Martin S. Taylor

This paper uses machine learning to predict genomic alterations from H&E images of liver tumors, revealing genotype-phenotype links and highlighting genetic background limitations.

This study shows that genetic background and environmental factors determine the trajectory of cancer evolution in mice, with distinct mutational paths despite similar final tumor states.

This study investigates how germline and somatic genetic variations affect mitochondrial DNA content, mutation patterns, and gene expression in liver tumors across four mouse strains.

The paper investigates how strand-asymmetric processes like replication and transcription influence DNA damage, repair, and mutagenesis, revealing mechanisms that shape cancer genome evolution.

The paper identifies mutational biases at germline regulatory sites, showing increased structural variants and insertions at transcription factor binding sites, with implications for evolution and disease.

The paper identifies genetic polymorphisms preferentially selected during cancer development by analyzing allele retention patterns in TCGA whole exome sequencing data.

The paper functionally annotates human long non-coding RNAs by systematically knocking down 285 lncRNAs and analyzing molecular phenotypes using CAGE sequencing.

The paper identifies an ER-localized NMD pathway that regulates the stability of ER-translated mRNAs through the interaction of NBAS and UPF1.

The paper analyzes whole genome sequencing data from an isolated Shetland population, finding increased ultra-rare variants in exonic and regulatory regions compared to mainland Scottish populations.

The paper shows splicing enhances expression of AT-rich genes by increasing mRNA stability and cytoplasmic localization, while GC-rich genes are less affected by splicing.