Hannah Rees

Machine learning models reveal environmental and genetic factors associated with the plant circadian clock

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.

Chromosome-scale genome assembly and de novo annotation of Alopecurus aequalis.

This study presents a chromosome-scale genome assembly and annotation of Alopecurus aequalis, revealing its genetic similarity to barley and expansion of herbicide resistance genes.

De novo annotation of the wheat pangenome reveals complexity and diversity of the hexaploid wheat pan-transcriptome

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

Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function

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.

Coordination of robust single cell rhythms in the Arabidopsis circadian clock via spatial waves of gene expression

The paper examines single-cell circadian rhythms in Arabidopsis seedlings, revealing robust oscillations and spatial waves of gene expression that coordinate clock activity across the plant.