James Han

The paper introduces DRAGEN, a scalable mosaic variant caller that detects low VAF variants with high accuracy and efficiency, and presents a benchmark for low-VAF variants.

This paper evaluates Illumina Constellation sequencing for rare disease research, showing it can detect structural variants and haplotype phasing with low DNA input and existing infrastructure.

This study benchmarks CNV callers for WGS clinical applications, finding DRAGEN v4.2 offers the best balance of sensitivity and precision for detecting deletions and some duplications.

The paper develops a benchmark for detecting low-frequency mosaic variants in the HG002 reference material using high-coverage sequencing data and validates its utility across multiple technologies.

The paper introduces a CN caller for KIV-2 repeats using short reads, showing high accuracy and demonstrating its correlation with Lp(a) levels and population-specific CN distributions.

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.

The paper introduces DRAGEN's somatic variant calling pipeline, demonstrating its speed and accuracy in whole-genome sequencing tumor-normal analysis compared to existing tools.

This paper introduces a machine learning method to estimate cross-sample contamination from variant call data using a support vector regression model trained on simulated datasets.