Pi-Chuan Chang

The paper introduces GBZ-base and GAF-base, indexed pangenome file formats for efficient subgraph and alignment extraction.

The paper assembles and characterizes 2,110 human centromeres, revealing novel haplotypes, mobile element insertions, and mutation rate variation across chromosomes.

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.

PanVariants improves variant calling by integrating k-mer and mapping methods, reducing errors and outperforming existing tools for SNVs, indels, and SVs.

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

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.

DeepSomatic is a deep learning method for detecting somatic variants from short-read and long-read sequencing data, outperforming existing callers, especially for indels.

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 creates a long-read benchmark for genetic variants using a large pedigree, improving variant calling performance and introducing new truth sets for tandem repeats and structural variants.

This study compares Element Avidity sequencing to Illumina sequencing, finding higher accuracy in mapping and variant calling, especially at lower coverages and in challenging genomic regions.

DeepPolisher improves genome assembly accuracy by reducing errors using a transformer model and phasing method, achieving over 70% indel error reduction.

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%.

Severus detects and characterizes somatic structural variations in tumor genomes using long reads, outperforming existing methods in accuracy and revealing clinically relevant rearrangements.

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

This paper introduces a local haplotype approximation method to improve variant calling accuracy across long-read sequencing platforms without increasing computational overhead.

The paper produces gapless assemblies of ape sex chromosomes for five great apes and a lesser ape, revealing divergence in Y chromosomes and evolutionary dynamics.

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 develops a deep-learning based RNA-seq germline variant caller that outperforms existing tools like Platypus and GATK.

The paper introduces a tool called best for efficiently quantifying sequencing errors in aligned reads and shows it is 16 times faster than existing methods.

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

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.

DeepConsensus improves PacBio HiFi read accuracy and assembly quality using a gap-aware transformer model.

DeepTrio improves variant calling in family trios using deep learning, achieving higher accuracy than DeepVariant, especially at lower coverages.

This study improves variant calling by integrating population data into deep learning models, enhancing precision and recall across diverse populations.