Charles M. Boone

This paper identifies Yra2's role in ubiquitin-mediated proteolysis of Cse4 to prevent its mislocalization and maintain chromosomal stability in budding yeast.

The paper uses a limit-pushing approach in yeast to measure protein abundance and toxicity, finding that overexpression tolerance varies with protein characteristics and can lead to distinct intracellular states.

The paper expands TheCellVision.org repository with two new single-cell phenomics projects on yeast intracellular organization and DUB biology.

The paper uses high-content imaging and machine learning to study how perturbing essential genes affects subcellular organization in yeast, revealing cascade effects and a threshold for cell death.

This study shows tioxazafen is bioactivated by cytochrome P450s into a proteasome disruptor, leading to toxicity in nematodes and potential human skin reactions.

The paper develops a barcoded yeast collection to map genotype-phenotype relationships across genetic diversity, revealing genetic variants linked to chemical responses.

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

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

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.

This paper identifies brilacidin as a novel antifungal agent effective against Cryptococcus neoformans, showing it reduces fungal survival in macrophages and potentiates existing antifungals.

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.

PIFiA is a self-supervised method for protein functional annotation from single-cell imaging data, outperforming existing approaches and enabling downstream analysis of protein organization.

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.

This study uses single-cell imaging to analyze protein dynamics in response to paralog deletion, revealing mechanisms of gene retention and functional divergence.

The paper shows that N-terminal acetylation by NatC protects proteins from degradation, enhancing longevity and motility, and that UBR4-KCMF1 targets unacetylated N-terminal Met followed by hydrophobic residues.

The paper identifies genes whose overexpression is adaptive under stress in budding yeast, revealing cellular requirements influenced by genetic background and environment.

The paper records proteomes for non-essential gene knock-outs in yeast to uncover principles of protein expression and annotate gene function.

BIONIC integrates biological networks using deep learning to improve accuracy and scalability, identifying novel gene interactions in yeast.

This study shows that the impact of a mistranslating tRNA variant depends on genetic background and mistranslation frequency in yeast, with implications for understanding tRNA variants in human disease.

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

The paper benchmarks structure descriptors and similarity coefficients using chemical-genetic interaction data to improve compound function prediction.

This study compares the effects of two tRNA variants causing different amino acid substitutions in yeast, showing distinct impacts on growth, heat shock response, and transcriptome changes.

The paper develops YETI, a yeast library with inducible gene expression, and shows low native expression can bias promoter-perturbing screens.

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

The paper identifies genetic interactions associated with protein overproduction and nuclear export overload in yeast using genetic profiling.

This study maps genetic interactions in FASN-deficient cells to identify genes involved in lipid uptake and metabolic adaptation.

This paper introduces BridGE, a method to detect pathway-level genetic interactions from GWAS data, revealing significant interactions in multiple diseases.

The paper develops CG-TARGET to predict compound biological processes by integrating chemical-genetic and genetic interaction networks, showing improved accuracy and FDR control.

The paper identifies HopZ1a, a T3SE from Pseudomonas syringae, as an acetyltransferase that targets plant kinesins to disrupt microtubule networks through yeast genetic and interaction screens.

The paper introduces MOSAIC, a database and web resource for chemical-genetic interaction data, enabling mode-of-action prediction and discovery of compound effects in yeast.