Biology subjects

Daley, M.

Publications and source records attributed to Daley, M..

4 recordsLinked to original sources

bioRxiv2026

OmniPath Metabo: chemical structures, interactions and mechanisms to study the metabolome

Mechanistic and functional analysis of omics data largely relies on the incorporation of prior knowledge; however, connecting metabolomics data and knowledge is a major methodological challenge. This is largely driven by the diverse prior knowledge being fragmented across many databases requiring the merging of different database records across chemical structures, identifiers, and varying levels of structural specificity. Hence, this limits mechanistic interpretation and functional characterisation of the metabolome. Here, we present OmniPath Metabo, a comprehensive, harmonized, metabolome-centric database covering metabolites, lipids, food-derived compounds, and small molecule drugs, along with their associated receptors, transporters, enzymes, reactions, allosteric regulators, and disease associations. OmniPath Metabo harmonizes attributes using controlled vocabularies and ontologies, structures and built-in cheminformatics to map identifiers and track ambiguity. OmniPath Metabo is built directly from 40+ original resources and is freely accessible via an interactive web app and API at metabo.omnipathdb.org. OmniPath Metabo enables dynamic, context-specific construction of subnetworks to serve dedicated purposes, such as cell-cell communication or integrated multi-omics metabolite-driven regulation, connecting reactions, allosteric regulation, metabolite-receptor and metabolite-transporter interactions. Combining it with the over 170 other resources in OmniPath, it can be used for integrated networks of signaling, gene regulation, and metabolism. We showcase the application of OmniPath Metabo by analysing publicly available metabolomics data of lung cancer cell lines and metabolic footprints to mutational patterns. In summary, OmniPath Metabo transforms fragmented resources into a harmonised prior knowledge framework for a mechanistic and functional analysis of the metabolome.

bioinformatics↗

bioRxiv2025

OmniPath: integrated knowledgebase for multi-omics analysis

Analysis and interpretation of omics data largely benefit from the use of prior knowledge. However, this knowledge is fragmented across resources and often is not directly accessible for analytical methods. We developed OmniPath (https://omnipathdb.org/), a database combining diverse molecular knowledge from 168 resources. It covers causal protein-protein, gene regulatory, miRNA, and enzyme-PTM (post-translational modification) interactions, cell-cell communication, protein complexes, and information about the function, localization, structure, and many other aspects of biomolecules. It prioritizes literature curated data, and complements it with predictions and large scale databases. To enable interactive browsing of this large corpus of knowledge, we developed OmniPath Explorer, which also includes a large language model (LLM) agent that has direct access to the database. Python and R/Bioconductor client packages and a Cytoscape plugin create easy access to customized prior knowledge for omics analysis environments, such as scverse. OmniPath can be broadly used for the analysis of bulk, single-cell and spatial multi-omics data, especially for mechanistic and causal modeling. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/675512v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@17c2b6borg.highwire.dtl.DTLVardef@1069835org.highwire.dtl.DTLVardef@1f2ce76org.highwire.dtl.DTLVardef@1d0b34f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Integrated metabolomics data analysis to generate mechanistic hypotheses with MetaProViz

With the growing number of metabolomics and lipidomics studies, robust strategies for bioinformatic analyses are increasingly important. However, the absence of standardized and reproducible workflows, coupled with ambiguous metabolite annotations, hampers effective analysis, particularly when integrating prior knowledge with metabolomics data. Moreover, the limited availability of comprehensive, curated prior knowledge further limits functional analyses and reduces the extraction of meaningful biological insights. Here we present MetaProViz (Metabolomics Processing, functional analysis and Visualization), a free open-source R package for metabolomics data analysis that integrates prior knowledge to generate mechanistic hypotheses (https://saezlab.github.io/MetaProViz/). MetaProViz offers a flexible framework consisting of five modules: processing, differential analysis, prior knowledge integration, functional analysis and visualisation, applicable to both intracellular and exometabolomics experiments. To improve functional analysis, we created the Metabolism Signature Database (MetSigDB), a collection of annotated metabolite sets. MetSigDB includes classical pathway-metabolite sets, metabolite-receptor and metabolite-transporter sets, and chemical class-metabolite sets. In addition, MetaProViz enables the conversion of gene sets to metabolite sets by using enzyme-metabolic reaction associations. In addition, MetaProViz translates between metabolite identifiers of commonly used databases, analyzes mapping ambiguities and completes missing annotations. The MetaProViz functional analysis toolkit includes sample metadata analysis, classical enrichment analysis and biologically informed clustering. We showcase MetaProViz functionalities using kidney cancer metabolomics data from cell lines, cell-culture media, and tumour tissue. We found increased methionine usage in clear-cell renal cell carcinoma (ccRCC) cell lines in line with decreased methionine levels in tumour samples. Further, we link this observation to enzymes and transporters crucial for overall survival in ccRCC and suggest that the increased methionine usage reflects the elevated DNA-hypermethylation landscape, a known characteristic in ccRCC. In summary, MetaProViz facilitates and improves the analysis and interpretation of metabolomics data. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/670781v1_ufig1.gif" ALT="Figure 1"> View larger version (79K): org.highwire.dtl.DTLVardef@10a7a39org.highwire.dtl.DTLVardef@de4c4forg.highwire.dtl.DTLVardef@64041dorg.highwire.dtl.DTLVardef@4a427c_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2019

A reference map of the human protein interactome

Global insights into cellular organization and function require comprehensive understanding of interactome networks. Similar to how a reference genome sequence revolutionized human genetics, a reference map of the human interactome network is critical to fully understand genotype-phenotype relationships. Here we present the first human \"all-by-all\" binary reference interactome map, or \"HuRI\". With ~53,000 high-quality protein-protein interactions (PPIs), HuRI is approximately four times larger than the information curated from small-scale studies available in the literature. Integrating HuRI with genome, transcriptome and proteome data enables the study of cellular function within essentially any physiological or pathological cellular context. We demonstrate the use of HuRI in identifying specific subcellular roles of PPIs and protein function modulation via splicing during brain development. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI thus represents an unprecedented, systematic reference linking genomic variation to phenotypic outcomes.

systems biology↗