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TRD 2: BMRB Integration

TRD 2: BMRB Integration . Presentations : Introduction Michael Gryk (TRD 2 Co-Leader) D emo Valérie Copie ́ (DBP 6 Investigator) ??? Eldon Ulrich ( TRD 2 Co-Leader). TRD 2 Introduction. data archive and retrieval. software integration. data interchange.

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TRD 2: BMRB Integration

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  1. TRD 2: BMRB Integration Presentations: Introduction Michael Gryk (TRD 2 Co-Leader) Demo ValérieCopié (DBP 6 Investigator) ??? Eldon Ulrich (TRD 2 Co-Leader)

  2. TRD 2 Introduction data archive and retrieval software integration data interchange

  3. TRD 2 Specific Aims 2.1 Extend CONNJUR data model to capture all relevant metadata to save the state of biomolecular NMR study. 2.2 M2M communication services between NMRbox and BMRB. 2.3 Enable CONNJUR as a deposition engine to BMRB. 2.4 Enhanced querying & data mining of BMRB from within NMRbox.

  4. TRD 2 Updates from CONNJUR 2.1 Extend CONNJUR data model to capture all relevant metadata to save the state of biomolecular NMR study. - New annotation scheme to foster reproducibility. 2.3 Enable CONNJUR as deposition engine to BMRB. - CONNJUR Workflow Builder supports export of workflows in NMR-STAR format.

  5. BMRB recent contributions • TRD 2.2. Machine to machine NMRbox-BMRB interactions – simple APIs • CS-Rosetta launch from NMRbox to BMRB and onto the OSG • DEVise assigned chemical shift visualization launch • FASTA sequence search of the BMRB archive • SQL search of the BMRB relational database • TRD 2.3. BMRB/wwPDB deposition construction and content • Modeled manual NMR data analysis annotation developed by the UCHC team in the NMR-STAR dictionary • Reviewed initial NMR-STAR output from CONNJUR WB system • TRD 2.4. Advanced BMRB query capability • SQL search of the BMRB relational database • Initiated project with BMRB consultant Yannis Ioannidis to develop a query interface for the BMRB curated database

  6. Advance query example • Select Glu CA chemical shift values and errors • where experimental method is NMR • where experimental method subtype is solution • where molecular assembly contains a single entity • where entity type is polypeptide • where physical state of the polypeptide is intrinsically • disordered

  7. Challenges for BMRB • TRD aim 2.2. Machine to machine NMRbox-BMRB interactions • Ensure 24/7/365 service for legitimate queries • Develop safeguards to protect against accidental and malicious attacks (i.e., runaway VM requests, overly compute intensive queries, deletion or insertion queries) • Maintain backward compatibility as the BMRB schemas evolve • TRD aim 2.3. BMRB/wwPDB deposition construction and content • Collaborate in the mapping of the CONNJUR data model to the NMR-STAR data model and data exchange applications • Provide standalone validation tools • Implement automated deposition validation and acceptance systems • TRD aim 2.4. Advanced BMRB query capability • Construct an interface that reflects the complexity of the NMR-STAR and curated database schemas, but is easy to navigate and capable of building valid complex queries • Database cleanup to improve query results

  8. Impact (BMRB) • Tremendous opportunity to integrate BMRB into the workflow of the biological NMR experimentalist and to interface BMRB with a wide variety of software • Improve user access to the BMRB archive • Reduce the time and effort required to construct a BMRB deposition • Enrich the content of individual BMRB depositions • Improve the consistency across BMRB entries

  9. Impacts (CONNJUR) 2.1 Additional metadata is critical to foster reproducibility. It serves dual purpose of allowing us to populate new instances of NMRbox. 2.3 Eases the burden on the NMR community for submitting data to the BMRB. As CONNJUR is capable of tracking larger amounts of intricate data than the spectroscopist is likely to be willing to provide – the BMRB depositions will be fuller.

  10. Acknowledgements BMRB John L. Markley Vincent Chen Yannis Ioannidis Miron Livny Dmitri Maziuk Jon Wedell R. Kent Wenger Hongyang Yao Many students NMRFAM Gabriel Cornilescu Larry Clos II Woonghee Lee Marco Tonelli William M. Westler Funding 1996 - 2014 NLM P41 2014 - 2019 NIGMS R01?

  11. Acknowledgments (CONNJUR) Matthew Fenwick Jeffrey C. Hoch Mark W. Maciejewski Mehdi Mobli R.J. Nowling Colbert Sesanker Jay Vyas Heidi J.C. Ellis Gerard Weatherby Rensselaer at Hartford: Timothy O. Martyn NIDDK/Agilent: Frank Delaglio Rowland Institute: Alan Stern

  12. DEVise data visualizations

  13. TRD 2: NMRbox-BMRB integration • Goal • Provide biological NMR spectroscopists active and easy access to BMRB/wwPDB resources from within their working environment • Approach • Implement automated retrieval of information relevant to the user’s research interest through NMRbox-BMRB machine-to-machine communication (TRD 2.2) • Facilitate the construction of BMRB/wwPDB depositions (TRD 2.3) • Interface the BMRB relational database, data visualization, and computational resources to the NMRbox environment to provide advanced in depth access to BMRB data and facilities for research activities (TRD 2.4)

  14. BMRB as a knowledge resource Metadata BMRB user Software services Imported data Visualization service Validation results User data and compute resources CHTC & OSG Evaluate Biological NMR data Search BMRB Retrieve Deposit Data interpretation Derived data External data links

  15. BMRB knowledge resource core content Metadata chemical structure, natural source, sample, experimental detail Imported data coordinates, restraints, phi-psi angles Validation results LACS, AVS, PANAV, CING, MolProbity Biological NMR data Derived data back calculated chemical shifts, BLAST alignments Data interpretation citations External data links PDB, UniProt, KEGG, PubChem

  16. BMRB curated schema for biophysical data Entry meta data Experimental results Common information BMRB value added updates

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