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| − | <newstitle>Disease Module Identification DREAM Challenge</newstitle> | + | |
| | + | <newstitle>Patterning in the inner ear</newstitle> |
| | <teaser> | | <teaser> |
| − | Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse protein- protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Assessing 75 contributed module identification methods revealed novel top-performing algorithms, and established resource of modules correspond to core disease-relevant pathways for studying human disease biology (more info is in <a href="https://synapse.org/modulechallenge"> the challenge webpage </a> and <a href="https://www. biorxiv. org/ content/ 10.1101/265553v2"> the paper</a> ). | + | In mammals, the organ of Corti in the inner ear contains cells that can convert mechanical vibrations induced by sound into a neural signal. Those so-called hair cells are distributed along the cochlea together with another type of cells (supporting cells) in a neatly arranged checkerboard pattern. How this pattern emerges during development from an homogeneous layer of epithelial cells is the object |
| − | <date> 7 Dec 2018</date> | + | of our latest publication. Together with the group of David Sprinzak at the University of Tel Aviv, we showed that mechanical forces acting |
| | + | on the tissue, together with delta-notch signaling, drive this patterning process. Our study, which combines experiments and modeling |
| | + | has been published in <a href = "https://www. nature. com/ articles/ s41467-020-18894-8"> Nature Communications </a>. |
| | + | <date> 12 October 2020 </date> |
| | </teaser> | | </teaser> |
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| − | <newstitle>PhenoMeNal: processing and analysis of metabolomics data in the cloud. </newstitle>
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| − | <teaser>
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| − | As a member of the PhenoMeNal (Phenome and Metabolome aNalysis) consortium we contributed to set up a system of Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardised, automated, and published analysis workflows. The paper is published in <a href =" https://doi.org/10.1093/gigascience/giy149"> GigaScience </a>.
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| − | <date> 28 Jan 2019</date>
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| − | </teaser>
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| − | <newstitle>Quantifying information transfer in the GPCR signaling system</newstitle> | + | <newstitle>Disease Module Identification DREAM Challenge</newstitle> |
| | <teaser> | | <teaser> |
| − | G-protein coupled receptors (GPCR) constitute one of the most widespread class of receptors used by cell to get information on their external environment. However it was still debated whether the GPCR signaling system can measure more that the presence or absence of a compound binding to the receptor. Together with the team of Vladimir Katanaev of the Department of Pharmacology and Toxicology at UNIL, we quantified, using tools from information theory, the amount of information that the GPCR system can provide to a cell and showed that it can distinguish between multiple concentration levels. The paper has just been published in <a href="https://www. nature. com/ articles/ s41467-018-02868-y"> Nature Communications</a>. | + | Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Assessing 75 contributed module identification methods revealed novel top-performing algorithms, and established resource of modules correspond to core disease-relevant pathways for studying human disease biology (more info is in <a href="https://synapse.org/modulechallenge"> the challenge webpage </a > and <a href="https://www. biorxiv. org/ content/ 10.1101/265553v2"> the paper</a> ). |
| − | <date> 28 Feb 2018</date> | + | <date>28 Jan 2019</date> |
| | </teaser> | | </teaser> |
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| − | <newstitle>Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy</newstitle> | + | <newstitle>PhenoMeNal: processing and analysis of metabolomics data in the cloud. </newstitle> |
| | <teaser> | | <teaser> |
| − | We invented ' 'Metabomatching'' as a method to identify candidate compounds from genetic associations to NMR features. In our recently published paper in <a href=" http:// journals.plos.org/ ploscompbiol/article?id=10. 1371/ journal.pcbi.1005839"> Plos Computational Biology </a> we extensively tested our approach on a large collection of real GWAS association results and saw better discovery of confirmed pathways than with other popular methods. | + | As a member of the PhenoMeNal (Phenome and Metabolome aNalysis) consortium we contributed to set up a system of Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project' s continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardised, automated, and published analysis workflows. The paper is published in <a href =" https:// doi.org/10. 1093/gigascience/ giy149"> GigaScience </a>. |
| − | <date> 1 Dec 2017</date> | + | <date> 7 Dec 2018</date> |
| | </teaser> | | </teaser> |
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