John I. Murray

This study investigates the distinct roles of TBX-35 and CEH-51 in C. elegans mesoderm lineage development using genetic and single-cell RNA sequencing approaches.

The paper updates the wRBP1.0 compendium of C. elegans RNA-binding proteins to 928 proteins, cataloging their expression and developmental phenotypes.

This study compares gene expression patterns across embryonic development in C. elegans and C. briggsae to identify evolutionary constraints and divergence.

The paper measures mRNA decay rates across C. elegans embryonic development using single-cell RNA-sequencing and identifies differences in mRNA stability across cell types and stages.

The paper shows that mutations in histone modifying enzymes cause ectopic gene expression in adult worms, leading to behavioral defects that may be reversible.

The paper estimates absolute transcript accumulation rates in early C. elegans embryos using single-cell RNA sequencing and single molecule transcript imaging, showing rapid accumulation of transcription factor mRNAs and identifying core promoter motifs involved in this process.

The paper identifies species-specific transposon-derived enhancers contributing to transcriptomic differences in human and chimpanzee hippocampal intermediate progenitors.

The paper identifies transcription factors that regulate anterior gene expression in C. elegans embryos through cis-regulatory elements and enhancer analysis.

The paper identifies regulatory mechanisms for posterior Hox genes nob-1 and php-3 in C. elegans, showing that anterior Hox gene ceh-13 and other factors activate these genes, influencing cell lineage and position.

The paper identifies UNC-130 as a lineage-specific repressor that enables convergent differentiation in C. elegans by allowing proper specification of glial cells from distinct lineages.

The paper maps lineage and transcriptome relationships in C. elegans embryogenesis using single-cell transcriptomics, revealing lineage-transcriptome correlation breakdown in late divisions.

The paper introduces DESCEND, a method to deconvolve true gene expression distributions from noisy scRNA-seq data, improving estimates of dispersion and burstiness while adjusting for covariates.

SAVER improves gene expression estimates in scRNA-seq by imputing zeros and enhancing low/moderate expression genes, improving downstream analysis accuracy.

This paper compares single cell RNA sequencing and single molecule RNA FISH for rare cell analysis, finding that higher transcriptome complexity per cell improves accuracy for small gene sets.