BRAvo : A tool for regulatory network assembly through Linked Open Data

Marie Lefebvre (LN2S (Nantes))
Thursday, May 24, 2018 - 10:30
Room Aurigny
Talk abstract: 

A few years ago, SyMeTRIC health actors have proposed personalized medicine approaches in the context of several pathologies or therapeutical approaches such as cancer, transplantation, cardiovascular, respiratory or metabolic diseases. In spite of pathological diversities, these actors share methodological and technological commonalities. In particular they strongly rely on the exploration and combination of several heterogeneous and massive biological and clinical datasets to discover multi-parameter pathological signatures and new biomarkers.

In order to assemble data arising from different scales, technologies or localities, huge efforts address the organization of biological knowledge through linked open databases. These databases are supposed to be automatically queryable in order to reconstruct regulatory and signaling networks. Nevertheless, assembling networks usually implies manual operations due to source-specific identification of biological entities and relationships, multiple life-science databases with redundant information and difficulty to recover the logical flow of a biological pathway.

In this talk, I will provide a framework based on Semantic Web technologies for automating the assembly of regulatory and signaling networks. To this purpose,  I developed BRAvo, an interactive web tool, allowing users to interact with the reconstruction process, and a command-line tool allowing to address larger scale models in a batch mode.

Our results show that BRAvo is able to retrieve networks of 5000 nodes from 200 input genes by querying the full PathwayCommons database in less than one hour. BRAvo can also provide interesting filters of data sources, depth reconstruction and biological entities type. Thanks to BRAvo, we are now able to address issues of heterogeneous data integration for biological network reconstruction intended for computational and predictive models.