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PubMed2025

De novo pyrimidine synthesis is a collateral metabolic vulnerability in NF2-deficient mesothelioma.

Pleural mesothelioma (PM) is one of the deadliest cancers, with limited therapeutic options due to its therapeutically intractable genome, which is characterized by the functional inactivation of tumor suppressor genes (TSGs) and high tumor heterogeneity, including diverse metabolic adaptations. However, the molecular mechanisms underlying these metabolic alterations remain poorly understood, particularly how TSG inactivation rewires tumor metabolism to drive tumorigenesis and create metabolic dependencies. Through integrated multi-omics analysis, we identify for the first time that NF2 loss of function defines a distinct PM subtype characterized by enhanced de novo pyrimidine synthesis, which NF2-deficient PM cells are critically dependent on for sustained proliferation in vitro and in vivo. Mechanistically, NF2 loss activates YAP, a downstream proto-oncogenic transcriptional coactivator in the Hippo signalling pathway, which in turn upregulates CAD and DHODH, key enzymes in the de novo pyrimidine biosynthesis pathway. Our findings provide novel insights into metabolic reprogramming in PM, revealing de novo pyrimidine synthesis as a synthetic lethal vulnerability in NF2-deficient tumors. This work highlights a potential therapeutic strategy for targeting NF2-deficient mesothelioma through metabolic intervention.

Pyrimidines↗

PubMed2026

Dynamic lysine acetylation and succinylation of platelet proteins regulates platelet storage lesion: mechanistic insights from multi-omics.

OBJECTIVES: Platelet storage lesion (PSL) severely impairs platelet function during storage, presenting a major hurdle in transfusion medicine; however, the dynamic interplay between global proteomic changes and post-translational modifications (PTMs) underlying these functional deteriorations remains insufficiently characterized. Here, we report the first comprehensive multi-omics analysis integrating global proteomics, acetylomics, and succinylomics to dissect the molecular dynamics during platelet storage. METHODS: We performed quantification of global proteomics, acetylome and succinylome based on TMT-labeled LC-MS/MS analysis, combined with antibody-affinity enrichment and purification. Dynamic molecular changes and functional transformation of platelet were also characterized under proper conditions stored for 1, 3, 5, 7 days, respectively. RESULTS: We systematically characterized 3,609 proteins, 1,308 acetylation sites, and 1,947 succinylation sites across multiple storage time points (D1, D3, D5, D7). We distinct temporal patterns of post-translational modifications, with succinylation showing more extensive coverage than acetylation in platelets. Pathway enrichment analysis revealed extensive metabolic reprogramming involving complement activation, energy metabolism, and cellular detoxification processes. The identification of specific motif patterns provided mechanistic insights into the functional specificity of these modifications. Random forest machine learning identified 20 core regulatory proteins representing critical nodes in PSL development. Furthermore, we employed real - time quantitative polymerase chain reaction (RT - QPCR) to measure the expression levels of key genes related to platelet function and PTM - associated pathways. CONCLUSION: By mapping the interplay between proteomic abundance shifts and PTM dynamics, this study provides a multidimensional understanding of PSL, establishing a foundational framework for optimizing storage protocols and enhancing transfusion safety.

Blood Platelets↗

PubMed2025

Evolution and applications of genome-scale metabolic models in yeast systems biology studies.

Genome-scale metabolic models (GEMs) can be used to simulate the metabolic network of an organism in a systematic and holistic way. Different yeast species, including Saccharomyces cerevisiae, have emerged as powerful cell factories for bioproduction. Recently, with the dedicated efforts from the scientific community, significant progress has been made in the development of yeast GEMs. Numerous versions of yeast GEMs and the derived multiscale models have been released, facilitating integrative omics analysis and rational strain design for different types of yeast cell factories. These advancements reflected the evolution and maturation of yeast GEMs together with a model ecosystem around them. This review will summarize the development and expansion of yeast GEMs and discuss their applications in yeast systems biology studies. It is anticipated that yeast GEMs will continue to play an increasingly important role in pioneering yeast physiological and metabolic studies in coming years.

Systems Biology↗

PubMed2025

Genome-based exploration of volatile flavor diversity from food yeast species.

Yeast shares a longer than 10 000-year history with humans in food fermentation by producing various volatile flavor compounds that contribute to the final taste and aroma of foods. Yeast-associated volatile flavor compounds include esters, benzenoids, sulfur compounds, and phenolic derivatives, which enhance the sensory complexity of fermented foods and beverages. Genome-scale technologies have advanced and transformed our understanding of the genetic and evolutionary drivers of volatile flavor diversity. The conventional approach to aroma enrichment and flavor balancing through single-strain optimization has been redefined through yeast cofermentation strategies, such as the pairing of Saccharomyces cerevisiae with nonconventional yeast species. This minireview summarizes the latest genomic insights into volatile flavor compound formation through ester, benzenoid, sulfur, and phenolic pathways in various yeast species and highlights the shaping of the next generation of food fermentation innovation via cofermentation combined with omics analysis, followed by a future perspective on synthetic biology for industrial applicability.

Volatile Organic Compounds↗

PubMed2025

Stress-induced altered expression of hippocampal nuclear and mitochondrial encoded genes in rats and cross-species genetic associations reveal molecular links to depression.

BACKGROUND: Mitochondria play a pivotal role in energy production, and their dysfunction not only hampers cells' ability to meet energy requirements but also contributes to the impairment of neural plasticity, a critical feature of depressive disorders. In this study, mitochondrial cross-omics analysis was carried out in the hippocampus of restraint rats to understand the role of mitochondria in depression pathophysiology. METHODS: The expression profiles of hippocampal mitochondrial and nuclear-encoded genes in mitochondrial fractions from restraint and handled control rats were obtained using high-throughput RNA sequencing. Weighted gene co-expression network analysis (WGCNA) was used to identify the gene co-expression and pathways associated with the restraint phenotype. Mutual Information Network algorithm tools Arance, CLR, and MRNET were additionally used to screen the functional modules and hub genes and their similarity with the WGCNA-based network analysis. Finally, cross-species homology followed by gene association analysis was conducted to obtain SNPs and haplotypes related to depression phenotype. RESULTS: A significant proportion of mitochondrial and nuclear-encoded genes showed differential regulation in the hippocampus of restraint rats. WGCNA and Mutual Information Network analysis yielded distinct functional modules significantly related to restraint phenotype. Further network analysis revealed distinct co-expression patterns associated with differentially expressed genes associated with these modules. Cross-species analysis showed 39 significantly associated SNPs with the depression phenotype, where the most significant SNP, rs10899570, was located within the TENM4 gene. Further, rs1573529 and rs10899570 were distributed into the linkage disequilibrium block where SNPs were highly correlated. Subsequent haplotype analysis showed that rs1573529 and rs10899570 were significantly associated with depressive behavior. CONCLUSIONS: The study demonstrates a significant impact of restraint stress on mitochondrial functions and genetic association, suggesting their critical role in depression pathophysiology.

Animals↗

PubMed2026

AI In Leukemia Diagnostics: Complementing the Pathologist's Role.

Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-generation sequencing, multi-omics analysis, and emerging computational frontiers such as quantum-inspired feature selection. This review outlines how contemporary AI tools can automate labor-intensive quantitation, flag diagnostically salient patterns, and standardize interpretation, while the pathologist or hematologist retains authority over validation, context-specific integration, and clinical decision-making. We present an illustrative "human-in-the-loop" workflow that embeds AI modules within current laboratory information systems, emphasizing points where expert oversight mitigates algorithmic bias and resolves discordant findings. We further map the validator-integrator role across morphology, flow cytometry, and genomic/multi-omic interpretation and provide practical training competencies and use cases for AI-assisted hematopathology. Beyond technical deployment, the article addresses the educational transformation required for sustainable adoption. Drawing on international competency frameworks, including the Digital Health Competencies in Medical Education Framework and recently proposed AI-specific Entrustable Professional Activities, we map core skills that future hematopathologists must master: data-science literacy, critical appraisal of AI outputs, and ethical governance. We highlight evaluated training models such as the Pathology Informatics Essentials for Residents curriculum, Stanford Artificial Intelligence in Machine and Imaging workshops, and College of American Pathologists bootcamps and propose integration strategies adaptable across resource settings. By pairing rigorous validation with targeted education, AI can elevate rather than eclipse the diagnostic role of the leukemia specialist, enabling more timely, reproducible, and personalized patient care.

Humans↗

PubMed2026

Multi-omics identifies lipid accumulation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cell lines: a case-control study.

BACKGROUND: In recent years, evidence has indicated a metabolic shift towards increased demand for lipids in various lymphoid cell populations from people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). We previously screened the mitochondrial function and gene expression of B cell-derived lymphoblastoid cell lines (LCLs) generated from the blood of people with ME/CFS to characterise a model for hypothesis discovery and testing, observing elevated expression of gene products facilitating amino acid and fatty acid degradation for energy. METHOD: In this follow-up study we have expanded this characterisation by profiling the polar metabolomes and non-polar lipidomes of an all-female cohort of 17 healthy control and 15 ME/CFS LCLs, and we integrated this new data with the previously generated proteomic and transcriptomic data. RESULTS: In the polar metabolome we detected no significantly altered individual features, while integrated multi-omic analysis by MetaboAnalyst indicated 15 dysregulated pathways. Next, in the non-polar lipidome, we identified that PC(O-38:4) had significantly reduced levels in ME/CFS LCLs and was almost entirely discriminative of ME/CFS status. Among all detected classes of lipids we found that triradylglycerolipids ("triglycerides"), diradylglycerolipids and fatty acids were the most significantly affected and were elevated, and that most lipids exhibited average levels higher than in healthy controls. BioPAN pathway analysis of the lipidomic data predicted a more-active gene product that we confirmed to be significantly elevated in both our proteomic and transcriptomic data, this being phosphatidylserine synthase 1 (PTDSS1), plus 7 other gene products that were concordantly altered in expression in the transcriptomic data. We also found that ME/CFS LCLs exhibited a significant tendency towards more saturated lipid content. CONCLUSIONS: LCLs generated from circulating B cells from people with ME/CFS show accumulation of lipids, skewed lipid profiles and altered activity of related metabolic enzymes such as PTDSS1. These findings will inform future hypothesis-driven studies of primary lymphoid cell populations from people with ME/CFS to dissect specific immunometabolic mechanisms that may be involved in the syndrome, particularly relating to intersections between lipid abnormalities and potential effects on immune cell effector functions.

Fatigue Syndrome, Chronic↗

PubMed2026

Radiogenomics predicts immune microenvironment heterogeneity and response to combination immunotherapy in hepatocellular carcinoma.

BACKGROUND: The combination of immune checkpoint inhibitors (ICIs) with anti-angiogenic agents is the preferred first-line therapy option for patients with advanced hepatocellular carcinoma (HCC), yet only a subset of patients responds, urging the quest for prediction biomarkers. We aimed to integrate genomics with radiology to propose an immune-derived radiogenomics biomarker of response to such combination immunotherapy and evaluate its added value in clinical context. METHODS: We integrated bulk RNA sequencing (RNA-seq) and proteomics data of 994 HCC patients with single-cell RNA-seq data of 11 samples across multiple datasets to identify an immune-related signature (IRS) that may influence sensitivity or resistance to such combined immunotherapy strategy, followed by verification of selected marker genes using immunohistochemistry and cytological experiments. We then trained/validated a cross-modality radiogenomics biomarker using machine learning based on TCIA database that was further tested in multi-scale independent cohorts covering 754 HCC patients. RESULTS: Integrative multi-omics analysis identifed a parsimonious 2-gene prognostic signature including KPNA2 and SMG5 that was significantly associated with immune heterogeneity and response to combination immunotherapy. Machine-learning pipeline exported the optimal 4-feature radiogenomics biomarker using support vector machine that significantly discriminated prognosis (hazard ratio 1.415–1.890; p < 0.05 for all) and modestly predicted response to ICI plus anti-angiogenic therapy (area under the curve 0.720–0.829) in independent retrospective series across major imaging modalities (computed tomography/magnetic resonance imaging). In a prospective neoadjuvant cohort, this biomarker also showed favorable performance for predicting pathological response and tumor recurrence, accompanied by biological validation through single-cell RNA-seq analysis of pre-treatment biopsies. CONCLUSIONS: Our study provides a cross-device-cross-modal radiogenomics biomarker that can improve patient selection for emerging ICI plus anti-angiogenic therapy with novel potential therapeutic targets in HCC.

Humans↗

PubMed2026

Brucellar spondylitis is associated with disturbance in gut microbiota and histamine metabolism associated inflammation.

BACKGROUND: The pathogenesis of brucellar spondylitis (BLS) has traditionally been considered to be primarily limited to local osteoarticular lesions. With the proposal of the "gut-spine axis" concept, the role of intestinal microecological dysbiosis in inflammatory spinal diseases has attracted in an increase of attention. The overactivated inflammatory cytokine network not only mediates bone destruction and intervertebral disc damage, but also forms a bidirectional interaction with gut microbiota dysbiosis through the "gut-spine axis," collectively driving disease progression. However, the inflammatory mechanism by which gut microbiota participates in the pathological process of BLS remains largely unclear. METHODS: This study recruited 20 BLS patients and 20 healthy donors. Multi-omics analysis including metagenomics, untargeted metabolomics, and targeted short-chain fatty acids (SCFAs) analysis, were used to compare the structural differences in gut microbiota between the two groups and screen for signature differential bacterial species. Plasma levels of histamine and histidine decarboxylase were measured by ELISA to clarify the role of differential histidine metabolic pathway in the disease. Additionally, plasma levels of lipopolysaccharide (LPS) and inflammatory cytokines (IL-1β, IL-6, IL-10, IL-17A, TNF-α) were detected by ELISA. The correlation between gut microbiota and inflammatory indicators was further analyzed. RESULTS: Compared to the healthy control group, the α-diversity of the gut microbiota in BLS patients was significantly reduced, with the microbial community structure exhibiting increased homogeneity. Beta diversity analysis revealed significant differences, suggesting that disease progression is associated with an overall imbalance in the gut microbiota and the deterioration of its specific structural composition. At the phylum level, the abundances of Actinomycetota, unclassified_d_Viruses, and Fusobacteriota were significantly increased in the gut microbiota of BLS patients compared to the control group, while the abundances of Bacillota and Pseudomonadota were significantly decreased. Further analysis revealed that, compared to the control group, the generic abundance of Enterococcus was significantly increased, while the proportions of Blautia, Faecalibacterium, Ruminococcus, Agathobacter, Roseburia, Clostridium, Eubacterium, Alistipes and Anaerobutyricum were significantly decreased. At the species level, the abundances of Enterococcus sp and Enterococcus-faecium were increased, whereas Blautia sp, Ruminococcus sp, Faecalibacterium sp, Faecalibacterium prausnitzii, Agathobacter rectalis, Eubacterium sp, Agathobacter sp, and Roseburia sp were decreased. Furthermore, untargeted metabolomics revealed that metabolites were enriched in the histidine metabolic pathway, and the levels of SCFAs including butyrate, isobutyrate, valerate, and 4-methylvalerate in the intestinal contents were reduced in BLS. Functional KEGG profiling revealed that key KOs involved in butyrate synthesis (e.g., K00074, K00172, K01640) and transport were globally downregulated in the patient group, whereas histidine decarboxylase KOs (K01693, K11755, K19787) that convert histidine to pro-inflammatory histamine were significantly enriched. The loss of butyrate-producing symbionts led to SCFAs deficiency and mucosal barrier disruption, creating ecological niches for facultatively anaerobic Enterococcus, which further exacerbated local inflammation via proteolytic fermentation and histamine production. Compared with the control group, BLS patients showed decreased plasma levels of IL-10, while levels of IL-1β, IL-6, IL-17A, and TNF-α were increased, and LPS levels were elevated. In addition, significantly elevated plasma pro-inflammatory LPS levels in patients with BLS suggest disruption of intestinal integrity and permeability. Correlation analysis indicated a close relationship between gut microbiota and inflammation. CONCLUSION: BLS is associated with gut microbiota dysbiosis and alterations in microbial metabolites, which may be linked to inflammatory responses and histamine metabolism. The differential microbial taxa identified in this study could be developed into a stool-based non-invasive diagnostic panel to facilitate early differentiation of BLS from other spinal disorders. Furthermore, restoring gut microbial balance through probiotic supplementation or dietary modulation may represent a promising adjunctive strategy to enhance the efficacy of standard antibiotic therapy and reduce disease recurrence.

Humans↗

PubMed2026

iModMix: integrative module analysis for multi-omics data.

SUMMARY: Integrative Module Analysis for Multi-omics Data (iModMix) is a biology-agnostic framework that enables the discovery of novel associations across any type of quantitative abundance data, including but not limited to transcriptomics, proteomics, and metabolomics. Instead of relying on pathway annotations or prior biological knowledge, iModMix constructs data-driven modules using graphical lasso to estimate sparse networks from omics features. These modules are summarized into eigenfeatures and correlated across datasets for horizontal integration, while preserving the distinct feature sets and interpretability of each omics type. iModMix operates directly on matrices containing expression or abundances for a wide range of features, including but not limited to genes, proteins, and metabolites. Because it does not rely on annotations (e.g., KEGG identifiers), it can seamlessly incorporate both identified and unidentified metabolites, addressing a key limitation of many existing metabolomics tools. iModMix is available as a user-friendly R Shiny application requiring no programming expertise (https://imodmix.moffitt.org), and as a Bioconductor R package for advanced users (https://bioconductor.org/packages/release/bioc/html/iModMix.html). The tool includes several public and in-house datasets to illustrate its utility in identifying novel multi-omics relationships in diverse biological contexts. AVAILABILITY AND IMPLEMENTATION: iModMix is freely available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/iModMix.html), and the example dataset package (iModMixData) is also available from Bioconductor (https://bioconductor.org/packages/release/ data/experiment/html/iModMixData.html). The R package source code and Docker are available from GitHub: https://github.com/biodatalab/iModMix. Shiny application can be accessed at: https://imodmix.moffitt.org.

Multiomics↗

PubMed2026

Integrative Multi-Omics Mendelian Randomization Analysis Identifies NIT2 as a Potential Metabolic Risk Gene in Hepatocellular Carcinoma.

BACKGROUND: Metabolic pathways are crucial in hepatocellular carcinoma (HCC) pathogenesis, but causal metabolic genes remain unclear. This study used Summary data-based Mendelian Randomization (SMR) and colocalization to identify metabolism-related genetic loci influencing HCC risk. METHODS: Differentially expressed genes in hepatic malignancy phenotype versus normal tissues from TCGA and GTEx were analyzed. Metabolism-related candidates were examined via SMR and colocalization using multi-omics data: methylation (mQTL), expression (eQTL), and protein (pQTL) quantitative trait loci. RESULTS: Multi-omics integration identified NIT2 as a key metabolic regulator for HCC. The cg13016775 locus of NIT2 was associated with elevated HCC risk at gene (OR = 1.618, 95% CI: 1.199-2.182) and protein (OR = 4.432, 95% CI: 1.783-11.018) levels. Colocalization supported a shared causal variant (PPH4 > 0.6), linking NIT2 to hepatocarcinogenesis via metabolic regulation. CONCLUSIONS: This study provides multi-omics evidence for NIT2 as a potential causal gene in HCC, enhancing understanding of metabolic contributions to HCC pathogenesis and highlighting integrative genomics for uncovering causal relationships.

Carcinoma, Hepatocellular↗

PubMed2026

BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.

MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Humans↗

PubMed2006

A flexible representation of omic knowledge for thorough analysis of microarray data.

BACKGROUND: In order to understand microarray data reasonably in the context of other existing biological knowledge, it is necessary to conduct a thorough examination of the data utilizing every aspect of available omic knowledge libraries. So far, a number of bioinformatics tools have been developed. However, each of them is restricted to deal with one type of omic knowledge, e.g., pathways, interactions or gene ontology. Now that the varieties of omic knowledge are expanding, analysis tools need a way to deal with any type of omic knowledge. Hence, we have designed the Omic Space Markup Language (OSML) that can represent a wide range of omic knowledge, and also, we have developed a tool named GSCope3, which can statistically analyze microarray data in comparison with the OSML-formatted omic knowledge data. RESULTS: In order to test the applicability of OSML to represent a variety of omic knowledge specifically useful for analysis of Arabidopsis thaliana microarray data, we have constructed a Biological Knowledge Library (BiKLi) by converting eight different types of omic knowledge into OSML-formatted datasets. We applied GSCope3 and BiKLi to previously reported A. thaliana microarray data, so as to extract any additional insights from the data. As a result, we have discovered a new insight that lignin formation resists drought stress and activates transcription of many water channel genes to oppose drought stress; and most of the 20S proteasome subunit genes show similar expression profiles under drought stress. In addition to this novel discovery, similar findings previously reported were also quickly confirmed using GSCope3 and BiKLi. CONCLUSION: GSCope3 can statistically analyze microarray data in the context of any OSML-represented omic knowledge. OSML is not restricted to a specific data type structure, but it can represent a wide range of omic knowledge. It allows us to convert new types of omic knowledge into datasets that can be used for microarray data analysis with GSCope3. In addition to BiKLi, by collecting various types of omic knowledge as OSML libraries, it becomes possible for us to conduct detailed thorough analysis from various biological viewpoints. GSCope3 and BiKLi are available for academic users at our web site http://omicspace.riken.jp.

Journal Article↗

PubMed2025

BioMedGraphica: An All-in-One Platform for Joint Textual Biomedical Prior Knowledge and Numeric Graph Generation.

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica , an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

biomedical knowledge graph↗

PubMed2026

CancerOmicsStudio (CoS): a web server for integrative and interpretable analysis of multi-omics cancer data.

MOTIVATION: Large-scale omics resources, including The Cancer Genome Atlas, Genomics of Drug Sensitivity in Cancer, and the Cancer Dependency Map, have become essential for cancer research. However, these datasets are distributed across different platforms, formats and analysis frameworks, which limits their practical use by researchers without extensive computational expertise. RESULTS: We developed CancerOmicsStudio (CoS), a web server for integrative and interpretable analysis of multi-omics cancer data across 33 cancer types. CoS provides five major modules: CosAI, Traditional Analysis, Drug Sensitivity, CRISPR Dependency and Single-Cell Tumor Microenvironment. The Traditional Analysis module supports expression comparison, diagnostic evaluation, survival analysis, enrichment analysis and gene correlation. The Drug Sensitivity and CRISPR Dependency modules enable systematic evaluation of gene-drug response associations and gene essentiality in cancer cell lines. The Single-Cell Tumor Microenvironment module supports tumor microenvironment analysis at single-cell resolution. In total, approximately 1.23 million results have been precomputed to enable rapid retrieval. CosAI further allows users to submit natural-language queries and obtain results through a Real-time Analysis as Retrieval framework, with responses summarized by a lightweight language model. AVAILABILITY AND IMPLEMENTATION: CancerOmicsStudio is freely available at Zenodo (doi: 10.5281/zenodo.18744990) and https://cos.wanglab.bio.

Humans↗

PubMed2025

Composition-on-composition regression analysis for multi-omics integration of metagenomic data.

MOTIVATION: Compositional data are frequently encountered in many disciplines, such as in next-generation sequencing experiments widely used in biomedical studies. Regression analysis with compositional data as either responses or predictors has been well studied. However, when both responses and predictors are compositional, the inventory of analysis tools is surprisingly limited, especially in the high-dimensional setting. Among the few existing methods, most of them rely on a log-ratio transformation to move compositional data from the simplex to real numbers. Yet, a serious weakness of these methods is their failure to handle the substantial fraction of zeroes observed in data collected from next-generation sequencing experiments. RESULTS: To investigate associations between two high-dimensional multi-omics compositions, we propose a composition-on-composition (COC) regression analysis method which does not require log-ratio transformations and hence can handle zeroes in the data. To account for high dimensionality, we estimate regression coefficients using a penalized estimation equation approach. Finally, inference procedures for COC regression are also proposed. Superior performance of COC is demonstrated through both comprehensive numerical simulations and case studies. AVAILABILITY AND IMPLEMENTATION: Source R codes to implement COC method is available at https://github.com/nrios4/COC.

Regression Analysis↗

PubMed2025

Polygenic enrichment analysis in multi-omics levels identifies cell/tissue specific associations with schizophrenia based on single-cell RNA sequencing data.

OBJECTIVE: Understanding the specific cellular origin and tissue heterogeneity in schizophrenia is critically important for exploring the disease etiology. This study aims to investigate these aspects by performing multiple analyses based on omics data. METHOD: We performed single-cell disease relevance score (scDRS) algorithm to link brain single-cell RNA sequencing (scRNA-seq) with schizophrenia risk across multi-omics scales at single-cell resolution. This approach identified cell types with overexpression of schizophrenia-related genes implicated by multi-omics panels (ATAC-seq, RNA-seq, TWAS, and GWAS). Schizophrenia-related genes from these multi-omics panels were extracted and combined with scRNA-seq data to calculate scDRS. Subsequently, the cell-type vs. disease association and tissue heterogeneity were assessed using scDRS for each omics panel. RESULTS: We identified two novel cell subpopulations in the brain that differentially express SCUBE3 (59 cells, 7.0 %) and FN1 (21 cells, 2.5 %). At the individual cell level, schizophrenia-associated cell subpopulations included microglial cell associated with ATAC-seq panel (Passociation = 0.002, Pheterogeneity = 0.009) and deep layer neuron suggestively associated with GWAS panel (Passociation = 0.033, Pheterogeneity = 0.017). At the brain tissue level, microglial cell was significantly associated with cortical plate in ATAC-seq panel (Passociation = 0.002, Pheterogeneity = 0.011). Gene level analysis identified several genes associated with schizophrenia across multi-omics panels. CONCLUSIONS: Our study outlines the signature of cell subpopulations, brain regions, and disease risk genes in schizophrenia at single-cell resolution across multi-omics scales. These findings provide a reference for future precision medicine approaches targeting specific cell types and brain regions in schizophrenia.

Schizophrenia↗

PubMed2026

Yomix: an interactive tool for the exploration of low-dimensional embeddings in omics data.

SUMMARY: In the analysis of diverse omics data, a common and important preliminary step involves computing low-dimensional embeddings using techniques such as PCA, UMAP, t-SNE, or variational autoencoders. These embeddings provide a global overview of sample distributions and their relationships, often serving as the basis for formulating biological hypotheses. To facilitate rapid and intuitive exploration of such low-dimensional embeddings, we developed Yomix, an interactive omics-agnostic visualization and data exploration tool. Yomix enables users to flexibly define subsets of interest using a lasso selection tool, instantly compute their feature signatures, and compare their distributions. Yomix is a fast and efficient tool for interactive exploration of diverse omics datasets. AVAILABILITY AND IMPLEMENTATION: Yomix and its documentation are publicly available at https://github.com/perrin-isir/yomix.

Software↗