Ultan McDermott

This study compares CRISPR knockout and interference methods using single-cell RNA sequencing to evaluate their strengths in functional genomics.

This study examines the long-term effects of chemotherapy on normal blood cells by analyzing mutation burdens and cell population structure in treated individuals.

The paper benchmarks CRISPR sgRNA design algorithms and creates smaller, efficient libraries for loss-of-function screens.

This paper develops an in silico model to predict personalized therapeutic combinations for NSCLC by simulating genetic profiles and testing drug combinations to overcome resistance.

The paper trains an ensemble classifier to predict cetuximab sensitivity in colorectal cancer PDXs using multi-omic data, achieving high accuracy and demonstrating its utility for therapeutic biomarker development.

The paper demonstrates that the 10x Genomics Gene Expression Flex protocol can profile mixed human and mouse cells in xenografts by using combined probe sets and a pipeline to remove cross-reactive signals.

The paper quantifies mismatch repair (MMR) activity in human tissues by analyzing mutation rates in individuals with constitutional MMR deficiency, revealing genotype-specific mutational signatures and the role of MMR components in DNA repair.

The paper identifies human essentiality genes enriched in successful oncology therapies and suggests integrating these metrics into drug discovery pipelines.

This study identifies two molecular subgroups of anaplastic meningioma with distinct survival outcomes and links them to specific genetic mutations.

The paper develops SLAPenrich to analyze pathway alterations in cancer hallmarks across 4,415 patients, revealing hallmark predominance and suggesting novel driver genes.

The paper quantifies proteomic and genomic variation in colorectal cancer cell lines to understand how mutations affect protein levels and drug responses.

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.

The paper introduces LOBICO, a logic modeling approach for binary inputs predicting continuous outputs, showing it outperforms single-gene predictors in cancer drug response.