Effective Identification and Annotation of Fungal Genomes

In the past few decades, the dangers of mycosis have caused widespread concern. With the development of the sequencing technology, the effective analysis of fungal sequencing data has become a hotspot. With the gradual increase of fungal sequencing data, there is now a lack of sufficient approaches for the identification and functional annotation of fungal chromosomal genomes. To overcome this challenge, this paper firstly deals with the approaches of the identification and annotation of fungal genomes based on short and long reads sequenced by using multiple platforms such as Illumina and Pacbio. Then this paper develops an automated bioinformatics pipeline called PFGI for the identification and annotation task. The experimental evaluation on a real-world dataset ENA (European Nucleotide Archive) shows that PFGI provides a user-friendly way to perform fungal identification and annotation based on the sequencing data analysis, and could provide accurate analyzing results, accurate to the species level (97% sequence identity).