Stanley Fields

The paper characterizes plant enhancer activity across multiple species and develops a deep learning model to predict enhancer strength and identify functional motifs.

Dosa covalently bars proteins for high-throughput biochemical analysis by linking variants to RNA barcodes via a methyltransferase, enabling assessment of protein function through direct biochemical assays.

The paper identifies small DNA elements in plants that function as both insulators and silencers depending on regulatory context, validated in multiple plant species.

This study investigates how plant enhancers integrate transcription factor activity, finding both cooperative and additive interactions in light-responsive enhancers.

The paper inserts random 50-base sequences into yeast introns and finds that most reduce splicing efficiency, likely due to RNA secondary structures, but existing models poorly predict these effects.

The paper introduces a proteomic technology called Miro to identify amino acid substitutions that affect protein function by using mistranslation and mass spectrometry.

The paper assesses the functional impact of sequence motifs in yeast 3' UTRs using random sequence libraries, revealing context-dependent regulatory effects.

This study uses single-cell ATAC-seq to analyze chromatin accessibility in Arabidopsis thaliana roots, revealing cell type-specific regulatory landscapes and integrating them with transcriptomes to infer gene regulatory networks.

MaveRegistry is a platform for tracking and sharing multiplexed variant effect assays to reduce redundancy and enable collaboration in variant interpretation.

The paper investigates how phage λ adapts to resistant hosts by analyzing mutational tolerance in its tail fiber protein, identifying variants that confer promiscuity or host-specific infectivity.

The paper identifies mutations in the yeast Hsf1 oligomerization domain that alter transcriptional programs and fitness under heat stress.

The paper applies UMAP to visualize genetic interactions in yeast transcriptome data, identifying protein complexes and novel interactions.

The paper identifies dominant negative polypeptides in yeast by analyzing DNA library depletion during cell growth using high-throughput sequencing.

This study uses single-cell RNA-seq to analyze gene expression in A. thaliana root cells, identifying cell-type-specific genes and developmental trajectories, and shows cell-type differences in heat stress responses.

The paper develops a multiplex HDR assay to assess the functional impact of ~1,700 BRCA1 variants, distinguishing loss-of-function from benign variants.

The paper identifies DNA-binding preferences of Ste12 in yeast and shows how mutations shift its preference toward mating or invasion.

The paper trains a CNN to predict translational efficiency of yeast 5' UTRs using synthetic sequences and validates it on native sequences and evolved designs.

The paper examines regulatory DNA variation in Arabidopsis thaliana, finding that chromatin accessibility differences often correlate with gene expression changes despite limited sequence variation.