Biology subjects

Wang, C.

Publications and source records attributed to Wang, C..

At least 145 records · Page 8Linked to original sources

bioRxiv2026

Pan-cancer virtual spatial transcriptomics from routine histology with Phoenix

Spatial transcriptomics links gene expression to tissue architecture, providing a mechanistic view of cellular organization. Yet existing datasets cover few donors and miss the complexity of human disease. Experimental costs remain prohibitive, and large-scale profiling is impractically slow for population-level studies. Accurate computational methods are urgently needed. Predicting gene expression from standard histology, however, remains an open problem, as current approaches transfer poorly to unseen cohorts and diseases. Here, we present Phoenix, a (latent) flow matching generative model that infers pan-cancer spatially resolved single-cell gene expression with high accuracy. Phoenix analyzes treatment response in silico: Applied to 763 head and neck cancer patients, it identified three new spatial biomarkers that we validated across two cancers (breast cancer, n = 84; ovarian cancer, n = 157) and treatment regimens (platinum, trastuzumab). Phoenix generalizes beyond carcinomas: In a large sarcoma cohort (802 tissue microarray cores), it accurately predicted cell-type-specific signatures in held-out samples and captured chemotherapy-induced immune remodeling. Phoenix also extends across species: In a mouse model, it accurately predicted the expression of pancreatic cancer lineage markers and the mutant mKras^G12D allele in silico. In total, we evaluated Phoenix on over 10,000 patients. Our results establish virtual spatial transcriptomics as a scalable framework for studying tissue organization, therapeutic response, and disease mechanisms.

bioinformatics↗

bioRxiv2026

Plastome convergence across heterotrophic plant lineages: genome reduction, extreme AT bias, high substitution rates, and functional persistence in the endoparasitic Mitrastemonaceae

The loss of photosynthesis triggers extreme plastid genome (ptDNA) decay, including complete genome loss. Of the multiple transitions to heterotrophy among angiosperms, the ptDNA status remains poorly defined in lineages such as the endophytic Mitrastemonaceae (Ericales). Adopting a panplastome perspective, we characterized genomic variation across Mitrastemon yamamotoi individuals, assembling two complete circular ptDNAs and re-evaluating all available genomic resources for the genus. Our results reveal a highly minimized ptDNA (18-26 kb) with extreme AT content (>77%) and loss of the typical quadripartite architecture. Despite the absence of the stabilizing inverted repeats, the Mitrastemon panplastome exhibits remarkable structural stability and collinearity among individuals. The reduced suite of 26 genes, which includes accD, infA, clpP, ycf1, ycf2, and the essential tetrapyrrole precursor trnE-UUC, exhibit elevated substitution rates. Evolutionary rate analyses (dN/dS) demonstrate that the core ribosomal suite remains under strong purifying selection ({omega}<1), confirming the organelles functional status. Furthermore, transcriptomic analysis identified a nearly complete set of nuclear-encoded DNA-RRR genes, with the notable exception of the MUTS2 surveillance system. The convergent loss of these homologs in Mitrastemon and another holoparasitic lineage may be linked to the shared structural instability and mutational bias. Our findings demonstrate that despite extreme genome compaction, accelerated substitution rates, and severe AT-bias, the Mitrastemon panplastome remains quite stable, providing a definitive genomic framework for understanding plastid evolution within the endoparasitic Mitrastemonaceae.

evolutionary biology↗

bioRxiv2026

Evaluation of Diffusion Tensor Imaging in the Corpus Callosum on a Portable 100 mT MRI System

Low-cost portable MRI has the potential to improve the accessibility of MRI, but new acquisition methods and protocols must be developed and evaluated to accommodate the reduction in SNR and greater impact of system imperfections. Diffusion tensor imaging (DTI) is a candidate tool for monitoring population health, but the bias and variance of quantitative diffusion tensor-derived metrics must be evaluated prior to designing such studies. DTI of the corpus callosum was performed on an in-house, portable 100 mT MRI system using a slab diffusion weighted Fast Spin Echo with radiofrequency (RF) encoding. Slice coverage was restricted to the corpus callosum to shorten scan time and reduce sensitivity to large rigid motion. In vivo DTI images were obtained in two healthy volunteers with nominal voxel size 50 mm3, scan time 25 minutes, and two different volunteers using nominal voxel size 25 mm3, scan time 35 minutes. Mean diffusivity (MD) and fractional anisotropy (FA) coefficients of variation were estimated in the 50 mm3 acquisition using a bootstrap approach and compared to resolution-matched data obtained on a conventional 1.5T system. MD / FA maps were compared quantitatively and qualitatively. Mean MD values in the corpus callosum obtained on the 100 mT system were within 10% of the reference 1.5T acquisition, but FAs were underestimated by 20-30%. The corpus callosum median MD coefficient of variation was 3.7%, and the median FA coefficient of variation was 7.5%. FA maps obtained at 100 mT had an elevated FA noise floor and color FA maps had lower apparent resolution but some white matter tracts were still distinguishable. HighlightsO_LIDiffusion Tensor Imaging (DTI) of the corpus callosum was performed on a portable 100 mT MRI scanner with 50 mm3 voxels in 25 minutes scan time. C_LIO_LIMean Diffusivity estimates in the corpus callosum obtained at 100 mT and 1.5T differed by less than 0.1 x 10-3 mm2/s. C_LIO_LISome white matter tracts were visible in color Fractional Anisotropy maps obtained at 100 mT but FA maps were underestimated by 20- 30% when compared to a resolution-matched 1.5T acquisition, and had lower apparent resolution. C_LI

bioengineering↗

bioRxiv2026

A Critical Sites-Driven and Light-weighted Protein Engineering Platform

Protein large language models (PLMs) provide a novel computational paradigm for the deep mining of sequence co-evolutionary information, significantly accelerating the generation of functional proteins for biotechnological and medical applications. However, the misalignment between zero-shot predicted evolutionary fitness and industrial application requirements leads to a limited success rate in acquiring beneficial mutations, while the high training cost presents another drawback of large models. Here, we developed CASPE (Critical Amino acids Streamline Protein Evolution), a lightweight protein engineering platform for the precise localization and adaptation of critical residues, consisting of the CAS (Critical amino acid sites) and APCNet (Amino acid Point Cloud Classification Network). CAS utilizes gradient activation mapping and multi-layer attention matrices to directly extract key information determining target properties from PLMs and transform it into explicit site importance indicators, without relying on additional structural information or prior knowledge. Working in tandem with APCNet, CASPE establishes a workflow encompassing the entire trajectory from site localization to residue prediction. CASPE achieved remarkable hit rates in identifying beneficial variants for thermostability (31.3-60%) and pH tolerance (40-80%), further uncovering the potential mechanisms of action at these key target sites. Directed evolution of phytase further validated the generalizability of CASPE. CASPET-Phytase achieved a 33.3% success rate in obtaining beneficial mutants, which was significantly better than FoldX (6.7%) and ESM2-t33 (13.3%). CASPE guides enzyme evolution towards precise, site-targeted optimization, providing an efficient computational framework for developing industrial enzymes.

bioengineering↗

bioRxiv2026

Prenatal THC exposure disrupts mitochondrial respiratory gene programs and delays medium spiny neuron maturation in the nucleus accumbens

Prenatal cannabis exposure (PCE) is increasingly prevalent and has been associated with adverse neurodevelopmental outcomes, yet its molecular impact on brain reward circuitry remains poorly defined. Here, we investigated transcriptional and epigenomic alterations in the nucleus accumbens (NAc) following prenatal {Delta}9-tetrahydrocannabinol exposure in a rat model using snRNA-seq and snATAC-seq analyses. PCE markedly suppressed the expression of genes involved in mitochondrial oxidative phosphorylation (OXPHOS) in the NAc on postnatal day 24 (P24), indicating reduced mitochondrial respiration capacity. This disrupted mitochondrial respiratory gene programming was accompanied by coordinated alterations in ribosomal and proteasomal pathways regulating protein homeostasis in NAc medium spiny neurons (MSNs), suggesting coupled disruption of cellular metabolism and neuronal maturation. snATAC-seq analysis revealed altered chromatin accessibility at promoter regions enriched for Nrf1 and Yy2 binding motifs, implicating Hrf1- and Yy2-associated transcriptional regulation of mitochondrial genes in MSNs following PCE. Moreover, an acute THC challenge in PCE offspring at P24 further exacerbated the suppression of genes involved in mitochondrial OXPHOS and MSN maturation. Together, these findings define a transcriptional and epigenetic framework through which PCE may perturb mitochondrial function and impair MSN maturation trajectories in the NAc, providing mechanistic insights into how PCE may alter the development of reward circuitry.

neuroscience↗

bioRxiv2026

Dysregulated tissue-resident lymphocytes drive age-associated emphysema by impairing alveolar regeneration

Aging is often associated with progressive tissue degeneration and chronic inflammation, yet the role of immune cells in mediating structural and functional decline in organs remains poorly defined. Here, we investigated immune-tissue interactions in the aged lung and identified emphysematous remodeling characterized by alveolar loss. Notably, aged lungs exhibited a marked expansion of tissue-resident lymphocytes (TRLs) with senescent features, accompanied by a significant reduction in alveolar stem/progenitor cell (AT2) abundance. In vivo adoptive T cell transfer and 3D immune-stem cell organoid assays revealed that these expanded TRLs suppressed AT2 growth via secretion of oncostatin M and interferon gamma. In vivo blockade of IL-7 receptor (IL-7R) reduced TRL accumulation in the lungs and ameliorated age-related emphysematous changes, including restoration of alveolar density. Our findings identify TRLs as key drivers of alveolar degeneration in aging and propose IL-7R inhibition as a therapeutic strategy to mitigate pulmonary decline. TeaserBlocking IL-7R clears harmful lymphocytes and helps rebuild the damaged air sacs of the aging lung.

cell biology↗

bioRxiv2026

A Generative AI Framework to Predict Cardiomyocyte Contraction Function from Single Static Images.

Understanding how cardiomyocyte structure governs contractile function is fundamental to cardiac biology and disease modeling, yet current approaches rely on time-resolved imaging and computationally intensive analysis. Here, we present a generative artificial intelligence (AI) framework that directly predicts contractile behavior of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from single static images. Our approach integrates a U-Net-based generator with a patch-based generative adversarial network (GAN) discriminator to translate morphological and sarcomere structural features into pixel-resolved contraction heatmaps. This U-Net-GAN model achieved high predictive accuracy, with structural similarity index (SSIM) values up to 0.84 using combined morphological and structural inputs. To further enhance performance and generalizability, we incorporated synthetic cell-function pairs generated via a generative AI StyleGAN2 framework, improving prediction accuracy and perceptual similarity. Importantly, region-specific and whole-cell analyses revealed that AI predictions capture biologically meaningful structure-function relationships, with sarcomere organization strongly associated with both contractile output and prediction fidelity. Reconstruction error emerged as an interpretable metric reflecting localized inefficiencies in sarcomere-to-contraction coupling. Together, this framework establishes a scalable and interpretable strategy for inferring cardiomyocyte function from static morphology, eliminating the need for time-lapse imaging. More broadly, this work positions generative AI as a powerful tool for bridging cellular structure and function, enabling high-throughput functional phenotyping and advancing in vitro cardiac modeling.

bioengineering↗

bioRxiv2026

A Unified Agent-Enabled Platform for Drug Repurposing across Molecular, Phenotypic, and Clinical Scales

Drug repurposing offers an effective path to new therapies, yet existing computational approaches rely on a single line of evidence and are rarely validated across biological scales. We present LinkD, an integrated framework that unifies diffusion-based affinity prediction, proteome-wide selectivity scoring, phenotypic validation, and population-scale clinical evidence. LinkD-Bind predicts binding across 14,981 drugs and 20,385 human targets, ranking first in 8 of 9 BindingDB, Davis, and KIBA evaluations, with the largest gains under cold-start conditions. LinkD-Select recovers 95.3% of known drug-target pairs by combining selectivity scoring and molecular docking. LinkD-Pheno integrates drug-sensitivity and CRISPR dependency data across 960 cancer cell lines, identifying 34 novel drug-gene pairs and recovering [~]85% of known targets among the top 50 candidates. Across 11.5 million individuals from Mount Sinai and UK Biobank, LinkD-prioritized {beta}-blockers propranolol (HR 0.82) and carvedilol (HR 0.92) reduced 5-year prostate cancer incidence relative to metoprolol, corroborated by ADRB2 docking and LNCaP growth inhibition. LinkD-Agent, which can effectively orchestrate all evidence layers, is served on a publicly available web platform (https://linkd-agent.onrender.com/), enabling a wide range of users to derive new drug repurposing opportunities through natural language queries.

bioinformatics↗

bioRxiv2026

Development of a Fully Non-Viral 1XX-enhanced BCMA CAR-T Cell Therapy for Multiple Myeloma

Multiple myeloma (MM) is a clonal plasma cell malignancy characterized by bone marrow infiltration, monoclonal immunoglobulin production, and microenvironmental dysregulation that leads to systemic organ damage. The advent of B-cell maturation antigen (BCMA)-directed chimeric antigen receptor (CAR) T-cell therapy has induced unprecedented responses and durability for patients with relapsed/refractory MM. These outcomes are rarely observed with prior salvage strategies, although relapse remains the predominant long-term challenge for most patients. The two currently approved BCMA CAR-T cell products use viral vectors to semi-randomly insert the CAR gene, which results in heterogeneous genomic composition and variability in efficacy, safety, and product consistency. To address these challenges, we integrated targeted CRISPR genome engineering with precise CAR transgene insertion at the T-cell receptor alpha constant (TRAC) locus, 1XX CAR signaling architecture to enhance potency and durability, and non-viral manufacturing with a single-stranded DNA repair template to improve efficiency and yield. This approach confers physiological CAR expression, reduces insertional mutagenesis, and improves persistence by mitigating tonic signaling and exhaustion. Our GMP manufacturing process consistently achieved high CAR integration (37.7-72.7%) and yields across all full-scale runs and met predefined release criteria for identity, purity, safety, and quality. In NSG mouse models of MM, the UCCT-BCMA-1 product exhibited exceptionally potent tumor control, CAR-T cell expansion 100-1000-fold greater than that of lentiviral constructs, and durable clearance of myeloma cells after multiple rechallenges. These findings establish a CRISPR-edited, fully non-viral manufacturing platform for next-generation 1XX-BCMA CAR-T therapies with enhanced persistence, safety, and efficacy. One Sentence SummaryCRISPR-engineered, TRAC-targeted 1XX-BCMA CAR-T therapy with improved safety, potency, and persistence in relapsed and refractory multiple myeloma.

cancer biology↗

bioRxiv2026

Multiscale transcriptomic organization of the human brain with DigitalBrain

The human brain varies across anatomical regions, cell types, development, aging and disease states, yet existing single-cell transcriptomic resources remain fragmented and difficult to integrate into a unified biological model. Here we present DigitalBrain, a human brain-specific atlas and foundation-model framework for organizing diverse and fragmented human brain transcriptomic data across scales. We first built DigitalBrain-Atlas, a harmonized whole-brain single-cell resource comprising 16.35 million transcriptomes from 2,143 donors across 165 brain regions, spanning the human lifespan and multiple neurological and clinical conditions. We then developed DigitalBrain-M1, a Transformer-based model that jointly encodes gene identity and expression magnitude to learn a shared embedding space for cells and genes. Across held-out datasets, DigitalBrain supported robust single-cell integration, clustering and cell-type annotation while preserving major biological structure and reducing technical fragmentation. Beyond these benchmarks, the learned embeddings revealed emergent large-scale hierarchical organization of the human brain, linking anatomically distinct regions into higher-order patterns consistent with known functional systems. Applied to human hippocampal aging, DigitalBrain identified cell-type-specific aging sensitive gene sets, highlighted dentate gyrus granule cells as a particularly age-sensitive population, and discovered selective reorganization of gene programs related to synaptic transmission, postsynaptic structure, membrane excitability and axon guidance during aging. Cross-dataset convergence was strongest at the level of functional modules and recurrent aging sensitive genes. Together, these results demonstrate that DigitalBrain is a brain-specific framework for mapping human brain organization across scales, and as an early step towards a complete virtual organ for the human brain.

bioinformatics↗

bioRxiv2026

Decoding Single-Cell Omics of Perturbation Responses Using DeSCOPE

Deciphering cellular responses to genetic perturbations is fundamental to modeling gene regulatory networks and understanding mechanisms that change cellular phenotypes. However, current computational approaches often fail to outperform simple baseline models, highlighting a critical bottleneck in their generalizability and robustness. Here, we present DeSCOPE, a lightweight conditional variational autoencoder framework for predicting genetic perturbation responses spanning transcriptomic, epigenomic, and broader multi-modal landscapes. We systematically benchmarked DeSCOPE across diverse datasets under two challenging out-of-distribution settings: unseen genes and unseen cell types. DeSCOPE uniquely surpasses simple baselines in the unseen gene scenario, and achieves substantially improved performance for unseen cell types while requiring fine-tuning with far fewer perturbed genes. Finally, DeSCOPE demonstrates superior performance in predicting combinatorial multi-gene perturbations. Overall, DeSCOPE serves as a versatile multi-modal virtual cell model that can effectively guide the design of therapeutic targets that change cellular phenotypes. DeSCOPE is available at https://github.com/wanglabtongji/DeSCOPE.

bioinformatics↗

bioRxiv2026

Exercise-induced Sweat Promotes Wound Healing in Diabetic Foot Ulcers

Persistent hyperglycemia impairs wound healing in diabetic patients, and severe cases may even lead to disability or death. Glycemic control alone cannot effectively prevent the occurrence of diabetic foot ulcers, a serious complication of diabetes. However, safe, efficient, and cost-effective therapies remain unavailable and are urgently needed. Using a novel sports medicine paradigm, we predicted, based on reverse-transcriptomics, that exercise-induced sweat has the potential to promote would healing in diabetic foot ulcers. Subsequent animal experiments demonstrated that sweat can indeed promote re-epithelialization and collagen deposition, upregulate the expression of the proliferation marker Ki-67, the angiogenesis marker CD31, and -SMA, and significantly accelerate wound healing in a mouse model of diabetic foot ulcers. This study provides a new direction for sports medicine and offers a novel therapeutic strategy for patients with diabetic foot ulcers.

zoology↗

bioRxiv2026

Structure-aware geometric graph learning for modeling protease-substrate specificity at scale

Protease-substrate specificity is central to cellular regulation and disease pathogenesis, and accurately modeling its structural determinants remains challenging. Substrate recognition is governed by spatial constraints and higher-order relationships that extend beyond local sequence motifs. Most computational approaches rely predominantly on motif-centric or sequence-based representations, limiting their ability to capture the geometric and relational structure underlying enzymatic specificity. Here, we introduce OmniCleave, a structure-aware geometric graph learning framework for modeling protease-substrate specificity at scale. OmniCleave is trained on 57,278 structure-informed protease-substrate pairs derived from 9,651 substrates spanning over 100 proteases across six distinct families. The framework integrates multi-scale structural graphs with higher-order protease relational topology, explicitly encoding spatial context and inter-protease dependencies within a unified geometric representation. This formulation moves beyond local pattern recognition and enables transferable modelling across six protease families. Across large-scale benchmarks, the framework consistently outperforms existing approaches and reveals interpretable geometric determinants underlying substrate recognition. Experimental validation confirms three novel caspase-3 substrates and 21 cleavage sites predicted by OmniCleave, supporting the biological relevance of the learned representations. Together, OmniCleave provides a scalable geometric framework for modeling protease-substrate specificity, with practical utility for systematic analysis of protease biology.

bioinformatics↗

bioRxiv2026

From nucleotides to semantics: genomic representation learning via joint-embedding predictive architecture

Decoding the regulatory syntax encoded in genomic sequences is a central objective in computational biology. Most existing genomic foundation models treat DNA as a language and adopt pretraining objectives from natural language processing. DNA sequences, however, lack explicit semantic boundaries and contain substantial evolutionary noise. Nucleotide-level reconstruction in a low-dimensional input space can therefore increase computational overhead and may yield representations with limited discriminative capacity. Downstream tasks often depend on expensive finetuning, which restricts practical use in many biology laboratories. Here we present GenoJEPA, a genomic representation learning framework based on joint-embedding predictive architecture. GenoJEPA combines continuous patching with semantic alignment, shifting the optimization from local base reconstruction to semantic alignment in latent space. Across 55 downstream tasks, GenoJEPA shows strong representational capacity and robust generalization while reducing parameter count and computational cost. The resulting semantic vectors from frozen GenoJEPA support lightweight GPU-free classifiers to achieve competitive accuracy. These results suggest a practical route towards efficient training and broad application of larger-scale genomic foundation models.

bioinformatics↗

bioRxiv2026

Cryo-EM elucidation of stoichiometric plasticity, asymmetric ligand recognition and allosteric coupling in human P2X2/3 heterotrimeric channels

P2X receptors are trimeric ATP-gated ion channels that assemble as homo- or heterotrimers, with heteromeric forms exhibiting intrinsic asymmetry that influences function. Here, we report four high-resolution cryo-EM structures of human P2X2/3 heterotrimers representing distinct functional states, including ATP-bound assemblies (P2X332 and P2X223), the apo form, and a ligand/ATP-bound closed conformation. The three ATP-binding sites show asymmetric recognition of MgATP{superscript 2}- and ATP-, and channel activation requires occupancy of only two MgATP{superscript 2}- molecules. Gefapixant binds a single allosteric site and selectively inhibits MgATP{superscript 2}-, but not ATP-, binding, indicating orthosteric-allosteric coupling within the heterotrimer. Structural features of the transmembrane domain define ion permeation, particularly for Ca{superscript 2}. Despite asymmetric ligand interactions, gating remains largely symmetric, with minor differences in desensitization. These findings provide a structural framework linking asymmetry to coordinated channel function and open avenues for subtype-selective therapeutic intervention.

biophysics↗

bioRxiv2026

Fatty acid scavenging enables cancer escape from KRAS inhibition

Although inhibitors of oncogenic KRAS have shown clinical efficacy1, resistance to KRAS inhibition is common2, and its molecular basis remains unclear. Here we show that KRASi-resistant cancer cells sustain mitochondrial bioenergetics through enhanced fatty acid (FA) metabolism, despite suppression of canonical KRAS signaling. Specifically, KRASi-resistant pancreatic cancer cells exploit macropinocytosis to scavenge FA released from adipose tissue, fueling beta-oxidation independently of KRAS-PI3K signaling. This adaptive metabolic program is driven by the adhesion G protein-coupled receptor ADGRB1, which activates non-canonical PI3K{gamma}-PAK1 signaling to stimulate macropinocytosis and maintain metabolic homeostasis under KRASi. Disruption of ADGRB1-PI3K{gamma} signaling dismantles this metabolic program and restores KRASi sensitivity. This pathway operates across multiple KRAS-mutated cancers and is associated with poor therapeutic response and outcome. These findings offer a promising strategy for overcoming KRASi resistance.

cancer biology↗

bioRxiv2026

Inflammatory memory disables a DDR1 degradation checkpoint to enable pancreatic cancer growth

Inflammation and stroma remodeling regulate pancreatic ductal adenocarcinoma (PDAC), but how or if these cues are integrated at the molecular level remains unclear. Here, we identify a metabolic checkpoint that controls the stability of the collagen receptor DDR1, and subsequent tumorigenesis. We show that defective Col-I remodeling deprives PDAC cells of the high affinity DDR1 ligand, [3/4]Col-I, resulting in reduced ATP and activation of AMPK. AMPK phosphorylates DDR1 at T519, promoting its recognition by the E3 ubiquitin ligase adaptor FBXW2 and subsequent degradation. Importantly, this degradation pathway can be disabled by inflammatory signaling. Exposure to inflammatory cytokines induces methylation-dependent silencing of FBXW2, which establishes an inflammatory memory that preserves DDR1 stability, enabling sustained ligand-triggered receptor oligomerization and downstream NF-{kappa}B-NRF2 signaling even in restrictive stromal environments. Together, these findings identify regulated receptor turnover as a mechanism through which stromal architecture, metabolic state, and inflammatory memory are integrated to control PDAC progression.

cancer biology↗

bioRxiv2026

Accurate detection of mosaic mutations at short tandem repeats from bulk sequencing data

Short tandem repeats (STRs) are among the most mutable regions of the human genome, yet their somatic mosaicism remains poorly characterized due to the technical challenges of distinguishing genuine mutations from high intrinsic polymorphism and sequencing noise. Here, we introduce BulkMonSTR, a computational framework that combines STR-specific error modelling with machine-learning classification to enable accurate detection of mosaic STR mutations from bulk next-generation sequencing data. BulkMonSTR identifies nucleotide-resolution mutations--including insertions, deletions, and single-nucleotide variants (SNVs)--and supports both control-independent and case-control study designs. Leveraging a comprehensive training dataset derived from pedigree-based validation and in silico spike-in simulations, our random forest classifier effectively discriminates true mosaic events from germline variants and technical artifacts. Benchmarking on simulated and real datasets demonstrates that BulkMonSTR achieves substantially improved precision and F1 scores across diverse coverages and variant allele frequencies. In normal samples, cancer samples and controlled in silico mixing experiments, BulkMonSTR consistently outperforms existing methods, capturing a broader spectrum of STR mutations--including those arising on non-reference alleles--while achieving high validation rates. By enabling systematic, genome-wide interrogation of STR mosaicism, BulkMonSTR provides a scalable foundation for investigating the contributions of somatic STR mutations to aging and disease.

bioinformatics↗