DATA ANNOTATION AND RELATIONS MODELING FOR INTEGRATED OMICS IN CLINICAL RESEARCH

Omics has massively permeated translational clinical research with numerous diseases being covered by Omics studies from the genome to the metabolome level. Integrating these disease specific Omics tracks appears a logical next step for building the fundament of Systems Biology and Systems Medicine. Here, coherence of individual Omics tracks regarding clinical hypothesis, samples and clinical descriptors, and finally data handling and integration become pivotal. We present a data integration, annotation and relations modeling concept for heterogeneous Omics data and workflows. With molecular features at the center of all Omics we link the result profiles from different Omics tracks characterizing a specific disease phenotype to a common human molecular reference network for allowing a seamless integration and subsequent support in interpretation of Omics screening results. Our concept rests on data structures for representing objects specified by metadata and content. For handling diverse Omics tracks a flexible structure for content is proposed allowing data representation at different levels of granularity as demanded by the type of Omics and specific type of data. Content on the molecular level includes deep annotation of molecular features on gene and protein level. Based on this annotation pair-wise relations between molecular objects are built, traversing the molecular annotation into a network of relations (molecular feature graph). Such a relation network is also built on the Omics data level, combining explicit relations derived from study setup and implicit relations generated by mining metadata and content (Omics data graph). Finally both graphs are merged utilizing the molecular feature level as common denominator, enabling a persistent integration and subsequently interpretation of Omics profiling results in the realm of a given clinical hypothesis. We present a case study on integrating transcriptomics and proteomics data on chronic kidney disease for demonstrating the feasibility of this concept.