The second version of the METHADA 2020 Training School -eHandsOn - Transcriptomic metadata management and data analysis will take place from December 1 to 4. José Tomás Matus is one of the organizers of the event and also one of the speakers. This time the course has the presence of more than 100 observers from all over the world.

After the success of the First Version of METHADA, held at I2SysBio in February 2020 and with representation from more than 10 European countries, will take place its second version in online format.

The rise of the latest technologies that combine physics, optics, chemistry and their application to molecular biology has led to high-throughput experiments, producing an explosion of publicly available data. These data range from next-generation sequencing (NGS) to transcriptomics, phenomics, metabolomics, and large-scale single-cell data.

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

This online training course addresses the management and analysis of transcriptomics data, and is organized into three modules. In the first unit, students will work to learn how to correctly annotate experiments and manage metadata to standardize the upload of experiments to public repositories (such as SRA and ENA). Secondly, attendees will receive training in the use of the A.I.R platform. ('Artificial Intelligence RNA-seq', from Sequentia Biotech S.L.) for the management of transcriptomic data, including resources freely available to the grapevine community. Finally, students will learn to use the GREAT (GRapevine Expression ATlas) platform for the analysis of public transcriptomic data from the vine.

It is possible to participate as a course listener by accessing here.

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