Henry N. Ward

Expanding TheCellMap.org to visualize a genome-scale genetic interaction network for a human cell line

The paper expands TheCellMap.org to include human genetic interactions from CRISPR-based analysis in HAP1 cells, enabling visualization and exploration of the network.

Quantitative analysis of genetic interactions in human cells from genome-wide CRISPR-Cas9 screens

The paper develops a method to quantify genetic interactions from CRISPR-Cas9 screens and identifies systematic variations in screen data.

A global genetic interaction map of a human cell reveals conserved principles of genetic networks

The paper constructs a genome-scale genetic interaction network from 4 million double mutants in HAP1 cells, revealing conserved genetic network principles across species and uncovering novel gene functions and cancer dependencies.

The PRC2.1 Subcomplex Opposes G1 Progression through Regulation of CCND1 and CCND2

The paper identifies PRC2.1's role in opposing G1 progression by regulating CCND1 and CCND2, showing its importance in cell proliferation across multiple cancer types.

A scalable platform for efficient CRISPR-Cas9 chemical-genetic screens of DNA damage-inducing compounds

The paper presents a scalable CRISPR-Cas9 platform for chemical-genetic screens of DNA damage-inducing compounds, identifying enriched modes-of-action and revealing late ICL repair pathway responses.

Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens

This study evaluates dimensionality reduction methods to normalize CRISPR screen data and improve functional gene network extraction from the DepMap.

Reproducibility metrics for CRISPR screens

The paper identifies limitations in current reproducibility metrics for CRISPR screens and advocates for context-sensitive metrics to better assess screen quality.

Major functional bias for mitochondrial complexes in genome-wide CRISPR screens

The paper introduces FLEX, a pipeline for benchmarking CRISPR screen data, and identifies a dominant mitochondria-associated signal in genome-wide screens.

MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin

MaGIC identifies monoallelic expressed genes using chromatin signatures from ChIP-seq data, providing a tool for analyzing MAE in human and mouse cells.