Enabling Omics Data Analysis at Scale
Bioinformatics Support to Understand Human Immunology
Goal
The Institute for Research in Biomedicine (IRB) in Bellinzona stands out as an exceptional institution with an expert team of more than 150 people dedicated to groundbreaking biomedical research to solve the greatest humanitarian challenges. As a university, it has made a name for itself by fostering an environment of innovation and academic excellence dedicated to nurturing the next generation of scientific minds.
The IRB was founded in 2000 with the goal of advancing the study of human immunology, with emphasis on the mechanisms of host defense. It houses 13 research groups working on challenges in immunology and other areas such as DNA repair, rare diseases, structural and cell biology. The studies into human biology in health and disease aim at a better understanding of pathophysiology and the development of new therapeutic approaches for infectious, inflammatory, degenerative and tumor diseases.
The IRB maintains a broad international network of collaborations and offers teaching and training programs for doctoral students from Swiss and foreign universities. The institute’s success to date can be measured by over 850 scientific publications and more than 120 completed dissertations by doctoral students from all over the world.
Challenges
Outsourcing data analysis to external service providers or internal bioinformaticians caused a time bottleneck and was not flexible enough. The researchers needed to visualize more and dig deeper into the data themselves.
The ability to re-analyze data in a timely manner must be ensured.
To support the scientists in understanding data analysis
Results with Omics Playground
Using Omics Playground has increased the number of analysis requests processed by more than 300% in a shorter period of time.
Additional bespoke/in-depth analyses of the same data sets can be performed flexibly and
without the involvement of the bioinformatics team.
Scientists are equipped with the right tools to understand their data analysis without the need for programming skills. They can process increasing amounts of data more efficiently and gain the holistic and comprehensive understanding needed from the data to advance their science.
