In the SOUND project, development of the most needed statistical and bioinformatic algorithms for personal multi-omic data was tightly matched with applications to cutting-edge clinical research in three most promising areas of (gen)omics-informed medicine: rare inherited genetic disease, cancer detection and targeted cancer drugs. The close linking of developers and end-users was also reflected in the composition of the partners. A flexible, extensible and open software infrastructure, a future version of the well-established and productivity-enhancing R/Bioconductor infrastructure, delivered these tools. SOUND thus lowered the barrier to entry both for clinician-scientists and for bioinformaticians. Clinician-scientists were empowered to make increasingly sophisticated uses of ‘omics-based technologies. Statisticians and bioinformaticians got ready access to tools and methods in the area of personalized medicine; as a community, they are better prepared to address pressing future needs of bioinformatics in clinical ‘omics research and applications.