David Gresham

The paper introduces a method for fast and robust collective posterior inference from multiple noisy replicates, achieving state-of-the-art accuracy and efficiency.

This study shows that high spontaneous aneuploidy rates, not drug-induced mutations, drive fluconazole resistance in yeast through chromosome duplication at ERG11.

The paper examines how bacterial, fungal, and functional diversity influence the distribution of antibiotic resistance genes in a hay field, finding that clinical ARG diversity is more linked to fungal diversity than bacterial diversity.

This study shows segmental copy number amplifications are more stable than aneuploidies when selection pressure is removed, with aneuploidies reverting quickly and losing fitness.

This study shows that quiescence in Candida albicans enhances survival during antifungal drug exposure by altering cellular properties and gene expression.

This paper introduces in-CAHOOTTS, a neural ODE framework for inferring gene regulatory networks from dynamic single-cell data, showing it can predict gene expression and identify regulators in yeast responses to rapamycin and cell cycle progression.

The paper shows CNVs act as reversible toggles between ecological strategies in yeast, with imbalanced ratios defining specialists and balanced ratios defining generalists.

This study uses stochastic modeling to show that intracellular replication of influenza A virus creates strong bottleneck effects, reducing genetic diversity and increasing genetic drift.

The paper examines gene expression changes in yeast strains with adaptive copy-number variants, finding correlations between CNVs and transcript abundance, but reduced proteomic effects due to post-transcriptional compensation.

The paper investigates how genomic architecture influences the formation and fitness of adaptive copy number variants (CNVs) in yeast, finding that template switching during DNA replication is a common mechanism for CNV generation.

This paper introduces a deep learning framework for gene regulatory network inference and latent transcription factor activity estimation, showing improved performance over existing methods.

This paper develops a deep learning model to estimate gene regulatory networks and RNA kinetics from single-cell RNA-seq data, showing improved recovery of known regulatory relationships and predictive power.

The paper investigates how copy number variants (CNVs) in yeast affect mutational tolerance and fitness, finding both increased tolerance in translation-related genes and decreased tolerance in mitochondrial function genes.

The paper introduces Inferelator 3.0, a tool for inferring gene regulatory networks from single-cell data, showing improved performance on yeast and mouse brain datasets.

The paper identifies differences in genetic interaction profiles of regulatory kinases between proliferative and quiescent yeast cells under various nutrient conditions.

The paper identifies that nanopore sequencing fails catastrophically at inverted duplicated DNA sequences, leading to incorrect base calls due to elevated current levels.

The paper develops a method to reconstruct gene regulatory networks from scRNA-seq data by barcoding gene deletion mutants in budding yeast across diverse environments.

The paper identifies factors involved in accelerated mRNA degradation in yeast during a nitrogen upshift by combining genetic screening and molecular analysis.

The paper introduces TrUMIseq adapters with unique molecular identifiers to improve the accuracy of quantitative sequencing by better distinguishing PCR duplicates from true molecular duplicates.

This study shows that adaptive evolution of gene expression involves tuning TF binding affinities while maintaining gene regulatory network topology in yeast.

The paper identifies transcript properties that explain ~50% of mRNA degradation rate variation in yeast, showing that translation-related factors dominate over regulatory mechanisms.

The paper measures absolute transcript abundances in yeast under different growth rates and finds that growth rate increases mRNA abundance across the transcriptome.