Louis Gevirtzman

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 introduces a matrix prior for latent Dirichlet allocation to improve cell type detection in small scATAC-seq datasets by leveraging information from existing 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.

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 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.