Rachel Rusholme‐Pilcher

This study compares the transcriptional profiles of two closely related fungi during early wheat root colonization, revealing differences in gene expression that underlie their distinct lifestyles.

This study develops ChronoGauge, a machine learning model that estimates plant circadian time from transcriptomic data, enabling analysis of environmental and genetic factors affecting the circadian clock.

This paper introduces PGGB, a pipeline for building pangenome graphs that includes all variation without bias, enabling variation detection, conservation measurement, recombination event detection, and phylogenetic inference.

This study annotates the wheat pan-transcriptome de novo and identifies gene diversity and expression patterns across multiple wheat cultivars and tissues.

This study uses whole genome sequencing to analyze hexaploid wheat/Ambylopyrum muticum introgression lines, identifying structural disruptions and gene expression patterns associated with introgression.

This paper uses machine learning to predict circadian gene expression in Arabidopsis using DNA sequence features, identifies key regulatory elements, and optimizes sampling strategies for circadian transcript prediction.

The paper identifies hidden genetic and epigenetic variation in polyploid wheat that contributes to local adaptation and suggests using methylation and transposon diversity for breeding.