BIOOPENER is aggregating multiple cancer and biomedical repositories featuring thousands of terminologies (concepts and properties) described differently even if they overlap. This overlap can be due to different naming conventions, textual descriptions, synonyms, and granularity of these cancer terminologies; it’s an open research problem to precisely identify an ontological term which best describe a given cancer terminology.

Ontology search services such as BioPortal and Ontology Lookup Service (OLS) contains over 500 ontologies covering distinct domains as Zebrafish anatomy to Epidemiology. However, they often suggest large, vague, or loose search results for a given terminology. This issue hinders the applicability of reuse and leads to situations where the same concept has different definitions or labels. Searching for the right concept in the most appropriate ontology is, therefore, a strenuous task since a significant number of available ontologies exist, in the same or in closely related domains, and will most likely share some concepts.

Figure 1: Searching Top-K Ontological Resources: An Open Research Problem.

Figure 1 depicts a situation where a user often gets different sets of search results while finding a best fit ontological concept or property for a given terminology. The BIOOPENER project deals with thousands of overlapping terminologies (concepts/properties) used within cancer data repositories. The project aims is to identifying/find the best fit (top-k) ontological concepts for terminologies used in different cancer studies and repositories. To achieve this goal, we are developing a system which is able to automatically and precisely locate the best ontology term to describe each concept featured in the aggregated database.

This work is led by Daniela Oliveira

One of the key obstacles in aggregating multisite & multi-speciality datasets is to discover relevant data sets from available data sources (proprietary or public). Once these relevant data sources/datasets are identified, then the second major challenge is to identify & establish links across these data sources/datasets. The discovered data sources and links are the key enablers for creating complex and federated queries across distributed cancer data repositories.

Figure 1: Linking – An Unsolved Problem.

Figure 1 depicts a scenario of linking two cancer repositories (e.g., COSMIC, TCGA) with the Linked Open Data (LOD) Cloud. The LOD cloud is growing, massive in size and it’s not a straightforward job to identify a useful chunk of data for a given case study.

Figure 2: Linking Cancer & Biomedical Repositories..

Figure 2 shows an example of how COSMIC and TCGA datasets are linked together (within BIOOPENER)

The COSMIC and TCGA datasets have been integrated using the owl:sameAs construct. For instance, MYH7 (a gene) is linked through owl:sameAs in COSMIC and TCGA datasets. MYH7 is an RDF entity of type Gene Symbol. Such links across repositories helps to understand and discover differentially expressed genes and potential biomarkers. Similarly, Figure 2 shows the owl:sameAs links between COSMIC and TCGA datasets for TCGA-13-0920 and TCGA-24-1850 (which are RDF resources of type Sample ID). Further, by enabling links between COSMIC and GO datasets, it is now possible to find changes in healthy cells based on their pathway activities to identify the disease related and biological process related pathways. For instance, the Ensemble ID (ENSMUSP00000018795) from COSMIC linked with the same Ensemble ID in GO dataset. This enabled exploring the GO processes and GO IDs associated, which have further links to KEGG and Reactome IDs.

This work is co-lead by Muntazir Mehdi and Qaiser Mehmood

The Next Generation Sequencing (NGS) technologies are producing a massive amount of sequencing datasets. Often, these datasets are published from the isolated and different sequencing facilities. Consequently, the processes of sharing and aggregating multisite sequencing datasets are thwarted by issues such as:

• the need to discover relevant data from different sources
• build scalable repositories
• the automation of data linkage
• the volume of the data
• efficient querying mechanisms
• and information rich intuitive visualisation

Figure 1: BIOOPENER Query Federation.

Figure 1 summarises BIOOPENER’s querying mechanism that retrieves results from distributed and large-scale cancer and biomedical repositories.

The BIOOPENER query federation provides a single-point-of access through which distributed data sources (COSMIC, TCGA, REACTOME, KEGG, GO SPARQL endpoints) can be queried in unison.

BIOOPENER makes use of an efficient indexing mechanism and a graph-level source selection method that reduces the number of sources required for processing federated SPARQL queries (without compromising recall) by:

1. efficient source selection as per the capabilities of genomics repositories
2. query planning mechanism to decompose a query and build resultant data set from several sub-queries
3. query optimisation to execute the sub-queries
4. and query execution mechanism retrieve and integrate results

The BIOOPENER R&D team follows the consideration that existing public data repositories will provide their greatest benefits by their interoperability with all other public data resources through the connectivity of related or shared semantic types (e.g., proteins, genes, diseases). The BIOOPENER project is a key initiate that brings together several independently and openly hosted cancer and drug repositories.

This work is co-lead by Muntazir Mehdi and Qaiser Mehmood