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PubMed2026

Network-based integration of metabolomics data from large-scale repositories.

INTRODUCTION: Public metabolomics data repositories such as MetaboLights and Metabolomics Workbench host rapidly growing volumes of raw data, processed results, and metadata. As data deposition becomes a prerequisite for funding and publication, there is an increasing need for tools that enable integration and joint reanalysis of datasets across studies to maximise reuse and reproducibility. OBJECTIVES: This study aims to enable large-scale integrative meta-analysis of public metabolomics data, exploiting harmonised metabolite annotations to identify robust multi-study metabolite and pathway signatures and to provide global visual overviews of repository content. METHODS: We developed a network-based integration framework operating at both the study (dataset) level and the metabolite or pathway level. Metabolite-level meta-networks integrate studies with shared biological context using co-occurrences of differential metabolites represented as bipartite graphs. Study-level networks compare observed metabolites for overall repository exploration. Networks can be explored interactively using a dedicated Python Dash app available at https://github.com/EloisaRL/Metabolomic-data-analysis-app/tree/main . RESULTS: As an example, the approach was applied to six COVID-19 plasma datasets from MetaboLights generated using LC-MS and NMR. Ten metabolites were identified as differential in at least three studies, including consistently up-regulated pyroglutamic acid, in agreement with the literature. Pathway-level networks provided an overview of shared biological processes across studies. A global network of 1,181 studies in Metabolomics Workbench demonstrated clustering by assay coverage and associated metadata, as expected. CONCLUSION: Network-based integration of harmonised metabolomics data enables robust cross-study analyses and highlights the critical importance of standardised annotation pipelines. Such approaches enhance the reuse, reproducibility, and impact of public metabolomics datasets, accelerating biological discovery.

Metabolomics↗

PubMed2026

Integrative multi-omics analyses suggest a candidate microbial metabolite-associated host gene network in ulcerative colitis.

Ulcerative colitis (UC) is associated with gut microbial dysbiosis, but the host molecular alterations potentially linked to microbially derived metabolites remain incompletely understood. We integrated Mendelian randomization (MR), microbial metabolite annotation, computational target prediction, colonic transcriptomics, network analysis, and machine learning. MiBioGen microbiome GWAS data were used as exposures and FinnGen Release 12 ULCERENTER as the outcome. Metabolites linked to MR-prioritized taxa were retrieved from GutMGene, and human targets were predicted using SwissTargetPrediction and SEA. UC-related genes were defined by integrating differential expression analysis and WGCNA and then intersected with predicted metabolite targets. MR prioritized one family and eight genera showing nominal genetically supported associations with UC, but none remained significant after Benjamini-Hochberg FDR correction. Three prioritized genera were linked to 15 microbe-metabolite records, corresponding to 13 unique metabolites; nine were retained for target prediction, yielding 277 unique predicted human targets. Transcriptomic analysis identified 1,530 DEGs and a 312-gene MEgrey60 module, with 273 overlapping genes, producing 1,569 unique UC-related genes. Their intersection with the 277 predicted targets yielded 47 candidate genes. Enrichment analyses highlighted mainly metabolic and lipid-related processes. Random Forest showed the highest mean AUC across the two independent external benchmarking cohorts, and SHAP prioritized EPHX1, HSD17B2, IGFBP5, and MMP10. IBDome analysis showed inflammation-associated expression differences in these genes. This study provides a genomics-informed, hypothesis-generating framework that prioritizes candidate microbe-metabolite-host relationships in UC for future experimental validation.

Humans↗

PubMed2026

Comparative Genome-Wide Association Studies of Metabolites and Grain-Related Traits in Common Wheat.

The metabolome is highly diverse and the closest layer to phenotype; therefore, it is commonly regarded as a bridge between the genome and phenome in plants. Here, we performed large-scale metabolome analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and 33 grain-related traits in a diverse panel of natural accessions and a recombinant inbred line (RIL) population. We identified a new network of 2286 associations between 947 metabolites and 33 grain-related traits. Systematic integration of metabolic genome-wide association study (mGWAS) and metabolic quantitative trait locus (mQTL) analyses identified 33 566 significant single-nucleotide polymorphisms (SNPs) and 3128 mQTL. Thirteen annotated metabolites co-localized within a physical interval on 7A. Integration of metabolite-based and phenotype-based GWAS and QTL revealed an overlapped region for gibberellin A4 (GA4) content and grain roundness on 4A. Phenotyping of an ethyl methanesulfonate (EMS)-induced mutant confirmed the role of TaSDR in regulating GA4 content and grain morphology. These findings provide novel insights into the metabolic pathways influencing key grain-related traits and advance our understanding of the complex molecular mechanisms regulating grain metabolites and phenotypes in wheat. The identified metabolic markers and candidate genes provide valuable targets for molecular breeding programs aimed at improving wheat yield and quality.

QTL↗

PubMed2026

LC-IMS-MS profiling of avocado acetogenins reveals tissue-dependent distribution and cultivar-specific metabolic signatures.

This study presents a comprehensive characterisation of acetogenin-related metabolites in avocado using an LC-IMS-MS workflow. A total of 26 metabolites were semi-quantified across peel, pulp and seed tissues from three cultivars (Hass, Bacon and Fuerte). The integration of ion mobility spectrometry enabled the generation of the first experimental database of collision cross section (CCS) values for avocado acetogenins, improving confidence in metabolite annotation. Results revealed a pronounced tissue-dependent distribution, with seeds and pulp as the primary reservoir of several acetogenins, whereas the peel consistently exhibited lower concentrations. In contrast, acetogenin levels remained largely stable throughout ripening. Clear cultivar-dependent differences were observed, with Hass displaying a distinct metabolic profile compared to Bacon and Fuerte. Multivariate analysis confirmed these findings, showing tissue-dependent cultivar differentiation. This study provides new insights into avocado chemical diversity and highlights the potential of avocado by-products as consistent and promising sources of bioactive acetogenins.

Persea↗

PubMed2026

DeepMASS v.2: An enhanced deep learning platform for large-scale discovery and structural annotation of unknown plant metabolites.

Determining the structures of unknown metabolites remains a fundamental bottleneck in plant metabolomics, as the vast chemical diversity of plant secondary metabolites far exceeds the coverage of existing spectral libraries. Here, we present DeepMASS v.2, a substantially enhanced platform for annotating unknown metabolites from liquid chromatography-tandem mass spectrometry data, designed to address this challenge at scale. DeepMASS v.2 leverages a semantic spectral representation model trained on millions of spectra from GNPS, NIST, and in-house resources. By integrating Spec2Vec-based embeddings with HNSW (hierarchical navigable small world) graph retrieval and a unified chemical space defined by molecular fingerprints, DeepMASS v.2 identifies structurally related neighbors of unknown spectra and ranks candidate structures according to their proximity to the predicted structural neighborhoods within chemical space. Benchmarking against Critical Assessment of Small Molecule Identification datasets and a curated natural product collection demonstrated that DeepMASS v.2 outperforms state-of-the-art in silico annotation tools, including SIRIUS, CFM-ID, MetFrag, and MS-Finder. Importantly, DeepMASS v.2 maintains strong performance for metabolites absent from spectral libraries, highlighting its capacity to annotate genuinely unknown compounds. Application of DeepMASS v.2 to large-scale plant metabolomics datasets demonstrated its ability to expand accessible metabolome coverage. Implemented as an intuitive web platform, DeepMASS v.2 provides the community with a scalable, interpretable, and high-throughput solution for structural annotation, enabling more comprehensive characterization of plant chemical diversity and accelerating natural product discovery in molecular plant science. The DeepMASS v.2 web server is publicly available at http://deepmass.cn.

Metabolomics↗

PubMed2026

Bacterial polar metabolites modulate β-amyloid toxicity and cholinergic dysfunction in models of Alzheimer's disease.

Alzheimer's disease is characterized by progressive neurodegeneration driven by β-amyloid (Aβ) toxicity, oxidative stress, and cholinergic dysfunction. In this study, we investigated whether polar metabolites derived from a cultivable bacterial isolate could modulate Aβ-associated neurodegenerative phenotypes in complementary experimental models. A bioactivity-guided approach identified an aqueous fraction with high antioxidant capacity in DPPH, FRAP, and ORAC assays. In a transgenic Drosophila melanogaster model expressing human Aβ, treatment with this fraction significantly reduced amyloid accumulation and attenuated neurodegenerative histopathological alterations. In human SH-SY5Y neuronal cultures, the metabolites improved cell viability under therapeutic, but not preventive, conditions following exposure to aggregated Aβ. The aqueous fraction also exhibited significant inhibitory activity against acetylcholinesterase and butyrylcholinesterase. Whole-genome sequencing assigned the bioactive isolate to the genus Providencia, with comparative genomic analyses suggesting its placement within a distinct taxonomic lineage. Metabolomic profiling by LC-ESI-MS/MS revealed a diverse set of polar metabolites, including metabolites putatively annotated based on spectral matching, previously associated with neuroprotective and cholinesterase-modulating activities. Collectively, these findings demonstrate that bacterial polar metabolites can modulate key pathological features of Alzheimer's disease, supporting their relevance for mechanistic studies of Aβ toxicity and cholinergic dysfunction.

Animals↗

PubMed2006

Host-derived media used as a predictor for low abundant, in planta metabolite production from necrotrophic fungi.

AIMS: Penicillium ser. Corymbifera strains were assayed on a variety of media and from infected Allium cepa tissues to evaluate the stimulation and in planta prediction of low abundance metabolites. METHODS AND RESULTS: Stimulated production of corymbiferones and the corymbiferan lactones were observed for Penicillium albocoremium, Penicillium allii, Penicillium hirsutum, Penicillium hordei and Penicillium venetum strains cultured on tissue media. Target metabolites were sporadically detected from strains cultured on common laboratory media (CYA, MEA and YES). Up to a 376 times increase in corymbiferone and corymbiferan lactone production was observed when culture extracts from CYA and A. cepa agar were compared by high pressure liquid chromatography with ultraviolet and mass spectrometry (LC-UV-MS). The novel metabolite corymbiferone B was purified and structure elucidated from a P. allii/A. cepa tissue medium extract. In planta expression of low abundance, target metabolites were confirmed from infected A. cepa tissue extracts by LC-UV-MS. CONCLUSIONS: Secondary metabolite production was directly dependent and influenced by media conditions, resulting in the stimulated production of low abundance metabolites on host-derived media. SIGNIFICANCE AND IMPACT OF THE STUDY: The use of macerated host tissue media can be applied in vitro to predict in planta expression of low abundance metabolites and aid in metabolite origin annotation during in planta metabolomic investigations at the host/pathogen interface.

Allium↗

PubMed2026

hypeR-GEM: connecting metabolite signatures to enzyme-coding genes via genome-scale metabolic models.

MOTIVATION: Enrichment analysis is a cornerstone of "omics" data interpretation, enabling researchers to connect analysis results to biological processes and generate testable hypotheses. Enrichment analysis in metabolomics poses distinct challenges for interpretation and multi-omics integration due to the lack of well-defined and consistent connections to well-curated gene-centered biological knowledge repositories. To address these challenges, we developed hypeR-GEM, a methodology and associated R package that adapts gene set enrichment analysis to metabolomics. hypeR-GEM leverages genome-scale metabolic models (GEMs) to infer reaction-based links between metabolites and enzyme-coding genes, enabling the mapping of metabolite signatures to gene signatures and their subsequent annotation via gene set enrichment analysis. RESULTS: We validated hypeR-GEM using paired metabolomics-proteomics and metabolomics-transcriptomics datasets by assessing whether genes mapped from metabolites significantly overlapped with differentially expressed proteins or transcripts. We further evaluated whether pathways enriched via hypeR-GEM-mapped genes corresponded to those derived from paired proteomic or transcriptomic data. In most datasets analyzed, both the predicted enzyme-coding genes and the associated enriched pathways showed significant concordance with independently derived omics signatures, supporting the utility and robustness of hypeR-GEM. Finally, we applied hypeR-GEM to the analysis of age-associated metabolic signatures from the New England Centenarian Study. The results revealed consistent enrichment of lipid-related pathways, aligning with the well-established role of lipid metabolism in aging, and highlighted additional pathways not captured in the metabolites' annotation, demonstrating hypeR-GEM's practical utility in a real-world use case. AVAILABILITY AND IMPLEMENTATION: The hypeR-GEM R package, documentation, and workflow examples are freely available at https://github.com/montilab/hypeR-GEM and archived at https://doi.org/10.5281/zenodo.20586748.

Metabolomics↗

PubMed2005

METLIN: a metabolite mass spectral database.

Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin.scripps.edu), a freely accessible web-based data repository, has been developed to assist in a broad array of metabolite research and to facilitate metabolite identification through mass analysis. METLINincludes an annotated list of known metabolite structural information that is easily cross-correlated with its catalogue of high-resolution Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrometry (MS/MS) spectra, and LC/MS data.

Biomarkers↗

PubMed2003

Personal metabolomics as a next generation nutritional assessment.

Nutrition research is in the process of addressing a series of questions related to the future of diet and health. Are all humans the same with respect to their response to diet? If not, humans must be fed differently according to the differences in their genetics and metabolic needs. Are those differences self-evident to the individual or their care-givers? If not, methods must be developed to measure the basis of differences between humans. Are the current sets of diagnostic biomarkers for disease appropriate and sufficient to distinguish the appropriate diets of humans for optimal metabolic health? If not, metabolites must be measured such that the differences in human metabolism are resolvable before they become diseased. Will a small subset of metabolic markers provide an indication of intended and unintended effects of diets that relate to overall metabolism? If not, comprehensive metabolic analyses (metabolomics) must be put in place to ensure that all aspects of health are accurately assessed. Inappropriate dietary choices are accelerating the development of chronic metabolic disease and threatening to overwhelm public health's ability to manage them. Nutrition and food sciences will need to collaborate with other scientific disciplines to develop and implement metabolic assessment technologies and to assemble annotated databases of metabolite profiles in humans, thus building the knowledge needed to link metabolism to diet and health. Biochemical and physiological research must be guided to define the mechanisms by which diet interacts with metabolism in different individuals. Integrating metabolism with the genetic and dietary variables that affect health is the role of nutrition sciences. Integrating personal nutritional value with food's other key values of safety, quality, comfort, delight, convenience and affordability is the role of food science. It is time for these two fields to address a common problem, metabolic health, with coordinated solutions.

Computational Biology↗

PubMed2026

Polyphenol-Rich Opuntia ficus-indica Cladodes: An Integrated Metabolomic, In Vivo and In Silico Study Supporting Their Hypolipidemic and Hepatoprotective Effects.

Background: Hyperlipidemia is a major risk factor for cardiometabolic disorders, including non-alcoholic fatty liver disease (NAFLD), and is closely associated with oxidative stress. Opuntia ficus-indica (OFI) cladodes are recognized as a rich source of bioactive phytochemicals; however, the molecular mechanisms underlying their metabolic benefits remain incompletely understood. Objectives: This study aimed to comprehensively evaluate the hypolipidemic and hepatoprotective potential of a polyphenol-rich O. ficus-indica cladode extract (OCE) using an integrated approach combining in vivo evaluation, untargeted metabolomics (UHPLC-Orbitrap-MS/MS), molecular docking, and ADMET prediction. Methods: Hyperlipidemic mice fed a high-fat diet (HFD) were treated with OCE, while molecular docking was performed on ten major annotated phytochemicals against twelve key proteins involved in lipid metabolism and cholesterol homeostasis, including HMGCR, FAS, PPARα, PCSK9, and NPC1L1, using simvastatin as the reference compound. Results: OCE treatment significantly improved plasma and hepatic lipid profiles, improved glucose homeostasis, and markedly reduced hepatic malondialdehyde (MDA) levels, indicating attenuation of oxidative stress. Histopathological analysis further supported a pronounced hepatoprotective effect, with a substantial reduction in hepatic steatosis. Untargeted metabolomics enabled the annotation of 102 metabolites, putatively identifying piscidic acid as the predominant phenolic constituent together with a diverse profile of flavonoids and phenolic acids. Molecular docking supported the potential contribution of these phytochemicals to the regulation of lipid metabolism through favorable interactions with multiple therapeutic targets, while ADMET prediction suggested an overall favorable pharmacokinetic and toxicity profile despite the lower intestinal permeability predicted for glycosylated derivatives. Conclusions: Overall, these findings support O. ficus-indica cladodes as a promising source of dietary bioactive compounds with potential applications in the nutritional management and prevention of hyperlipidemia and related cardiometabolic disorders.

Animals↗

PubMed2026

13C Stable Isotope Tracing-Based MFA Reveals the Contribution of Glucose to Glycolytic and TCA Fluxes and Its Application in Depression Research.

Metabolomics is widely applied to dissect metabolic pathways and their correlations with biological phenotypes. Unlike genomics and proteomics, metabolites exhibit substantial heterogeneity in chemical structure, physicochemical properties, and biological origin. Accordingly, pathway enrichment and annotation relying merely on alterations in metabolite abundance are prone to incomplete coverage, ionization bias, and ambiguous annotation, which inevitably impair the accuracy of pathway interpretation. Metabolic flux analysis (MFA) coupled with stable isotope-resolved metabolomics (SIRM) offers a powerful quantitative framework for tracing in vivo carbon flow and estimating reaction fluxes across key metabolic nodes. Glucose metabolism lies at the core of systemic energy homeostasis; however, most current investigations are confined to cell lines or in vitro systems, and a simple, easy-to-implement computational pipeline for in vivo glucose flux analysis in animal models is still lacking. Herein, we established an in vivo 13C-labeling-based MFA workflow to trace and resolve the systemic metabolic fate of glucose in rats. The pipeline covers tracer administration, sample preparation, LC-MS detection, isotopologue data acquisition and correction, construction of a glucose-metabolism-related metabolite database, MFA model establishment, and metabolic flux quantification. By infusing rats with [U-13C6]-glucose and [U-13C3]-sodium L-lactate, we precisely characterized the in vivo metabolic fates of circulating glucose and lactate and quantified their respective contributions to glycolytic flux and tricarboxylic acid (TCA) cycle flux. We further applied this workflow to profile energy metabolic reprogramming in depression. The results revealed a systemic shift toward aerobic glycolysis in rats exposed to chronic unpredictable mild stress (CUMS). Overall, the expanded application of this MFA strategy can provide mechanistic and quantitative insights into the regulation of metabolic pathways.

Animals↗

PubMed2006

Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors.

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. We have used a metabolite profiling approach to test the hypothesis that quantitative signatures of primary metabolites can be used to characterize molecular changes in ovarian tumor tissues. Sixty-six invasive ovarian carcinomas and nine borderline tumors of the ovary were analyzed by gas chromatography/time-of-flight mass spectrometry (GC-TOF MS) using a novel contamination-free injector system. After automated mass spectral deconvolution, 291 metabolites were detected, of which 114 (39.1%) were annotated as known compounds. By t test statistics with P < 0.01, 51 metabolites were significantly different between borderline tumors and carcinomas, with a false discovery rate of 7.8%, estimated with repeated permutation analysis. Principal component analysis (PCA) revealed four principal components that were significantly different between both groups, with the highest significance found for the second component (P = 0.00000009). PCA as well as additional supervised predictive models allowed a separation of 88% of the borderline tumors from the carcinomas. Our study shows for the first time that large-scale metabolic profiling using GC-TOF MS is suitable for analysis of fresh frozen human tumor samples, and that there is a consistent and significant change in primary metabolism of ovarian tumors, which can be detected using multivariate statistical approaches. We conclude that metabolomics is a promising high-throughput, automated approach in addition to functional genomics and proteomics for analyses of molecular changes in malignant tumors.

Cluster Analysis↗

PubMed2026

Comparative genomics approaches to identify genomic regions associated with the antimicrobial activity of Pseudomonas protegens PBL3.

The environmental bacterium Pseudomonas protegens PBL3 has antagonistic activity against the plant pathogenic bacterium Burkholderia glumae, an important pathogen in rice. The antimicrobial activity of P. protegens PBL3 was found in the bacteria-free secreted fraction (secretome), but the specific molecules, as well as the genetic basis of that activity, have not been identified. In this study, we integrated genomic information with antimicrobial assays on P. protegens PBL3 and additional six Pseudomonas spp. strains, to identify putative genomic regions in P. protegens PBL3 associated with antimicrobial activity. We hypothesized that Pseudomonas spp. strains with antimicrobial activity against B. glumae have conserved genes with P. protegens PBL3 that are absent in strains lacking activity. Comparative genomics analyses with anvi'o and progressiveMauve, and using P. protegens PBL3 as the reference genome, revealed 188 genes uniquely present in antimicrobial-producing strains. Seven of those genes were annotated as biosynthetic gene clusters predicted to encode secondary metabolites; additional genes were grouped into 25 contiguous clusters with functions annotated as secretion, signal transduction, regulation, transport/efflux, carbohydrate metabolism and one with an additional uncharacterized function. Altogether, this study uncovered a complex and multi-functional network of candidate genes, suggesting that the antimicrobial activity in P. protegens PBL3 is not limited to biosynthetic pathways but also involves additional regulatory, metabolic and export modules to synthesize and deploy antimicrobials.

Pseudomonas↗

PubMed2006

Structural and biochemical characterization of gentisate 1,2-dioxygenase from Escherichia coli O157:H7.

Gentisic acid (2,5-dihydroxybenzoic acid) is a key intermediate in aerobic bacterial pathways that are responsible for the metabolism of a large number of aromatic compounds. The critical step of these pathways is the oxygen-dependent reaction catalysed by gentisate 1,2-dioxygenase which opens the aromatic ring of gentisate to form maleylpyruvate. From gentisic acid, the cell derives carbon and energy through the conversion of maleylpyruvate to central metabolites. We have confirmed the annotation of a gentisate 1,2-dioygenase from the pathogenic O157:H7 Escherichia coli strain and present the first structural characterization of this family of enzymes. The identity of the reaction product was revealed using tandem mass spectroscopy. The operon responsible for the degradation of gentisate in this organism exhibits a high degree of conservation with the gentisate-degrading operons of other pathogenic bacteria, including the Shiga toxin-producing E. coli O103:H2, but does not appear to be present in non-pathogenic strains. The acquisition of the gentisate operon may represent a special adaptation to meet carbon source requirements under conditions of environmental stress and may provide a selective advantage for enterohaemorrhagic E. coli relative to their non-pathogenic counterparts.

Amino Acid Sequence↗

PubMed2025

Spatial Metabolomics Reveals the Role of Penicillic Acid in Cheese Rind Microbiome Disruption by a Spoilage Fungus.

Microbial interactions in cheese rinds influence community structure, food safety, and product quality. But the chemical mechanisms that mediate microbial interactions in cheeses and other fermented foods are generally not known. Here, we investigate how the spoilage mold Aspergillus westerdijkiae chemically inhibits beneficial cheese-rind bacteria using a combination of omics technologies. In cheese rind community and co-culture experiments, A. westerdijkiae strongly inhibited most cheese rind community members. In co-culture with Staphylococcus equorum, A. westerdijkiae strongly affected bacterial gene expression, including upregulation of a putative bceAB gene cluster that is associated with resistance to antimicrobial compounds in other bacteria. Mass spectrometry imaging (MSI) revealed spatially localized production of secondary metabolites, including penicillic acid and ochratoxin B at the fungal-bacterial interface. Integration of LC-MS/MS and genome annotations confirmed the presence of additional bioactive metabolites, such as notoamides and circumdatins. Fungal metabolic responses varied by bacterial partner, suggesting species-specific chemical strategies. Notably, penicillic acid levels increased 2.5-fold during interaction with Brachybacterium, and experiments with purified penicillic acid showed inhibition of a range of cheese rind bacteria. These findings show that A. westerdijkiae deploys a context-dependent arsenal of mycotoxins and other metabolites, disrupting microbial community assembly in cheese rinds.

Aspergillus westerdijkiae↗

PubMed2026

Whole-genome sequencing and analysis of the endophytic fungus Alternaria alternata Y-2 from Leymus chinensis.

To explore the genetic basis and functional potential of beneficial symbiosis between the endophytic fungus Alternaria alternata Y-2 and its host Leymus chinensis, we performed Illumina-based draft whole-genome sequencing and systematic bioinformatic analysis. Although this assembly does not reach telomere-to-telomere completeness, it provides high-quality gene-level information for gene prediction, functional annotation, carbohydrate-active enzyme (CAZyme) identification, and secondary metabolite biosynthetic gene cluster analysis. The final genome size of A. alternata Y-2 was 34,383,676 bp with a GC content of 51.0%, containing 12,724 predicted protein-coding genes, 90 tRNAs, and 12 rRNAs. BUSCO assessment showed 98.9% completeness, supporting the high quality of this draft genome. A total of 12,627 genes were successfully annotated in the NCBI NR database, and 17,183 genes were functionally categorized using GO terms. In total, 448 CAZyme genes and 21 secondary metabolite biosynthetic gene clusters were identified, which are potentially involved in lignocellulose degradation, cellular redox homeostasis and biosynthesis of bioactive metabolites. Based on ITS sequence alignment, NR annotation, and phylogenetic analysis of single-copy orthologous genes, the strain was confidently identified as A. alternata. This study firstly reports the draft genome of an endophytic A. alternata strain derived from L. chinensis and provides valuable genetic resources for exploring the endophytic lifestyle, stress tolerance, and bioactive metabolite potential of this fungus.

Alternaria↗

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↗