Researchers develop algorithm for machine learning to identify fungi genomes

In California, as a proof of concept, researchers developed an algorithm that “taught” a computer how to classify 101 representative genomes of Dothideomycetes, the largest class of fungi, by lifestyles. The machine “learned” to identify data generated in part through the 1000 Fungal Genomes Project, including 55 newly sequenced species.

The class Dothideomycetes includes fungi that obtain nutrients from decaying organic matter (saprobes), and many plant pathogens known to infect most major food crops and feedstocks for biomass and biofuel production. Learning about the ecology and evolution of Dothideomycetes could provide more insights into how these fungi have adapted to stress and host specificity, particularly regarding the effects of climate change.

A team led by researchers at the U.S. Department of Energy (DOE) Joint Genome Institute (JGI), a DOE Office of Science User Facility located at Lawrence Berkeley National Laboratory (Berkeley Lab), has generated a more accurate phylogenetic tree tracking the evolution of Dothideomycetes fungi. The work appeared in the June issue of Studies in Mycology.