Nathan Skene

This paper challenges the assumption of selective vulnerability in Parkinson's disease by analyzing post-mortem data and finding substantial neuronal loss beyond dopaminergic and pigmented populations.

This study shows that single-cell experiments often lack power to detect small-effect genes, even with 600 donors, and highlights the impact of sequencing depth and cell counts on results.

The paper presents a method to isolate rare cell populations from human post-mortem brain with high purity, demonstrating 100% microglial purity using fluorescence-activated nuclear sorting.

The paper systematically identifies cell type-specific mechanisms for 86.7% of rare disease phenotypes using transcriptomic data and the Human Phenotype Ontology, with results accessible via an interactive web portal.

This paper shows that trustworthy agentic genome interpretation requires executing validated skills as code rather than relying on models to reason or retrieve information, ensuring deterministic and auditable results.

orthogene simplifies gene mapping across species and identifier systems, providing a unified framework for reproducible and scalable workflows.

MotifPeeker benchmarks epigenomic profiling methods using motif enrichment as a metric, providing an HTML report with minimal input.

This study conducts a genome-wide association analysis of generalised anxiety symptom severity, identifying 80 significant variants and showing genetic correlations with other mental and physical health traits.

This study identifies genetic risk factors for panic attacks and disorder through genome-wide meta-analysis and links them to multiple body systems and neuronal functions.

This study benchmarks CUT&Tag against ENCODE ChIP-seq for histone modifications, finding 54% average recall of known peaks and comparable peak caller performance.

This paper presents a single-nucleus RNA sequencing atlas of human craniofacial development, revealing cell type contributions to facial variation and disease risk.

This study shows that histone mark function, genomic distance, and cellular states collectively influence gene expression predictions using deep learning models, with no single mark consistently predicting expression across contexts.

This paper introduces a deep learning model, Enformer Celltyping, that predicts cell type-agnostic epigenomic profiles and outperforms existing methods, while highlighting limitations in genetic variant effect predictions.

The paper optimizes TIP-seq peak calling parameters to improve peak precision and alignment with known motifs.

This paper uses GPT-4 to automatically annotate the severity of phenotypic abnormalities in the Human Phenotype Ontology, achieving high accuracy in clinical characteristic identification.

The paper reanalyzes an Alzheimer's disease snRNA-Seq dataset using improved methods, finding 549 times fewer differentially expressed genes at an FDR of 0.05.

EpiCompare is an R package that compares and quality controls epigenomic peak files, providing an interactive HTML report with minimal user input.

This paper reanalyzes differential expression methods in scRNA-seq using corrected simulations, finding pseudobulk methods outperform mixed models and pseudoreplication approaches.

This study identifies the cellular specificity of Leigh syndrome genes using transcriptomic analyses and finds enrichment in neuronal cells and pre-synaptic structures.

MungeSumstats standardises and quality controls GWAS summary statistics, enabling secondary analysis and meta-analysis by converting them into a common format.

scFlow provides a scalable, reproducible pipeline for analyzing single-cell RNA-seq data, demonstrating its utility with public datasets and extensibility as an open-source tool.

This study identifies cell types associated with brain complex traits using GWAS and single-cell transcriptomics, revealing novel roles for oligodendrocytes in Parkinson's disease.

The paper shows that single nucleus RNA-Seq fails to detect microglial activation genes in human brain samples compared to single cell RNA-Seq.

This study identifies genetic loci associated with household income and links them to biological pathways and cognitive ability, showing genetic differences between income and education.

The paper introduces pciSeq to map inhibitory neurons in mouse hippocampus CA1, identifying 16 interneuron classes with spatial arrangement matching ground truth.

This study uses conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders, finding some variants with reduced pleiotropy effects.

This study identifies 202 genome-wide significant loci for insomnia using a large sample, linking them to specific brain cell types and pathways.

The paper identifies synaptic protein combinations that underlie diverse innate and learned behaviors in mice, showing their role in behavioral repertoire expansion and disease susceptibility.

The paper introduces a Bayesian framework to prioritize risk genes for complex diseases by integrating gene-set information with variant counts, identifying novel epilepsy risk genes and linking them to immune pathways and drug targets.

The paper maps cell types in the mouse nervous system using single-cell RNA sequencing and identifies distinct astrocyte and oligodendrocyte subtypes with specific molecular and spatial characteristics.