Anna Fijarczyk

This study uses population genomics and machine learning to identify genetic markers predictive of antifungal resistance in Candida albicans, highlighting the need for expanded resistance catalogues.

The paper examines how aspen genetic groups in Mexico and Canada influence soil microbiome nitrogen acquisition strategies across different ecosystems.

The paper evaluates genomic offset models in black spruce using common gardens to predict fitness under climate change, finding model reliability varies with environmental and genetic factors.

The paper curates AMR mutations in fungi to create FungAMR, identifies universal resistance mutations, and provides a tool to screen genomes for AMR mutations.

The paper presents a cost-effective method for constructing barcoded libraries for genes of any length using low-cost DNA synthesis and standard cloning technologies.

The paper shows that hybrid yeast adaptation is slowed by Haldane's sieve due to slower loss of heterozygosity and incomplete dominance of adaptive mutations.

The paper evaluates 4,000 Erg11 variants to determine azole resistance and fitness costs, finding 33% resistance, 88% cross-resistance, and minimal fitness cost.

This study identifies two distinct pathways for resistance to the antifungal 5-FC in yeast, showing that ecological factors influence which resistance mechanisms dominate.

The paper sequences the genome of an ancient beer yeast strain from Quebec and determines its origin and relationship to other yeast strains.