Translational science, research and development (R&D) organizations and hospitals are starting to break down the barriers to open data collaboration. Most recently we’ve seen Johnson & Johnson agree on clinical trial data sharing with Yale School of Medicine. Other pharma companies are looking at removing cultural barriers by bringing teams closer together, both in mindset and location. But how easy is it to share information when it comes to multiple different omics data (multi-omics)?
As the industry continues to grow, so too does the data. Moving away from a genome-centric approach to a holistic understanding of the broader biological process affords the bioinformatics industry access to new multi-omics data. The challenge lies not so much in the volumes but in processing and analyzing the data. This information is usually obtained in different conditions and accumulated via high throughout technologies. It can benefit from collaborative input across organizations, but this can push legal boundaries.
Data management systems can help to support issues around consent, data privacy and security, providing appropriate data provenance and metadata in line with collaboration agreements. This remains a key issue as, naturally, patients’ data can only be used if it has been consented.
Complex multi-omics datasets also require a structured approach to capture and manipulation. Segal Cancer Centre recently adopted this type of approach to alleviate the analysis bottlenecks that typify translational science. Here, multi-omics data will play a key role in enhancing the knowledge-base they are building based on data sharing and collaboration.
A structured approach to data management provides the building blocks to allow clinical researchers to capture and curate their data. It enables them to manage ontologies and to integrate, search and visualize data from multi-omics sources. All this lends itself to a collaborative, results-sharing culture in life sciences which should be welcomed.