R Waterston

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

The paper presents a catalog of transcription factor binding sites in Drosophila and C. elegans, revealing tissue-specific regulatory relationships using machine learning.

This study creates a single-cell gene expression atlas of C. elegans, identifying cell types, housekeeping genes, and intercellular interactions using scRNA-seq and ChIP-seq data.

The paper presents the first single cell ATAC-seq study in C. elegans, identifying tissue-specific chromatin accessibility patterns aligned with existing RNA-seq data.

YAAT identifies functionally-related gene groups by integrating co-expression and phylogenetic conservation data, revealing biases in gene set enrichment interpretation.

This study compares mock IP and DNA input controls in ChIP-seq, showing that mock IP better removes spurious sites, while DNA input has unique advantages, and proposes combining both for improved binding site detection.

The paper introduces PhenoMIP, a method using molecular inversion probes to phenotype C. elegans mutant strains, finding that ~80% show decreased fitness compared to N2.

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 MIP-MAP, a method to map temperature-sensitive mutations in C. elegans using molecular inversion probes, achieving high-resolution genetic mapping with high-throughput sequencing.

This paper introduces sci-RNA-seq, a combinatorial indexing method for single-cell RNA sequencing, and applies it to profile 42,035 C. elegans cells, identifying 27 cell types and their specific genes.