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The I2SysBio will host the METHADA 2020 - Training School on Transcriptomic Metadata Handling and Data Analysis, funded by the COST ACTION INTEGRAPE (CA 17111)

  • December 14th, 2019

The Training School METHADA 2020 - Transcriptomic Metadata Handling and Data Analysis, is being organized in our institute and will be held on

February 5, 6 and 7. José Tomás Matus is the organizer of the event and also one of the 5 speakers.



The rise of the latest technologies that combine physics, optics, chemistry and their application to molecular biology has led to high-throughput experiments, yielding an explosion of publicly available data. This data ranges from Next Generation Sequencing (NGS) to transcriptomics, phenomics, metabolomics to large scale single cell data.

In the case of transcriptomics, which generates to date the biggest amount of data compared to other omics, protocols for data submission are not fully standardized for grapevine data and not controlled by the research community. Public available gene expression datasets have a hidden true potential in the light of data reanalysis and integration. In line with the FAIR (Findable Accessible Interoperable Reusable) principles our next challenge as a community relies on correct sample and experiment annotations, using controlled vocabularies to ensure both human readability and computational tractability.

This training course addresses transcriptomics data handling and analysis, and it is organized in two modules. On the first unit, students will work to learn how to correctly annotate experiments and handle metadata in order to exploit standards and bio-ontologies for data annotation. Secondly, attendees will be trained in a reduced set of foundational skills to analyse and explore transcriptomic datasets, including resources freely available for the grapevine community. All students will learn on how to use Jupyter Notebook, an open-source language-agnostic web application (it supports over 40 programming languages including Python and R) that allows a wide range of workflows in data science and scientific computing without burdening the users with installation and maintenance tasks.

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