Identification of fungi in shotgun metagenomics datasets.
Donovan PD, Gonzalez G, Higgins DG, Butler G, Ito K
PLoS One. 2018. doi: 10.1371/journal.pone.0192898
COMMENT: In recent years, there has been a gradual shift from studying isolated species to studying their interactions in an environment that is more representative of their ecological niche. This shift is reflected in the increased use of nucleic acid sequencing directly from an environmental sample with no prior knowledge of the species that are present.
The mycobiome is the fungal component of the microbiome. The term was first used in 2010, in reference to the human oral mycobiome. The number of mycobiome publications has increased at an average rate of ~60% each year since 2012 (as of late 2017). Nevertheless, this area remains understudied compared to bacterial microbiomes as most metagenomic studies have used 16S rRNA amplicon sequencing to identify bacteria. However, the decreasing cost of sequencing has resulted in a gradual shift away from amplicon analyses and towards shotgun metagenomic sequencing. Shotgun metagenomic data can be used to identify a wide range of species, but have rarely been applied to fungal identification.
In this article the authors describe FindFungi, a sequence classification pipeline, and use it to identify fungal sequences in public metagenome datasets. They focus primarily on animal metagenomes, especially those from pig and mouse microbiomes. Using this tool they identified fungi in 39 of 70 datasets comprising 71 fungal species. At least 11 pathogenic species with zoonotic potential were identified, including Candida tropicalis. They identify Pseudogymnoascus species from 13 Antarctic soil samples initially analyzed for the presence of bacteria capable of degrading diesel oil. They also show that Candida tropicalis and Candida loboi are likely the same species. In addition, they identify several examples where contaminating DNA was erroneously included in fungal genome assemblies.
The authors claim that FindFungi can be applied to any shotgun metagenomics dataset.