eXframe: A Semantic Web Platform for Genomic Experiments

Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation. The FAIR Guiding Principles for scientific data management and stewardship (http://www.nature.com/articles/sdata201618) has been an effective rallying-cry for EU and USA Research Infrastructures. FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has 8 years of experience of asset sharing and data infrastructure ranging across European programmes (SysMO and EraSysAPP ERANets), national initiatives (de.NBI, German Virtual Liver Network, UK SynBio centres) and PI's labs. It aims to support Systems and Synthetic Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges of and approaches to sharing, credit, citation and asset infrastructures in practice. I'll also highlight recent experiments in affecting sharing using behavioural interventions. http://www.fair-dom.org http://www.fairdomhub.org http://www.seek4science.org Presented at COMBINE 2016, Newcastle, 19 September. http://co.mbine.org/events/COMBINE_2016

Technology Education

What is eXframe?
• reusable framework for creating online data
repositories
• upgraded version based on Drupal 7 (drupal.org)
• structured annotation of experiments, bio-materials and
assays
• publishes Semantic Web data automatically (RDF &
SPARQL endpoint)
• first instance of upgraded version - Stem Cell
Commons (http://stemcellcommons.org)

Architecture
• re-factored second generation of
eXframe
• updated experimental model
mapped to ontologies
• Drupal RDF modules used to
generate RDF
• RDF store (SPARQL endpoint)
powered by ARC2 PHP library

Data Model
• experiment (obi:investigation);
meta-data mapped to Dublin
Core (dc)
• researchers & citations mapped
to foaf & bibo respectively
• experiments are comprised of
bioassays (obo:assay)
• bioassays have replicates
(efo:replicate)
• replicates are associated with
biomaterials (obo:specimen)

Biomaterials
• biomaterials are deeply annotated, key examples:
- organism (NCBI Taxonomy)
- tissue (FMA: Foundation Model of Anatomy)
- cell type (CL: Cell Type ontology)
- disease state (DO: Disease ontology)
- treatment compound (CheBI: Chemical Entities of
Biological Interest)
• )

Sample RDF from
Stem Cell Commons
Stem Cell Commons

SPARQL queries
• flexible query system with
SPARQL
• allows graph queries
• integration with other endpoints
• sample query on right: “find
experiments done on mouse,
hematopoietic stem cells”.

Security
• Stem Cell Commons: selected experiments are accessible
only to researchers from Harvard Stem Cell Institute
• created two stores:
- public with limited data
- private with all data

Conclusion
• groups & institutions can create databases simply by
configuring eXframe
• structured repository serves as institutional memory
and facilitates publication
• automatic RDF generation & SPARQL endpoint lowers
barrier to Semantic Web adoption

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The Royal Society of Chemistry has an archive of published journals and books stretching back to 1841. In the past decade we have digitized this archive and semantically enriched our frontfile data with chemical structures linked to our free online chemical compound database, ChemSpider. In this talk we will survey our recent efforts to extract all kinds of data – chemical structures, experimental and bibliographic data – from both our backfile and frontfile. We will also discuss our future work to extract chemical reactions to host in our ChemSpider Reactions database and will discuss the potential applications of optical structure recognition technologies for converting structure images to structures as well as using similar techniques to convert experimental spectral data into interactive data formats. A key aspect of this project is the delivery of a crowdsourcing platform for the interactive annotation and validation of the extracted data.

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