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Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes. We acknowledge the support of the developers of PhenoTips, which was used in the past by RD-Connect and NeurOmics as the primary tool to collate phenotypic data. We would also like to thank the leaders and members of the Instituto Nacional de Bioinformática (INB) and ELIXIR for their support and collaboration throughout the years. RD-Connect (RD-Connect, an integrated platform connecting registries, biobanks, and clinical bioinformatics) received funding from the Seventh Framework (FP7) Programme of the European Union under grant agreement No 305444. Data were analyzed using the RD-Connect GPAP, which received funding from EU projects Solve-RD, EJP-RD (grant numbers H2020 779257, H2020 825575), Instituto de Salud Carlos III (Grant numbers PT13/0001/0044, PT17/0009/0019; Instituto Nacional de Bioinformática, INB), ELIXIR-EXCELERATE (Grant number EU H2020 #676559) and ELIXIR Implementation Studies (Remote real-time visualization of human rare disease genomics data (RD-Connect) stored at the EGA ELIXIR. 2017-2018; ELIXIR IT-2017-INTEGRATION, Rare Disease Infrastructure ELIXIR, 2019-2020 and the Beacon ELIXIR, 2019-2021). The RD-Connect GPAP has leveraged developments funded through project VEIS (001-P-001647 co-financed by the European Regional Development Fund of the European Union in the framework of the Operational Program FEDER of Catalonia 2014-2020 with the support of the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya) and URD-Cat (PERIS SLT002/16/00174, Departament de Salut, Generalitat de Catalunya). The research leading to these results has received funding from Consequitur (Newton Fund UK/Turkey, MR/N027302/1), BBMRI-LPC (EU FP7 #313010), NeurOmics (EU FP7 #305121), the Economic Development Department of the Navarra Government (Grant number 001114112017), the European Reference Network for Rare Neurological Diseases (Project ID number 739510) and NIH, National Institute of Child Health and Human Development (1R01HD103805-01). We acknowledge the support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Program/Generalitat de Catalunya. We also acknowledge the support of the Generalitat de Catalunya through Departament de Salut and Departament d'Empresa i Coneixement and Co-financing by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) with funds from the European Regional Development Fund (ERDF) corresponding to the 2014-2020 Smart Growth Operating Program. HL receives support from the Canadian Institutes of Health Research (Foundation Grant FDN-167281), the Canadian Institutes of Health Research and Muscular Dystrophy Canada (Network Catalyst Grant for NMD4C), the Canada Foundation for Innovation (CFI-JELF 38412), and the Canada Research Chairs program (Canada Research Chair in Neuromuscular Genomics and Health, 950-232279).

Many patients experiencing a rare disease remain undiagnosed even after genomic testing. Reanalysis of existing genomic data has shown to increase diagnostic yield, although there are few systematic and comprehensive reanalysis efforts that enable collaborative interpretation and future reinterpretation. The Undiagnosed Rare Disease Program of Catalonia project collated previously inconclusive good quality genomic data (panels, exomes, and genomes) and standardized phenotypic profiles from 323 families (543 individuals) with a neurologic rare disease. The data were reanalyzed systematically to identify relatedness, runs of homozygosity, consanguinity, single-nucleotide variants, insertions and deletions, and copy number variants. Data were shared and collaboratively interpreted within the consortium through a customized Genome-Phenome Analysis Platform, which also enables future data reinterpretation. Reanalysis of existing genomic data provided a diagnosis for 20.7% of the patients, including 1.8% diagnosed after the generation of additional genomic data to identify a second pathogenic heterozygous variant. Diagnostic rate was significantly higher for family-based exome/genome reanalysis compared with singleton panels. Most new diagnoses were attributable to recent gene-disease associations (50.8%), additional or improved bioinformatic analysis (19.7%), and standardized phenotyping data integrated within the Undiagnosed Rare Disease Program of Catalonia Genome-Phenome Analysis Platform functionalities (18%). Supported by Generalitat de Catalunya through Departament de Salut (SLT002/16/00174 to URD-Cat consortium) and Departament d’Empresa i Coneixement and the CERCA Program; FP7 and H2020 EU projects RD-Connect, Solve-RD, and EJP-RD grants FP7 305444, H2020 779257, and H2020 825575 for CNAG-CRG; Spanish Ministry of Science and Innovation to the EMBL partnership and through the Instituto de Salud Carlos III grants PT13/0001/0044 and PT17/0009/0019 for CNAG-CRG and grants PI16/01048, PI19/01310, PI18/00451, PI18/00498, and PI21/00935 for IDIBAPS (Instituto Nacional de Bioinformática); ELIXIR Implementation Studies (CNAG-CRG); Centro de Investigaciones Biomédicas en Red de Enfermedades Raras; Centro de Excelencia Severo Ochoa grant SEV-2016-0571 (CNAG-CRG) and grant CEX 2018-000806-S (ISGLOBAL); and cofinancing with funds from the European Regional Development Fund by the Spanish Ministry of Science and Innovation corresponding to the Programa Operativo FEDER Plurirregional de España (POPE) 2014 to 2020 and by the Secretaria d’Universitats i Recerca, Departament d’Empresa i Coneixement of the Generalitat de Catalunya corresponding to the Programa Operatiu FEDER de Catalunya 2014 to 2020.

Els sistemes de monitoratge són essencials avui dia a complexes xarxes de computadors. Aquests ens garanteixen que totes les errades produïdes a la xarxa són detectades i informen els operadors per tal de solucionar­-les. Aquest treball està dividit en dues parts, teòrica i pràctica, per aproximar als sistemes de monitoratge i ensenyar com funcionen. Les eines de monitoratge ens permeten el control sobre una llarga varietat de serveis i processos utilitzant diferents protocols, tècniques per verificar i representar gràficament les dades, per exemple, estats dels serveis de xarxa, l'estat dels servidors webs o sensors de temperatura. Los sistemas de monitorización son esenciales hoy en día en complejas redes de computadores. Estos nos garantizan que todos los errores producidos en la red son detectadas e informan los operadores para solucionarles. Este trabajo está dividido en dos partes, teórica y práctica, para aproximar a los sistemas de monitorización y enseñar cómo funcionan. Monitoring systems are essential nowadays in complex computer networks. They guarantee us that all errors in the network are detected and reported to solucionarles operators. This paper is divided into two parts, theoretical and practical, to approximate monitoring systems and teach how they work.