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PubMed2005

From XML to RDF: how semantic web technologies will change the design of 'omic' standards.

With the ongoing rapid increase in both volume and diversity of 'omic' data (genomics, transcriptomics, proteomics, and others), the development and adoption of data standards is of paramount importance to realize the promise of systems biology. A recent trend in data standard development has been to use extensible markup language (XML) as the preferred mechanism to define data representations. But as illustrated here with a few examples from proteomics data, the syntactic and document-centric XML cannot achieve the level of interoperability required by the highly dynamic and integrated bioinformatics applications. In the present article, we discuss why semantic web technologies, as recommended by the World Wide Web consortium (W3C), expand current data standard technology for biological data representation and management.

Algorithms↗
PubMed2025

DNA methylation and multi-omics profiling of T cells uncovers chemotactic pathways and proliferation-linked hypomethylation in narcolepsy type 1.

Narcolepsy type 1 (NT1) is a chronic sleep disorder caused by a loss of orexin-producing cells in the brain and involves autoimmune mechanisms, including the presence of autoreactive T cells. In this study, we performed genome-wide DNA methylation analysis using both CD4+/CD8+ T cells from 42 NT1 patients and 42 controls across discovery and replication cohorts. To identify methylation changes more robustly associated with the disease, we prioritized differentially methylated regions (DMRs) over single-site differentially methylated positions (DMPs). Furthermore, to validate and interpret DMP-level associations, we integrated genome-wide genotype and gene expression data obtained from the same individuals. As a result, the DMR analysis identified 15 reproducible DMRs in CD4+ T cells and 5 in CD8+ T cells, with most DMRs shared between the two cell types. Shared DMRs included regions associated with CCL5 (p = 2.1E-02) and CCR4 (p = 8.3E-03). Integrative analysis with genotype and gene expression data also showed that the DMP related to S100A4, which promotes lymphocyte migration through CCR5 and CXCR3 receptors, was associated with the disease in CD4+ T cells. Pathway analysis of genes identified through both the DMR and integrative analyses indicated enrichment in cell chemotaxis-related pathways, suggesting that aberrant chemokine-mediated cell migration plays a central role in NT1 pathogenesis. Further, NT1-associated methylation changes were predominantly hypomethylation events, significantly enriched in non-promoter, non-CpG island regions (p = 1.74E-102). We further observed that global hypomethylation levels were correlated with hypoSC, a mitotic index estimated from methylation data, highlighting increased T cell proliferation in NT1.

Humans↗
PubMed2026

Dynamic transcriptomic landscape from bulk RNA-seq reveals critical mmu-miR-181a-5p/hif1a and mmu-miR-101a-3p/col1a1 modules for deep second-degree burn wound healing.

Burn injuries constitute a significant global health challenge, with deep partial-thickness burns (deep second-degree) posing particular clinical concerns due to prolonged healing and high scarring risks stemming from reticular dermis damage. Current therapeutic strategies remain largely empirical, reflecting limited understanding of stage-specific regulatory mechanisms. This study systematically investigated the molecular basis of deep partial-thickness burn repair by establishing murine models and performing RNA-seq analysis across healing phases (0, 3, 7, 14 days post-burn, dpb). Integrated bioinformatics revealed pivotal ceRNA and PPI networks, identifying hif1a (hypoxia-responsive immunomodulator) and col1a1 (ECM remodeling hub) as nodal regulators. Mechanistically, mmu-miR-101a-3p and mmu-miR-181a-5p were validated as post-transcriptional repressors of col1a1 and hif1a, respectively. Our work pioneers the discovery of the mmu-miR-181a-5p/hif1a and mmu-miR-101a-3p/col1a1 axes as master regulators of burn repair, offering novel therapeutic targets. The multi-omics dataset and molecular networks established herein provide a foundational resource for wound healing research.

MicroRNAs↗
PubMed2026

Multiancestry genome-wide association and multiomics analyses elucidate spatiocellular features of multiple sclerosis genetics.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by demyelination disseminated in space and time. Here we performed a genome-wide association study (GWAS) using 688 MS cases and 205,199 controls from the Japanese population and identified significant associations in the major histocompatibility complex region and a population-specific risk variant in 11q24. Through cross-population GWAS meta-analyses using a total of 29,374 cases and 1,843,563 controls from 4 ancestral populations, we identified 22 novel susceptibility loci. Integration of GWAS and single-cell and single-nucleus RNA sequencing of peripheral blood mononuclear cells and subcortical lesions from patients with MS revealed enrichment of genetic risk factors for MS in CD4+ T helper cell lineage and regulatory T cells, as well as in endothelial cells. Furthermore, spatial transcriptomics of subcortical lesions demonstrated spatial and temporal heterogeneity in associations with MS genetic risk. Our study demonstrates the value of investigation of spatiocellular features of disease genetics across diverse populations and omics modalities.

Humans↗
PubMed2025

Exon inclusion signatures enable accurate estimation of splicing factor activity.

Splicing factors control exon inclusion in messenger RNAs, shaping transcriptome and proteome diversity. Their catalytic activity is regulated by multiple layers, making single-omic measurements on their own fall short in identifying which splicing factors underlie a phenotype. Here, we posit that splicing factor activity can be estimated from changes in exon inclusion. To test this hypothesis, we benchmarked methods for constructing splicing factor→exon networks and estimating splicing factor activity. We found that combining RNA-seq perturbation-based networks with VIPER (Virtual Inference of Protein Activity by Enriched Regulon analysis) accurately captures splicing factor activation as modulated by multiple regulatory layers. This approach integrates splicing factor regulation into a single score derived solely from exon inclusion signatures, allowing functional interpretation of heterogeneous conditions. As a proof of concept, we identify recurrent cancer splicing programs, revealing oncogenic- and tumor suppressor-like splicing factors missed by conventional methods. These programs correlate with patient survival and key cancer hallmarks: initiation, proliferation, and immune evasion. Altogether, we show splicing factor activity can be accurately estimated from exon inclusion changes, enabling comprehensive analyses of splicing regulation with minimal data requirements.

VIPER↗
PubMed2025

Multi-omics analyses provide insights into the molecular basis for salt tolerance of Phyla nodiflora.

The perennial herbaceous plant, Phyla nodiflora (Verbenaceae), which possesses natural resistance to multiple abiotic stresses, is widely used as a pioneer species in island ecological restoration. Due to the lack of information about its genome, the mechanism underlying its tolerance to environmental stresses, such as salinity, is almost entirely unknown. Here, we report on the high-quality genome of P. nodiflora that is 403.07 Mb in size, and which was assembled and anchored onto 18 pseudo-chromosomes. Genomic synteny revealed that P. nodiflora underwent two whole genome duplication events, which promoted the expansion of genes related to environmental adaptation and the biosynthesis of secondary metabolites. An integrated genomic and transcriptomic analysis suggested that salt stress tolerance in P. nodiflora is associated with the expansion and activated expression of genes related to abscisic acid (ABA) homeostasis and signaling. The expansion of ZEP family genes may contribute to the consistent increase in ABA levels under salt stress. Lysine acetylomic analysis revealed that exposure to salt led to widespread protein deacetylation, with these proteins primarily involved in signal transduction, carbohydrate transport and metabolism, and transcription regulation. Deacetylation of glutathione S-transferase increased enzymatic activities in response to salt-induced oxidative stress. Collectively, the genomic, transcriptomic, and lysine acetylomic analyses provide profound insight into the molecular basis of the adaptation of P. nodiflora to salt stress, and will be helpful to engineer salt-tolerant plants for ecological restoration.

Salt Tolerance↗
PubMed2026

Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.

Cancer remains a major global health burden, with approximately 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022. While advances in radiological imaging, molecular profiling, and clinical data have enhanced the interpretation of disease progression, the availability of multiple such modalities still does not meet the needs of a large patient population. This narrative review focuses on the role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology. Multimodal frameworks such as Pathomic Fusion illustrate how complementary histopathological and genomic information can be integrated for cancer diagnosis and prognostic modeling. Multimodal models have demonstrated potential in virtual biopsy, cancer screening, prognostic prediction, radiotherapy planning, intraoperative guidance, and clinical-trial design using digital twins and synthetic control arms. The major limitations of incorporating multimodal artificial intelligence and machine learning in oncology include data heterogeneity, demographic or institutional biases, and reproducibility challenges that hinder translation. Accordingly, appropriate data-governance strategies, fairness audits, and privacy-preserving approaches such as federated learning should be considered where appropriate. Future progress will depend on the development of standardized benchmarking datasets, robust external validation, seamless integration with electronic health records and picture archiving and communication systems, and the implementation of explainable, secure, and clinically validated multimodal artificial intelligence frameworks that support precision oncology in routine clinical practice.

deep learning↗
PubMed2006

HiRes--a tool for comprehensive assessment and interpretation of metabolomic data.

UNLABELLED: The increasing role of metabolomics in system biology is driving the development of tools for comprehensive analysis of high-resolution NMR spectral datasets. This task is quite challenging since unlike the datasets resulting from other 'omics', a substantial preprocessing of the data is needed to allow successful identification of spectral patterns associated with relevant biological variability. HiRes is a unique stand-alone software tool that combines standard NMR spectral processing functionalities with techniques for multi-spectral dataset analysis, such as principal component analysis and non-negative matrix factorization. In addition, HiRes contains extensive abilities for data cleansing, such as baseline correction, solvent peak suppression, removal of frequency shifts owing to experimental conditions as well as auxiliary information management. Integration of these components together with multivariate analytical procedures makes HiRes very capable of addressing the challenges for assessment and interpretation of large metabolomic datasets, greatly simplifying this otherwise lengthy and difficult process and assuring optimal information retrieval. AVAILABILITY: HiRes is freely available for research purposes at http://hatch.cpmc.columbia.edu/highresmrs.html

Algorithms↗
PubMed2025

Multi-ancestry genome-wide and transcriptome-wide association analyses identified new risk loci and genes for inflammatory bowel disease.

To advance genetic understanding of inflammatory bowel disease (IBD), we conducted genome-wide association meta-analyses of 63,415 IBD cases of European and East Asian descendants and identified 90 previously unknown risk loci. Integrating multi-ancestry transcriptome-wide association studies (TWAS), cell type-specific TWAS, alternative splicing (AS-WAS), and alternative polyadenylation (APA-WAS) analyses using RNA-seq data from normal colon tissues of 707 European and 364 East Asian individuals, we uncovered 506 high-confidence IBD risk genes, including 384 not previously reported. These genes converge on immune regulation, microbial interaction, and other pathways central to IBD pathogenesis, with over half showing transcriptional dysregulation supported by single-cell and spatial omics analyses. Notably, 46 risk genes are targeted by 225 drugs that have been approved or in Phase II/III trials, including sulfasalazine already used in IBD therapy. Our study findings deepen the understanding of IBD genetics and support the development of precision medicine for its prevention and treatment.

GWAS↗
PubMed2026

Secondary metabolite profiling of rare Micromonospora spp. from cold desert of NW Himalayas via multi-omics analysis.

INTRODUCTION: The genus Micromonospora is a prolific producer of specialized metabolites with pharmacological and agronomic relevance. Natural products derived from the genus Micromonospora have a distinctive chemical diversity and enormous therapeutic potential, thus represent a potential source for drugs and drug leads. OBJECTIVE: To explore the biosynthetic potential of four Micromonospora strains isolated from cold desert of NW Himalayas through genome mining and to correlate predicted biosynthetic gene clusters with chemical features detected by untargeted LC-HRMS metabolomics. METHOD: High-quality genomes were annotated for BGCs and matched against untargeted LC-HRMS features (peak picking, alignment, and annotation to chemical classes). Each isolate was grown in triplicate, and fermented broth was pooled for further metabolomic studies. RESULTS: By integrating genomic and metabolomic approaches, specialized biosynthetic gene clusters and strain-based putative metabolite classes were identified. LRS1 showed elevated xanthines (RiPP/siderophore), LRS3 had phenolic glycosides (hybrid PKS/NRPS), LRS4 showed 70-fold hydroxycinnamate enrichment (Type II PKS), and LRS5 displayed p-benzoquinone enrichment (Type III PKS). The metabolite profile of each strain aligned with its predicted biosynthetic gene cluster composition. CONCLUSION: Under a single growth regime, each Micromonospora strain exhibits a distinct metabolomic profile. This metabologenomics workflow can be further explored to isolate specialized metabolites with potential therapeutic and agricultural value.

Micromonospora↗
PubMed2026

Potential mitochondria-associated pathogenic genes in sepsis: a multi-omics Mendelian randomization study.

BACKGROUND: Mitochondrial dysfunction has been implicated in the pathophysiology of sepsis. However, human genetic evidence linking mitochondria-related genes to sepsis susceptibility remains limited. This study aimed to identify mitochondria-related genes associated with sepsis risk using a multi-omics Mendelian randomization framework. METHODS: Summary-data-based Mendelian randomization (SMR) was applied using sepsis genome-wide association study (GWAS) summary statistics from the UK Biobank and FinnGen databases. Expression, methylation, single-cell, and protein quantitative trait loci (QTLs) were used as genetic instruments. Colocalization analyses were conducted to evaluate whether SMR associations were driven by shared genetic variants. Expression of prioritized candidate genes was further examined in clinical septic samples, and correlations with disease severity (SOFA scores) were assessed. RESULTS: SMR analysis prioritized 13 mitochondria-related genes associated with sepsis risk. Immune cell-specific eQTL analysis suggested that genetically predicted SURF1 expression in memory B cells and naïve T cells was associated with sepsis risk. Differential expression of 12 candidate genes was confirmed in septic patients by qPCR, and PPOX expression showed a negative correlation with SOFA scores. Integration of mQTL and eQTL data supported a regulatory relationship between methylation at cg06661924 and AK4 expression. Increased genetically predicted AK4 expression was associated with higher sepsis risk (OR = 1.21, 95% CI 1.02-1.42). Protein-level analysis identified DUT as a potential sepsis-associated candidate, with consistent evidence across streptococcal and pneumococcal septicemia subtypes. Subtype analyses also suggested heterogeneous genetic signals across different sepsis subtypes. CONCLUSION: This study prioritized several mitochondria-related genes associated with sepsis susceptibility based on human genetic evidence. These findings provide candidate targets for further mechanistic and translational investigation.

Humans↗
PubMed2005

Xylella and Xanthomonas Mobil'omics.

The gamma-proteobacterium Xanthomonadales groups two closely related genera of plant pathogens, Xanthomonas and Xylella. Whole genome sequencing and comparative analyses disclosed a high degree of identity and co-linearity of the chromosome backbone between species and strains. Differences observed are usually clustered into genomic islands, most of which are delimited by genetic mobile elements. Focus is given in this paper to describe which groups of mobile elements are found and what is the relative contribution of these elements to Xanthomonas and Xylella genomes. Insertion sequence (IS) elements have invaded the Xanthomonas genome several times, whereas Xylella is rich in phage-related regions. Also, different plasmids are found inhabiting the bacterial cells studied here. Altogether, these results suggest that the integrative elements such as phages and transposable elements as well as the episomal plasmids are important drivers of the genome evolution of this important group of plant pathogens.

Amino Acid Sequence↗
PubMed2025

Insights into dill (Anethum graveolens) flavor formation via integrative analysis of chromosomal-scale genome, metabolome and transcriptome.

INTRODUCTION: Dill (Anethum graveolens) is a significant medicinal herb belonging to the Apiaceae family. Owing to its high levels of volatile organic compounds (VOCs), dill is commonly utilized for essential oil extraction and medicine purpose. However, the biosynthesis of the crucial VOC in dill remains obscure. OBJECTIVES: Identify the key VOCs related to the flavor formation in dill and dissect the regulatory mechanism of their synthesis. METHODS: The dill chromosomal-level genome was constructed by PacBio HiFi, Hi-C, and BGISEQ second generation sequencing and assembly. The VOCs in dill leaves were identified through GC-MS. The potential mechanism involved in regulating the VOC accumulation in dill flavor formation was analyzed by multi-omics analysis. RESULTS: A 1.17 Gb chromosome-scale genome of dill with a contig N50 of 10.78 Mb was constructed. A total of 46,538 genes were annotated across 11 assembled chromosomes. Comparative genomics analysis suggested that transposable element insertions, especially LTR-Gypsy, have contributed to the evolution and expansion of the dill genome. The flavor formation of dill was mainly attributed to terpenoids, especially α-phellandrene, β-ocimene, and o-cymene. The contribution of expansion and replication of terpenoid synthesis pathway genes, especially terpene synthase (TPS), to the abundant terpenoid production of dill was identified. Differential gene expression patterns observed at various developmental stages and tissues provided key candidate genes for the regulation of terpenoid synthesis, as well as transcription factors. The different accumulation of esters and aromatics also affected the flavor formation of dill. The key genes implicated in the synthesis of anethole, namely AIS and AMT were further identified. CONCLUSION: This study constructed the chromosome level genome and identified the main VOCs and related key genes in flavor formation of dill, shedding lights on our understanding of terpenoid biosynthesis but also offered guidance for future genetic research on molecular breeding in Anethum graveolens.

Transcriptome↗
PubMed2026

Convergent mitochondrial impairment and apoptosis driven by simultaneous down-regulation of multiple genes at 11p11.2 in Alzheimer's disease.

Genome-wide association studies (GWAS) and multi-omics analyses have identified numerous risk loci and thousands of potential causal genes associated with Alzheimer's disease (AD). However, the synergistic pathogenic contributions of multiple low-risk causal genes within a single locus remain poorly understood. Polygenic synergism at the 11p11.2 locus was systematically examined in AD pathogenesis. Three causal genes (MTCH2, NDUFS3, and PSMC3) exhibited coordinated down-regulation in both AD patients and AD mouse models. Individual knockdown in cultured cells altered mitochondrial function and disrupted AD-associated pathways, as revealed by transcriptomic profiling. Integrated RNA-seq analysis and experimental validation demonstrated that the concurrent down-regulation of all three genes synergistically enhanced mitochondrial reactive oxygen species (ROS) generation and activated the caspase-7-mediated apoptotic pathway. Notably, pharmacological caspase inhibition with Q-VD-OPh attenuated neuronal apoptosis, ameliorated memory deficits, and reduced Aβ plaque deposition in APP/PS1 mice. Simultaneous down-regulation of multiple genes at the 11p11.2 locus contributed to mitochondrial dysfunction and apoptosis in AD, highlighting polygenic synergism as a key pathogenic mechanism.

Animals↗
PubMed2003

Interplay of transcriptomics and proteomics.

Despite the obvious attractions of parallel profiling of transcripts and proteins on a global 'omic' scale, there are practical and biological differences involved in their application. Transcriptomics is now a robust, high-throughput, cost-effective technology capable of simultaneously quantifying tens of thousands of defined mRNA species in a miniaturized, automated format. Conversely, proteomic analysis is currently much more limited in breadth and depth of coverage owing to variations in protein abundance, hydrophobicity, stability, size and charge. Nevertheless, transcriptomic and proteomic data can be compared and contrasted provided the studies are carefully designed and interpreted. Differential splicing, post-translational modifications and data integration are among some of the future challenges to tackle.

Biotechnology↗
PubMedDate not supplied

Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis.

In this review, metabonomics, a combination of data-rich analytical chemical measurements and chemometrics for profiling metabolism in complex systems, is described and its applications are reviewed. Metabonomics is typically carried out using biofluids or tissue samples. The relevance of the technique is reviewed in relation to other '-omics', and it is shown how the methods can be applied to physiological evaluation, drug safety assessment, characterization of genetically modified animal models of disease, diagnosis of human disease, and drug therapy monitoring. The different types of analytical data, mainly from nuclear magnetic resonance spectroscopy and mass spectrometry, are summarized. The outputs from a metabonomics study allow sample classification, for example according to phenotype, drug safety or disease diagnosis, and interpretation of the reasons for classification yields information on combination biomarkers of effect. Transcriptomic and metabonomic data is currently being further integrated into a holistic understanding of systems biology. An assessment of the possible future role and impact of metabonomics is presented.

Age Factors↗
PubMed2006

Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice.

A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.

Animals↗
PubMed2026

Biological Foundation Models for Complex Disease Research and Clinical Translation.

Complex diseases, including cancer, rare genetic disorders, neurodevelopmental and psychiatric conditions, and neurodegenerative diseases, arise from interactions among genetic variation, gene regulation, and cellular states that are difficult to capture using a single data type or biological scale. Biological foundation models address this challenge by treating nucleotides and genes as tokens and learning representations that can be transferred to downstream biomedical and clinical tasks. In this review, we examine two major model classes, genomic sequence foundation models and cell foundation models, and compare their tokenization strategies, model architectures, pretraining objectives, and adaptation methods. We summarize their emerging applications in regulatory variant interpretation, disease-associated cell-state analysis, drug-response prediction, and therapeutic target discovery across complex diseases. We distinguish applications supported by experimental or retrospective validation from those that remain primarily computational or conceptual. We further discuss key challenges to clinical translation, including multimodal data integration, model interpretability, benchmarking, patient-specific prediction, and privacy protection. We highlight future opportunities to integrate biological foundation models with emerging frameworks of medical digital twins, agentic AI, and federated learning. By linking model design to translational goals, this review provides a practical framework for evaluating biological foundation models and their readiness for complex disease research and clinical use.

biological foundation model↗