Colin A M Semple

This study identifies chromothripsis as a common driver of pleural mesothelioma, linked to gene deletions and poor patient survival.

The paper maps RNA-DNA interactions in 16 human cell types, showing cell-type-specific and dynamic networks that correlate with gene regulation and disease traits.

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 tracks genomic changes in TNBC during neoadjuvant chemotherapy, finding that whole-genome duplication events drive structural alterations and contribute to tumor evolution and response to treatment.

The paper identifies two divergent structural variation trajectories in HGSOC that impact patient survival and reveals novel candidate driver genes and mtDNA mutations associated with prognosis.

This study uses machine learning on ATAC-seq and RNA-seq data to predict CTCF-anchored chromatin loops during human meiosis, revealing dynamic changes in loop structure and function across meiotic stages.

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 identifies context-dependent gene essentiality in GBM using CRISPR screening and develops a platform to link biomarkers to therapeutic targets.

The paper identifies regionally enriched rare deleterious exonic variants in the UK and Ireland using exome sequencing data, highlighting potential implications for genetic screening and counseling.

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 analyzes cancer mutations in protein structures to identify novel driver genes by distinguishing mutational patterns in tumour suppressors and oncogenes.

IMPROVE-DD integrates multiple phenotype data sources to improve variant evaluation in developmental disorders, showing better diagnostic accuracy and fewer false positives.

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

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 develops genome-wide models of DNA double strand break (DSB) susceptibility to identify structural variants (SVs) under positive or purifying selection in cancer.

This study shows that a DISC1 mutation linked to mental illness disrupts NMDAR dynamics and synaptic function in neurons, affecting excitatory synapses and vesicle dynamics.

The paper identifies chromatin loop anchors (LAPs) as sites of genomic instability in cancer and recombination hotspots in the germline, showing enrichment for structural variants and meiotic recombination.

The paper identifies circadian regulation of snoRNA and their coordination with rRNA maturation in nuclear RNA sequencing data.