Justin Wagner

T2T-HG002v1.1, a complete diploid HG002 assembly lacking detectable errors over 99.4% of its length, enables genome-based benchmarking of assemblies, sequencing reads, and phased variant calls.

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.

The paper describes a comprehensive genomic dataset from a tumor-normal pair with broad consent, generated using multiple sequencing technologies for benchmarking somatic variant detection.

The paper creates a variant benchmark for X and Y chromosomes using a complete assembly of HG002, highlighting challenges in variant detection in complex regions.

The paper introduces genomic stratifications for human reference genomes GRCh37, GRCh38, and T2T-CHM13v2.0 to benchmark variant caller performance in difficult regions.

StratoMod predicts variant calling errors using interpretable machine learning to help design better variant calling pipelines.

GrapHiC improves Hi-C read imputation by integrating Hi-C and ChIP-seq data in a graph framework, enhancing performance on sparse and cross-cell-type inputs.

scGrapHiC uses deep learning to predict cell-type-specific scHi-C contact maps from scRNA-seq data, improving recovery of Topologically Associating Domains by 23.2% over baselines.

This paper evaluates deep-learning methods for improving Hi-C resolution using experimentally generated datasets and finds they perform poorly on sparse data, with retraining offering limited improvement.

The paper benchmarks small and large variants in tandem repeats across 86 samples, creating a benchmark dataset and comparison method for TR variant calling.

The paper introduces a pangenome research toolkit to analyze complex genomic variations across multiple scales, improving the interpretation of repetitive and clinically relevant genes.

The paper evaluates sequencing and assembly methods to create high-quality diploid human genome assemblies, finding that long reads and haplotype sorting improve accuracy and completeness.

This paper expands genome benchmarking to include difficult-to-map regions using linked and long reads, identifying more false negatives in variant calling.

The paper evaluates variant calling in difficult genomic regions using short- and long-read sequencing, showing improved performance with new methods and multi-technology approaches.

A gapless 3.055 billion bp human genome from HiFi and ultra-long nanopore reads resolves all centromeric arrays and acrocentric p-arms, adding 182 Mbp of new sequence.

The T2T-CHM13 reference genome improves read mapping and variant calling across diverse samples, reducing false positives and enabling novel variant discovery.

This paper creates a comprehensive benchmark for challenging medically-relevant autosomal genes, improving variant recall from 8% to 100% by addressing reference-specific false duplications.

The paper develops a diploid assembly-based benchmark for variants in the MHC region using HG002 data, identifying 21496 small variants with high concordance to clinical HLA types.

The paper assembles and annotates a population-specific Ashkenazi human reference genome, showing improved contiguity and completeness compared to GRCh38.

The paper develops a reproducible pipeline to integrate sequencing datasets for high-confidence SNP, indel, and reference calls in five human genomes, showing improved variant detection compared to prior methods.

The paper introduces an R package for handling 16S rRNA databases and marker-gene survey data, providing consistent interfaces for database comparison and data structure development.

Metaviz provides an interactive web tool for exploring and visualizing metagenomic data through heatmaps, bar plots, and PCA scatter plots.