ParaDB: A manually curated database containing genomic annotation for the human pathogenic fungi Paracoccidioides spp.

Abstract

BackgroundThe genus Paracoccidioides consists of thermodymorphic fungi responsible for Paracoccidioidomycosis (PCM), a systemic mycosis that has been registered to affect ~10 million people in Latin America. Biogeographical data subdivided the genus Paracoccidioides in five divergent subgroups, which have been recently classified as different species. Genomic sequencing of five Paracoccidioides isolates, representing each of these subgroups/species provided an important framework for the development of post-genomic studies with these fungi. However, functional annotations of these genomes have not been submitted to manual curation and, as a result, ~60-90% of the Paracoccidioides protein-coding genes (depending on isolate/annotation) are currently described as responsible for hypothetical proteins, without any further functional/structural description.Principal findingsThe present work reviews the functional assignment of Paracoccidioides genes, reducing the number of hypothetical proteins to ~25-28%. These results were compiled in a relational database called ParaDB, dedicated to the main representatives of Paracoccidioides spp. ParaDB can be accessed through a friendly graphical interface, which offers search tools based on keywords or protein/DNA sequences. All data contained in ParaDB can be partially or completely downloaded through spreadsheet, multi-fasta and GFF3-formatted files, which can be subsequently used in a variety of downstream functional analyses. Moreover, the entire ParaDB environment has been configured in a Docker service, which has been submitted to the GitHub repository, ensuring long-term data availability to researchers. This service can be downloaded and used to perform fully functional local installations of the database in alternative computing ecosystems, allowing users to conduct their data mining and analyses in a personal and stable working environment.ConclusionsThese new annotations greatly reduce the number of genes identified solely as hypothetical proteins and are integrated into a dedicated database, providing resources to assist researchers in this field to conduct post-genomic studies with this group of human pathogenic fungi.