Integrating ELIXIR Italy into ELIXIR activities

The implementation study project plan of ELIXIR Italy consists of six activities that aim to boost the cooperation with existing ELIXIR activities and are expected to deepen the interaction between ELIXIR-IIB, the Joint Research Unit embodying the Italian Node, and ELIXIR. The partners involved have already established contacts with other ELIXIR Nodes and the relevant ELIXIR Platforms and Services in order to ensure an advantageous outcome for all the involved parties.

Rare Diseases Infrastructure

This Study will build on recent developments across the RD community by aligning and securely interconnecting existing international infrastructures (RD-Connect, European Genome-phenome Archive (EGA), and tranSMART) with the general ELIXIR infrastructure. Tasks will develop upon services provided by ELIXIR Nodes and international standards such as those from the Global Alliance for Genomics and Health (GA4GH).

Software Best Practices

To raise the quality and sustainability of research software

This study will promote the production, adopting, promoting and measuring information standards and best practices applied to software development life cycle. We have published four simple recommendations to encourage best practices in research software and the Top 10 metrics for life science software good practices. 

The next steps are to:

Web application for fast 3D structure visualisation with residue conservation

The collaboration between EMBL-EBI and ELIXIR Czech Republic enabled the development and release of a reusable, web based tool for linking sequence conservation information with biomolecular structural data.

As part of the project, the team at the ELIXIR Czech Republic (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, IOCB) pooled their expertise in linking sequence data with structure stability, interactions and functions with EMBL-EBI’s experience in providing sequence similarity search (HMMER service).

Strategic Development of DOME Recommendation for Machine learning Focus Group

Machine Learning (ML) enables computers to assist humans in making sense of large and complex data sets. With the fall in the cost of high-throughput technologies, large amounts of omics data are being generated and made accessible to researchers. Analysing these complex high-volume data is not trivial, and the use of classical statistics cannot explore their full potential.