iGENE: Application for filamentous and yeast genomic identification

• Autor(a) principal: Antunes, Selena Dias Borborema
• Data de Publicação: 2022
• Outros Autores: Rodrigues de Oliveira , Heveraldo, D'Angelis, Marcos Flávio Silveira, Xavier, Mauro Aparecido de Sousa, Silva, Fabiana Brandão Alves, Oliveira, Dario Alves de, Leite, Luciana Nobre, Santos, Josiane dos, Barbosa, Frederico Santos, Xavier, Alessandra Rejane Ericsson de Oliveira
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/25103
• topic: Biotecnología; Visión por computador; Procesamiento de imágenes; Identificación de microorganismos; Perfil de restricción; Biblioteca.; Biotecnologia; Visão computacional; Processamento de imagens; Identificação de microrganismos; Perfil de restrição; Biblioteca.; Biotechnology; Computer vision; Image processing; Identification of microorganisms; Library.

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.