Daniel Cameron

This paper presents near-complete, haplotype-resolved assemblies of a pancreatic cancer genome and normal tissue, identifying extensive somatic variants and epigenetic changes in previously excluded regions.

DRAGEN provides fast and accurate genome analysis across 3,202 genomes, outperforming existing methods in variant detection speed and accuracy for various types including SNV, indel, STR, SV, and CNV.

svaRetro and svaNUMT are R packages for annotating retrotransposed transcripts and nuclear mitochondrial DNA integration in genome sequencing data, evaluated using simulations and benchmarking datasets.

The paper compiles a comprehensive reference dataset of true somatic structural variations by analyzing melanoma and normal cell lines with multiple sequencing technologies and experimental validation.

The paper introduces the GA4GH Variation Representation Specification (VRS), a framework for precise and federated representation of molecular variation, with a reference implementation and community resources available online.

VIRUSBreakend identifies viral DNA integration with high sensitivity and low false discovery rate using single breakend variant calling in cancer genomes.

GRIDSS2 identifies somatic structural variants by analyzing single breakends and phasing, achieving a 3.1% false negative rate in metastatic cancers and uncovering a novel duplication signature.

A three-tool chain calls somatic structural variants, purity, ploidy and copy number from paired tumour/normal whole genomes, then clusters breakpoints into derivative chromosome structures and gene fusions.

The paper uses whole genome optical mapping to reveal complex chained fusion events and novel sequences in cancer genomes that are not detected by short-read sequencing.

GRIDSS detects genomic rearrangements with high sensitivity and specificity using a positional de Bruijn graph assembler, outperforming existing methods on simulated and tumor data.