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REACTOMEgsa

Extending the REACTOME Pathway Database for multi-omics biomedical data analysis

Total Cost €

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EC-Contrib. €

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Partnership

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Project "REACTOMEgsa" data sheet

The following table provides information about the project.

Coordinator
EUROPEAN MOLECULAR BIOLOGY LABORATORY 

Organization address
address: Meyerhofstrasse 1
city: HEIDELBERG
postcode: 69117
website: http://www.embl.de

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Project website https://reactome.github.io/ReactomeGSA
 Total cost 97˙727 €
 EC max contribution 97˙727 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2017
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2018
 Duration (year-month-day) from 2018-08-01   to  2019-07-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    EUROPEAN MOLECULAR BIOLOGY LABORATORY DE (HEIDELBERG) coordinator 97˙727.00

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 Project objective

The increasing availability of high-throughput ‘omics technologies results in unprecedented opportunities for precision medicine and biomedical research. With the increasing availability of large amounts of data (‘big data’), data analysis and interpretation have become a major bottleneck.

Pathway analysis techniques are used to incorporate existing biological knowledge into the data analysis and allow researchers to focus on the interpretation of regulated biological processes. Despite the existence of many advanced pathway analysis algorithms, most leading pathway analysis resources still rely on simplistic ‘gene set over representation’ analyses (ORA) and cannot integrate data from different ‘omics approaches. Reactome is one of the most popular resources for pathway information. Its open-access data model, powerful web interface, and stringent manual curation and peer-review provide an ideal foundation for this project’s developments.

In this project, I will extend Reactome towards an analysis platform for multi-omics biomedical studies. I will replace its current ORA approach with more sophisticated pathway algorithms supporting transcriptomics, microarray, proteomics, and metabolomics data. Next, I will extend Reactome to analyse datasets of samples that cannot be attributed to a phenotype. The extended version of Reactome will be able to derive one expression value per pathway which can then be correlated with clinical parameters to identify clinical relevant biological processes. This will make Reactome a prime resource for multi-omics biomedical studies. I anticipate that the successful completion of this project will allow me to integrate and extend my current skills as a medical doctor and a bioinformatician towards systems biology studies. The additional gained experience in project management and communication of scientific results will form the basis for my envisaged career to start my own interdisciplinary biomedical research group.

 Publications

year authors and title journal last update
List of publications.
2019 Johannes Griss, Wolfgang Bauer, Christine Wagner, Martin Simon, Minyi Chen, Katharina Grabmeier-Pfistershammer, Margarita Maurer-Granofszky, Florian Roka, Thomas Penz, Christoph Bock, Gao Zhang, Meenhard Herlyn, Katharina Glatz, Heinz Läubli, Kirsten D. Mertz, Peter Petzelbauer, Thomas Wiesner, Markus Hartl, Winfried F. Pickl, Rajasekharan Somasundaram, Peter Steinberger, Stephan N. Wagner
B cells sustain inflammation and predict response to immune checkpoint blockade in human melanoma
published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-019-12160-2
Nature Communications 10/1 2020-01-22
2019 Johannes Griss, Goran Vinterhalter, Veit Schwämmle
IsoProt: A Complete and Reproducible Workflow To Analyze iTRAQ/TMT Experiments
published pages: 1751-1759, ISSN: 1535-3893, DOI: 10.1021/acs.jproteome.8b00968
Journal of Proteome Research 18/4 2020-01-22
2019 Johannes Griss, Florian Stanek, Otto Hudecz, Gerhard Dürnberger, Yasset Perez-Riverol, Juan Antonio Vizcaíno, Karl Mechtler
Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins
published pages: 1477-1485, ISSN: 1535-3893, DOI: 10.1021/acs.jproteome.8b00377
Journal of Proteome Research 18/4 2020-01-22

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The information about "REACTOMEGSA" are provided by the European Opendata Portal: CORDIS opendata.

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