Saez Lab
We are a research group at Heidelberg University and the European Bioinformatics Institute, part of the European Molecular Biology Laboratory (EMBL-EBI).
Our goal is to acquire a functional understanding of the deregulation of signalling networks in disease and to apply this knowledge to develop novel therapeutics. We focus on cancer, heart failure, auto-immune and fibrotic disease. Towards this goal, we integrate big "omics" data with mechanistic molecular knowledge into statistical and machine learning methods. To this end, we have developed a range of tools in different areas of biomedical research, mainly using the programming languages R and Python.
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| BioCypher A unifying framework for biomedical research knowledge graphs | CellNOpt Train logic models of signaling against omics data | CollecTRI Collection of Transcriptional Regulatory Interactions | CORNETO Unified framework for network inference problems |
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| COSMOS Mechanistic insights across multiple omics | Decoupler Infer biological activities from omics data using a collection of methods | DOT Optimization framework for transferring cell features from a reference data to spatial omics | GRETA Snakemake pipeline for benchmarking multimodal gene regulatory network inference methods |
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| LIANA+ Framework to infer inter- and intra-cellular signalling from single-cell and spatial omics | MetaProViz Metabolomics functional analysis and visualization | MISTy Explainable machine learning models for single-cell, highly multiplexed, spatially resolved data | NetworkCommons Context specific networks from omics data and prior-knowledge |
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| ocEAn Metabolic enzyme enrichment analysis | OmniPath Networks, pathways, gene annotations from 180+ databases | ParTIpy Archetypal analysis to identify functional trade-offs in biological data | PROGENy Activities of canonical pathways from transcriptomics data |
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- BioChatter - A platform for the biomedical application of Large Language Models
- BioServices - Python package to access Bioinformatics Web Services
- Birewire - R package for the randomisation of bipartite graphs
- CARNIVAL - Causal reasoning to explore mechanisms in molecular networks
- DREAMTools - Code used in the scoring of DREAM challenges
- DoRothEA - Transcription factor activity inference
- DrugVsDisease - R/Cytoscape pipeline to compare drug and disease gene expression profiles
- GDSCTools - Python library dedicated to the study of pharmacogenomic relationships
- Kasumi - Identification of spatially localized neighborhoods of intra- and intercellular relationships from spatial omics
- MEIGO - Global optimization toolbox including metaheuristic and Bayesian methods
- MetalinksDB - Database of protein-metabolite and small molecule ligand-receptor interactions
- PHONEMeS - Logic modeling of phosphoproteomics
- SLAPenrich - R package to identify pathway-level enrichments of genetic alterations
- ScAPE - Single-cell Analysis of Perturbational Effects
- lipyd - Python module for lipidomics LC MS/MS data analysis
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Repositories
- CollecTRI Public
Gene regulatory network containing signed transcription factor-target gene interactions
- omnipath-present Public
- SpaCEy Public
- pypath Public
Python module for prior knowledge integration. Builds databases of signaling pathways, enzyme-substrate interactions, complexes, annotations and intercellular communication roles.
- MetaProViz Public
R-package to perform metabolomics pre-processing, differential metabolite analysis, metabolite clustering and custom visualisations.
- annnet Public
AnnNet (Annotated Network) is a unified, high-expressivity graph platform designed to bring the convenience of AnnData-style annotated containers to networks, multilayer structures, and hypergraphs.
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