Antonis Rokas

The paper investigates the evolutionary origins of protein novelty in Saccharomycotina yeasts, identifying two distinct modes of de novo and divergent gene evolution and their structural implications.

The paper identifies Nvj1 orthologs across diverse yeast lineages and shows functional divergence in NVJ formation and sequence motifs.

This study compares phenotypic, metabolomic, and genomic variation between two Aspergillus species to determine factors distinguishing opportunistic pathogens from non-pathogens.

The paper shows processed pseudogenes are dynamic evolutionary elements, not inert, and links their abundance to life-history traits and cancer-related dynamics across vertebrates.

The paper expands a previous finding of a correlation between glucose uptake rates and cell surface area-to-volume ratio across 11 yeast species to 282 species, finding additional correlations with genome size and growth rate.

The paper identifies convergent evolution of aerobic fermentation in yeasts through divergent genetic mechanisms affecting shared glycolytic genes.

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 identifies proto-point centromeres in Saccharomycodales yeast, showing they evolved from retrotransposon LTRs, supporting the hypothesis that point centromeres originated from ancestral retrotransposons.

The paper discovers three novel ancient whole genome duplications in Saccharomycotina yeasts, suggesting WGD may be more common in fungi than previously thought.

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 links fungal secondary metabolites to their biosynthetic gene clusters using genomic and chemical similarity, identifying 22 known pairs and hypothesizing 38 others in Aspergillus fischeri.

This paper identifies population-specific transcriptomic shifts in Aspergillus species, showing how transcriptomic reorganization drives secondary metabolic diversification and domestication in A. oryzae.

The paper analyzes genomic data from 250 Aspergillus flavus isolates to show that pathogenicity is strongly linked to population structure, with population D containing most clinical isolates and harboring genes related to zinc ion binding and lipid metabolism.

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

This study identifies gestation length as a key driver of brain size and litter size variation in eutherian mammals using phylogenetic path analysis.

This study uses AlphaFold2 to analyze how metabolism influenced enzyme structural evolution across 400 million years by examining 11,269 enzyme structures and their metabolic reactions.

This study uses machine learning to identify gene families associated with oxidative stress resistance in yeasts and validates their roles experimentally.

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.

OrthoHMM improves ortholog group inference using Hidden Markov Models, showing significant precision gains over existing methods in benchmark tests.

The paper uses machine learning to analyze Aspergillus species distribution and predicts climate change impacts on their ranges and diversity.

The paper investigates how specialization in yeast sugar transporters restricts evolutionary paths by analyzing sequence-function relationships and evolutionary history.

This study identifies convergent gene loss in bee-associated lactic acid bacteria, linking it to phenotypic traits like fructophily through phylogenomic and machine learning analyses.

This study analyzes gestation length variation across eutherian mammals and identifies adaptive shifts linked to life history traits and evolutionary transitions.

This study examines strain variation in a non-pathogenic fungus, identifying factors linked to virulence differences across strains.

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.

This study identifies greater sequence variation in non-coding regions of A. fumigatus genes compared to protein-coding regions, suggesting a role in phenotypic differences between strains.

The paper shows that the fungal gene ENB1, acquired from a bacterial operon, enables iron uptake in W/S clade yeasts and evolved through horizontal transfer and gene loss.

The paper expands mitochondrial genome sampling across Saccharomycotina yeasts by extracting sequences from nuclear genome datasets, revealing trends in size, GC content, and gene presence.