DecoFungi: a web application for automatic characterisation of dye decolorisation in fungal strains

• Main Authors: César Domínguez, Jónathan Heras, Eloy Mata, Vico Pascual
• Format: Article
• Language: English
• Published: BMC 2018-02-01
• Series: BMC Bioinformatics
• Subjects: Fungal strains; Dye decolorisation; Image analysis; Deep learning; Transfer learning
• Online Access: http://link.springer.com/article/10.1186/s12859-018-2082-9

Abstract Background Fungi have diverse biotechnological applications in, among others, agriculture, bioenergy generation, or remediation of polluted soil and water. In this context, culture media based on color change in response to degradation of dyes are particularly relevant; but measuring dye decolorisation of fungal strains mainly relies on a visual and semiquantitative classification of color intensity changes. Such a classification is a subjective, time-consuming and difficult to reproduce process. Results DecoFungi is the first, at least up to the best of our knowledge, application to automatically characterise dye decolorisation level of fungal strains from images of inoculated plates. In order to deal with this task, DecoFungi employs a deep-learning model, accessible through a user-friendly web interface, with an accuracy of 96.5%. Conclusions DecoFungi is an easy to use system for characterising dye decolorisation level of fungal strains from images of inoculated plates.