Gerton A. Lunter

The paper develops treeLFA, a topic modeling method using ICD10 priors to identify multimorbidity clusters in UK Biobank, and finds genetic loci associated with these clusters.

The paper benchmarks small-variant genotyping in polyploids using Octopus and other tools on synthetic and real datasets, finding Octopus has 25% fewer errors on average.

MLV is a web-based tool for visualizing, analyzing, and annotating genomic datasets, enabling multi-scale interaction and integration of multiple data tracks.

Octopus is a haplotype-based variant caller that accurately detects germline and somatic variants in diverse scenarios, including tumours and parent-offspring trios.

This study evaluates the ability of long-read sequencing to detect somatic variants in a lymphoma patient, highlighting the need for specialized tools and the advantages of long reads for structural variants.

DeepC predicts chromatin interactions from DNA sequence using deep learning and transfer learning, generalizing across cell types and resolutions.

The paper uses a Bayesian method to infer ancient demographic events and finds evidence of migration from Eurasian ancestors into Africa 40-70 thousand years ago.

This paper introduces a Bayesian model to identify vaccine-specific BCR sequences by analyzing B cell receptor repertoires across time and individuals, achieving 69% sensitivity.

The paper develops a platform combining ATAC-seq, DNaseI footprints, NG Capture-C, and machine learning to identify causal variants and genes for polygenic human traits.

The paper introduces an equivariant Bayesian CNN that predicts recombination hotspots and identifies PRDM9 binding motifs with high accuracy.

This paper introduces TE-NGS, a targeted sequencing protocol and computational pipeline to identify polymorphic and novel transposable element insertions in human genomes, demonstrating high sensitivity and precision.