Biology subjects

Jeong, H.-H.

Publications and source records attributed to Jeong, H.-H..

9 recordsLinked to original sources

bioRxiv2026

BARCS: beta-binomial regression for multivariate CRISPR screen design

Pooled CRISPR screens increasingly use longitudinal, donor-adjusted, and factorial designs, but beta-binomial screen methods have largely remained limited to pairwise comparisons. BARCS extends the library-total-conditional beta-binomial model to guide-level regression with an arbitrary design matrix, enabling direct estimation of time, covariate, and interaction effects. In four replicate-complete Cas13 screens, adding the intermediate time point modestly improved essential-gene recovery. Applying the same non-targeting-control scaling rule to BARCS, MAGeCK-MLE, edgeR-QL, DESeq2, and limma--voom produced similar calibration across all five methods, while the four alternatives ranked essential genes more strongly than BARCS. In an ordered-bin IL2RA screen, donor-adjusted BARCS recovered more validated regulators with fewer total calls than the matched four-bin MAGeCK-MLE fit, and cross-fitted controls exposed excess guide-level significance. Simulations showed gains from dispersion moderation and control-based denominators, but seed-specific results exposed denominator sensitivity and a null grid localized substantial gene-level error to correlated-guide aggregation rather than dispersion alone. Aggregation-matched control scaling reduced but did not eliminate this error. An external audit prompted by concerns about beta-binomial false discoveries showed that the reported CB2 null-discovery count disappeared when full-library totals were restored. This corrected one denominator-dependent result but did not refute the broader calibration concern; nominal-level calibration remained unresolved. BARCS therefore contributes a multivariable extension of the library-total-conditional beta-binomial model together with an explicit account of where its inference is valid: guide-level coefficients are supported by independent biological libraries, whereas gene-level summaries and partitioned-bin designs require correlation-aware aggregation or joint modelling that the present implementation provides diagnostically rather than generatively. We report this boundary because complex pooled designs make it consequential, not because it is unique to the beta-binomial model.

bioinformatics↗

bioRxiv2025

MARRVEL-MCP enables natural language variant interpretation through autonomous workflow construction

AO_SCPLOWBSTRACTC_SCPLOWRare disease variant interpretation requires navigating multiple genomic databases with strict input formats and synthesizing heterogeneous evidence, creating barriers for non-experts and cognitive burdens even for specialists. MARRVEL exemplifies this challenge by requiring precise queries (e.g., HGVS notation) and returning complex, difficult-to-synthesize outputs. To address this input-output asymmetry, we developed MARRVEL-MCP, a natural language interface enabling large language models (LLMs) to perform end-to-end variant interpretation via structured tool access. This work demonstrates the impact of context engineering--the deliberate design of domain-aware tool environments and information scaffolding--in reshaping the role of model scale in genomics. MARRVEL-MCP equips LLMs with 39 tools spanning gene and variant utilities, pathogenicity databases, phenotype resources, expression atlases, ortholog data, and literature APIs. Without hard-coded pipelines, LLMs autonomously infer workflows, performing named-entity recognition, identifier normalization, and multi-database synthesis from narrative queries. Using 45 expert-curated questions, lightweight models (3B-20B parameters) with MARRVEL-MCP matched or outperformed larger models lacking tool access. A 20B-parameter model (gpt-oss-20b) achieved a 95% pass rate versus 33% without MARRVEL-MCP, approaching state-of-the-art proprietary performance. Although accuracy remains below the 100% required for autonomous diagnosis and token usage is considerable, these results show that well-designed context can compensate for limited model capacity. These findings establish context engineering as a core principle for biomedical AI, enabling efficient, scalable integration of LLMs with curated genomic resources. MARRVEL-MCP is available at https://github.com/hyunhwan-bcm/MARRVEL_MCP/.

bioinformatics↗

bioRxiv2023

Genetic Inactivation of Beta-Catenin Attenuates and Its Activation Aggravates Desmoplakin Cardiomyopathy

AimMutations in the DSP gene encoding desmoplakin, a constituent of the desmosomes at the intercalated discs (IDs), cause a phenotype that spans arrhythmogenic cardiomyopathy (ACM) and dilated cardiomyopathy (DCM). It is typically characterized by biventricular enlargement and dysfunction, severe myocardial fibrosis, cell death, and arrhythmias. The canonical WNT (cWNT)/{beta}-catenin signaling pathway is implicated in the pathogenesis of ACM. Given that {beta}-catenin, an indispensable co-transcriptional regulator of the cWNT pathway, is also a member of the IDs, we genetically inactivated or activated {beta}-catenin to determine its role in the pathogenesis of the desmoplakin cardiomyopathy. Methods and ResultsThe Dsp gene was conditionally deleted in cardiac myocytes concomitant with the genetic inactivation or activation of {beta}-catenin using the tamoxifen-inducible MerCreMer mice. Inactivation and activation of {beta}-catenin were achieved upon deletion of its transcriptional domain and degrons, respectively. Analysis of cardiac myocytes transcripts and proteins showed marked dysregulation of the cWNT/{beta}-catenin pathway in the DSP-deficient mouse cardiac myocytes (Myh6-McmTam:DspF/F), as indicated by increased expression of cWNT/{beta}-catenin targets along with its inhibitors and isoforms of its key co-effectors. Genetic inactivation of {beta}-catenin in the Myh6-McmTam:DspF/F mice prolonged survival, improved cardiac function, reduced cardiac arrhythmias, and attenuated myocardial fibrosis, and cell death caused by apoptosis, necroptosis, pyroptosis, i.e., PANoptosis, whereas its activation had the opposite effects. Inactivation of {beta}-catenin was associated with partial restoration of the suppressed genes involved in OXPHOS, whereas its activation has the opposite effect. The beneficial effects were independent of the changes in the transcript levels of the cWNT target genes. ConclusionThe cWNT/{beta}-catenin was markedly dysregulated in the cardiac myocytes from a mouse model of DC. Inactivation of {beta}-catenin attenuated the phenotype partly through the recovery of OXPHOS genes whereas its activation had deleterious effects. The findings suggest suppression of {beta}-catenin might be beneficial in desmoplakin-cardiomyopathy. SummaryGenetic inactivation of {beta}-catenin improved desmoplakin cardiomyopathy, in part through the restoration of expression of genes involved in oxidative phosphorylation, whereas its activation was deleterious.

genetics↗

bioRxiv2022

Literature-based predictions of treatments for genetic disease pathology

Identifying genetic modifiers of disease-causing genes can guide drug discovery for treatment of genetic disorders, but selecting promising drugs to test requires extensive and up-to-date knowledge of drug-gene and gene-gene relationships. To address this challenge, we present PARsing ModifiErS via Abstract aNnotations (PARMESAN), a computational tool that searches PubMed for information on these relationships, and assembles them into one knowledgebase. PARMESAN then hypothesizes on undiscovered drug-gene relationships, assigning an evidence-based score to each hypothesis. We compare PARMESANs drug-gene hypotheses to all of the drug-gene relationships displayed by DrugBank, and see a strong correlation between the prediction score and the predictive accuracy--such that predictions scoring above 10 are 11 times more likely to be correct than incorrect. This publicly available tool provides an automated way to prioritize drug screens to target the most-promising drugs to test, thereby saving time and resources in the development of therapeutics for genetic disorders.

bioinformatics↗

bioRxiv2021

A comprehensive evaluation of CRISPR lineage recorders using TraceQC

The CRISPR-Cas9 genome editing-based lineage tracing system is emerging as a powerful tool to track cell lineages at unprecedented scale and resolution. However, the complexity of CRISPR-Cas9 induced mutations has raised challenges in lineage reconstruction, which requires a unique computational analysis framework. Meanwhile, multiple distinctive CRISPR-based high-throughput lineage recorders have been developed over the years in which the data analysis is incompatible across platforms. To address these challenges, first, we present the TraceQC, a cross-platform open-source package for data processing and quality evaluation of CRISPR lineage tracing data. Second, by using the TraceQC package, we performed a comprehensive analysis across multiple CRISPR lineage recorders to uncover the speed and distribution of CRISPR-induced mutations. Together, this work provides a computational framework for the CRISPR lineage tracing system that should broadly benefit the design and application of this promising technology.

bioinformatics↗

bioRxiv2021

Association of CXCR6 with COVID-19 severity: Delineating the host genetic factors in transcriptomic regulation

BackgroundThe coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with [~]80% asymptomatic or mild cases and [~]5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. MethodsWe implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. ResultsWe discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on the lung and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis-expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung resident memory CD8+ T (TRM) cells, we found a 3.32-fold decrease of cell proportion and lower expression of CXCR6 in the severe than moderate patients using the BALF transcriptomic dataset. Pro-inflammatory transcriptional programs were highlighted in TRM cells trajectory from moderate to severe patients. ConclusionsCXCR6 from the 3p21.31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung TRM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis. We illustrate one potential mechanism of host genetic factor impacting the severity of COVID-19 through regulating the expression of CXCR6 and TRM cell proportion and stability. Our results shed light on potential therapeutic targets for severe COVID-19.

genetics↗

bioRxiv2020

The bone microenvironment invigorates metastatic seeds for further dissemination

Metastasis has been considered as the terminal step of tumor progression. However, recent clinical studies suggest that many metastases are seeded from other metastases, rather than primary tumors. Thus, some metastases can further spread, but the corresponding pre-clinical models are lacking. By using several approaches including parabiosis and an evolving barcode system, we demonstrated that the bone microenvironment facilitates breast and prostate cancer cells to further metastasize and establish multi-organ secondary metastases. Importantly, dissemination from the bone microenvironment appears to be more aggressive compared to that from mammary tumors and lung metastases. We further uncovered that this metastasis-promoting effect is independent from genetic selection, as single cell-derived cancer cell populations (SCPs) exhibited enhanced metastasis capacity after being extracted from the bone microenvironment. Taken together, our work revealed a previously unappreciated effect of the bone microenvironment on metastasis evolution, and suggested a stable reprogramming process that engenders cancer cells more metastatic.

cancer biology↗

bioRxiv2020

TraceQC: An R package for quality control of CRISPR lineage tracing data

MotivationThe CRISPR-based lineage tracing system is emerging as a powerful new sequencing tool to track cell lineages by marking cells with irreversible genetic mutations. Accurate reconstruction of cell lineages from CRISPR-based data is sensitive to noise. Quality control is critical for filtering out low-quality data points. Yet, existing quality control tools for RNA-seq and DNA-seq do not measure features specific for the CRISPR-linear tracing system. ResultsWe introduce TraceQC, a quality control package to overcome challenges with measuring the quality of CRISPR-based lineage tracing data and help in interpreting and constructing lineage trees. AvailabilityThe R package is available at https://github.com/LiuzLab/TraceQC. Contactzhandong.liu@bcm.edu or hyun-hwan.jeong@bcm.edu

bioinformatics↗

bioRxiv2018

CRISPRCloud2: A cloud-based platform for deconvoluting CRISPR screen data

The simplicity and cost-effectiveness of CRISPR technology have made high-throughput pooled screening approaches available to many. However, the large amount of sequencing data derived from these studies yields often unwieldy datasets requiring considerable bioinformatic resources to deconvolute data; a feature which is simply not accessible to many wet labs. To address these needs, we have developed a cloud-based webtool CRISPRCloud2 that provides a state-of-the-art accuracy in mapping short reads to CRISPR library, a powerful statistical test that aggregates information across multiple sgRNAs targeting the same gene, a user-friendly data visualization and query interface, as well as easy linking to other CRISPR tools and bioinformatics resources for target prioritization. CRISPRCloud2 is a one-stop shop for labs analyzing CRISPR screen data.

bioinformatics↗