An interconnected data infrastructure to support large-scale rare disease research.

The Solve-RD project brings together clinicians, scientists, and patient representatives from 51 institutes spanning 15 countries to collaborate on genetically diagnosing ("solving") rare diseases (RDs). The project aims to significantly increase the diagnostic success rate by co-analyzing data from thousands of RD cases, including phenotypes, pedigrees, exome/genome sequencing, and multiomics data. Here we report on the data infrastructure devised and created to support this co-analysis. This infrastructure enables users to store, find, connect, and analyze data and metadata in a collaborative manner. Pseudonymized phenotypic and raw experimental data are submitted to the RD-Connect Genome-Phenome Analysis Platform and processed through standardized pipelines. Resulting files and novel produced omics data are sent to the European Genome-Phenome Archive, which adds unique file identifiers and provides long-term storage and controlled access services. MOLGENIS "RD3" and Café Variome "Discovery Nexus" connect data and metadata and offer discovery services, and secure cloud-based "Sandboxes" support multiparty data analysis. This successfully deployed and useful infrastructure design provides a blueprint for other projects that need to analyze large amounts of heterogeneous data.

Glia Open Access Database (GOAD): A comprehensive gene expression encyclopedia of glia cells in health and disease.

Recently, the number of genome-wide transcriptome profiles of pure populations of glia cells has drastically increased, resulting in an unprecedented amount of data that offer opportunities to study glia phenotypes and functions in health and disease. To make genome-wide transcriptome data easily accessible, we developed the Glia Open Access Database (GOAD), available via www.goad.education. GOAD contains a collection of previously published and unpublished transcriptome data, including datasets from isolated microglia, astrocytes and oligodendrocytes both at homeostatic and pathological conditions. It contains an intuitive web-based interface that consists of three features that enable searching, browsing, analyzing, and downloading of the data. The first feature is differential gene expression (DE) analysis that provides genes that are significantly up and down-regulated with the associated fold changes and p-values between two conditions of interest. In addition, an interactive Venn diagram is generated to illustrate the overlap and differences between several DE gene lists. The second feature is quantitative gene expression (QE) analysis, to investigate which genes are expressed in a particular glial cell type and to what degree. The third feature is a search utility, which can be used to find a gene of interest and depict its expression in all available expression data sets by generating a gene card. In addition, quality guidelines and relevant concepts for transcriptome analysis are discussed. Finally, GOAD is discussed in relation to several online transcriptome tools developed in neuroscience and immunology. In conclusion, GOAD is a unique platform to facilitate integration of bioinformatics in glia biology.