Data standards for 'omic' science.
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Databases, Nucleic Acid↗
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Results for “omics”
Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.
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In silico biology through "omics".
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Computer Simulation↗
'Omic diagnostics trip up on way to clinic.
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Gene Expression Profiling↗
New 'omics tools for fission yeast.
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Gene Library↗
Beyond in silico prediction: multi-omics to identify a pathogenic deep intronic HNRNPK variant in Au-Kline syndrome.
Pathogenic variants in HNRNPK are associated with autosomal dominant Au-Kline syndrome (AKS, Au-Kline-Okamoto syndrome, OMIM #616580). This syndrome is characterized by developmental delay and intellectual disability, hypotonia, and distinctive facial features. Despite the use of whole-genome sequencing (WGS) as a powerful diagnostic tool, we nearly dismissed a novel intronic variant (NM_031263.4(HNRNPK):c.214-55 T > A) affecting HNRNPK splicing and function. Although commonly used bioinformatic splice prediction tools, including SpliceAI and PDIVAS, yielded inconclusive results, Face2Gene analysis indicated a high phenotypic similarity to AKS. Characteristic facial features described by Choufani et al. [1] supported the clinical diagnosis of AKS. Subsequent functional studies demonstrated aberrant splicing with intron retention, and DNA methylation profiling revealed a positive HNRNPK-specific episignature. These insights and the de novo status support an evaluation as likely pathogenic. This case report supports the relevance of facial analysis and comprehensive variant validation strategies, particularly for deep intronic variants with ambiguous in silico splicing predictions.
Journal Article↗
Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization.
Alzheimer's disease (AD) is a multifactorial neurodegenerative condition in which accumulating genetic and molecular evidence implicates dysregulation of peripheral immune processes in disease pathogenesis. Nevertheless, the contribution of distinct peripheral immune cell subsets and associated gene regulatory landscapes to AD risk remains incompletely defined. To address this gap, we integrated single-cell expression quantitative trait loci (sc‑eQTL) data from the OneK1K cohort with AD GWAS summary statistics. We systematically interrogated immune cell-specific genes for their contributions to AD risk by integrating genetic causal inference with Bayesian colocalization analyses, and identified 24 eGenes that passed both the MR significance threshold (P < 0.05) and the criterion for strong shared genetic signals (PP.H4 > 0.8). Notable candidates included GATS, HLA-DOB, HLA-DQA1, PM20D1, and others, with each gene demonstrating a cell-type-specific association restricted to its corresponding immune cell type, such as monocytes, CD8 + T cells, or B cells. Independent peripheral blood single-cell transcriptomic data further supported disease-associated shifts in cell-type-specific expression patterns in AD. Phenome-wide association studies (PheWAS) indicated limited associations with off-target traits, indicating a favorable safety profile for therapeutic intervention, with the exceptions of B4GALNT3, PM20D1, and CNN2. Integration of immune gene targets with pharmacological databases yielded three candidate compound, including NSC321521 (targeting HLA-DQA1), phenoxybenzamine (targeting GSTP1), and rimexolone (targeting BIN1). Among these compounds, Predicted blood-brain barrier permeability was observed only for phenoxybenzamine and rimexolone, with docking studies indicating stable interactions, such as those between NSC321521 and HLA-DQA1, phenoxybenzamine and GSTP1, and rimexolone and BIN1. This integrative approach highlights key immune‑cell‑specific genes involved in AD and proposes repurposable drugs with central nervous system potential, paving the way for more targeted immunomodulatory strategies in AD.
Humans↗
Integrative omics of the genetic basis for wheat WUE and drought resilience reveal the function of TaMYB7-A1.
Improving wheat drought resilience and water use efficiency (WUE) is critical for sustaining productivity under increasing water scarcity. Here, we integrate genome-wide association study (GWAS), expression quantitative trait locus (eQTL) mapping, population-transcriptome analysis, and summary-data-based mendelian randomization (SMR), followed by functional validation using indexed EMS mutants and transgenic lines, to systematically identify key WUE regulators. GWAS across water conditions in 228 accessions identifies 73 quantitative trait loci (QTLs) for WUE-traits. Transcriptome profiling of 110 diverse accessions reveals 28 drought-responsive modules. eQTL mapping uncovers 146,966 regulatory variants, including condition-specific hotspots associated with key drought-related pathways. Integrative analysis underscores 85 high-confidence candidate genes, notably TaMYB7-A1. Overexpression of TaMYB7-A1 enhances photosynthesis, WUE, root development, and grain yield under drought condition by activating TaPIP2;2-B1 (water transport), TaRD20-D1 (stomatal regulation), and TaABCB4-B1 (root growth), reflecting reduced water loss and improved physiological resilience. Our study presents a comprehensive regulatory map and robust targets for wheat drought adaptation and resilient cultivar breeding.
Triticum↗
OmicsPred as a centralized resource for genetic prediction of multi-omic traits.
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Journal Article↗
Sex-specific biological aging clocks across organs and omics.
Sex differentially shapes aging, neurodevelopment and neurodegenerative diseases such as Alzheimer's disease (AD). However, most biological aging clocks (artificial intelligence-predicted age minus chronological age) were trained on sex-pooled samples and implicitly assume sex invariance.Here we developed 38 sex-specific biological aging clocks across 15 organ systems. We first demonstrate the importance of sex-stratified training for constructing sex-specific healthy normative references and then reveal marked divergence between female and male clocks. Key genetic parameters and Mendelian randomization results indicate that organ-specific aging liability and its relationships to cardiometabolic, endocrine and mental traits are configured differently in females and males. Proteomic analyses identify distinct, organ-resolved synaptic, immune, vascular and metabolic networks that differentially track female and male biological aging. In longitudinal survival analyses, sex-specific clocks predict whole-body systemic diseases and all-cause mortality in a sex-dependent and organ-dependent manner. Further analyses reveal sex-dependent associations between the brain aging clock and cognitive decline trajectory during a preclinical AD clinical trial. Sex-stratified clocks may offer distinct value by defining biological age against sex-appropriate normative references and revealing sex-dependent genetic, molecular and clinical signatures that pooled models may obscure. Meanwhile, sex-pooled and sex-interaction approaches remain valuable, as human aging and disease also share fundamental biological similarities between females and males. Together, these findings reveal sex-specific biological aging signatures in aging, AD and systemic health, highlighting the need for explicitly sex-stratified modeling approaches.
Journal Article↗
Single-cell multi-omic detection of DNA methylation and histone modifications reconstructs the dynamics of epigenomic maintenance.
DNA methylation and histone modifications encode epigenetic information. Recently, major progress was made to measure either mark at a single-cell resolution; however, a method for simultaneous detection is lacking, preventing study of their interactions. Here, to bridge this gap, we developed scEpi2-seq. Our technique provides a readout of histone modifications and DNA methylation at the single-cell and single-molecule level. Application in a cell line with the FUCCI cell cycle reporter system reveals how DNA methylation maintenance is influenced by the local chromatin context. In addition, profiling of H3K27me3 and DNA methylation in the mouse intestine yields insights into epigenetic interactions during cell type specification. Differentially methylated regions also demonstrated independent cell-type regulation in addition to H3K27me3 regulation, which reinforces that CpG methylation acts as an additional layer of control in facultative heterochromatin.
DNA Methylation↗
Pathogenicity of Cadophora luteo-olivacea on Quercus robur and multi-omics characterization of antagonism by Trichoderma atroviride.
Pedunculate oak (Quercus robur L.) is a foundation tree species in European forests and reforestation programs, but nursery propagated seedlings can harbor cryptic trunk diseases pathogens. Cadophora luteo-olivacea, known from grapevine trunk diseases, has been detected in oak nurseries, yet its pathogenicity on oak and interactions with antagonistic fungi remain unclear. We fulfilled Koch's postulates for C. luteo-olivacea isolate CZ_395 on Q. robur seedlings under experimental inoculation conditions and quantified growth reduction of C. luteo-olivacea by Trichoderma atroviride isolate CZ_180 in dual culture. Proteomic and metabolomic profiling of the contact zone was performed at two post contact sampling points, 4 and 8 dpi, to identify candidate molecular signatures associated with the interaction. Inoculated seedlings developed extensive stem lesions (mean 11.9 cm), whereas controls showed minimal wound response (mean 0.9 cm; p < 0.001). In dual culture, T. atroviride reduced the visible colony development and radial growth of C. luteo-olivacea under the tested in vitro conditions. Contact zone proteomics revealed 257 differentially abundant proteins at 8 days, including cell wall targeting hydrolases, secreted proteases, oxidoreductases (44 upregulated), and transporters. Metabolomics detected contact specific changes in amino acids, central carbon intermediates, and lipid-associated features, including reduced ergosterol. This study demonstrates that C. luteo-olivacea can induce necrotic lesions in Q. robur under experimental inoculation conditions and identifies proteomic and metabolomic signatures associated with the interaction between T. atroviride and C. luteo-olivacea, providing a basis for nursery risk assessment and future evaluation of biocontrol potential.
Quercus↗
A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.
The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.
Epidermal Growth Factor↗
Multi-omics insights into the molecular signature and prognosis of hypopharyngeal squamous cell carcinoma.
Approximately two-thirds of hypopharyngeal squamous cell carcinoma (HPSCC) cases are diagnosed at advanced stages, with the worst prognosis among head and neck squamous cell carcinomas (HNSCCs). Identifying biomarkers for high-risk patients requiring aggressive treatment is crucial. We present mutational, transcriptomic, and proteomic studies of 103 Chinese HPSCC patients and observe a higher prevalence and poorer prognosis in males. Estrogen response pathways are up-regulated, and proteins phosphorylated by protein kinase C (PKC) and cyclin-dependent kinases (CDKs) are aberrantly regulated in HPSCC. We identify aberrant copy number regions including SOX2(3q26.33), FGFR(8p11.23), CCND1(11q13.3), CDKN2A/2B(9p21.3), and MYC(8q24.21). Human papillomavirus (HPV) status combined with highly mutated genes, such as SYNE1 in HPV(-) and MUC4 in HPV(+) patients, were assessed as prognosis markers. A predictive model involving clinical factors and expression of six genes was established and cross-site validated. These findings open new opportunities for stratifying high-risk patients and molecular targets for personalized therapeutic strategies.
Humans↗
A nucleolar stress gene signature enables quantitative scoring across multi-omics contexts.
The nucleolus is essential for ribosome biogenesis and cellular homeostasis, and its dysfunction can induce nucleolar stress, a process implicated in cancer and other diseases. However, nucleolar stress is commonly inferred from morphological changes or a limited set of functional assays, and quantitative approaches based on gene expression profiles remain lacking. Here, we integrate literature curation with multi-dataset screening to define a nucleolar stress gene signature and develop a nucleolar stress score (NuS) applicable to bulk transcriptomics, single-cell transcriptomics, proteomics, and spatial transcriptomics. Using this framework, we show in colorectal cancer models that oxaliplatin induces nucleolar stress, suppresses nascent rRNA synthesis, and activates p53 signaling, whereas these responses are attenuated in oxaliplatin-resistant cells. Combined with a ribosome biogenesis activity score (RiboSis), NuS captures related but distinct dimensions of nucleolar function and stratifies tumors into functional states associated with clinical outcomes. NuS-based analysis of perturbational transcriptomes further prioritizes compounds with putative nucleolar stress-inducing activity. Collectively, this study provides a quantitative framework for evaluating nucleolar stress and illustrates its applications in disease stratification and drug mechanism discovery.
Cell Nucleolus↗
Nutrigenomics and nutrigenetics: the 'omics' revolution in nutritional science.
The execution of the Human Genome Project has brought forth a wealth of information about the structure of the genome, which can now be used to study how the interplay between our genes and factors from the environment such as nutrition relate to a state of health or disease. To enable such studies, novel technologies have been designed in particular to monitor the activity of multiple genes simultaneously at the level of the RNA by transcriptomics, or the level of the proteins by proteomics. In addition, genome information has boosted approaches to study the role of genetic variation to explain individual differences in responses to nutrition, underlying in part the susceptibility for nutrition-related disorders. These new areas of science referred to as 'nutrigenomics' and 'nutrigenetics' respectively, will increase our fundamental knowledge of the interaction between life processes and our diet or specific components thereof, which may in time lead to the development of novel functional foods to improve the health status of the general population, and to the personalized diet to prevent the onset of nutrition-related disorders in genetically predisposed individuals.
Genetics↗
[To apprehend the complexity of living at the era of the << Omics >>].
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Biochemistry↗
OMICS and brain tumour biomarkers.
Currently, brain tumours are diagnosed by surgical biopsy and light microscopic examination of tissue, with immunohistochemistry in difficult cases. We review research in the field of brain tumour diagnosis and discuss several new approaches. In future, tumour type, optimal treatment, and prognosis could be obtained by studying the gene (genomics), protein (proteomics) or metabolite (metabolomics) content of tumour cells. These techniques generate complex data, analysed using techniques such as pattern recognition software to identify biomarker signatures of different tumours. Compared with individual biomarkers, biomarker signatures appear to increase diagnostic accuracy and may produce an improved brain tumour classification system.
Biomarkers, Tumor↗
Combination of 'omics' data to investigate the mechanism(s) of hydrazine-induced hepatotoxicity in rats and to identify potential biomarkers.
To gain novel insight into the molecular mechanisms underlying hydrazine-induced hepatotoxicity, mRNAs, proteins and endogenous metabolites were identified that were altered in rats treated with hydrazine compared with untreated controls. These changes were resolved in a combined genomics, proteomics and metabonomics study. Sprague-Dawley rats were assigned to three treatment groups with 10 animals per group and given a single oral dose of vehicle, 30 or 90 mg kg(-1) hydrazine, respectively. RNA was extracted from rat liver 48 h post-dosing and transcribed into cDNA. The abundance of mRNA was investigated on cDNA microarrays containing 699 rat-specific genes involved in toxic responses. In addition, proteins from rat liver samples (48 and 120/168 h post-dosing) were resolved by two-dimensional differential gel electrophoresis and proteins with changed expression levels after hydrazine treatment were identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry peptide mass fingerprinting. To elucidate how regulation was reflected in biochemical pathways, endogenous metabolites were measured in serum samples collected 48 h post-dosing by 600-MHz 1H-NMR. In summary, a single dose of hydrazine caused gene, protein and metabolite changes, which can be related to glucose metabolism, lipid metabolism and oxidative stress. These findings support known effects of hydrazine toxicity and provide potential new biomarkers of hydrazine-induced toxicity.
Animals↗