Challenge
The client has large and rapidly growing private datasets from previous experiments and upcoming technologies. These experiments take place across many years and look at different species under many different conditions. The Life Science Data that are recorded are varied, including expression data from microarrays and RNAseq, and they are held in multiple locations.
Altogether this makes it hard to find relevant data (or even know it exists). Even when it is found, it is challenging to aggregate and re-use data from these experiments because of the differing ways it has been recorded over time and the pain of retrieving data points across studies in a comparable way.
Ideally the analysis of internal data would be complemented by the inclusion of selected publicly available datasets, but again, meaningfully comparing internal data to data in the format it’s available from public datasets is hard.
Finally, while individual solutions may exist for some of these challenges, they face the problem of being hard to use by non-technical users or difficult to integrate into downstream analyses and visualizations that scale.
Solution
We are working with a top 3 agriscience company to deploy Genestack ODM (ODM), our enterprise software solution for data cataloguing, metadata curation, and multi-omics APIs.
By indexing multiple Life Science Data types whatever their location and providing a cloud-based, modern interface, ODM enables scientists to discover and query Life Science Data - both past, current, and extensible for future types - about biological entities of interest across distributed public and internal datasets from a single screen without needing prior knowledge of that data’s existence or location.
Powerful curation tools within ODM enable curators to ensure the completeness, consistency, and validity of study, sample, and analysis metadata, while our templates and ontologies enable the harmonisation and validation of metadata making it comparable and re-usable.
Finally ODM’s comprehensive and well-documented APIs allow full integration with upstream and downstream processes. Large scale integrative analyses can be easily visualised through applications (such as R Shiny, Spotfire, Jupyter notebooks) consuming the output from simple API calls.
More than just a software solution though, the collaborative, long term thinking approach that this partnership brings will enable the client to improve their customers’ productivity now and in the future through the continued development of cutting edge products and technologies.