Marizeth Groenewald

The paper uses machine learning to identify genomic signatures of antifungal drug resistance in Saccharomycotina yeasts, finding that natural resistance patterns differ from clinical isolates.

The paper examines filamentation morphology evolution in Saccharomycotina yeasts using integrated genomic, metabolic, and environmental data from 1,154 strains, finding that filamentation is common and linked to phylogeny and environmental factors.

The paper identifies genomic factors limiting Saccharomycotina fungi's ability to infect plants, finding they lack metabolic enzymes used by other phytopathogens.

The paper investigates the relationship between gene family expansions and metabolic traits in yeasts, finding that keystone gene families drive multiple metabolic innovations.

The paper analyzes gene family evolution across nearly all known Saccharomycotina yeast species, finding distinct evolutionary patterns with gene loss linked to metabolic and genomic changes.

The paper identifies three yeast lineages with reduced DNA repair genes and high evolutionary rates, showing how gene loss can lead to persistent hypermutator phenotypes.

The paper analyzes codon usage patterns across 1,154 Saccharomycotina strains, identifying biases, correlations with GC content and tRNA-ome size, and order-specific classification using machine learning.