Big Data analysis and reproducibility: new challenges and needs of the post-genomics era

Reproducibility constitutes an important criterion for the evaluation of the quality of research and robustness of results. However, while big data are accumulating at a fast pace, data management, integration, sharing and interpretation become rather challenging. The massive amount of different kinds of data produced need standardised approaches for their production and processing. To enable researchers to assure and ensure the quality of data and research results, uniform and widely adopted procedures, pipelines, data repositories, bioinformatics tools and data-sharing platforms are mandatory. Many efforts in this direction have been made in the last decade by scientists and by the governing bodies, funding agency and publishers. The application of FAIR (Findable, Accessible, Interoperable, Reusable) and Open Science principles to life sciences research are the most important results produced by the combination of all these efforts. Nevertheless, a lot of work is still necessary for these principles and their application to be integrated and fully implemented by the whole scientific community. Obstacles are represented by many different factors, the most important being the still unequal pre- and post-degree education of researchers from different countries of the EU, difficulties of scientists to adopt standards and standard operating procedures (SOP) because of their lack or because of missing guidelines, to mention only the most relevant ones. In this scenario, the EMBnet workshop will discuss main factors affecting reproducibility of life sciences research and explore the need for standards with a particular focus on the reproducibility of computer-aided drug design and metagenomic assemblies.