Adam M. Novak

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.

Lancet2 improves somatic variant calling with faster runtime and better performance, especially for InDels, using enhanced methods and machine learning.

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

This paper introduces pangenome-aware DeepVariant, a variant caller that improves accuracy by using a pangenome reference, outperforming linear-reference-based DeepVariant by up to 25.5%.

The paper introduces tools to efficiently index and query annotations in pangenome graphs using GAF format, demonstrating applications on human pangenome data.

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

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

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.

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 haplotype-aware graph indexes to improve the representation of genetic variation in variation graphs.

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 identifies human-specific NOTCH2NL genes linked to neurodevelopmental disorders and shows they affect cortical neurogenesis through Notch signaling.

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