Biology subjects

Bian, J.

Publications and source records attributed to Bian, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Inflammation-induced of Clec18a inhibits immune responses via Trpm7-dependent Cbl activation

Innate immune responses must be tightly controlled to maintain immuno-homeostasis. However, the regulatory mechanisms underlying the activation and early stages of the innate immune response remain poorly defined. Here, we found the soluble C-type lectin Clec18a is up-regulated in multiple inflammatory diseases, including sepsis and influenza, and secreted within minutes upon activation in multiple immune cells, including macrophages, dendritic cells (DCs), and T cells. Clec18a-myeloid-knockout mice exhibited reduced survival and exacerbated inflammation than the wild-type. However, exogenous recombinant Clec18a administration alleviated inflammation both in vitro and in vivo. Mechanistically, we identified macrophage-inducible C-type lectin (Mincle) as the receptor of Clec18a. Clec18a-Mincle binding triggers Trpm7-dependent calcium influx, which activates the ubiquitin E3 ligase Cbl and leads to Mincle-Cbl-MyD88-Trpm7 complex formation and degradation. Cbl deficiency, calcium inhibition and Trpm7 knockdown abrogated Clec18a-mediated immune suppression. Thus, our study revealed Clec18a as a critical checkpoint on the initiation of innate immune response through an intrinsic crosstalk between CLR and TLR signaling.

immunology↗

bioRxiv2026

Corpusome, a cross-body-site human microbiome corpus for representation learning

Machine-learning models of the human microbiome are trained mostly on stool samples from single cohorts, limiting cross-body-site representation and cross-study generalization. Progress is constrained less by algorithms than by the absence of a harmonized multi-body-site corpus carrying the technical metadata needed to model, rather than ignore, batch structure. Here we release Corpusome, a harmonized two-tier cross-body-site human microbiome corpus for representation learning: a harmonized corpus of 187,546 human microbiome samples integrating standardized profiles from curatedMetagenomicData, the American Gut Project, and the EBI MGnify platform. Corpusome follows a two-tier design preserving both functional depth and cross-body-site breadth: a shotgun tier (22,588 samples, 93 studies) with species- and pathway-level profiles, and a 16S tier (164,958 samples, from a full pull of 708 MGnify studies) with genus-level profiles extending coverage to oral, skin, respiratory, and urogenital sites. It spans six body sites and two modalities, with harmonized metadata for batch-aware modelling. Body-site signal exceeds technical/source variance in the 16S tier by approximately 2.4-fold.

microbiology↗

bioRxiv2026

SenSASP: A Unified, Multi-Layer Database of Senescence and SASP Genes

Research on cellular senescence and the senescence-associated secretory phenotype (SASP) draws on independently curated gene resources that differ in scope, identifiers, and update cycles, making cross-resource integration error-prone. We unified four widely used resources, CellAge, GenAge, the SenMayo signature, and the Reactome Cellular Senescence pathway, onto a single canonical identifier (the Ensembl gene ID) and enriched every gene with three annotation layers absent from all four inputs: cross-species conservation, tissue and cell-type expression, and high-confidence protein-protein interactions. Unification collapsed 1,460 summed source entries into 1,250 unique genes (210 redundant entries removed, 14.4%) while preserving full source provenance: 173 genes are corroborated by two or more resources and two (IL6, JUN) by all four. The three annotation layers reach 95.8%, 97.9%, and 93.0% of genes, with 89.4% annotated across all three. A 500-gene random sample of identifier mappings was validated against HGNC and Ensembl (98.0% exact match). The result, SenSASP, is a single, machine-readable, provenance-tracked database of harmonized identifiers and net-new functional context, illustrated here with a gene-prioritization score and a tissue-expression atlas. SenSASP is freely available at https://xuan13hao.github.io/sensasp/

cell biology↗

bioRxiv2026

GatorPrism: Prototype-Conditioned Routing across Coalition Graph Experts for Spatial Multi-Omics Integration

Spatial multi-omics integration requires balancing cross-omics consensus with modality-specific signals whose importance varies across tissue locations. We present GatorPrism, a self-supervised coalition graph mixture-of-experts framework that explicitly separates cross-omics consensus from modality-specific structure and adaptively integrates their contributions across tissue locations. A joint expert encodes an intersection-based consensus graph, while modality-private experts capture mixed spatial-molecular structures; a prototype-conditioned router assigns spot-specific coalition weights. GatorPrism is trained end-to-end to preserve shared and modality-specific neighborhoods, align co-registered modalities, maintain spatial coherence, and prevent routing collapse. Across eight spatial multi-omics datasets, GatorPrism achieved strong performance across nine clustering metrics against nine competing methods. In human tonsil, inferred domains were supported by concordant RNA and ADT markers and distinct functional programs. In embryonic mouse brain, routing profiles revealed anatomically localized shared, RNA-private, and ATAC-private states supported by transcriptomic, chromatin-accessibility, and motif evidence. These results establish GatorPrism as an accurate and interpretable framework that reveals how shared and modality-specific molecular signals organize tissue structure. Source code is available at https://github.com/Gator-Group/GatorPrism.

bioinformatics↗

bioRxiv2026

GatorDuo: Global-Consistency Dual-Graph Refinement With Pseudo-Label Agreement for Spatial Transcriptomics

Spatial transcriptomics (ST) measures gene expression together with spatial coordinates, enabling spatial domain identification of coherent tissue regions. Many recent approaches rely on graph-based modeling to combine spatial neighborhoods and transcriptomic (gene-expression) similarity, yet neighborhood construction is often unreliable under sparsity and technical noise. As a result, spurious cross-domain shortcut edges can persist in static graphs and propagate misleading signals during message passing, ultimately blurring domain boundaries and weakening cluster separability. In this paper, we propose GatorDuo, a topology-aware dual-graph contrastive self-supervised framework for robust spatial domain identification that couples gene-expression similarity with spatial proximity through complementary neighborhood graphs. GatorDuo introduces global-consistency-based graph refinement that uses a pseudo-label agreement mask to suppress cross-domain shortcut edges in both views, thus stabilizing neighborhood topology for representation learning. To avoid manual tuning of domain resolution, GatorDuo further employs a contextual bandit reinforcement-learning strategy to adaptively select the clustering granularity (the number of clusters) used for refinement. The refined view-specific embeddings are integrated via a hybrid-routing Mixture-of-Experts (MoE) module to generate a unified embedding, optimized with contrastive objectives augmented by an MoE-alignment term. Across eight public benchmarks spanning sequencing- and imaging-based ST at spot and single-cell resolution, and compared with ten representative baselines, GatorDuo consistently delivers strong and robust spatial domain identification performance across multiple clustering metrics, while yielding informative unified embeddings that can support downstream biological analyses.

bioinformatics↗

bioRxiv2026

Rewiring of 3D Enhancer-Promoter Interactome Underlies Diabetic Endothelial Dysfunction

BackgroundDiabetic vascular complications are driven by endothelial dysfunction, yet the role of 3D genome organization in this process is unknown. We sought to define the alterations in chromatin architecture in diabetic endothelium and identify the key regulators involved. MethodsWe generated a high-resolution 3D epigenomic atlas of diabetic endothelial cells from mouse models and human subjects using H3K27ac HiChIP, complemented by ChIP-seq, ATAC-seq, and RNA-seq. A human cohort was used to assess protein expression in diabetic versus non-diabetic endothelial cells. To identify JUNB-interacting proteins, we performed rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME), with protein-protein interaction validated by co-immunoprecipitation. Functional validation was performed using in vitro, ex vivo, and in vivo approaches, including endothelial-specific knockdown in a diabetic hindlimb ischemia model. ResultsMulti-omics profiling revealed extensive enhancer reprogramming in diabetic endothelium, with AP-1 binding motifs being consistently and selectively enriched in downregulated enhancers across three distinct diabetic models. Analysis of a human cohort confirmed significantly reduced JUNB protein levels in diabetic endothelial cells. We identified widespread disruption of JUNB-anchored enhancer-promoter interactions, which underlies transcriptional repression of key endothelial genes. RIME and co-immunoprecipitation established the E3 ubiquitin ligase RBBP6 as a direct JUNB interactor that promotes its polyubiquitination and proteasomal degradation in response to hyperglycemia. Human cohort analysis further showed reciprocal elevation of RBBP6 in diabetic endothelial cells. Either JUNB overexpression or RBBP6 knockdown restored enhancer-promoter connectivity, reactivated vasoprotective transcriptional programs, and rescued endothelial function. Critically, endothelial-specific knockdown of Rbbp6 in diabetic mice restored endothelium-dependent vasorelaxation and improved perfusion recovery after hindlimb ischemia, independent of systemic glucose levels. ConclusionsOur study unveils a novel mechanism whereby hyperglycemia induces enhancer reprogramming and disrupts endothelial 3D genome architecture through RBBP6-mediated degradation of JUNB. The RBBP6-JUNB axis is established as a crucial link between metabolic stress and epigenomic reprogramming in vascular disease, presenting a promising therapeutic target for diabetic vasculopathy.

genomics↗

bioRxiv2025

GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics

Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of cellular heterogeneity, but its rich, complementary structure across cells and genes remains underexploited, especially in the presence of technical noise and sparsity. Effectively leveraging this multi-scale structure is essentially an information fusion problem that requires integrating heterogeneous graph-based views of cells and genes into robust low-dimensional representations. In this paper, we introduce GatorSC, a unified representation learning framework that models scRNA-seq data through multi-scale cell and gene graphs and fuses them with a Mixture-of-Experts architecture. GatorSC constructs a global cell-cell graph, a global gene-gene graph, and a local gene-gene graph derived from neighborhood-specific subgraphs, and learns graph neural network embeddings that are adaptively fused by a gating network. To learn noise-robust and structure-preserving embeddings without labels, we couple graph reconstruction and graph contrastive learning in a unified self-supervised objective applied to both cell- and gene-level graphs. We evaluate GatorSC on 19 publicly available scRNA-seq datasets covering diverse tissues, species, and sequencing platforms. Experiments showed that GatorSC consistently outperforms state-of-the-art deep generative, graph-based, and contrastive methods for cell clustering, gene expression imputation, and cell-type annotation. The learned embeddings are used for accurate trajectory inference, recovery of canonical marker gene programs, and cell-type-specific pathway signatures in an Alzheimers disease single-nucleus dataset. GatorSC provides a flexible foundation for comprehensive single-cell transcriptomic analysis and can be readily extended to multi-omic and spatial modalities.

bioinformatics↗

bioRxiv2025

NPAS2 attenuates VSMC phenotypic switching in ascending thoracic aortic aneurysm via LPCAT3/PC-PUFA2S-mediated ferroptosis

Ascending thoracic aortic aneurysm (ATAA) is a life-threatening disorder with limited therapeutic options, critically linked to vascular smooth muscle cell (VSMC) phenotypic switching. Neuronal PAS Domain Protein 2 (NPAS2), a circadian rhythm-related transcription factor, regulates diverse physiological processes. However, the biological functions and underlying regulatory mechanisms of NPAS2 in VSMCs during ATAA pathogenesis remain to be elucidated. NPAS2 expression decreased in ATAA patients, PDGF-BB-treated HASMCs and BAPN-administrated mice. NPAS2 depletion facilitated VSMC phenotype switching. VSMC-specific NPAS2 knockout (NPAS2SMKO) mice exhibited aggravated ATAA. Mechanistically, NPAS2 depletion attenuates its transcriptional repression of LPCAT3, subsequently elevating the accumulation of phosphatidylcholines with two polyunsaturated fatty acyl chains (PC-PUFA2S), thereby promoting ferroptosis-induced VSMC phenotype switching and accelerating ATAA progression. Targeting NPAS2 represents a potential therapeutic strategy for ATAA treatment.

molecular biology↗

bioRxiv2025

GatorST: A Versatile Contrastive Meta-Learning Framework for Spatial Transcriptomic Data Analysis

IntroductionRecent advances in spatial transcriptomics (ST) technologies have revolutionized our understanding of cellular functions by providing gene expression profiles with rich spatial context. Effectively learning spatial representations is crucial for downstream analyses and requires robust integration of spatial information with transcriptomic data. While existing methods have shown promise, they often fail to adequately capture both local (neighbor-level) and global (tissue-wide) spatial contexts. Moreover, they tend to rely heavily on augmentation strategies, which can introduce noise and instability. ObjectivesThis study aims to introduce and demonstrate a novel, versatile framework called GatorST, which explicitly combines graph-based modeling with advanced learning strategies to generate spatially informed representations of ST data. GatorST is designed to improve various downstream tasks, including identification of spatial domains, gene expression imputation, batch effect removal, and trajectory inference. MethodsGatorST constructs a spot-spot graph by connecting each node to its k nearest spatial neighbors and extracts two-hop neighborhood subgraphs to capture local context. At the global level, gene expression profiles are clustered using soft K-means to generate pseudo-labels, which serve as weak supervision signals within a contrastive learning framework. This process encourages the alignment of embeddings with shared pseudo-labels while separating those with different labels. GatorST further adopts an episodic training strategy inspired by meta-learning, wherein each episode consists of a support set for contrastive optimization and a disjoint query set for embedding classification, guided by the pseudo-labeled data. This design enables the model to classify unseen samples based on learned embeddings, thereby enhancing its generalization to new spatial contexts. ResultsComprehensive comparisons with fifteen state-of-the-art methods across fourteen spatial transcriptomics datasets demonstrate that GatorST consistently achieves superior performance in identifying spatial domains, imputing gene expressions, and removing batch effects. The results showcase the versatility and strong generalization capabilities of GatorST across diverse tissue types and experimental settings. ConclusionGatorST effectively integrates spatial topology and global gene expression through graph-based modeling, pseudo-labeling, and contrastive meta-learning. This framework generates biologically meaningful representations and significantly improves key downstream tasks, including spatial domain identification, gene expression imputation, batch effect removal, and trajectory inference.

bioinformatics↗

bioRxiv2025

Hyper-Glycosylation as a Central Metabolic Driver of Alzheimers Disease

Alzheimers disease (AD) is a neurodegenerative disorder characterized by devastating degenerative decline. Metabolic disruptions are widely observed, yet their involvement in the molecular etiology of AD remains underexplored. Utilizing spatial metabolomics, lipidomics, and glycomics in both mouse models and human post-mortem samples, we identified a hyper-glycosylation phenotype as a hallmark of AD. To investigate the underlying mechanisms and whether the observed effect was a driver of the observed decline, we developed an advanced spatial isotopic tracing pulse-chase method to study the dynamics of N-linked glycans. Our analysis revealed enhanced glycan biosynthesis in AD mouse models. Based on these findings, we performed genetic and dietary interventions to modulate glycan biosynthesis. Genetic knockdown of glycan biosynthetic enzymes ameliorated the hyper-glycosylation and improved cognitive and behavioral outcomes in AD mice. In contrast, oral glucosamine supplementation drove hyper-glycosylation and exacerbated cognitive and behavioral deficits. To assess the clinical relevance of these findings, we conducted a retrospective analysis of a large population of patients with mild cognitive impairment (MCI), AD, and Alzheimers Disease Related Dementias (ADRD) stratified by glucosamine use, leveraging electronic health records. Consistently, glucosamine supplementation was associated with increased mortality in AD and ADRD patient cohorts, and significantly elevated progression from MCI to AD compared to age-matched controls. Collectively, our findings establish hyper-glycosylation as a pathological driver of AD and highlight glycan metabolism as an actional target in the fight against AD.

neuroscience↗

bioRxiv2025

SEHI-PPI: An End-to-End Sampling-Enhanced Human-Influenza Protein-Protein Interaction Prediction Framework with Double-View Learning

Influenza continues to pose significant global health threats, hijacking host cellular machinery through protein-protein interactions (PPIs), which are fundamental to viral entry, replication, immune evasion, and transmission. Yet, our understanding of these host-virus PPIs remains incomplete due to the vast diversity of viral proteins, their rapid mutation rates, and the limited availability of experimentally validated interaction data. Additionally, existing computational methods often struggle due to the limited availability of high-quality samples and their inability to effectively model the complex nature of host-virus interactions. To address these challenges, we present SEHI-PPI, an end-to-end framework for human-influenza PPI prediction. SEHI-PPI integrates a double-view deep learning architecture that captures both global and local sequence features, coupled with a novel adaptive negative sampling strategy to generate reliable and high-quality negative samples. Our method outperforms multiple benchmarks, including state-of-the-art large language models, achieving a superior performance in sensitivity (0.986) and AUROC (0.987). Notably, in a stringent test involving entirely unseen human and influenza protein families, SEHI-PPI maintains strong performance with an AUROC of 0.837. The model also demonstrates high generalizability across other human-virus PPI datasets, with an average sensitivity of 0.929 and AUROC of 0.928. Furthermore, AlphaFold3-guided case studies reveal that viral proteins predicted to target the same human protein cluster together structurally and functionally, underscoring the biological relevance of our predictions. These discoveries demonstrate the reliability of our SEHI-PPI framework in uncovering biologically meaningful host-virus interactions and potential therapeutic targets.

bioinformatics↗

bioRxiv2025

Temporal mapping of the anti-tumor effects of nanobody-based MSLN.CAR-T cell therapy in metastatic solid tumors

Studies on the dynamic changes occurring in the tumor microenvironment (TME) following CAR-T cell therapy have been confounded by host lymphodepletion, multiple dosing and immunodeficient models. Here, a nanobody-based, mouse mesothelin-targeting CAR-T cell (A101) was developed, achieving effective primary tumor suppression, metastasis reduction, and improved survival after a single dose in immunocompetent, syngeneic mouse models without lymphodepletion. Temporal tumor profiling using RNA sequencing revealed initial downregulation of cell proliferation genes followed by upregulation of inflammation, epithelial-to-mesenchymal-transition (EMT) and extracellular matrix (ECM) modification genes in the CAR-T-treated tumors relative to mock-T-treated controls. This phenotype was reversed at a later timepoint which coincided with downregulation of immunosuppressive Cd274+ Lcn2+ neutrophils and upregulation of anti-tumor P2rx1+ Nrf2- neutrophils. At the same time, upregulation of Ccl2+ in fibroblasts and a more immunomodulatory macrophage phenotype was observed in CAR-T-treated tumors, indicating a tumor adaptation mechanism. This study demonstrates complex dynamic changes in the TME, and highlights time-dependent responses of solid tumors to CAR-T cell therapy. It further highlights Lcn2+ neutrophils and Ccl2+ fibroblasts as potential therapeutic targets for improving CAR-T cell anti-tumor efficacy. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=176 SRC="FIGDIR/small/640438v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@88ec4org.highwire.dtl.DTLVardef@1d4fa64org.highwire.dtl.DTLVardef@182e11aorg.highwire.dtl.DTLVardef@1889182_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract.C_FLOATNO Tumor responses to A101 CAR-T cell treatment. C_FIG

immunology↗

bioRxiv2024

Microglia-specific NF-κB signaling is a critical regulator of prion-induced glial inflammation and neuronal loss

Prion diseases are a group of rare and fatal neurodegenerative diseases caused by the cellular prion protein, PrPC, misfolding into the infectious form, PrPSc, which forms aggregates in the brain. This leads to activation of glial cells, neuroinflammation, and irreversible neuronal loss, however, the role of glial cells in prion disease pathogenesis and neurotoxicity is poorly understood. Microglia can phagocytose PrPSc, leading to the release of inflammatory signaling molecules, which subsequently induce astrocyte reactivity. Animal models show highly upregulated inflammatory molecules that are a product of the Nuclear Factor-kappa B (NF-{kappa}B) signaling pathway, suggesting that this is a key regulator of inflammation in the prion-infected brain. The activation of the I{kappa}B kinase complex (IKK) by cellular stress signals is critical for NF-{kappa}B-induced transcription of a variety of genes, including pro-inflammatory cytokines and chemokines, and regulators of protein homeostasis and cell survival. However, the contribution of microglial IKK and NF-{kappa}B signaling in the prion-infected brain has not been evaluated. Here, we characterize a primary mixed glial cell model containing wild-type (WT) astrocytes and IKK knock-out (KO) microglia. We show that, when exposed to prion-infected brain homogenates, NF-{kappa}B-associated genes are significantly downregulated in mixed glial cultures containing IKK KO microglia. Mice with IKK KO microglia show rapid disease progression when intracranially infected with prions, including an increase in microglia and reactive astrocytes, and accelerated loss of hippocampal neurons and associated behavioral deficits. These animals display clinical signs of prion disease early and have a 22% shorter life expectancy compared to infected wild-type mice. Intriguingly, PrPSc accumulation was significantly lower in the brains of infected animals with IKK KO microglia compared to age-matched controls, suggesting that accelerated disease is independent of PrPSc accumulation, highlighting a glial-specific pathology. Conversely, primary mixed glia with IKK KO microglia have significantly more PrPSc accumulation when exposed to infected brain homogenates. Together, these findings present a critical role in NF-{kappa}B signaling from microglia in host protection suggesting that microglial IKK may be involved in sufficient clearance of prions.

molecular biology↗

bioRxiv2024

Graph Contrastive Learning as a Versatile Foundation for Advanced scRNA-seq Data Analysis

Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles. However, cell clustering is technically challenging, as raw scRNA-seq data have various analytical issues, including high dimensionality and dropout values. Existing research has developed deep learning models, such as graph machine learning models and contrastive learning-based models, for cell clustering using scRNA-seq data and has summarized the unsupervised learning of cell clustering into a human-interpretable format. While advances in cell clustering have been profound, we are no closer to finding a simple yet effective framework for learning high-quality representations necessary for robust clustering. In this study, we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. This framework facilitates the generation of high-quality representations crucial for cell clustering. Our scSimGCL incorporates cell-cell graph structure and contrastive learning to enhance the performance of cell clustering. Extensive experimental results on simulated and real scRNA-seq datasets suggest the superiority of the proposed scSimGCL. Moreover, clustering assignment analysis confirms the general applicability of scSimGCL, including state-of-the-art clustering algorithms. Further, ablation study and hyperparameter analysis suggest the efficacy of our network architecture with the robustness of decisions in the self-supervised learning setting. The proposed scSimGCL can serve as a robust framework for practitioners developing tools for cell clustering. The source code of scSimGCL is publicly available at https://github.com/zhangzh1328/scSimGCL.

bioinformatics↗

bioRxiv2024

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics.

bioinformatics↗

bioRxiv2024

OCTOPUS: Disk-based, Multiplatform, Mobile-friendly Metagenomics Classifier

Portable genomic sequencers such as Oxford Nanopores MinION enable real-time applications in clinical and environmental health. However, there is a bottleneck in the downstream analytics when bioinformatics pipelines are unavailable, e.g., when cloud processing is unreachable due to absence of Internet connection, or only low-end computing devices can be carried on site. Here we present a platform-friendly software for portable metagenomic analysis of Nanopore data, the Oligomer-based Classifier of Taxonomic Operational and Pan-genome Units via Singletons (OCTOPUS). OCTOPUS is written in Java, reimplements several features of the popular Kraken2 and KrakenUniq software, with original components for improving metagenomics classification on incomplete/sampled reference databases, making it ideal for running on smartphones or tablets. OCTOPUS obtains sensitivity and precision comparable to Kraken2, while dramatically decreasing (4-to 16-fold) the false positive rate, and yielding high correlation on real-word data. OCTOPUS is available along with customized databases at https://github.com/DataIntellSystLab/OCTOPUS and https://github.com/Ruiz-HCI-Lab/OctopusMobile.

molecular biology↗

bioRxiv2024

Kernel-smoothed permutation for extreme P-value estimation in genetic association studies

In genetic studies, permutation tests serve as a cornerstone to estimate P-values. This is because researchers may design new test statistics without a known closed-form distribution, or the assumption of a well-established test may not hold. However, permutation tests require vast number of permutations which is proportional to the magnitude of the actual P-values. When it comes to genome-wide association studies where multiple-test corrections are routinely conducted, the actual P-values are extremely small, requiring a daunting number of permutations that may be beyond the available computational resources. Existing models that reduce the required number of permutations all assume a specific format of the test statistic to exploit its specific statistical properties. We propose Kernel-smoothed permutation which is a model-free method universally applicable to any statistic. Our tool forms the null distribution of test statistics using a kurtosis-driven transformation, followed by a kernel-based density estimation (KDE). We compared our Kernel-smoothed permutation to Naive permutation using statistics from known closed-form null distributions. Based on three frequently used test statistics in association studies, i.e., t-test, sequence kernel association test (SKAT), and chi-squared test, we demonstrated that our model reduced the required number of permutations by a magnitude with the same or higher accuracy.

bioinformatics↗

bioRxiv2023

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Transcriptome-wide association study (TWAS) is an emerging model leveraging gene expressions to direct genotype-phenotype association mapping. A key component in TWAS is the prediction of gene expressions; and many statistical approaches have been developed along this line. However, a problem is that many genes have low expression heritability, limiting the performance of any predictive model. In this work, hypothesizing that appropriate denoising may improve the quality of expression data (including heritability), we propose AE-TWAS, which adds a transformation step before conducting standard TWAS. The transformation is composed of two steps by first splitting the whole transcriptome into co-expression networks (modules) and then using autoencoder (AE) to reconstruct the transcriptome data within each module. This transformation removes noise (including nonlinear ones) from the transcriptome data, paving the path for downstream TWAS. We showed two inspiring properties of AE-TWAS: (1) After transformation, the transcriptome data enjoy higher expression heritability at the low-heritability spectrum and possess higher connectivity within the modules. (2) The transferred transcriptome indeed enables better performance of TWAS; and moreover, the newly formed highly connected genes (i.e., hub genes) are more functionally relevant to diseases, evidenced by their functional annotations and overlap with TWAS hits.

bioinformatics↗