Development of an extensive OMICs Atlas

Since the start of BBMRI, extensive datasets have been generated at genome, epigenome, transcriptome and metabolome level for 10 thousand of individuals from tens of Dutch biobanks. By screening this wealth of omics data in large-scale analyses, BBMRI researchers have identified a plethora of links between these layers of cellular organization, thus uncovering insight into how molecular pathways are involved in health and disease. The unique atlas generated by this endeavour has been made accessible to the general public as an online resource: http://bbmri.researchlumc.nl/atlas

Screenshot of the network view of a gene query.

What makes the BBMRI atlas special is that it contains associations between many different biological layers. “Developing a browser that is able to represent such complex data in a straightforward, intuitive fashion is quite challenging,” says Jan Bert van Klinken from the Leiden University Medical Center who developed the online atlas. “This is why we chose to show association data also as an interactive, layered network, in addition to text tables as is standard for omics browsers. For example, the network view provides users with comprehensive insights in the regulation of gene expression by regulatory elements and genetic variants.”

Currently the browser part of the atlas contains the genome, epigenome and transcriptome level, and will be extended with the metabolome and phenotype layer at the end of this year when more of the BBMRI studies currently under review have been published. This will constitute a truly unique repository within the life sciences that allows researchers to view how variations at the level of the DNA percolate through each layer and ultimately affect our health.

Another important feature of the atlas is that queries can be sent directly to the server through an application programming interface, which makes it possible to include the atlas in computational pipelines. “We want to go one step further to increase the atlas’ value to the community by making all of the data Findable Accessible Interoperable and Reusable (FAIR),” says Van Klinken. The FAIR initiative has as long-term goal to optimize the reuse research data. To comply with the FAIR data principles, the association meta data of each study will be uploaded to a FAIR data point and the functionality of the API will be extended to linked data.

September 14, 2018