira de Computação. https://doi.org/10.5753/wics.2020.11044

Teixeira, R. L. P., Teixeira, C. H. S. B., Brito, M. L. d. A., & Silva, P. C. D. (2019). Os discursos acerca dos desafios da siderurgia na indústria 4.0 no Brasil. Brazilian Journal of Development, 5(12), 28290–28309. https://doi.org/10.34117/bjdv5n12-016

Tsuchida, S., Umemura, H., & Nakayama, T. (2020). Current status of matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry (MALDI-TOF MS) in clinical diagnostic microbiology. Molecules, 25(20), 4775. https://doi.org/10.3390/molecules25204775

Wang, C., Gao, X., Wang, S., & Liu, Y. (2020). A smartphone-integrated paper sensing system for fluorescent and colorimetric dual-channel detection of foodborne pathogenic bacteria. Analytical and Bioanalytical Chemistry, 412(3), 611–620. https://doi.org/10.1007/s00216-019-02208-z

Zhu, X., Yan, S., Yuan, F., & Wan, S. (2020). The applications of nanopore sequencing technology in pathogenic microorganism detection. Canadian Journal of Infectious Diseases and Medical Microbiology, 2020, 1–8. https://doi.org/10.1155/2020/6675206

Published

21/01/2022

How to Cite

ANTUNES, S. D. B. .; RODRIGUES DE OLIVEIRA , H.; D’ANGELIS, M. F. S. .; XAVIER, M. A. de S. .; SILVA, F. B. A. .; OLIVEIRA, D. A. de .; LEITE, L. N. .; SANTOS, J. dos .; BARBOSA, F. S. .; XAVIER, A. R. E. de O. . iGENE: Application for filamentous and yeast genomic identification . Research, Society and Development, [S. l.], v. 11, n. 2, p. e13011225103, 2022. DOI: 10.33448/rsd-v11i2.25103. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/25103. Acesso em: 22 nov. 2024.

Issue

Section

Health Sciences