Jordan M. Eizenga

The paper introduces Centrolign, a tool for aligning centromeric regions, revealing mutation dynamics and enabling variant calling in human centromeres.

460 haplotypes from 230 genomes, selected to maximize common-variant coverage, capture 99.43% of All of Us common small variants and halve structurally unreliable regions and base errors versus HPRC1.

The paper analyzes chromosome 22q11.2 haplotypes to show how structural variations in specific regions correlate with microdeletion and inversion risks across populations.

HMM-Flagger evaluates genome assemblies by detecting structural errors using read coverage modeling, showing improved accuracy in human assemblies.

The paper uses long-read sequencing to identify pathogenic variants in autism cases, finding three pathogenic mutations and nine candidate structural variants missed by short-read sequencing.

The paper presents T2T reference genomes from a multi-generational pedigree to study genetic and epigenetic inheritance across the complete genome.

The paper updates Giraffe to map both long and short reads to large pangenome graphs, showing improved speed and accuracy compared to existing tools.

The paper introduces a method to construct diploid sequence graphs from haploid sequence graphs to represent haplotype pairings and their similarity.

The paper introduces a personalized pangenome approach to improve short-read mapping and variant calling by reducing reference bias and errors.

The paper introduces methods for haplotype-resolved de novo assembly using Shasta and GFAse, showing improved assembly quality with newer Oxford Nanopore reads.

The paper introduces Minigraph-Cactus, a pangenome pipeline that constructs pangenome graphs directly from whole-genome alignments, demonstrating improved performance in mapping and variant calling across human and Drosophila data.

The paper presents a draft human pangenome reference that improves variant discovery and structural variant detection compared to existing references.

The paper shows recombination between heterologous human acrocentric chromosomes using pangenome data, revealing pseudo-homologous regions linked to Robertsonian translocations.

This paper introduces BiWFA, an algorithm that performs gap-affine sequence alignment in O(s) memory while maintaining O(ns) time complexity, improving memory efficiency for large-scale alignments.

This paper introduces a toolchain for haplotype-aware transcript analysis using spliced pangenome graphs, improving RNA-seq mapping accuracy and enabling haplotype-specific expression quantification without prior haplotype characterization.

The paper introduces a pedigree-aware workflow using pangenome graphs to improve variant calling in rare disease analysis, detecting more deleterious variants in probands than unaffected siblings.

The paper improves the memory and runtime complexity of the WFA algorithm and integrates its scoring system into standard alignment frameworks.

The paper introduces Giraffe, a pangenome mapper that improves genotyping of SNVs, indels, and SVs in 5,202 human genomes using the vg toolkit.

The paper introduces a haplotype-aware variant calling pipeline for nanopore and PacBio HiFi data, achieving high accuracy in variant detection and de novo assembly polishing.

The paper develops efficient algorithms for distance calculation and seed clustering on sequence graphs to improve mapping algorithms.

The paper introduces two C++ libraries for efficient manipulation of pangenomic graphs, showing improved performance over existing tools.

This paper introduces a Bayesian framework to detect gene expression outliers in single samples by creating a consensus background distribution without requiring a manual comparison set.

The paper demonstrates that variation graphs using the vg toolkit improve structural variant genotyping compared to traditional methods and VCF-based catalogs.

The paper introduces vg, a toolkit for creating and using variation graphs as genome references, improving read mapping and variant calling accuracy over linear references.

This paper introduces a new algorithm called the flow procedure for linearizing genome sequence graphs, which outperforms existing methods in key metrics.

The paper introduces snarls and ultrabubbles for bidirected graphs to represent genomic variations and presents an algorithm for their detection using cactus graphs.

The paper discusses the use of genome graphs to reduce reference bias in human genomics by improving read mapping, variant calling, and haplotype determination.

This paper introduces a haplotype model based on variation graphs to represent complex genetic variation, improving upon traditional methods limited to linear haplotype panels.

This paper introduces genome graphs as an alternative to linear reference genomes, showing they improve read mapping and variant calling compared to the standard GRCh38 reference.

The paper introduces bundle structures and extends ultrabubbles to describe genetic sites in sequence graphs with linear-time identification.