Users of NGS technologies, producing large and numerous distinct types of omics data, demands statistical methods to combat data and knowledge fragmentation and inappropriate procedures for data analysis. Yet, the current a gap between the available tools for analysis of a single omics data-type versus the requirement of biomedical scientists to understand the integrated results derived from several omics data-types, threatens to further increase due to the accelerated capacity of data production.STATegra will therefore improve and develop new statistical methods enabling accurate collection and integration of –omics data while providing user-friendly packaging of STATegra tools targeting biomedical scientists. To close the gap between the present sub-optimal utilization of –omics data and the power of statistics, STATegra develops statistical methods targeting efficient experimental design, data gathering, missing data, noisy data, current knowledge, meta-analysis and integrative data analysis.Importantly, STATegra facilitates understanding and use of –omics data by forcing abstract concepts including knowledge, design, dirty data, visualization, causality and integration to be embedded in a real yet prototypical biomedical context. STATegra is positioned to ensure that the collective output of the statistical STATegra methods is relevant and subject to “statistical and experimental” validation and iteration. STATegra mimics a user-driven IT development strategy, by involving real biomedical users as beta-testers.To deliver beyond current exploratory tools, our consortium accumulates the necessary strong statistical, technological, and molecular expertise. The strong lead by research intensive SMEs, with proven track-record in software deployment, translates STATegra to a wide existing community base. STATegra accelerates the production of relevant statistical tools impacting a broad community of biomedical scientists in research, industry, and healthcare.