Abigail Leavitt LaBella

The paper identifies genes near tRNAs in fungal genomes that are enriched in proteasome regulation, ion transport, and rRNA, suggesting local co-regulation of protein synthesis and quality control.

The paper identifies lineage-specific tRNA sequence conservation and variation in modifying enzymes across Saccharomycotina using genomic and sequencing data.

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 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.

This study identifies a genome-wide association for neonatal jaundice, revealing distinct genetic pathways compared to adult bilirubin metabolism.

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.

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.

This study identifies genetic loci associated with gestational duration and preterm delivery, showing maternal and fetal genetic effects and antagonistic pleiotropy with birth weight.

BioKIT provides a unified toolkit for processing and analyzing diverse sequence data, demonstrating its utility through quality assessments and codon usage analysis across multiple datasets.

The paper shows that galactose metabolism genes can be horizontally transferred between yeast species, allowing for the reacquisition of a lost trait after millions of years.