Julio Saez-Rodriguez
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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, auto-immune and fibrotic disease. Towards this goal, we integrate big (‘Omics’) data with mechanistic molecular knowledge into statistical and machine learning methods, and we share our tools as free open-source packages.
We are at the European Bioinformatics Institute (EMBL-EBI) and at the Institute for Computational Biomedicine at the Medical Faculty of Heidelberg University and Heidelberg University Hospital. We are also part of the Molecular Medicine Partnership Unit (MMPU) between the Medical Faculty of the University of Heidelberg and the European Molecular Biology Laboratory (EMBL) and of ELLIS Heidelberg.
Finally, we support scientific crowdsourcing, specifically collaboratives competitions, through the DREAM challenges.
Check below the publications section to see some of our recent work.
Julio Saez-Rodriguez
More...Erika Schulz
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More...Ricardo O. Ramirez-Flores
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More...Ahmet Sureyya Rifaioglu
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More...Bárbara Zita Peters Couto
More...Miguel Hernandez
More...Jovan Tanevski Tanevski Lab
More...Rebecca Terrall Levinson
More...Olga Ivanova
More...Nadine Tüchler
More...Mira Burtscher
More...Lorna Wessels
More...Tim Rose
More...Loan Vulliard
More...Remi Trimbour
More...Chiara Schiller
More...Jennifer Habbes
More...Thorben Söhngen
More...Macabe Daley
More...The following people spent over 6 months with us:
Name | Duration | Position |
---|---|---|
Fabian Fröhlich | 2022-2023 | PostDoc |
Katharina Zirngibl | 2021-2023 | PostDoc |
Marzia Sidri | 2021-2022 | project manager |
Bartosz Bartmanski | 2021-2021 | Postdoc |
Arezou Rahimi | 2021 -2024 | postdoc |
Hanna Schumacher | 2021 - 2023 | Bioinformatician |
Daniel Dimitrov | 2020-2024 | PhD Student |
Eleanor Fewings | 2020-2021 | Bioinformatician |
Rosa Hernansaiz Ballesteros | 2019-2021 | PostDoc |
Igor Bulanov | 2019-2020 | intern |
Minoo Ashtiani | 2019-2020 | intern |
Alice Driessen | 2019-2020 | intern |
Ana Victoria Ponce-Bobadilla | 2019-2020 | Postdoc |
Alberto Valdeolivas Urbelz | 2019-2020 | Postdoc |
Javier Perales-Patón | 2018-2022 | Postdoc |
Charlie Pieterman | 2018-2018 | intern |
Anika Liu | 2018-2018 | Master Thesis |
Hyojin Kim | 2017-2020 | Phd student |
Nicolas Palacio-Escat | 2017-2020 | PhD Student |
Panuwat Trairatphisan | 2017-2019 | Postdoc |
Bence Szalai | 2017-2018 | Postdoc |
Ferenc Tajti | 2017-2018 | Intern |
Francesco Ceccarelli | 2017-2018 | intern |
Vigneshwari Subramanian | 2017-2018 | Postdoc |
Christian Holland | 2017 -2021 | PhD Student |
Mahmoud Ibrahim | 2017 -2017 | Postdoc |
Enio Gjerga | 2016-2020 | Phd student |
Luis Tobalina Segura | 2016-2019 | Postdoc |
Melanie Rinas | 2016-2019 | Postdoc |
Mi Yang | 2015-2019 | PhD Student / Postdoc |
Angeliki Kalamara | 2015-2019 | PhD Student |
Jakob Wirbel | 2015-2017 | Intern & Master Thesis |
Fatemeh Ghavidel | 2015-2016 | Postdoc (w O Stegle & A Brazma) |
Pisanu Buphamalai | 2015-2016 | Trainee (w. M Brehme) |
Ricardo Ramirez | 2015-2016 | Trainee |
Luz Garcia-Alonso | 2014-2018 | Postdoc |
Johannes Stephan | 2014-2015 | Postdoc (w O Stegle) |
Claudia Hernandez | 2014-2015 | Trainee |
Martí Bernardo-Faura | 2013-2015 | Postdoc |
Ioannis Melas | 2013-2014 | Postdoc |
Vitor Costa | 2013-2013 | Master Thesis |
Luca Cerone | 2013-2013 | Postdoc |
Emanuel Gonçalves | 2012-2017 | PhD Student |
Michael Schubert | 2012-2016 | PhD Student |
Michael Menden | 2011-2016 | PhD student |
Thomas Cokelaer | 2011-2015 | Staff Scientist |
Martijn van Iersel | 2011-2012 | Postdoc |
Federica Eduati | 2011 - 2017 | Postdoc |
Francesco Iorio | 2010-2017 | Postdoc / Senior Bioinformatician |
Aidan MacNamara | 2010-2014 | Postdoc |
Camille Terfve | 2010-2014 | PhD Student |
David Henriques | 2010-2011 | Master Thesis |
Daniel Dimitrov et al. Nat Cell Biol, 2024
Lobentanzer et al. Mol Syst Biol, 2024
Rahimi et al. Nat Commun, 2024
Burtscher et al. Mol Syst Biol, 2024
Schäfer et al. Nat Immunol, 2024
Ramirez et al. Elife, 2023
Müller-Dott et al. Nucleic Acids Res, 2023
Badia-I-Mompel et al. Nat Rev Genet, 2023
Lobentanzer et al. Nat Biotechnol, 2023
Sciacovelli, Dugourd, et al. Nat Commun, 2022
Kuppe, Ramirez Flores, Li, et al. Nature, 2022
Dimitrov et al. Nat Commun, 2022
Tanevski et al. Genome Biol, 2022
Türei et al. Mol Syst Biol, 2021
Dugourd et al. Mol Syst Biol, 2021
Holland et al. Genome Biol, 2020
Eduati et al. Mol Syst Biol, 2020
Garcia-Alonso et al. Genome Res, 2019
Menden et al. Nat Commun, 2019
Schubert et al. Nat Commun, 2018
Türei et al. Nat Methods, 2016
Iorio et al. Cell, 2016
Hill et al. Nat Methods, 2016
Ghasemi et al. Nat Commun, 2024
Lanzer et al. Basic Res Cardiol, 2024
Daniel Dimitrov et al. Nat Cell Biol, 2024
Dimitrov et al. Nat Cell Biol, 2024
White et al. Nat Commun, 2024
Paton et al. Nucleic Acids Res, 2024
Gegner et al. Clin Proteomics, 2024
Maier et al. Nucleic Acids Res, 2024
Lobentanzer et al. Mol Syst Biol, 2024
Unger et al. Nat Rev Nephrol, 2024
Dugourd et al. bioRxiv, 2024
Müller-Dott et al. bioRxiv, 2024
Wünnemann et al. bioRxiv, 2024
Tetzlaff et al. bioRxiv, 2024
Tanevski et al. bioRxiv, 2024
Titeca et al. bioRxiv, 2023
Erawijantari et al. medRxiv, 2023
Dimitrov et al. bioRxiv, 2023
Burtscher et al. bioRxiv, 2023
Lobentanzer et al. arxiv, 2023
Lerma-Martin, Badia-i-Mompel et al. bioRxiv, 2022
Ma et al. bioRxiv, 2020
A unifying framework for biomedical research knowledge graphs
More about BioCypher
BioCypher is an open framework for creating versatile knowledge graphs that are grounded in ontologies, with a particular focus on user-friendliness and usability. We streamline the entire process of knowledge representation from primary resources to downstream usage in reproducible and containerised pipelines, and maintain a community for developing and sharing these pipelines.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/BioCypher | biocypher.org/ |
Lobentanzer et al., Nat Biotechnol, 2023 |
Find causal paths upstream of transcription factors in signaling networks from transcriptomics
More about CARNIVAL
CARNIVAL (CAusal Reasoning for Network identification using Integer VALue programming) allows to derive perturbed signalling pathways topology after drugs perturbation based on gene expression data. CARNIVAL is available as an R package, also in Bioconductor.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/carnival | saezlab.github.io/CARNIVAL |
Liu et al., NPJ Syst Biol Appl, 2019 |
Create logic models of signaling networks and train them with perturbation data
More about CellNOpt
CellNetOptimizer (CellNOpt) is a toolbox for creating logic-based models of signal transduction networks, and training them against high-throughput biochemical data, and is freely available both for R and matlab.
Code Repository | Website | Publication |
---|---|---|
github.com/cellnopt/cellnopt | saezlab.github.io/CellNOptR/ |
Gjerga E et al., Bioinformatics, 2020 |
Collection of Transcriptional Regulatory Interactions
More about CollecTRI
The CollecTRI-derived regulons contain signed transcription factor (TF) – target gene interactions compiled from 12 different resources. This collection provides an increased coverage of transcription factors and was benchmarked against other known GRNs, showing a superior performance in identifying perturbed TFs based on gene expression data using the knockTF data sets.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/CollecTRI | github.com/saezlab/CollecTRI |
Müller-Dott et al., Nucleic Acids Res, 2023 |
Mechanistic integration of multi-omics with prior knowledge into causal networks
More about COSMOS
COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets. COSMOS leverages extensive prior knowledge of signaling pathways, metabolic networks, and gene regulation with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. This pipeline can provide mechanistic explanations for experimental observations across multiple omic data sets.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/COSMOS/ | saezlab.github.io/cosmosR/ |
Dugourd et al., Mol Syst Biol, 2021 |
Estimate biological activities from omics data
More about decoupleR
Many methods allow us to extract biological activities from omics data using information from prior knowledge resources. decoupleR is a Bioconductor and Python package containing different statistical methods to extract these signatures within a unified framework. It can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomic, gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites targeted by a kinase.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/decoupler-py | decoupler-py.readthedocs.io/en/latest/index.html |
Badia-i-Mompel et al., Bioinformatics Advances, 2022 |
Manually curated human regulons of genes downstream of Transcription Factors
More about DoRothEA
A collection of genes controlled by Transcription Factors (regulons) obtained from different resources from manually curated to inferred from transcriptomic. It can be used to estimate Transcription Factors activities using decoupler.
An optimization framework for transferring cell features from a reference data to spatial omics
More about DOT
DOT is an optimization framework for transferring cell features (such as cell types/states) from a reference data (such as single-cell RNA-seq) to spots/cells in spatial omics across diverse platforms, tissue types, and resolutions. DOT captures heterogeneity in cell populations, utilizes modality specific features such as spatial information, and offers absolute abundance at different locations.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/DOT | saezlab.github.io/DOT |
Rahimi et al., arxiv, 2023 |
Framework to infer inter- and intra-cellular signalling from single-cell and spatial omics
More about LIANA+
LIANA+ is a scalable framework to decode coordinated inter- and intra-cellular signalling events from single-cell and spatially-resolved omics data. It enables comparative analyses across experimental conditions and supports the incorporation of diverse molecular mediators, such as those identified via multi-omics datasets. Thereby, LIANA+ provides an all-in-one solution to contemporary cell-cell communication inference.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/liana-py | liana-py.readthedocs.io |
Dimitrov et al., bioRxiv, 2023 |
Explainable machine learning models for single-cell, highly multiplexed, spatially resolved data
The Multiview Intercellular SpaTial modeling framework (MISTy) is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/mistyR/ | saezlab.github.io/mistyR/ |
Tanevski et al., bioRxiv, 2020 |
Molecular prior knowledge from more than 170 databases. Pathways, intercellular communication and more.
More about OmniPath
OmniPath is a comprehensive collection of molecular prior knowledge such as literature curated human and rodent signaling pathways, enzyme-substrate interactions, protein complexes, molecular annotations (function, localization and many others) and inter-cellular communication roles. OmniPath is built by pypath, a powerful Python module for molecular networks and pathways analysis. It is available by a web service at https://omnipathdb.org/, an R/Bioconductor package OmnipathR, a Python client and the OmniPath Cytoscape app.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/pypath | omnipathdb.org/ |
Türei et al., Mol Syst Biol, 2021 Türei et al., Nat Methods, 2016 Ceccarelli F et al., Bioinformatics, 2019 |
Collection of target genes (footprints) of signaling pathways
More about PROGENy
PROGENy (Pathway RespOnsive GENes) is a collection of target genes of diverse signaling pathways. It can be used to infer activity of signaling pathways from transcriptomics data using decoupler.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/progeny | saezlab.github.io/progeny/ |
Schubert et al., Nat Commun, 2018 Holland et al., Biochim Biophys Acta Gene Regul Mech, 2019 |
R package for the randomisation of bipartite graphs.
More about Birewire
Birewire is an R package implementing high-performing routines for the randomisation of bipartite graphs preserving their node degrees (i.e. Network Rewiring), through the Switching Algorithm (SA) . BiRewire analytically estimates the number of Switching Steps to be performed in order for the similarity between the original network and its rewired version to reach a plateau (i.e. to achieve the maximal level of randomness) according to the lower bound.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/BiRewire | saezlab.github.io/BiRewire/ |
Iorio et al., Bmc Bioinformatics, 2016 |
Find causal paths upstream of transcription factors in signaling networks from transcriptomics
More about CARNIVAL
CARNIVAL (CAusal Reasoning for Network identification using Integer VALue programming) allows to derive perturbed signalling pathways topology after drugs perturbation based on gene expression data. CARNIVAL is available as an R package, also in Bioconductor.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/carnival | saezlab.github.io/CARNIVAL |
Liu et al., NPJ Syst Biol Appl, 2019 |
Create logic models of signaling networks and train them with perturbation data
More about CellNOpt
CellNetOptimizer (CellNOpt) is a toolbox for creating logic-based models of signal transduction networks, and training them against high-throughput biochemical data, and is freely available both for R and matlab.
Code Repository | Website | Publication |
---|---|---|
github.com/cellnopt/cellnopt | saezlab.github.io/CellNOptR/ |
Gjerga E et al., Bioinformatics, 2020 |
Unified framework for network inference problems
More about CORNETO
CORNETO is a unified framework for integrating common network inference problems in biology through constraint programming. It supports tasks like the contextualisation of signalling networks or inference of metabolic networks from Genome-Scale Models with full support of Flux Balance Analysis. It provides a flexible Python API for model creation and optimisation with a wide variety of solvers.
Code Repository | Website |
---|---|
github.com/saezlab/corneto | github.com/saezlab/corneto |
Mechanistic integration of multi-omics with prior knowledge into causal networks
More about COSMOS
COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets. COSMOS leverages extensive prior knowledge of signaling pathways, metabolic networks, and gene regulation with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. This pipeline can provide mechanistic explanations for experimental observations across multiple omic data sets.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/COSMOS/ | saezlab.github.io/cosmosR/ |
Dugourd et al., Mol Syst Biol, 2021 |
An optimization framework for transferring cell features from a reference data to spatial omics
More about DOT
DOT is an optimization framework for transferring cell features (such as cell types/states) from a reference data (such as single-cell RNA-seq) to spots/cells in spatial omics across diverse platforms, tissue types, and resolutions. DOT captures heterogeneity in cell populations, utilizes modality specific features such as spatial information, and offers absolute abundance at different locations.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/DOT | saezlab.github.io/DOT |
Rahimi et al., arxiv, 2023 |
Code used in the scoring of DREAM challenges.
More about DREAMTools
DREAMTools provides the code used in the scoring of DREAM challenges that pose fundamental questions about system biology and translational medicine.
Code Repository | Website | Publication |
---|---|---|
github.com/dreamtools/dreamtools | dreamtools.readthedocs.io/en/latest/ |
Cokelaer et al., F1000Res, 2015 |
R/Cytoscape pipeline to compare drug and disease gene expression profiles.
More about DrugVsDisease
DrugVsDisease (DvD) provides a pipeline, available through R or Cytoscape, for the comparison of drug and disease gene expression profiles from public microarray repositories.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/DrugVsDisease | saezlab.github.io/DrugVsDisease/ |
Pacini et al., Bioinformatics, 2012 |
Python library dedicated to the study pharmacogenomic relationships.
More about GDSCTools
GDSCTools is an open-source Python library dedicated to the study pharmacogenomic relationships in the context of the GDSC (Genomics of Drug Sensitivity in Cancer) project. The main developer is Thomas Cokelaer (Institut Pasteur), and it is a joint effort with the groups of Mathew Garnett (Sanger Institute) and Julio Saez-Rodriguez.
GDSCTools is hosted by GitHub and documented here.
Code Repository | Website | Publication |
---|---|---|
github.com/CancerRxGene/gdsctools | gdsctools.readthedocs.io/en/master/ |
Cokelaer et al., Bioinformatics, 2018 |
Estimate ligand-receptor interactions from single-cell transcriptomics using a variety of resources and methods
More about LIANA
LIANA: a LIgand-receptor ANalysis frAmework to estimate ligand-receptor interactions from single-cell transcriptomics using different resources and methods. Specifically, it decouples the methods from their corresponding resources, and enables any method to be used with any resource. It further calculates a consensus across the different methods.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/liana | saezlab.github.io/liana/ |
Dimitrov et al., Nat Commun, 2022 |
Framework to infer inter- and intra-cellular signalling from single-cell and spatial omics
More about LIANA+
LIANA+ is a scalable framework to decode coordinated inter- and intra-cellular signalling events from single-cell and spatially-resolved omics data. It enables comparative analyses across experimental conditions and supports the incorporation of diverse molecular mediators, such as those identified via multi-omics datasets. Thereby, LIANA+ provides an all-in-one solution to contemporary cell-cell communication inference.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/liana-py | liana-py.readthedocs.io |
Dimitrov et al., bioRxiv, 2023 |
Python module for lipidomics LC MS/MS data analysis
More about lipyd
Code Repository | Website |
---|---|
github.com/saezlab/lipyd | saezlab.github.io/lipyd |
Global optimization toolbox including metaheuristic and Bayesian methods.
More about MEIGO
MEIGO is a global optimization toolbox that includes a number of metaheuristic methods as well as a Bayesian inference method for parameter estimation. It is developed jointly with the group of Julio Banga.
MEIGO is described in (Egea et al, BMC Bioinformatics, 2014), and hosted here.
Website | Publication |
---|---|
gingproc.iim.csic.es/meigo.html |
Egea et al., Bmc Bioinformatics, 2014 |
Metabolic enzyme enrichment analysis
More about ocEAn
OCEAN: a method that defines metabolic enzyme footprint from a curated reduced version of the recon2 reaction network. The metabolic enzyme footprints are used to explore coordinated deregulations of metabolite abundances with respect to their position relative to metabolic enzymes. This is similar to Kinase-substrate and TF-targets enrichment analyses.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/ocean | saezlab.github.io/ocean |
Sciacovelli, Dugourd, et al., Nat Commun, 2022 |
Build causal signaling networks from untargeted mass-spectrometry Phosphoproteomics and prior knowledge
More about PHONEMeS
PHONEMEs builds logic models from discovery mass-spectrometry based Phosphoproteomic data.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/PHONEMeS/ | saezlab.github.io/PHONEMeS/ |
Terfve et al., Nat Commun, 2015 Gjerga et al. et al., J Prot Research, 2021 |
R package to identify pathway-level enrichments of genetic alterations.
More about SLAPenrich
SLAPEnrich, a statistical method implemented in an open source R package, to identify pathway-level enrichments of genetic alterations.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/SLAPenrich | saezlab.github.io/SLAPenrich/ |
Iorio et al., Sci Rep, 2018 |
A platform for the biomedical application of Large Language Models
More about BioChatter
BioChatter is an open framework for collecting and providing functionalities around the application of Large Language Models (LLMs), including connectivity to knowledge graphs created in BioCypher. We cover aspects such as Retrieval Augmented Generation and prompt engineering in the biomedical context, and pay particular respect to evaluating model performance of proprietary and open-source LLMs.
Code Repository | Website | Publication |
---|---|---|
github.com/biocypher/biochatter | biocypher.github.io/biochatter |
Lobentanzer et al., arxiv, 2023 |
A unifying framework for biomedical research knowledge graphs
More about BioCypher
BioCypher is an open framework for creating versatile knowledge graphs that are grounded in ontologies, with a particular focus on user-friendliness and usability. We streamline the entire process of knowledge representation from primary resources to downstream usage in reproducible and containerised pipelines, and maintain a community for developing and sharing these pipelines.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/BioCypher | biocypher.org/ |
Lobentanzer et al., Nat Biotechnol, 2023 |
Python package to access Bioinformatics Web Services.
More about BioServices
BioServices is a Python package that provides access to many Bioinformatics Web Services (e.g., UniProt) and a framework to easily implement Web Service wrappers (based on WSDL/SOAP or REST protocols).
Code Repository | Website | Publication |
---|---|---|
github.com/cokelaer/bioservices | pypi.python.org/pypi/bioservices |
Cokelaer et al., Bioinformatics, 2013 |
Collection of Transcriptional Regulatory Interactions
More about CollecTRI
The CollecTRI-derived regulons contain signed transcription factor (TF) – target gene interactions compiled from 12 different resources. This collection provides an increased coverage of transcription factors and was benchmarked against other known GRNs, showing a superior performance in identifying perturbed TFs based on gene expression data using the knockTF data sets.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/CollecTRI | github.com/saezlab/CollecTRI |
Müller-Dott et al., Nucleic Acids Res, 2023 |
Manually curated human regulons of genes downstream of Transcription Factors
More about DoRothEA
A collection of genes controlled by Transcription Factors (regulons) obtained from different resources from manually curated to inferred from transcriptomic. It can be used to estimate Transcription Factors activities using decoupler.
Database of protein-metabolite and small molecule ligand-receptor interactions
More about MetalinksDB
Interactions between proteins and metabolites are key for cellular function, from the catalytic breakdown of nutrients to signaling. An important case is cell-cell communication, where cellular metabolites are secreted into the microenvironment and initiate a signaling cascade by binding to an intra- or extracellular receptor of another cell. While protein-protein mediated cell-cell communication is routinely inferred from transcriptomic data, for metabolite-protein interactions this is challenging due to the limitations of high-throughput single-cell and spatial metabolomics technologies, together with the absence of comprehensive prior knowledge resources that include metabolites. Here we report MetalinksDB, a comprehensive and flexible database of intercellular metabolite-protein interactions that is a magnitude larger than existing ones. MetalinksDB can be tailored to specific biological contexts such as diseases, pathways, or tissue/cellular locations by querying subsets of interactions using the web interface (https://metalinks.omnipathdb.org/) or the knowledge graph adapters.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/MetalinksDB | metalinks.omnipathdb.org/ |
Farr et al., bioRxiv, 2023 |
Molecular prior knowledge from more than 170 databases. Pathways, intercellular communication and more.
More about OmniPath
OmniPath is a comprehensive collection of molecular prior knowledge such as literature curated human and rodent signaling pathways, enzyme-substrate interactions, protein complexes, molecular annotations (function, localization and many others) and inter-cellular communication roles. OmniPath is built by pypath, a powerful Python module for molecular networks and pathways analysis. It is available by a web service at https://omnipathdb.org/, an R/Bioconductor package OmnipathR, a Python client and the OmniPath Cytoscape app.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/pypath | omnipathdb.org/ |
Türei et al., Mol Syst Biol, 2021 Türei et al., Nat Methods, 2016 Ceccarelli F et al., Bioinformatics, 2019 |
Collection of target genes (footprints) of signaling pathways
More about PROGENy
PROGENy (Pathway RespOnsive GENes) is a collection of target genes of diverse signaling pathways. It can be used to infer activity of signaling pathways from transcriptomics data using decoupler.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/progeny | saezlab.github.io/progeny/ |
Schubert et al., Nat Commun, 2018 Holland et al., Biochim Biophys Acta Gene Regul Mech, 2019 |
The wordcloud is based on the number of joint publications with our collaborators
We are thankful to our present and past collaborators, including (in alphabetic order):
Duration | Name | Agency |
---|---|---|
2024-2030 | Functional cartography of intestinal host-microbiome interactions (CartoHostBug) |
European Research Council (Synergy Grant) |
2022-2026 | German Research Council (DFG) | |
2022-2025 | German Ministry of Education and Research (BMBF) | |
2021-2026 | DECIDER: Improved clinical decisions via integrating multiple data levels to overcome chemotherapy resistance in high-grade serous ovarian cancer |
European Union |
2021-2026 | LiSyM-Krebs: Mechanism-based Multiscale Model to Dissect the Tipping Point from Liver Cirrhosis to Hepatocellular Carcinoma |
German Ministry of Education and Research (BMBF) |
2021-2025 | Dissecting IgA nephropathy via integration of multi-omics data |
German Research Council (DFG) |
2021-2024 | DeepSC2: Deep learning for single-cell genomics in cancer |
German Ministry of Education and Research (BMBF) |
2021-2023 | Changes in the microbiome in PsA patients undergoing biologics therapy |
Centers for Personalized Medicine Baden-Württemberg |
2021 | Analysis of essentiality in cancer using CRISPR-Cas9 screenings |
Sanofi |
2020-2026 | MSCare-A Systems Medicine Approach to Stratification of Cancer Recurrence |
German Ministry of Education and Research (BMBF) |
2020-2024 | Analysis of multi-omics and spatial transcriptomics data |
GSK |
2020-2021 | BioDATEN : Bioinformatics DATa Environment |
Ministry of Science, Research and Art, Baden Wuerttemberg |
2020-2021 | Single cell resolution of human chronic kidney disease for precision medicine in nephrology |
DFG German Research Council |
2020 - 2024 | StrategyCKD: System omics to unravel the gut-kidney axis in Chronic Kidney Disease |
European Uunion H220-MSCA-ITN |
2020 - 2022 | HPC Center of Excellence in Personalised Medicine – PerMedCoE |
European Union (H2020-EU.1.4.1.3) |
2019-2022 | individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology |
European Union H2020 Health |
2019-2022 | LaMarcK: Longitudinal Multiomics Characterization of disease using prior Knowledge |
German Ministry of Education and Research (BMBF) |
2019-2022 | Validating microfluidics-based personalized cancer therapy in mouse models |
DFG German Research Council |
2018-2028 | Informatics for Life |
Klaus-Tschira Stiftung |
2017-2021 | TransQST: Translational quantitative systems toxicology to improve the understanding of the safety of medicines |
EU IMI Innovative Medicines Initiative |
2017-2018 | Analysis of mass spectrometry proteomics and drug treatment |
OncoSignature/Acrivon |
2016-2020 | LiSym: Liver systems medicine |
BMBF |
2016-2019 | SYS4MS: Personalizing health care in Multiple Sclerosis using sytems medicine tools |
BMBF |
2016-2018 | PrECISE: Personalized engine for Cancer integrative study and evaluation |
EU H2020 Health |
2016-2018 | CPTAC DREAM Challenge |
National Cancer Institute (USA) |
2015-2019 | Joint Research Center for Computational Biomedicine |
Bayer AG |
2015-2018 | SyMBioSys : Systematic Models for Biological Systems Engineering Training Network |
EU H2020 Health MSCA-ITN |
2014-2016 | Novel Target Identification Through Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cancer Cell Lines |
Center for Therapeutic Target Validation |
2014-2016 | Target identification and validation using pathway activities derived from functional genomics data |
Center for Therapeutic Target Validation |
2013-2014 | EU H2020 Health | |
2012-2015 | EU H2020 Health | |
2012-2014 | Understanding drug mode of action via statistical integration of functional genomic studies and literature-derived signalling networks |
Medical Research Council (UK) |
2011-2012 | Analysis of mass spectrometry phosphoproteomics data in the context of insulin signal processing and functional alterations thereof |
Sanofi |
For all positions, candidates should email their CV and a letter of interest to jobs.saez {at} uni-heidelberg.de, including names of (ideally 3) references. The letter of interest has to be tailored to our group, mentioning projects or articles of our group that you find interesting, and explaining how you would fit in our group. Please also provide a pointer to a code repository if possible. Non-specific applications without this tailored expression of interest or sent to a different address will not be considered.