Daniel J. Garry

STEVE evaluates cell-type annotation robustness in scRNA-seq datasets through subsampling, novel cell detection, and benchmarking against ground-truth labels.

MCAREDB integrates scRNA-seq datasets from mouse cardiac regeneration studies to enable comparative analysis of gene expression and pathways across developmental and injury contexts.

This paper develops a CRISPR-based therapy targeting Shiga toxin genes in STEC infections, showing efficacy in reducing bacterial load and mitigating disease in animal models.

Proformer predicts gene expression from promoter sequences using a hybrid transformer model with improved performance over conventional designs.

This study identifies Hedgehog signaling as a regulator of differentiation timing through a heterochronic gene regulatory network in cardiac and neural lineages.

SeATAC is a tool that uses a conditional variational autoencoder to detect differential chromatin accessibility in ATAC-seq data, outperforming existing methods in multiple tasks.

The paper re-evaluates the efficiency of a CRISPR-Cas9 method for generating conditional knockout alleles in mice, finding very low success rates and high rates of undesired alleles.

This paper introduces a novel algorithm to integrate heterogeneous temporal scRNA-seq datasets and reconstruct lineage trajectories, identifying Ebf1 as a key transcription factor for mouse endothelial development.

DrImpute imputes dropout events in scRNA-seq data, improving statistical tools that do not account for these events.