Biology subjects

Yoo, B.

Publications and source records attributed to Yoo, B..

15 recordsLinked to original sources

bioRxiv2026

Hunting for microsatellite instability in long-read data with Owl

Microsatellite instability (MSI) is a key biomarker of mismatch repair deficiency and response to immunotherapy, yet most existing genomic detection methods are optimized for short-read sequencing and rely on small panels of homopolymer markers, limiting the ability to characterize genome-wide and motif-specific patterns of instability. Here we present Owl, a bioinformatic tool for quantifying MSI from long-read (PacBio) genomic data. Owl leverages a genome-wide marker set of more than 140,000 microsatellite repeats ranging from 1-6 bp in length to measure MSI across a phased genome. Using a wrap-around alignment algorithm, Owl constructs repeat-length distributions at each marker site and flags somatic instability using the coefficient of variation. We applied Owl to screen for markers with stable coverage, phasing, and baseline variation across 131 diverse genomes from the Human Pangenome Reference Consortium, where Owl scores ranged from 1.4% to 5.4% of markers exceeding the instability threshold. When applied to 19 cancer cell lines and one diffuse astrocytoma tumor-normal pair, Owl identified five MSI-high genomes with 15-18% unstable markers and showed close concordance with an Illumina DRAGEN MSI assay for the astrocytoma sample. Motif-level analyses revealed shared enrichment of short homopolymer and dinucleotide (A- and AT-rich) repeats across MSI-high cancers, and additionally uncovered a distinct pattern of elevated GGAA microsatellite instability in Ewing sarcoma cell lines, consistent with the known role of the EWS::FLI1 fusion protein at GGAA-rich regulatory elements. Owl is implemented in Rust and integrated into the PacBio HiFi Somatic workflow, providing a scalable framework for MSI analysis from long-read sequencing focused on repeat instability specifically in tumor samples.

bioinformatics↗

bioRxiv2025

A comprehensive bulk and single-cell transcriptional atlas of pediatric leukemias

Advancements in understanding the molecular factors driving pediatric leukemias have led to an ever-increasing volume and diversity of data being generated. Recent studies are moving beyond DNA-based profiling to incorporate transcriptional data, enhancing the characterization of these cancers and informing clinical decisions. However, many existing datasets focus on specific leukemia subtypes, limiting the extraction of broader clinically relevant insights. Here, we present a comprehensive dataset of bulk and single-cell transcriptional data from 69 pediatric leukemia patients, encompassing eight leukemias. This is one of the most diverse pediatric leukemia datasets published to date. Through comparative analysis and explainable machine learning, we demonstrate the utility of these datasets in improving pediatric leukemia characterization and supporting the integration of transcriptional data into clinical testing.

cancer biology↗

bioRxiv2025

Integrated multi-omic analysis reveals novel subtype-specific regulatory interactions in pediatric B-cell acute lymphoblastic leukemia

Molecular subtyping of pediatric B-cell acute lymphoblastic leukemia (B-ALL) has improved patient outcomes through stratification and selection of targeted therapies. Despite extensive genomic and transcriptomic profiling of this cancer, few studies to date have characterized the proteomic landscape, although proteins are the direct targets of many therapeutic agents. In this study, we demonstrate the utility of multi-omic integration of global transcriptomic, proteomic, and phosphoproteomic profiles of samples from patients diagnosed with either of two B-ALL subtypes - Ph- like (BCR::ABL1-like) and ETV6::RUNX1. Through individual and multi-omic analysis, we recapitulate known transcriptomic findings and identify novel subtype-specific proteomic and phosphoproteomic biomarkers. Our findings suggest a previously undescribed role for calcium-dependent signaling processes in Ph-like B-ALL, which has the potential to serve as a novel avenue for targeted treatments. By integrating multiple omics modalities, we identify not only features of interest but also begin to unravel the regulatory interactions driving subtype-specific mechanisms of leukemogenesis. This integrated analytic approach paves the way for enhanced precision medicine for precise subtyping and treatment selection for pediatric leukemia patients. Mass spectrometry data generated in this study have been deposited in MassIVE under accession MSV000097955.

cancer biology↗

bioRxiv2024

StarPhase: Comprehensive Phase-Aware Pharmacogenomic Diplotyper for Long-Read Sequencing Data

Pharmacogenomics is central to precision medicine, informing medication safety and efficacy. Phar-macogenomic diplotyping of complex genes requires full-length DNA sequences and detection of structural rearrangements. We introduce StarPhase, a tool that leverages PacBio HiFi sequence data to diplotype 21 CPIC Level A pharmacogenes and provides detailed haplotypes and supporting visualizations for HLA-A, HLA-B, and CYP2D6. StarPhase diplotypes have high concordance with benchmarks where 99.5% are either exact matches or minor discrepancies. Manual inspection of the 0.5% mismatches indicates they were correctly called by StarPhase. With StarPhase, we update or correct 26.2% of GeT-RM pharmacogenomic diplotypes. Population distributions from StarPhase mostly reflect those of the All of Us cohort, while also highlighting gaps in existing pharmacogenomic databases that long-read sequencing can fill. With a single HiFi whole genome sequencing assay, StarPhase enables robust PGx diplotyping even as additional pharmacogenes and haplotypes are discovered.

bioinformatics↗

bioRxiv2024

A robust benchmark for detecting low-frequency variants in the HG002 Genome In A Bottle NIST reference material.

Somatic mosaicism is an important cause of disease, but mosaic and somatic variants are often challenging to detect because they exist in only a fraction of cells. To address the need for benchmarking subclonal variants in normal cell populations, we developed a benchmark containing mosaic variants in the Genome in a Bottle Consortium (GIAB) HG002 reference material DNA from a large batch of a normal lymphoblastoid cell line. First, we used a somatic variant caller with high coverage (300x) Illumina whole genome sequencing data from the Ashkenazi Jewish trio to detect variants in HG002 not detected in at least 5% of cells from the combined parental data. These candidate mosaic variants were subsequently evaluated using >100x BGI, Element, and PacBio HiFi data. High confidence candidate SNVs with variant allele fractions above 5% were included in the HG002 draft mosaic variant benchmark, with 13/85 occurring in medically relevant gene regions. We also delineated a 2.45 Gbp subset of the previously defined germline autosomal benchmark regions for HG002 in which no additional mosaic variants >2% exist, enabling robust assessment of false positives. The variant allele fraction of some mosaic variants is different between batches of cells, so using data from the homogeneous batch of reference material DNA is critical for benchmarking these variants. External validation of this mosaic benchmark showed it can be used to reliably identify both false negatives and false positives for a variety of technologies and detection algorithms, demonstrating its utility for optimization and validation. By adding our characterization of mosaic variants in this widely-used cell line, we support extensive benchmarking efforts using it in simulation, spike-in, and mixture studies.

bioinformatics↗

bioRxiv2024

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies now offer potential advantages in terms of repeat mapping and variant phasing. We present DeepSomatic, a deep learning method for detecting somatic SNVs and insertions and deletions (indels) from both short-read and long-read data, with modes for whole-genome and exome sequencing, and able to run on tumor-normal, tumor-only, and with FFPE-prepared samples. To help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available a dataset of five matched tumor-normal cell line pairs sequenced with Illumina, PacBio HiFi, and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples and technologies (short-read and long-read), DeepSomatic consistently outperforms existing callers, particularly for indels.

bioinformatics↗

bioRxiv2022

Analysis of Inbred Mouse strains' High-Impact Genotype-phenotype Hypotheses (AIMHIGH) reveals novel disease-causing candidate genes

Inbred mouse strains reveal the molecular basis of mammalian traits and diseases, particularly recessive ones. We utilized mouse community curated resources to set up an automated screen to discover novel testable gene function hypotheses. Using 11,832 community contributed strain-differentiating experiments and trait presence/absence scoring, we searched for all experiments where strains can be split by their phenotypic values (e.g., high vs. low responders). Then, using 48 sequenced strains, we found one or more candidate gene for each experiment where homozygous high-impact variants (such as stopgain, frameshifts) segregate strains into these same binary grouping. Our approach rediscovered 212 known gene-phenotype relationships, almost always highlighting potentially novel causal variants, as well as thousands of gene function hypotheses. To help find the most exciting hypotheses, we improved the state of the art in machine learning driven literature-based discovery (LBD). Reading on our top 3 ranked candidate genes per experiment reveals 80% of rediscovered relationships, compared to 5% reading at random. We proposed 1,842 novel gene-phenotype testable hypotheses using our approach. We built a web portal at aimhigh.stanford.edu to allow researchers to view all our testable hypotheses in detail. Our open-source code can be rerun as more sequenced strains and phenotyping experiments become available.

genomics↗

bioRxiv2021

Towards a Comprehensive Variation Benchmark for Challenging Medically-Relevant Autosomal Genes

The repetitive nature and complexity of multiple medically important genes make them intractable to accurate analysis, despite the maturity of short-read sequencing, resulting in a gap in clinical applications of genome sequencing. The Genome in a Bottle Consortium has provided benchmark variant sets, but these excluded some medically relevant genes due to their repetitiveness or polymorphic complexity. In this study, we characterize 273 of these 395 challenging autosomal genes that have multiple implications for medical sequencing. This extended, curated benchmark reports over 17,000 SNVs, 3,600 INDELs, and 200 SVs each for GRCh37 and GRCh38 across HG002. We show that false duplications in either GRCh37 or GRCh38 result in reference-specific, missed variants for short- and long-read technologies in medically important genes including CBS, CRYAA, and KCNE1. Our proposed solution improves variant recall in these genes from 8% to 100%. This benchmark will significantly improve the comprehensive characterization of these medically relevant genes and guide new method development.

genomics↗

bioRxiv2021

Analysis of Structural Variation Among Inbred Mouse Strains Identifies Genetic Factors for Autism-Related Traits

The genomes of six inbred strains were analyzed using long read (LR) sequencing. The results revealed that structural variants (SV) were very abundant within the genome of inbred mouse strains (4.8 per gene), which indicates that they could impact genetic traits. Analysis of the relationship between SNP and SV alleles across 53 inbred strains indicated that we have a very limited ability to infer whether SV are present using short read sequence data, even when nearby SNP alleles are known. The benefit of having a more complete map of the pattern of genetic variation was demonstrated by identifying at least three genetic factors that could underlie the unique neuroanatomic and behavioral features of BTBR mice that resemble human Autism Spectrum Disorder (ASD). Similar to the genetic findings in human ASD cohorts, the identified BTBR-unique alleles are very rare, and they cause high impact changes in genes that play a role in neurodevelopment and brain function.

genomics↗

bioRxiv2021

20-hydroxyecdysone (20E) signaling regulates amnioserosa morphogenesis during Drosophila dorsal closure: Ecdysone receptor modulates gene expression in a complex with the AP-1 component, Jun

Steroid hormones influence diverse biological processes throughout the animal life cycle, including metabolism, stress resistance, reproduction, and lifespan. In insects, the steroid hormone, 20-hydroxyecdysone (20E), is the central regulator of molting and metamorphosis, and has been shown to play roles in tissue morphogenesis. For example, amnioserosa contraction, which is a major driving force in Drosophila dorsal closure (DC), is defective in embryos mutant for 20E biosynthesis. Here, we show that 20E signaling modulates the transcription of several DC participants in the amnioserosa and other dorsal tissues during late embryonic development, including the zipper locus, which encodes for non-muscle myosin II heavy chain. Canonical 20E signaling typically involves the binding of Ecdysone receptor (EcR) and Ultraspiracle heterodimers to ecdysone-response elements (EcREs) within the promoters of ecdysone-responsive genes to drive their expression. During DC, we provide evidence that 20E signaling instead acts in parallel to the JNK cascade via a direct interaction between EcR and the AP-1 component, Jun, which together binds to genomic regions containing AP-1 binding sites but no EcREs to control gene expression. Our work demonstrates a novel mode of action for 20E signaling in Drosophila that likely functions beyond DC, and may provide further insights into mammalian steroid hormone receptor interactions with AP-1.

developmental biology↗

bioRxiv2020

Benchmarking challenging small variants with linked and long reads

Genome in a Bottle (GIAB) benchmarks have been widely used to help validate clinical sequencing pipelines and develop new variant calling and sequencing methods. Here, we use accurate linked reads and long reads to expand the prior benchmarks in 7 samples to include difficult-to-map regions and segmental duplications that are not readily accessible to short reads. Our new benchmark adds more than 300,000 SNVs, 50,000 indels, and 16 % new exonic variants, many in challenging, clinically relevant genes not previously covered (e.g., PMS2). For HG002, we include 92% of the autosomal GRCh38 assembly, while excluding problematic regions for benchmarking small variants (e.g., copy number variants and reference errors) that should not have been in the previous version, which included 85% of GRCh38. By including difficult-to-map regions, this benchmark identifies eight times more false negatives in a short read variant call set relative to our previous benchmark.We have demonstrated the utility of this benchmark to reliably identify false positives and false negatives across technologies in more challenging regions, which enables continued technology and bioinformatics development.

genomics↗

bioRxiv2019

2D-HELS-AA MS Seq: Direct sequencing of tRNA reveals its different isoforms and multiple dynamic base modifications

We report a direct method for sequencing tRNAPhe without cDNA by combining 2-dimensional hydrophobic RNA end-labeling with an anchor-based algorithm in mass spectrometry-based sequencing (2D-HELS-AA MS Seq). The entire tRNAPhe was sequenced and the identity, location and abundance of all 11 base modifications were determined. Changes in ratios of wybutosine and its depurinated form under different conditions were quantified, pointing to the ability of our technology to determine dynamic changes of nucleotide modifications. Two truncated isoforms at 3CCA tail of the tRNAPhe (75 nt CC, 80% and 74 nt C, 3%) were identified in addition to the 76 nt tRNAPhe with a full-length 3CCA tail (17%). We also discovered a new isoform with A-G transitions at both the 44 and 45 positions in the tRNAPhe variable loop.\n\nOne Sentence SummaryDirect 2D-HELS-AA MS Seq of tRNA reveals different isoforms and base modifications

biochemistry↗

bioRxiv2019

Homeodomain-interacting protein kinase (Hipk) is required for nervous system and muscle structure and function

Homeodomain-interacting protein kinases (Hipk) have been previously associated with cell proliferation and cancer, however, their effects in the nervous system are less well understood. We have used Drosophila melanogaster to evaluate the effects of altered Hipk expression on the nervous system and muscle. Using genetic manipulation of Hipk expression we demonstrate that knockdown and over-expression of Hipk produces early adult lethality, partially due to the effects on the nervous system and muscle involvement. We find that optimal levels of Hipk are critical for the function of dopaminergic neurons and glial cells in the nervous system, as well as muscle. Furthermore, manipulation of Hipk affects the structure of the larval neuromuscular junction (NMJ) and increases motor neuron axonal branching. Hipk regulates the phosphorylation of the synapse-associated cytoskeletal protein Hts (adducin) and modulates the expression of two important protein kinases, Calcium-calmodulin protein kinase II (CaMKII) and Partitioning-defective 1 (PAR-1), all of which may alter neuromuscular function and influence lethality. Hipk also modifies the distribution of an important nuclear protein, TBPH, the fly orthologue of TAR DNA-binding protein 43 (TDP-43), which may have relevance for understanding motor neuron diseases.

neuroscience↗

bioRxiv2019

Differential chromatin accessibility in developing projection neurons is correlated with transcriptional regulation of cell fate

We are only just beginning to catalog the vast diversity of cell types in the cerebral cortex. Such categorization is a first step toward understanding how diversification relates to function. All cortical projection neurons arise from a uniform pool of progenitor cells that lines the ventricles of the forebrain. It is still unclear how these progenitor cells generate the more than fifty unique types of mature cortical projection neurons defined by their distinct gene expression profiles. Here we compare gene expression and chromatin accessibility of two subclasses of projection neurons with divergent morphological and functional features as they develop in the mouse brain between embryonic day 13 and postnatal day 5 in order to identify transcriptional networks that diversity neuron cell fate. We find groups of transcription factors whose expression is correlated with chromatin accessibility, transcription factor binding motifs, and lncRNAs that define each subclass and validate the function of a family of novel candidate genes in vitro. Our multidimensional approach reveals that subclass-specific chromatin accessibility is significantly correlated with gene expression, providing a resource for generating new specific genetic drivers and revealing regions of the genome that are particularly susceptible to harmful genetic mutations by virtue of their correlation with important developmental genes.

developmental biology↗

bioRxiv2019

A general LC/MS-based RNA sequencing method for direct analysis of multiple-base modifications in RNA mixtures

A complete understanding of the structural and functional potential of RNA requires understanding of chemical modifications and noncanonical bases; this in turn requires advances in current sequencing methods to be able to sequence not only canonical ribonucleotides, but at the same time directly sequence these nonstandard moieties. Here, we present the first direct and modification type-independent RNA sequencing method via integration of a hydrophobic end-labeling strategy with of 2-D mass-retention time LC/MS analysis to allow de novo sequencing of RNA mixtures and enhance sample usage efficiency. Our method can directly read out the complete sequence, while identifying, locating, and quantifying base modifications accurately in both single and mixed RNA samples containing multiple different modifications at single-base resolution. Our method can also quantify stoichiometry/percentage of modified RNA vs. its canonical counterpart RNA, simulating a real biological sample where modifications exist but may not be 100% at a particular site of the RNA. This method is a critical step towards fully sequencing real complex cellular RNA samples of any type and containing any modification types and can also be used in the quality control of modified therapeutic RNAs.

biochemistry↗