Richard Dobson

This study benchmarks TE detection tools on long-read data, showing significant variation in performance and highlighting tool-specific strengths and limitations.

This paper develops a deep learning model using SNP microarray data to predict ALS risk with high accuracy across diverse populations and genotyping platforms.

The paper investigates oligogenic risk in ALS using genome sequencing data, finding that multiple rare variants in known ALS genes increase disease risk but not clinical outcomes.

This study uses GWAS summary statistics to identify shared genetic loci across neuropsychiatric diseases and finds HLA region associations with potential shared causal variants.

The paper identifies sex-associated DNA methylation differences in ALS using a large-scale epigenome-wide association study and links them to survival outcomes.

This study analyzes genetic and clinical data from PLS patients to assess the role of genetic testing and identify potential disease-associated variants.

Mutafy identifies high-quality mutant protein structures from the PDB for given human genes, annotates them with biomedical data, and provides interactive visualization.

The paper analyzes CAV1 and CAV2 expression in ALS brain tissue and finds higher expression in ALS patients, associated with increased survival and slower disease progression.

The paper evaluates bioinformatics tools for detecting human endogenous retroviral insertions in short-read genome sequencing data, finding performance variability across datasets and recommending a consensus approach.

This study uses molecular dynamics and clinical data to show structural and dynamic differences between WTL and MBR SOD1 variants, linking these to distinct ALS clinical outcomes.

This study identifies NEFH tail domain mutations as risk factors for ALS through meta-analysis and variant burden analysis in case-control studies and whole-genome sequencing data.

DNAscan2 is a versatile, scalable, and user-friendly NGS analysis pipeline that supports variant detection and end-to-end data processing.

RetroSnake detects HERVs in NGS data using an end-to-end pipeline validated by wet-lab experiments, producing annotated insertions in an interactive HTML file.

This study performs GWAS on quantitative AD biomarkers and cognitive traits, identifying new loci associated with memory performance and cognitive decline.

This study investigates genetic overlap and causality between blood plasma proteins and Alzheimer's Disease using polygenic risk scores and Mendelian Randomization, finding associations but no strong causal links.

COPILOT automates GWAS quality control for Illumina genotyping data, improving sample call rates and identifying data issues across two cohorts.

This study identifies TREM2-dependent genes and pathways in Alzheimer's disease by analyzing RNA sequencing data from brain samples, revealing its impact on microglia, oligodendrocytes, and endothelial cells.

The paper conducts GWAS on CSF Alzheimer's disease biomarkers, identifying genetic associations with NF-L, YKL-40, and Ng levels.

This study uses Mendelian Randomization to investigate causal relationships between blood metabolites and Alzheimer's Disease, identifying several metabolites as potential causal factors.

The paper conducts a genome-wide association study (GWAS) on Alzheimer’s disease CSF biomarkers in the EMIF-AD dataset, identifying novel genetic signals and confirming existing associations.

This study identifies seven novel genetic loci for Alzheimer's disease using a large GWAS and highlights microglia and immune pathways as key contributors to disease pathology.

The paper integrates lipidomics and proteomics data to identify modules associated with Alzheimer's disease phenotypes and risk loci in immune and lipid pathways.

The paper develops two gene expression classification models for Alzheimer’s Disease using blood-derived data, finding that including mixed controls improves specificity but reduces sensitivity.

This study compares transcriptomic profiles of asymptomatic and symptomatic Alzheimer's brains to identify early molecular changes associated with disease vulnerability.

DNAscan is a fast and memory-efficient pipeline for analyzing DNA NGS data, capable of detecting SNVs, indels, SVs, and other variants with minimal computational resources.

This paper performs a meta-analysis of Alzheimer's disease brain transcriptomic data to identify differentially expressed genes and pathways specific to AD, finding 323-1023 genes per brain region and enriched pathways in protein metabolism and viral components.

ALSgeneScanner is a tool that provides automated, detailed reports on ALS-related gene variants from whole genome or exome sequencing data, aiding non-specialists in interpretation.

The paper describes the development and deployment of an open-source information retrieval and extraction system in a UK hospital to improve data flow and governance while supporting clinical and research needs.

This study examines the transcriptional effects of Trem2 loss in mouse brain, revealing disruptions in cell co-expression networks and highlighting a potential role for Trem2 in endothelial cell homeostasis.

This study identifies 9 novel genetic loci and 215 potential causative genes associated with Alzheimer's disease risk through a large genome-wide association analysis.