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At least 19 recordsLinked to original sources

PubMed2026

CpGene: a web application for epigenetic signature identification from DNA methylation arrays.

MOTIVATION: DNA methylation (DNAme) is the best studied epigenetic mechanism that plays pivotal role in tissue differentiation and epigenetic disruption has been correlated to diverse disease types (e.g. cancer, metabolic disorders). While various DNAme array platforms have been discovered, data analysis remains a challenging task which often requires in-depth bioinformatic expertise. Here, we developed a user-friendly web-based application for data analysis and visualization that accommodates users ranging from early-career basic/translational researchers to experienced bioinformaticians. RESULTS: CpGene is a web application for analyzing DNA methylation array data. It supports Illumina 450K, EPIC, and EPICv2 methylation array platforms and processes .idat files with integrated preprocessing, normalization, and quality control. Biomarker discovery is available through either classic differential methylation point analysis or machine learning-based feature selection as well as gene enrichment analysis. Results are summarized with clear visualizations, to aid interpretation. By combining these functions in a unified interface, CpGene streamlines methylation analysis and helps identify CpG sites and genes with biological and clinical relevance. AVAILABILITY AND IMPLEMENTATION: CpGene is openly accessible as a web service through http://cpgene.duckdns.org:8001/ and it's source code is available on https://github.com/kostaslazaros/cpgenene.

DNA Methylation↗

PubMed2026

shinyDeepGxP: a user-friendly R shiny app for predicting surface protein abundance from scRNA-seq expression using deep learning in blood cells.

MOTIVATION: Understanding accurate immune cell heterogeneity and function in single-cell datasets requires access to protein-level information, which is often unavailable due to experimental limitations. RESULTS: We present shinyDeepGxP, an interactive web application featuring our deep learning model, DeepGxP, for predicting surface protein abundance from single-cell RNA-sequencing (scRNA-seq) data. This platform makes DeepGxP accessible to researchers without programming skills. Users can upload scRNA-seq count matrices and use "Predict Protein" to predict the abundance of 224 biologically relevant surface proteins. shinyDeepGxP provides visualizations to help identify distinct cell populations based on predicted protein profiles. Moreover, users can choose "Explore Model" to reveal key RNA predictors and their associated biological pathways for each protein. Overall, shinyDeepGxP is a user-friendly, freely available web tool that provides protein-level detail for RNA-only single-cell datasets, enabling multimodal discovery without additional experiments. AVAILABILITY AND IMPLEMENTATION: shinyDeepGxP can be launched on https://shiny.crc.pitt.edu/deepgxp/.

Journal Article↗

PubMed2025

PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.

Recent advancements in spatial transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost for current ST data generation techniques restricts the large-scale application of ST. Consequently, maximization of the use of available resources to achieve robust statistical power for ST data is a pressing need. One fundamental question in ST analysis is detection of differentially expressed genes (DEGs) under different conditions using ST data. Such DEG analyses are performed frequently, but their power calculations are rarely discussed in the literature. To address this gap, we developed PoweREST, a power estimation tool designed to support the power calculation for DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments and after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application that allows users to interactively calculate and visualize study power along with relevant parameters.

Gene Expression Profiling↗

PubMed2026

MaizeGDB Phylostrata Tool: exploring evolutionary origins of maize proteins.

MOTIVATION: Phylostratigraphic analysis identifies the evolutionary origins and level of conservation of proteins, facilitating research in evolutionary biology and comparative genomics. RESULTS: We developed the MaizeGDB Phylostrata Tool, a custom web application that enables users to explore the evolutionary origins of proteins in maize (Zea mays), a globally important crop and model organism. This tool features interactive visualizations and detailed gene pages incorporating subcellular localization, Gene Ontology (GO) terms, and links to resources for homologs, facilitating comparison of gene functions across evolutionary time. The tool also provides downloadable links for full-proteome phylostratigraphic results for 26 maize inbreds (B73 and the NAM founders). From these, we identified genome- and subgenome-wide trends, finding that more conserved proteins tended to be longer and more highly expressed. Finally, we provide code including updates to the "phylostratr" R package to make it more robust against taxonomic updates, as well as example scripts for phylostratigraphic analysis and web tool development for researchers and curators of other species. AVAILABILITY AND IMPLEMENTATION: The MaizeGDB Phylostrata Tool is freely available at https://phylostrata.maizegdb.org. Scripts used for the analysis and web tool are available at https://github.com/LTibbs/PhylostrataWebtool.

Journal Article↗

PubMed2024

PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions.

Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

Differentially expressed genes↗

PubMed2026

Discovery and validation of a multi-protein panel for predicting non-fatal major adverse cardiovascular events in diabetic kidney disease.

OBJECTIVE: To identify plasma protein biomarkers associated with incident non-fatal major adverse cardiovascular events (MACE) in diabetic kidney disease (DKD) patients. RESEARCH DESIGN AND METHODS: We analyzed 317 DKD patients from the UK Biobank. Plasma proteomics and clinical data (demographics, metabolism, renal function) were integrated. In an exploratory discovery phase, three sequential Cox regression models (crude, socio-demographic-adjusted, socio-demographic-metabolic adjusted) screened non-fatal MACE-associated proteins. To prevent information leakage, the cohort was then randomly split into training (70%) and testing (30%) sets; machine-learning feature selection, hyperparameter optimization, and final model development were performed exclusively within the training set. The associated proteins were input into the four-step machine-learning pipeline (LASSO-Cox, random survival forest, Boruta, XGBoost-Cox). Predictive performance was validated using Kaplan-Meier survival analyses, longitudinal trajectory modeling, and ROC benchmarking. An interactive web application was deployed for clinical implementation. RESULTS: Of 1,463 plasma proteins, 561 were associated with non-fatal MACE across Cox models, with 14 overlapping proteins. Nine core proteins (ANG, IL1R1, CXCL14, ESAM, PTGDS, HAVCR1, FGFR2, IGSF8, CCL3) were validated: ANG showed the strongest non-fatal MACE association (HR = 3.88, 95%CI 2.33-6.48, p<0.001), and all high-expression groups had elevated non-fatal MACE risk. GO/KEGG enrichment highlighted inflammatory-immune pathways like positive regulation of MAPK cascade, Cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway as key mechanisms. The model integrating proteins, demographic factors, and clinical variables achieved the highest predictive performance across non-fatal MACE (AUC = 0.768), myocardial infarction (MI) (0.808), and stroke (0.816) outcomes, with superior stability in cross-validation. CoxBoost + Elastic Net framework was selected as the optimal framework via benchmarking of 101 algorithms. The model demonstrated favorable calibration in high-risk patients and yielded positive net clinical benefit across decision thresholds of 5% to 45%. The web tool (https://jiangli2941.github.io/MACE-prediction-v2/) enables input of 28 variables, outputs non-fatal MACE risk status, risk probability, and highlights abnormal indicators. CONCLUSION: Plasma proteomics combined with machine learning identifies robust non-fatal MACE predictors in DKD.

Humans↗

PubMed2026

Systematic Review and Transcriptomic Meta-analysis of Environmental Enrichment Reveal Core Molecular Programs of Brain Plasticity.

Environmental enrichment (EE) paradigms in rodents have long demonstrated that enhanced sensory, cognitive, social, and motor stimulation positively impacts brain function, improving learning, memory, and neuroplasticity. These effects have significant implications for understanding cognitive development and mitigating cognitive decline and brain aging. While numerous transcriptomic studies have explored EE-induced molecular changes, a unified view of the genes and pathways consistently modulated remains lacking. To address this gap, we performed a systematic review and meta-analysis. We conducted a comprehensive PubMed search for all studies published up to February 2025 that matched all the following inclusion criteria: (1) employed EE paradigms; (2) were conducted on rodents; (3) utilized genome-wide transcriptomic methods; (4) examined brain regions or neuronal populations. The 323 retrieved articles were manually screened for relevance to the study aims and data availability. Datasets from 20 eligible RNA-seq reports were reprocessed using a unified analysis pipeline and subjected to a meta-analysis with three complementary statistical methods. Despite considerable heterogeneity across studies, our integrative analysis identified consistent gene expression signatures linked to synaptic function, plasticity and their transcriptional regulation. In particular, our findings highlight the upregulation of the activity-dependent transcriptional program, including Fos and Jun family members. These molecular insights advance our understanding of how EE impacts on neuronal and behavioral outcomes, and may inform therapeutic strategies aimed at replicating or enhancing EE benefits. To promote open science and foster further research, we developed an accessible web application, mEEtaBrain, that enables the neuroscience community to navigate and interrogate our meta-analysis results. Substantial methodological heterogeneity across source studies increased variability in the meta-analysis outcomes. The use of stressors or disease models, particularly in rat studies, introduced a major confounding factor and limited reliable interspecies comparison. Overall, the studies exhibited a low to moderate risk of bias.

Neuronal Plasticity↗

PubMed2024

A research synthesis of humans, animals, and environmental compartments exposed to PFAS: A systematic evidence map and bibliometric analysis of secondary literature.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are a class of widely used anthropogenic chemicals. Concerns regarding their persistence and potential adverse effects have led to multiple secondary research publications. Here, we aim to assess the resulting evidence base in the systematic secondary literature by examining research gaps, evaluating the quality of reviews, and exploring interdisciplinary connections. METHODS: This study employed a systematic evidence-mapping approach to assess the secondary literature on the biological, environmental, and medical aspects of exposure to 35 fluorinated compounds. The inclusion criteria encompassed systematic reviews published in peer-reviewed journals, pre-prints, and theses. Comprehensive searches across electronic databases and grey literature identified relevant reviews. Data extraction and synthesis involved mapping literature content and narrative descriptions. We employed a modified version of the AMSTAR2 checklist to evaluate the methodological rigour of the reviews. A bibliometric data analysis uncovered patterns and trends in the academic literature. A research protocol for this study was previously pre-registered (osf.io/2tpn8) and published (Vendl et al., Environment International 158 (2022) 106973). The database is freely accessible through the interactive and user-friendly web application of this systematic evidence map at https://hi-this-is-lorenzo.shinyapps.io/PFAS_SEM_Shiny_App/. RESULTS: Our map includes a total of 175 systematic reviews. Over the years, there has been a steady increase in the annual number of publications, with a notable surge in 2021. Most reviews focused on human exposure, whereas environmental and animal-related reviews were fewer and often lacked a rigorous systematic approach to literature search and screening. Review outcomes were predominantly associated with human health, particularly with reproductive and children's developmental health. Animal reviews primarily focused on studies conducted in controlled laboratory settings, and wildlife reviews were characterised by an over-representation of birds and fish species. Recent reviews increasingly incorporated quantitative synthesis methodologies. The methodological strengths of the reviews included detailed descriptions of study selection processes and disclosure of potential conflicts of interest. However, weaknesses were observed in the critical lack of detail in reporting methods. A bibliometric analysis revealed that the most productive authors collaborate within their own country, leading to limited and clustered international collaborations. CONCLUSIONS: In this overview of the available systematic secondary literature, we map literature content, assess reviews' methodological quality, highlight data gaps, and draw research network clusters. We aim to facilitate literature reviews, guide future research initiatives, and enhance opportunities for cross-country collaboration. Furthermore, we discuss how this systematic evidence map and its publicly available database benefit scientists, regulatory agencies, and other stakeholders by providing access to current systematic secondary literature on PFAS exposure.

Bibliometrics↗

PubMed2024

TFinder: A Python Web Tool for Predicting Transcription Factor Binding Sites.

Transcription is a key cell process that consists of synthesizing several copies of RNA from a gene DNA sequence. This process is highly regulated and closely linked to the ability of transcription factors to bind specifically to DNA. TFinder is an easy-to-use Python web portal allowing the identification of Individual Motifs (IM) such as Transcription Factor Binding Sites (TFBS). Using the NCBI API, TFinder extracts either promoter or gene terminal regulatory regions, through a simple query of NCBI gene name or ID. It enables simultaneous analysis across five different species for an unlimited number of genes. TFinder searches for Individual Motifs in different formats, including IUPAC codes and JASPAR entries. Moreover, TFinder also allows de novo generations of a Position Weight Matrix (PWM) and the use of already established PWM. Finally, the data are provided in a tabular and a graph format showing the relevance and the P-value of the Individual Motifs found as well as their location relative to the Transcription Start Site (TSS) or the terminal region of the gene. The results are then sent by email to users facilitating the subsequent data analysis and sharing. TFinder is written in Python and freely available on GitHub under the MIT license: https://github.com/Jumitti/TFinder. It can be accessed as a web application implemented in Streamlit at https://tfinder-ipmc.streamlit.app. Resources are available on Streamlit "Resources" tab. TFINDER strength is that it relies on an all-in-one intuitive tool allowing users inexperienced with bioinformatics tools to retrieve gene regulatory regions sequences in multiple species and to search for individual motifs in a huge number of genes.

Transcription Factors↗

PubMed2026

Scalable near-real-time Bayesian phylogenetics for outbreaks with Delphy.

Pathogen genomic analysis is central to tracking, understanding and containing outbreaks1-13, but the complexity and cost of state-of-the-art phylogenetic tools limit global access and impact. Here we introduce Delphy, an exact reformulation of Bayesian phylogenetics14-17 designed to transform its speed, scalability and accessibility while retaining Bayesian state-of-the-art accuracy. Delphy's central data structure, an explicit mutation-annotated tree, takes advantage of the high sequence similarity of large-scale epidemic datasets18-20 for efficient tree exploration and convergence. By reproducing key analyses from recent major epidemics, including Ebola1,21, Zika2, SARS-CoV-2 (ref. 22), mpox3,4 and H5N1 (refs. 23,24), we demonstrate state-of-the-art accuracy with up to 2-3 orders of magnitude improvements in speed. Assessing Delphy's scalability, we show that a simulated dataset of 100,000 sequences can be analysed within a day. We distribute Delphy as a client-side web application that enables local, interactive analysis of raw data on the user's machine. Delphy automatically identifies key viral lineages and mutations, as well as their emergence and prevalence through time, with quantified uncertainties grounded in Bayesian theory. Delphy establishes Bayesian phylogenetics as a fast, accessible frontline tool for future outbreak response.

Journal Article↗

PubMed2025

MelanoDB: A dataset of clinical and molecular features of patients with advanced melanoma treated with MAPK inhibitors.

MAPK inhibitors (MAPKi) have revolutionized the treatment of patients with advanced melanoma. However, primary and acquired resistance mechanisms limit their efficacy. Predicting MAPKi response from the tumor baseline features remains challenging due to the limited size of patient cohorts. Therefore, we collected data from nine different patient cohorts (total n = 417 patients with advanced melanoma treated with MAPKi) to identify clinical and molecular features. Our curated dataset, named MelanoDB, includes whole or partial exome sequencing data for 191 patients, copy number alteration information for 66 patients, and gene expression data for 132 patients. We provide a web application to explore the integrated dataset and data distribution across the collected studies, and we share this dataset with the scientific community according to the Findable, Accessible, Interoperable, Reusable (FAIR) principles.

Humans↗

PubMed2026

GRNContext: an interactive web platform for contextualized gene regulatory networks visualization across human cancers.

SUMMARY: While current Gene Regulatory Network (GRN) databases provide comprehensive reference maps of potential interactions between transcription factors and target genes, they do not specify which regulatory interactions are active within specific biological contexts. This limitation is particularly critical in cancer, where transcriptional programs are inherently tissue-specific. To address this gap, we developed GRNContext, an interactive web platform designed for the visualization, exploration, and comparative analysis of gene regulatory networks contextualized across 33 cancer types from The Cancer Genome Atlas (TCGA). Our approach uses the TFLink human reference GRN as a starting point and integrates TCGA transcriptomic profiles to infer cancer-specific regulatory activity. Regulatory relevance was assessed using complementary machine learning and statistical methods, which were unified into a consensus score to prioritize and filter the most relevant candidate regulators for each target gene. By providing both curated context-specific GRNs and a user-friendly platform, GRNContext constitutes a comprehensive and accessible resource that supports mechanistic investigations, hypothesis generation, and translational research focused on transcriptional regulation in cancer. AVAILABILITY AND IMPLEMENTATION: GRNContext is supported by all major browsers and freely available on the web at https://apps.cienciavida.org/grncontext. It is implemented as a client-server web application featuring a FastAPI backend and a React frontend utilizing Cytoscape.js for interactive network visualization, all containerized via Docker for cross-platform compatibility.

Humans↗

PubMed2025

CNV-Finder: Streamlining Copy Number Variation Discovery.

Copy Number Variations (CNVs) play pivotal roles in the etiology of complex diseases and are variable across diverse populations. Understanding the association between CNVs and disease susceptibility is significant in disease genetics research and often requires analysis of large sample sizes. One of the most cost-effective and scalable methods for detecting CNVs is based on normalized signal intensity values, such as Log R Ratio (LRR) and B Allele Frequency (BAF), from Illumina genotyping arrays. In this study, we present CNV-Finder, a novel pipeline integrating deep learning techniques on array data, specifically a Long Short-Term Memory (LSTM) network, to expedite the large-scale identification of CNVs within predefined genomic regions. This facilitates efficient prioritization of samples for time-consuming or costly subsequent analyses such as Multiplex Ligation-dependent Probe Amplification (MLPA), short-read, and long-read whole genome sequencing. We incorporate four genes to establish our methods-Parkin (PRKN), Leucine Rich Repeat And Ig Domain Containing 2 (LINGO2), Microtubule Associated Protein Tau (MAPT), and alpha-Synuclein (SNCA)-which may be relevant to neurological diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), Progressive Supranuclear Palsy (PSP), or related disorders such as essential tremor (ET). By training our models on expert-annotated samples and validating them across diverse cohorts, including those from the Global Parkinson's Genetics Program (GP2) and additional dementia-specific databases, we demonstrate the efficacy of CNV-Finder in accurately detecting deletions and duplications. Our pipeline outputs app-compatible files for visualization within CNV-Finder's interactive web application. This interface enables researchers to review predictions and filter displayed samples by model prediction values, LRR range, and variant count in order to explore or confirm results. Our pipeline integrates this human feedback to enhance model performance and reduce false positive rates. Through a series of comprehensive analyses and validations using visual inspection, MLPA, short-read, and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied size, probe density, and noise. Our findings highlight the significance of contextual understanding and human expertise in enhancing the precision of CNV identification, particularly in complex genomic regions like 17q21.31. The CNV-Finder pipeline is a scalable, publicly available resource for the scientific community, available on GitHub (https://github.com/GP2code/CNV-Finder; DOI 10.5281/zenodo.14182563). CNV-Finder not only expedites accurate candidate identification but also significantly reduces the manual workload for researchers, enabling future targeted validation and downstream analyses in regions or phenotypes of interest.

Copy Number Variation (CNV)↗

PubMed2025

Visualization using NIPTviewer support the clinical interpretation of noninvasive prenatal testing results.

BACKGROUND: Noninvasive prenatal testing (NIPT) is increasingly used to screen for fetal chromosomal aneuploidy by analyzing cell-free DNA (cfDNA) in peripheral maternal blood. The method provides an opportunity for early detection of large genetic abnormalities without an increased risk of miscarriage due to invasive procedures. Commercial applications for use at clinical laboratories often take advantage of DNA sequencing technologies and include the bioinformatic workup of the sequence data. The interpretation of the test results and the clinical report writing, however, remains the responsibility of the diagnostic laboratory. In order to facilitate this step, we developed NIPTviewer, a web-based application to visualize and guide the interpretation of NIPT data results. RESULTS: NIPTviewer has a database functionality to store the NIPT results and a web interface for user interaction and visualization. The application has been implemented as part of a novel analysis pipeline for NIPT in a diagnostic laboratory at Uppsala University Hospital. The validation data set included 84 previously analyzed plasma samples with known results regarding chromosomes 13, 18, 21, X and Y. They were sequenced in six different experiments, uploaded to NIPTviewer and assigned to a clinical laboratory geneticist for interpretation. The results of all previously analyzed samples were replicated. CONCLUSION: NIPTviewer facilitates NIPT results interpretation and has been implemented as part of a NIPT analysis routine that was accredited by the national accreditation body for Sweden (Swedac).

Humans↗

PubMed2026

ChemGenXplore: an interactive tool for exploring and analysing chemical genomic data.

MOTIVATION: Chemical genomics is a powerful high-throughput approach to systematically link phenotypes to genotypes. However, the vast datasets generated remain challenging to explore due to the lack of integrated, interactive tools for visualization and analysis. Existing workflows often require multiple independent software tools, limiting data accessibility and collaboration. Therefore, we created a user-friendly platform that enables efficient exploration and sharing of chemical genomics data. RESULTS: We developed ChemGenXplore, a web-based Shiny application designed to streamline the visualization and analysis of chemical genomic screens. It offers two primary functionalities: one for exploring pre-implemented datasets and another for analysing user-uploaded datasets. ChemGenXplore enables users to visualize phenotypic profiles, assess gene-gene and condition-condition correlations, perform GO and KEGG enrichment analysis, and generate customizable, interactive heatmaps. To further support collaborative research, ChemGenXplore also facilitates the comparative analysis of chemical genomic and other omics datasets. By consolidating these features into a single interactive and accessible tool, ChemGenXplore facilitates data sharing, enhances reproducibility, and promotes collaboration within the research community. AVAILABILITY AND IMPLEMENTATION: ChemGenXplore is freely accessible as a web application at https://chemgenxplore.kaust.edu.sa/. Source code and documentation, including instructions for local installation, are provided on GitHub (https://github.com/Hudaahmadd/ChemGenXplore). A Docker image is also available on DockerHub (https://hub.docker.com/r/hudaahmad/chemgenxplore) to ensure reproducibility and simplify installation.

Software↗

PubMed1991

[Effects of the PAF antagonist WEB 2086 on hypoxia and angiotensin II-induced pulmonary vasoconstriction in the isolated perfused rat lung].

Using isolated blood-perfused lung preparations of rats, we tested the influence of the PAF antagonist WEB 2086 on vasoconstriction triggered by hypoxia or angiotensin II (A II). If a constant flow was pre-set, changes in the prepulmonarily measured pressure were directly related to the changes of resistance in the pulmonary flow. WEB 2086 reduced the hypoxically conditioned vasoconstriction (HPV) when using blood as perfusion medium, the effect being dependent on the dose (ED50 = 127.3 +/- 21.1 mg/l). HPV was lowered on the average by 82% if the full pharmacologic dose of 800 mg/l WEB 2086 was added to the perfusate. The A II response was weakened to a lesser degree (by 45%). If plasma was used as perfusate, the pressure increase in response to hypoxic stimulation or A II was less marked. However, the relative effect of the PAF antagonist was analogous (attenuation by 83% or 53%, respectively). In chronically hypoxic animals (3 weeks at 10% O2) the relative pressure drop in the lesser circulation after application of WEB 2086 (400 mg/l; HPV; blood as perfusate) was definitely more pronounced (p less than 0.001). The fact that WEB partly antagonises the pulmonary vasoconstriction triggered both by alveolar hypoxy and by angiotensin II, seems to indicate that in both constrictive stimuli PAF participates in the complex mediator mechanism or that WEB 2086 exercises a non-specific vasodilatory effect on the pulmonary flow.

Angiotensin II↗

PubMed2026

Structure-centric searching enables global mapping of the public metabolome.

Searching and learning from aggregated public metabolomics data spanning thousands of studies remained largely inaccessible. Here we present StructureMASST, a web-based application enabling scalable, structure-centric searches across public metabolomics repositories using molecule names or chemical representations. It queries a precomputed knowledgebase of 2.19 billion spectral matches and 420 million metadata links, supports modification-tolerant and mass-shift searches, and maps chemical structures across taxonomy, biological context and environmental conditions to accelerate discovery.

Journal Article↗

PubMed2025

SBMLtoOdin and Menelmacar: interactive visualisation of systems biology models for expert and non-expert audiences.

SUMMARY: Computational models in biology can increase our understanding of biological systems, be used to answer research questions, and make predictions. Accessibility and reusability of computational models is limited and often restricted to experts in programming and mathematics. This is due to the need to implement entire models and solvers from the mathematical notation models are normally presented as. Here, we present SBMLtoOdin, an R package that translates differential equation models in SBML format from the BioModels database into executable R code using the R package odin, allowing researchers to easily reuse models. We also present Menelmacar, a web-based application that provides interactive visualisations of these models by solving their differential equations in the browser. This platform allows non-experts to simulate and investigate models using an easy-to-use interface. AVAILABILITY AND IMPLEMENTATION: SBMLtoOdin is published under the open source Apache 2.0 licence at https://github.com/bacpop/SBMLtoOdin and can be installed as an R package. The code for the Menelmacar website is published under the MIT License at https://github.com/bacpop/odinviewer, and the website can be found at https://biomodels.bacpop.org/.

Software↗