Biology subjects

Mananes, D.

Publications and source records attributed to Mananes, D..

3 recordsLinked to original sources

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↗

bioRxiv2024

Modeling causal signal propagation in multi-omic factor space with COSMOS

Understanding complex diseases requires approaches that jointly analyze omics data across multiple biological layers, including signaling, gene regulation, and metabolism. Existing data-driven multi-omics analysis methods, such as multi-omics factor analysis (MOFA), can identify associations between molecular features and phenotypes, but they are not designed to integrate existing mechanistic molecular knowledge, which can provide further actionable insights. We introduce an approach that connects data-driven analysis of multi-omics data with systematic integration of mechanistic prior knowledge using COSMOS+ (Causal Oriented Search of Multi-Omics Space). We show how factor analysis output can be used to estimate activities of transcription factors and kinases as well as ligand-receptor interactions, which in turn are integrated with network-level prior-knowledge to generate mechanistic hypotheses about paths connecting deregulated molecular features. We apply this approach on a novel multi-omics dataset of cell line models of breast cancer resistance to evaluate the ability of such mechanistic hypotheses to identify resistance drivers, as well as a breast cancer patient cohort. Our approach offers an interpretable framework to generate actionable insights from multi-omic data particularly suited for high dimensional datasets. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/603538v3_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1de0eb1org.highwire.dtl.DTLVardef@19690a7org.highwire.dtl.DTLVardef@1f2f0ddorg.highwire.dtl.DTLVardef@a36d5a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

SpatialDDLS: An R package to deconvolute spatial transcriptomics data using neural networks

SummarySpatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available plaXorms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data. SpatialDDLS leverages single-cell RNA sequencing (scRNA-seq) data to simulate mixed transcriptional profiles with predefined cellular composition, which are subsequently used to train a fully-connected neural network to uncover cell type diversity within each spot. By comparing it with two state-of-the-art spatial deconvolution methods, we demonstrate that SpatialDDLS is an accurate and faster alternative to the available state-of-the art tools. Availability and implementationThe R package SpatialDDLS is available via CRAN-The Comprehensive R Archive Network: https://CRAN.R-project.org/package=SpatialDDLS. A detailed manual of the main functionalities implemented in the package can be found at https://diegommcc.github.io/SpatialDDLS. Contactfscabo@cnic.es Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗