Magdalena Zarowiecki

The paper develops a tool to assess tumour contamination in normal samples using whole-genome sequencing data, finding contamination in various cancer types and highlighting risks to variant calling.

GenePy identifies potential missed biallelic diagnoses in the 100KGP by scoring variants and analyzing phenotypic overlaps, finding 14% of undiagnosed participants may have a missed diagnosis.

The paper introduces MuAt, a deep learning model that learns representations of somatic mutations to predict tumour types and subtypes with high accuracy.

The paper identifies an intermediate-effect variant in UMOD associated with chronic kidney disease risk and shows it has reduced disease severity compared to canonical ADTKD mutations.

The paper identifies gene-specific mutational signatures caused by replication errors and endogenous DNA damage using CRISPR-Cas9 knockouts and whole-genome sequencing.

OMA standalone is a tool for orthology inference across public and custom genomes, enabling analysis of both existing and user-provided data.