Genomics
Proteomics
Metabolomics
Data analysis, integration & stewardship
Online workshop series “Strategies to overcome your challenges in multi-omics data integration”
X-omics is inviting you to an online workshop series on challenges and solutions for multi-omics data integration.
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Thursday, June 18, 2020 10 AM - 12 PM CET
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Data standards and multi-omics data integration In the first workshop, you will learn about common standards for different types of omics data, public databases, and FAIR omics data. We invited experts that will explain how to lay the foundations for successful multi-omics data integration. |
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Thursday, June 22, 2020 12 PM - 2 PM CET |
Linked data in practice: An RDF-based approach with SPARQLing-genomics We look at an example of how to create and query linked data. |
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Thursday, June 25, 2020 2 PM - 4 PM CET |
Showcases of multi-omics data integration The third session will highlight showcases of multi-omics data integration. You will learn first-hand what challenges other researchers experienced and how they approached them. |
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Tuesday, June 30, 2020 10 AM - 12 PM CET |
Pitch your own multi-omics project Finally, we invite you to pitch your own multi-omics project and discuss it with other researchers and experts from the field. Pitch your project |
Save the dates!
More details on the program will be added in the coming days.
Registration will be open soon!
The X-omics project management team.
Big data in the fight against Covid-19 – Podcast
Peter-Bram ‘t Hoen, our core team member and leader of the data pillar was invited to give his opinion on the use of big data in the fight against the Covid-19 pandemic.
On Tuesday 19th May he talked about the Personal Health Train project as an efficient way of making health data accessible In the NPO podcast “De Dag”
Interested in what he has to say? Listen to the podcast:
Webinar of the Nightingale Metabolomics platform
About the improvements and other developments at Nightingale Health.
Where 
Online
When 
June 10 2020 - 10am - 11am CET
About the improvements and other developments at Nightingale Health.
#1: Data standards and multi-omics data integration
In the first workshop, you will learn about common standards for different types of omics data, public databases, and FAIR omics data. We invited experts that will explain how to lay the foundations for successful multi-omics data integration.
Where Online
When June 18 2020 - 10am - 12pm CET
Anna Niehues
Anna Niehues is a postdoc at Radboudumc, Nijmegen, where she works at the Center for Molecular and Biomolecular Informatics (CMBI), led by Peter-Bram ’t Hoen, and the Translational Metabolic Laboratory, led by Alain van Gool. She is currently involved in the X-omics project and the European EATRIS-Plus project. Anna previously studied Biosciences at the University of Münster (WWU) in Germany, where she also obtained her PhD. She has been working as a bioinformatician on mass spectrometry data analysis in the context of quantitative proteomics, and structural analysis of carbohydrate oligo- and polymers.
Within the X-omics project, she works as data scientist in the data analysis, integration and stewardship pillar, together with Peter-Bram ’t Hoen (work package leader) and Gurnoor Singh (data steward) from Radboudumc and other X-omics collaborators across the Netherlands. She is interested in the integration of mass spectrometry-derived data such as metabolomics data with other types of omics data in order to e.g. increase the power of biomarker studies or gain additional insights into the relationships between different molecular levels and their association with disease phenotypes. She is also interested in FAIR data analysis workflows which, alongside FAIR data, provide the means for reproducibility of multi-omics data analyses.
Gurnoor Singh
Gurnoor Singh works as a data-steward at the CMBI. After completing his Masters in Life Science informatics from the University of Bonn, Gurnoor pursued a Ph.D. in developing linked data graphs from genomics data from Wageningen University & Research. For the X-omics project, Gurnoor is working towards standardizing meta-data and data schema for harmonizing and fairify X-omics data workflows. He is also involved in the FAIR Genomes project, which aims to develop a national guideline to promote optimal (re)use of NGS data in research and healthcare. Gurnoor Singh strongly believes in the usability of the FAIR data principles in the creation of semantic data infrastructure for diverse X-omics data.
Gurnoor and Anna are coordinating the data FAIRification and multi-omics integration tasks, respectively, of the first X-omics data integration and FAIRification demonstrator, which is a collaboration between X-omics and NTR. They are both also involved in conducting various workshops for adequate data management and planning for research data.
The publications of the X-omics consortium will be listed here!
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2020
Dekker, LJM. et al. (2020). Metabolic changes related to the IDH1 mutation in gliomas preserve TCA-cycle activity: An investigation at the protein level. FASEB J. 34, 3646 – 3657.
PMID: 31960518Lamers, MM. et al. (2020). SARS-CoV-2 productively infects human gut enterocytes. Science.
PMID: 32358202Zajec, M. et al. (2020). Integrating Serum Protein Electrophoresis with Mass Spectrometry, A New Workflow for M-Protein Detection and Quantification. J Proteome Res.
PMID: 31895568 -
2019
Körner, A. et al. (2019). Inhibition of ∆24-dehydrocholesterol reductase activates pro-resolving lipid mediator biosynthesis and inflammation resolution. Proc Natl Acad Sci U S A. 116, 20623 – 20634.
PMID: 31548397Lageveen-Kammeijer, GSM. et al. (2019). Highly sensitive CE-ESI-MS analysis of N-glycans from complex biological samples. Nat Commun. 10, 2137.
PMID: 31086181Palmblad, M. et al. (2019). Automated workflow composition in mass spectrometry-based proteomics. Bioinformatics. 35, 656 – 664.
PMID: 30060113van der Kant, R. et al. (2019). Cholesterol Metabolism Is a Druggable Axis that Independently Regulates Tau and Amyloid-β in iPSC-Derived Alzheimer’s Disease Neurons. Cell Stem Cell. 24, 363-275.
PMID: 30686764Weintraub, ST. et al. (2019). Special Issue on Software Tools and Resources: Acknowledging the Toolmakers of Science. J Proteome Res. 18, 575.
PMID: 30704244Lacobucci, C. et al. (2019). First Community-Wide, Comparative Cross-Linking Mass Spectrometry Study. Anal Chem. 91, 6953-6961.
PMID: 31045356Masson, GR. et al. (2019). Recommendations for performing, interpreting and reporting hydrogen deuterium exchange mass spectrometry (HDX-MS) experiments. Nat Methods. 16, 595-602.
PMID: 31249422Ressa, A. et al. (2019). PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis. J Proteome Res. 18, 576-584.
PMID: 30525654Greisch, JF. et al. (2019). Expanding the mass range for UVPD-based native top-down mass spectrometry. Chem Sci. 10, 7163-7171.
PMID: 31588283van der Laarse, SAM. et al. (2019). Targeting proline in (phospho)proteomics. FEBS J.
PMID: 31863553van der Laan, T. et al. (2019). Fast LC-ESI-MS/MS analysis and influence of sampling conditions for gut metabolites in plasma and serum. Sci Rep. 9, 12370.
PMID: 31451722 -
older
Van Damme, T. et al. (2017). Mutations in ATP6V1E1 or ATP6V1A …. Am. J. Hum. Genet. 100, 216–227.
van Gool, A. J. et al. (2017). Bridging the translational innovation gap …. Nat. Rev. Drug Discov.
Snijder, J. et al. (2017). Structures of the cyanobacterial circadian oscillator …. Science 355, 1181–1184.Blokzijl, F. et al. (2016). Tissue-specific mutation accumulation in human …. Nature 538, 260–264.
Jansen, E. J. R. et al. (2016). ATP6AP1 deficiency causes an immunodeficiency …. Nat. Commun. 7, 11600.
Liepe, J. et al. (2016). A large fraction of HLA class I ligands are …. Science 354, 354–358.
Tarailo-Graovac, M. et al. (2016). Exome Sequencing and the … .N. Engl. J. Med. 374, 2246–2255.
van Karnebeek, C. D. M. et al. (2016). NANS-mediated synthesis of sialic acid …. Nat. Genet. 48, 777–784.Huch, M. et al. (2015). Long-term culture of genome-stable bipotent stem cells…. Cell 160, 299–312.
Liu, F., Rijkers, et. al. (2015). Proteome-wide profiling of protein a…. Nat. Methods 12, 1179–84.Dane, A. D. et al. (2014). Integrating metabolomics profiling …. Anal. Chem. 86, 4110–4.
Hussein, S. M. I. et al. (2014). Genome-wide characterization of…. Nature 516, 198–206.
Tegtmeyer, L. C. et al. (2014). Multiple Phenotypes in … N. Engl. J. Med. 370, 533–542.
van Heesch, S. et al. (2014). Genomic and functional overlap between somatic and …. Cell Rep. 9, 2001–10.Altelaar, A. F. M., et. al. (2013). Next-generation proteomics: …. Nat. Rev. Genet. 14, 35–48.
Gonzalez-Covarrubias, V. et al. (2013). Lipidomics of familial longevity. Aging Cell 12, 426–34.
Kettleborough, R. N. W. et al. (2013). A systematic genome-wide analysis of…. Nature 496, 494–7.
Low, T. Y. et al. (2013). Quantitative and qualitative proteome characteristics. Cell Rep. 5, 1469–78.
Peironcely, J. E. et al. (2013). Automated pipeline for de novo …. Anal. Chem. 85, 3576–83.
Raterink, R.-J et. al. (2013). T. Three-phase electroextraction: …. Anal. Chem. 85, 7762–8.van der Kloet, F. M. et al. (2012). Discovery of early-stage biomarkers …Metabolomics 8, 109–119.
This research was (partially) funded by NWO, project 184.034.019