Difference between revisions of "News"
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| + | <newstitle>Disease Module Identification DREAM Challenge</newstitle> | ||
| + | <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/10.1101/265553v1"> the paper</a>). | ||
| + | </teaser> | ||
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| + | <newstitle>PhenoMeNal: processing and analysis of metabolomics data in the cloud. </newstitle> | ||
| + | <teaser> | ||
| + | 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>. | ||
| + | </teaser> | ||
