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 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.
Julio Saez-Rodriguez
More...Erika Schulz
More...Lydia Roeder
More...Hanna Schumacher
More...Attila Gabor
More...Aurélien Dugourd
More...Jovan Tanevski
More...Martín Garrido Rodríguez-Córdoba
More...Denes Turei
More...Jan Lanzer
More...Ricardo O. Ramirez-Flores
More...Arezou Rahimi
More...Sebastian Lobentanzer
More...Ahmet Sureyya Rifaioglu
More...Pablo Rodríguez Mier
More...Ece Kartal
More...Chang Lu
More...Christina Schmidt
More...José Liñares Blanco
More...Maria Puschhof
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 |
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 |
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
Wolf et al. Hepatol Commun, 2023
Lanzer et al. Bmc Med, 2023
Badia-I-Mompel et al. Nat Rev Genet, 2023
Lobentanzer et al. Nat Biotechnol, 2023
Sun et al. Mol Cell Proteomics, 2023
Polychronidou et al. Mol Syst Biol, 2023
Heumos et al. Nat Rev Genet, 2023
Nangaku et al. Kidney Int, 2023
Solé et al. Cell Mol Immunol, 2023
Salovska et al. Clin Transl Med, 2023
Paton et al. bioRxiv, 2023
Dimitrov et al. bioRxiv, 2023
Burtscher et al. bioRxiv, 2023
Masarapu et al. Res Sq, 2023
Lanzer et al. bioRxiv, 2023
Lobentanzer et al. arxiv, 2023
Baghdassarian et al. bioRxiv, 2023
Müller-Dott et al. bioRxiv, 2023
Kennedy et al. bioRxiv, 2023
Ramirez et al. bioRxiv, 2023
Valdeolivas et al. bioRxiv, 2023
Wijaya et al. bioRxiv, 2023
Andorra et al. Res Sq, 2023
Rahimi et al. arxiv, 2023
Niarakis et al. bioRxiv, 2022
Lerma-Martin, Badia-i-Mompel et al. bioRxiv, 2022
Ghasemi et al. bioRxiv, 2022
Triantafyllidis et al. bioRxiv, 2022
White et al. bioRxiv, 2022
Sun et al. bioRxiv, 2022
Ma et al. bioRxiv, 2020
A unifying framework for biomedical research knowledge graphs
More about BioCypher
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/BioCypher | biocypher.org/ |
Lobentanzer et al et al., arxiv, 2022 |
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 |
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.
Estimate ligand-receptor interactions from single-cell transcriptomics from different resources and methods
More about LIANA
The continuous developments of single-cell RNA-Seq (scRNA-Seq) have sparked an immense interest in understanding intercellular crosstalk. Multiple tools and resources aiming the investigation of cell-cell communication (CCC) were published recently. However, these methods and resources are usually fixed combinations of a tool and its corresponding resource, although potentially any resource could be combined with any method. To this end, we built LIANA—a framework to decouple the tools from their corresponding resources. LIANA is able to infer cell-cell communication from scRNA-Seq using 6 tools and 15 resources in a single framework. This way not only it makes the methods more accessible, but also facilitates their comparison and benchmark.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/liana | saezlab.github.io/liana/ |
Dimitrov et al., Nat Commun, 2022 |
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 |
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 |
Molecular prior knowledge from more than 100 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 |
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 |
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 |
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 |
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 |
Interface between R and Cytoscape.
More about Cyrface
Cyrface establishes an interface between R and Cytoscape by using different Java-R libraries, e.g. Rserve, RCaller. Cyrface can be used as a Cytos cape plug-in, e.g. to run R commands within Cytoscape, or used as a library to allow your plug-in to connect to R.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/cyrface/ | saezlab.github.io/cyrface/ |
Gonçalves et al., F1000Res, 2013 |
Cytoscape plug-in for SBGN maps.
More about CySBGN
CySBGN is a Cytoscape plug-in that extends the use of Cytoscape visualization and analysis features to SBGN maps. CySBGN adds support to Cytoscape to import, export, visualize, validate and analyse SBGN maps.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/cysbgn | saezlab.github.io/cysbgn/ |
Gonçalves et al., Bmc Bioinformatics, 2013 |
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 |
User-friendly interface to analyze multiomic data using footprint methods
More about FUNKI
FUNKI (FUNctional analysis KIt) is an user-friendly interface developed in R, and designed using Shiny, to analyze multiomic data (mainly transcriptomics, phosphoproteomics and metabolomics) using footprint methods. FUNKI provides an interface for DoRothEA, PROGENy, KinAct, CARNIVAL and COSMOS. All methods run on multiple or differential analysis of bulk data for human samples. Besides, DoRothEA, PROGENy and CARNIVAL can also handle data coming from single-cell experiments and mouse.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/ShinyFUNKI | https://saezlab.github.io/ShinyFUNKI/ |
Hernansaiz-Ballesteros et al., arxiv, 2021 |
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 |
An R package for interactive network guided connectivity mapping.
More about iNRG-cMap
iNRG_cMap (iterative NetwoRk Guided connectivity Mapping) a strategy that refines these unbiased approaches by making use of prior knowledge about the analysed compounds to compute refined transcriptional signatures of drug response. Making use of transcriptional data from the Connectivity Map and building on the MANTRA method, iNRG_cMap is able to disentangle spurious effects due to non-relevant secondary drug effects, thus enhancing the predictive power of the resulting refined signatures.
Code Repository | Website | Publication |
---|---|---|
github.com/saezlab/iNRG_cMAP | saezlab.github.io/iNRG_cMAP/ |
Iorio et al., Plos One, 2015 |
Python package to infer kinase activities from phosphoproteomics datasets.
More about KinAct
KinAct is a python package with different computational methods to infer kinase activities from phosphoproteomics datasets.
Code Repository | Website |
---|---|
github.com/saezlab/kinact | saezlab.github.io/kinact/ |
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 |
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 |
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 |
---|---|---|
2021-2025 | DECIDER: Improved clinical decisions via integrating multiple data levels to overcome chemotherapy resistance in high-grade serous ovarian cancer |
European Union |
2021-2024 | LiSyM-Krebs: Mechanism-based Multiscale Model to Dissect the Tipping Point from Liver Cirrhosis to Hepatocellular Carcinoma |
German Ministry of Education and Research (BMBF) |
2021-2024 | 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 |
2020-2022 | MSCare-A Systems Medicine Approach to Stratification of Cancer Recurrence |
German Ministry of Education and Research (BMBF) |
2020-2022 | 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 |
2019-2021 | 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.d
– We are continuously looking for postdoctoral fellows, PhD students, and staff scientists to better understand and treat diseases including cancer and heart disease by analyzing multi-omics data sets, including single-cell and spatially resolved data. Candidates interested in using bioinformatics, machine learning, and mathematical modeling to analyze big data to advance personalized medicine are encouraged to contact us.
– We have opportunities for student assistants (HiWi), master/bachelor theses, and internships. In general, these are for a period of six months or longer, although shorter internships of 3-4 months are possible, in particular for local students. Besides the general information above, please include information on the lectures you have attended in your bachelor and (if already there) master courses.