HoJoon Lee

The paper introduces CLONEID, a framework for organizing clone-resolved data to enable longitudinal genotype-to-phenotype mapping in experiments with repeated molecular and phenotypic measurements.

This study uses spatial single-cell analysis to identify 10 cell phenotypes in stage III colorectal cancers and links specific spatial features to recurrence risk.

KmerSV visualizes and annotates structural variants using k-mers and the Human Pangenome reference.

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.

The paper develops a nanopore sequencing strategy to measure cfDNA methylomes in cancer patients, enabling longitudinal monitoring during treatment.

The paper develops a method to detect complex structural variants across human genomes and finds they are associated with phenotypic hotspots and evolutionary changes.

The paper analyzes the Human Pangenome to identify conserved and polymorphic structural variations across diverse haploid assemblies.

The paper describes the development of a gastric cancer registry integrating clinical and genomic data to support translational research.

KmerKeys provides a web tool for efficient k-mer-based querying of genome assemblies and variant databases.

This paper introduces HEMnet, a deep learning model that transfers molecular labels from immunohistochemistry to H&E images to identify cancer cells without manual annotation.

This study identifies a consensus microbiome for colorectal cancer by analyzing 924 tumors across eight RNA-Seq datasets, finding 114 microbial species consistently associated with tumors.

The paper develops a high-coverage sequencing approach to profile microsatellite and chromosomal instability in colorectal cancer, revealing co-occurrence of MSI and CIN features.

The paper develops a method using k-mer sequences to validate somatic mutations in cancer genome sequencing data, showing it effectively identifies true positive mutations including insertions and deletions.

The paper identifies TP53 genomic deletions as a poor prognosis marker in Stage III colorectal cancer using a new low-coverage whole-genome sequencing protocol.

CRISPRpic provides fast and precise mutation calling by counting exact matches and pattern searches in CRISPR sequencing data.

The paper introduces a targeted sequencing method using linked read sequencing of CRISPR DNA targets to assemble large genomic regions and detect structural variants with high precision.

SVEngine simulates genome structural variations with cancer clonal evolution features, producing sequence data with variants and ground truth BED files.