 Hello, I'm Arno Knijn, I work at the Italian National Institute of Health, the Instituto Superiore Sanitain in Rome in Italy, the Department of Food Safety, Nutrition and Veterinary Public Health. My lightning talk will be about the Arias Genomics Italian Public Health Surveillance System, which we set up at our institute. First, let's have a look at the project's objectives. With the change in surveillance from PFGA based to next generation sequencing based, there was the need to set up an information system for the collection of both genomic and epidemiological data, especially because the system would be used by users with a wide variety of technical skills. Specific requirements were that the system had to have a low learning curve, there had to be a short chain of information exchange. And although the user interface had to be simple, it also had to be complete, which translated in essential comprehensive outcomes together with complete data available for users with more advanced bioinformatics skills. The stakeholders included public health workers with different backgrounds, the parties in hospitals and the data. But in order to give them a feedback of how that data relates to that of other regions, they also consumed the data, which makes them more eager to use the system. In fact, in Italy, public health care is federated at a regional level, so the platform had the important role to overcome data silos. Because of this, the platform was organized so that everyone can see a limited set of the national data. Every region has its own project where they can insert the data and have total control of it. Part of this data is then shared in a national project with read-only access to all regions, so that every region has knowledge of what is happening at a national level, putting their own data in a wider context. Furthermore, every region can launch manually analytical pipelines on the national data. So this was the theoretical part. Now, in practice, Areas Genomics is made up of two open source platforms, a read on one hand for data management and surveillance, and our Galaxy cluster areas on the other hand for data analysis. The workflow consists of a regional lab creating a new sample in their project in Iridda, and upon upload of the NGS sequences, an automated pipeline is launched, which is elaborated by Areas. The user interface is always Iridda. The user does not interact directly with Areas. The automatic pipeline is called Fantastic, and it performs assembly, typing, and clustering of the sequences. In basis of the sample organism, which in our case would be either Shikatoxin-producing X-curriculum stack, or Listeria monocytogenis. And also in basis of the sequencing platform, which can be either Illumina or Iron Torrents. You can see the workflow of Fantastic in the poster we have here at the conference. At the end of each analysis, Areas returns the various results to Iridda. Analytical metadata is then written to the samples record and can be analyzed in aligned lists together with the other samples for an immediate comparison. And moreover, when the pipeline using core genome MMLSAT detects a cluster, that is, if the other deposited samples have an analytic difference less than a certain cut-off level, which we have set at 7 LAILs for Listeria and at 10 LAILs for stack, a notification is automatically sent to all stakeholders involved in the cluster. In order to give the users the possibility to further investigate the data within the system, several additional pipelines have been developed, which can be launched on selected samples. There is a pipeline that creates a summary report of the samples with some simple pivot charts. We've got a pipeline that performs neighbor joining analysis of the Laila distances, and it outputs phylogenetic tree. We've got a pipeline that outputs phylogenetic tree over SNP analysis performed by PopPunk, and a pipeline that returns the viral types of the selected samples in a table for easier comparison. In conclusion, Arias Genomics currently hosts the Italian National Surveillance System for Infections by Listeria Monocytogenes and the local surveillance system for infections by stack. Users have given positive feedback on the system. In fact, although submission of data is on a voluntary basis in Italy, the system is now used by a majority of the regions. Federal clusters, persistence in time and location have been highlighted by the system, and the system is also used to respond to urgent inquiries for Europe. This work has been made possible by my colleagues at the Instituto Superiore di Sanità working on stack and on Listeria, and of course by the two communities working on Irida and on Galaxy that are doing a terrific job. Thank you.