1. Data Curation and Management activities within the UCT Computational Biology Group Dr Nicky Mulder

2. Outline Activities at UCT: – High-throughput biology data – Sequence annotation – DAS annotation development Issues we face A note on standards and ontologies

3. High-throughput biology data Close ties with CPGR Microarray data storage –BASE Proteomics data: – Annotation –pipeline required – Storage –LIMS required

4. BASE BioArray Software Environment Open source database for storage of array- type data Manages raw data (images) and annotations Has limited LIMS options Can include specifications for MIAME compliance

5. BASE Sample Information

7. BASE experimental info

8. Proteomics Data Still in progress Peptide identification programs Additional cross-linking from results to public database annotations Storage of experimental data and resulting identifications Include MIAPE compliance Linking to genomics data –standards required

9. Sequence Annotation 1 Paeano pipeline for annotation of cDNAs from non-model organisms Uses collection of publicly available and custom software Results are stored under projects Links provided to array data in BASE

10. Sequence Annotation 2 Glossina (Tsetse) EST annotation project Held annotation jamboree at UWC Worked with Twiki tool developed by JBIRC Data to be submitted to public databases

11. Twiki system

13. DAS Annotation Tool Distributed Annotation System –allows viewing of annotation from different sources Can overlay your own data/annotation Facilitates information sharing without issue of updates Repositories distributed in different geographical locations Extension of DASTy2 –developed at NBN Development of DAS annotation tool underway

14. DASTy

15. Links to other DAS viewers

16. DAS annotation tool Collaborative visual annotation tool - Annotation - Comments - Sequences - Features - Non positional features - Methodology of trust on a collaborative annotation process

17. Data curation and management issues HTB software licenses are expensive Open Source not always maintained Ensuring regular backups (data size) Keeping data up to date Researchers leave data after project –not updated to new versions Privacy –researchers share data only with collaborators, patient data is private Sharing and linking data

18. Standards and ontologies Use a controlled vocabulary (controlled list of terms) or ontology (set of terms with relations) Enables easy data retrieval and sharing Easy comparison of results from different labs Compatibility with other labs/databases world- wide Ease of uploading data into public databases Unambiguous report of research

19. Open Biomedical Ontologies Central location for accessing well-structured controlled vocabularies and ontologies for use in the biological and medical sciences Provides simple format for ontologies Scope include anatomy, phenotype, development, disease, “omics”, experiment, etc. http://obo.sourceforge.net

20. Data exchange standards Microarray standards –MIAME and MAGE Proteomics Standards Initiative (PSI) Systems Biology Markup Language (SBML) – computer-readable format for representing models of networks Biological Pathways Exchange (BioPAX) –format for representing pathways

21. Conclusions Some tools in place for curation and management of different data types Need better education of researchers to encourage this Ontologies and standards are important in digital data curation and management, need to encourage compliance with international standards

22. Acknowledgements Funding: Collaborations: – CPGR – Researchers at UCT