ResFungi

ResFungi enables in silico searches for candidate antifungal drug resistance genes using a database and Hidden Markov Model profile to facilitate the study of antifungal resistance mechanisms.

Key Features:

• Antifungal Drug Resistance Database: Contains resistance genes for seven drug classes: azoles, fluconazole, voriconazole, itraconazole, flucytosine, micafungin, and caspofungin.
• HMM Profile: Provides a Hidden Markov Model profile focused on antifungal resistance genes for sequence-based candidate gene identification.
• Candidate Gene Identification: Identifies candidate genes with functional annotations consistent with known antifungal resistance mechanisms and genes or enzymes linked to antifungal resistance in previous studies.
• Species-Independent Search: Enables resistance gene discovery without prior knowledge of the species or genes.

Scientific Applications:

• Resistance Gene Discovery: Identifies candidate antifungal resistance genes, including those with transport functions involved in azole resistance.
• Wet Lab Experiment Design: Facilitates the design of laboratory experiments to study antifungal resistance mechanisms by narrowing down candidate genes.
• Resistance Mechanism Elucidation: Helps unravel genetic mechanisms related to antifungal resistance, including transport-based azole resistance.
• Cost Reduction in Resistance Studies: Reduces experimental costs by enabling preliminary in silico screening of candidate genes.

Methodology:

ResFungi was built using a Hidden Markov Model profile and validated using species with known resistance genes. The database contains 261 candidate genes identified through HMM-based searches with functional annotations consistent with previously described resistance mechanisms.