Mathew J. Garnett

STEER-seq: a recombinase based platform for pooled multimodal single-cell perturbation screening

STEER-seq enables pooled single-cell perturbation screening in hiPSCs by using defined genomic integration and barcode tracking to study transcription factor effects on cell identity.

Structure-function analysis of PI3K signalling cascade base editing screens in cancer cells

The paper uses CRISPR base editing and structural analysis to identify functional residues in the PI3K pathway, revealing potential drug targets and allosteric pockets.

BEstimate: a computational tool for the design and interpretation of CRISPR base editing experiments

BEstimate designs and interprets CRISPR base editing experiments by predicting gRNA targets, off-targets, and providing functional annotations.

Perturbation-guided mapping of colorectal cancer cell states to causal mechanisms

This paper develops a continual learning framework to map colorectal cancer cell states to causal mechanisms through integration of single-cell data and perturbation experiments.

CRISPR screens in the context of immune selection identify CHD1 and MAP3K7 as mediators of cancer immunotherapy resistance

The paper identifies CHD1 and MAP3K7 as mediators of cancer immunotherapy resistance through CRISPR screens and shows their potential as biomarkers for immunotherapy response.

On-target mutations confer resistance to WRN helicase inhibitors in Microsatellite Unstable Cancer Cells.

This study identifies on-target WRN mutations as resistance mechanisms in MSI cancer cells to WRN helicase inhibitors and suggests combination therapies to prevent resistance.

Genetic interaction library screening with a next-generation dual guide CRISPR system

The paper introduces a next-generation dual guide CRISPR system for efficient genetic interaction screening, achieving low undesired guide pairs and balanced expression, and demonstrates its application in colorectal cancer cells.

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

The paper introduces a deep learning model to augment cancer cell line multi-omic data, increasing the number of profiles and uncovering drug resistance mechanisms.

High-throughput phenotyping of single nucleotide variants by linking transcriptomes to genotypes in single cells

This study links single-cell transcriptomes to genotypes to assess the functional impact of single nucleotide variants, classifying missense variants into three categories.

Benchmark data and software for assessing genome-wide CRISPR-Cas9 screening pipelines

The paper provides a dataset and R package to benchmark CRISPR-Cas9 screening pipelines using HT-29 cells and a commercial guide RNA library.

Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities

The paper presents a pan-cancer proteomic map of 949 cell lines, revealing protein-specific biomarkers of cancer vulnerabilities and showing proteomic data's superior predictive power for drug response.

CoRe: A robustly benchmarked R package for the identification of core fitness genes in genome-wide pooled CRISPR knock-out screens

CoRe identifies core-fitness genes from CRISPR-Cas9 screens, outperforming existing tools and providing more reliable results.

Integrated cross-study datasets of genetic dependencies in cancer

The paper integrates two large CRISPR-Cas9 screens to improve the detection of gene dependencies and cancer vulnerabilities by addressing biases and batch effects.

Minimal genome-wide human CRISPR-Cas9 library

The paper designs a smaller genome-wide CRISPR-Cas9 library with reduced size while maintaining sensitivity and specificity, enhancing its applicability in complex models.

Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens

This study integrates drug sensitivity data with CRISPR screens to identify drug mechanisms and biomarkers for cancer therapy.

Stratification and prediction of drug synergy based on target functional similarity

The paper develops workflows to predict drug synergy based on target functional similarity and validates them in breast and colorectal cancer cell lines.

CELLector: Genomics Guided Selection of Cancer in vitro Models

CELLector selects relevant cancer cell lines based on patient genomic data to improve in vitro model relevance and bridge cell line screens to primary tumor sub-cohorts.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

The paper develops patient-specific signaling pathway models using microfluidic screenings and logic-based modeling to predict personalized cancer therapies.

Rapid proteotyping reveals cancer biology and drug response determinants in the NCI-60 cells

This study rapidly generates proteomic data for NCI-60 cancer cell lines and identifies drug response determinants by integrating proteomic, genetic, and transcriptomic data.

JACKS: joint analysis of CRISPR/Cas9 knock-out screens

JACKS improves hit identification in CRISPR/Cas9 screens by jointly analyzing data from the same guide RNA library, reducing required cell numbers by 2.5-fold.

Tandem duplications lead to loss of fitness effects in CRISPR-Cas9 data

The paper shows that targeting tandem amplified regions in CRISPR-Cas9 screens reduces cellular fitness, while chromosomal duplications have minimal effects.

Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting

CRISPRcleanR reduces false positives in CRISPR-Cas9 screens by correcting gene-independent responses without assuming copy number status.

Integrative analysis of pharmacogenomics in major cancer cell line databases using CellMinerCDB

CellMinerCDB integrates pharmacogenomic data from multiple cancer cell line databases to identify drug response determinants, reproducibility, and novel genomic signatures.

A screen for combination therapies in BRAF/NRAS wild type melanoma identifies nilotinib plus MEK inhibitor as a synergistic combination

Genome-wide chemical mutagenesis screens allow unbiased saturation of the cancer genome and identification of drug resistance mutations.

This paper describes a method to identify drug resistance mutations in cancer using genome-wide chemical mutagenesis and next-generation sequencing, enabling the discovery of both known and novel resistance mechanisms.