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Data Standards for Data Integration

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Data Standards for Data Integration chemical biology domain caspase activity PubChem standards cheminformatics nomenclature activity semantic enzyme reporter viability fluorescence binding based programming data sets knowledge search screening technology end point thesauri article object enzyme substrate based PDSP XML natural language high-thoughput screeningversioning (HTS) software classificationData Standards for Data Integrationpolysemes biological pathways Beta-Lactamase Induction specificity dehydrogenase activity GFP induction annotation properties subject indexing schemes Fluorogenic substrate Stephan Schürerche mical probes information exchange ChemBank ATP Luciferin Coupled servers classesUniversity of Miami synonyms search tool biological assay controlled vocabulary biomedical knowledge disease networks SPARC Workshop, structureNIH, Feb 25-26 2015 concepts taxonomies small molecule meta-data subject headings calcium redistribution cyclic AMP redistribution RDF OWL novel chemical tools indexing library pharmaceutical [email protected] web authorized terms individuals structural biology tags homographs energy transfer Types of data standards • Reporting guideline (checklist) specifies what information need to be captured about an experiment for a particular purpose • (Controlled) vocabulary terminological resource that provides the identification and definition of entities • Data exchange format is a specification how data are encoded to be computer-readable / -processable • Data structure refers to organization of data, data schema, entity relations 2 BioSharing Standards (http://www.biosharing.org) O. Search all of BloSharlng LOG IN OR REGISTER biosharing COMMUNITY CONTENT SUMMARY ABOUT O<:> Contribute by submitting a standard Found a bug? Please tell us! Standards BioSharing standards have been partly compiled by linking to BioPortal, MIBBI and the Equator Network. Or you can filter on MIBBI Foundry reporting guidelines or 080 Foundry terminology artifacts. arch for tandar O. Search MHHF ~Reset X REPORTING GUIDELINE Showing records 1 • 50 of 69. View as Grid View as Table D No Publlc1tlon [J Hl!IS Publication ~ No M1lnt1lner ~ Has M1lnulner AMIS ARRIVE BioDBCore Standard Type C'ear Article Minimum Information Standard Animals in Research: Reporting In Vivo .. Core Attributes of Biological Databases REPORTING GUIOELINE REPORTING GUIOELINE REPORTING GUIOELINE REPORTING GUIDELINE EXCHANGE FORMAT ~Systems S Systems ~Systems a L.:., Publications L.:., Publications L.:., Publications TERMINOLOGY ARTIFACT a a a 3 Checklists – Minimum Information Guidelines a mibbi Bioscience reporting guidelines and tools @ Portal Minimum Information guidelines from diverse bioscience communities @ Foundry • If you want to register your checklist to MIBBI, please contact the BioSharing team • Excel spreadsheet and XML document (schema) describing all registered projects @ About Bioscience projects registered with MIBBI CIMR Core Information for Metabolomics Reporting I !. x GIATE Guidelines for Information A bout Therapy Experiments MIABE Minimal Information About a Bioactive Entity I !. x MIABiE Minimum Information About. a Biofilm Experiment MIACA Minimal Information About a Cellular Assay MIAME Minimum Information About. a Microarray Experiment I !. x MIAPA Minimum Information About a Phylogenetic Analysis MIAPAR Minimum Information About. a Protein Affinity Reagent MIAPE Minimum Information About a Proteomics Experiment MIAPegAE Minimum Information About. a Peptide Array Experiment MIARE Minimum Information About a RNAi Experiment I !. x MIASE Minimum Information About. a Simulation Experiment I !. x MIASPPE Minimum Information About Sample Preparation for a Phosphoproteomics Experiment MIATA Minimum Information About. T Cell Assays MICEE Minimum Information about a Cardiac Electrophysiology Experiment MIDE Minimum Information required for a DMET Experiment MIFlowCyj Minimum Information for a Flow Cytometry Experiment I !. x 4 Minimum Information Standard may not exist Regenbase: Integration of diverse data related to nerve regeneration in the context of spinal cord injury http://regenbase.org 5 Vocabulary vs. ontology • Controlled vocabularies / thesauri • describe what things mean (link terms to human description) • Entities with identity criteria • Share knowledge in a common language • Natural language synonyms for search and text mining 6 Vocabulary vs. ontology • Ontologies • Contains entities (classes) and their relationships (object properties) • Capture / abstract knowledge using logical axioms • Explicit specification (OWL-DL) • Building formal (computable) models • Computing with knowledge (reasoning engines) • Foundation of Semantic Web information systems 7 Ontology resources NCBO Bioportal EBI Ontology Lookup Service OLS OBO Foundry 8 Metadata specifications Metadata: Data not directly measured in an experiment (or obtained in a study) Why metadata: • Facilitate data replicability, reproducibility, reuse • Interpret results, perform data analysis, hypotheses • Repurpose data for other projects • Information systems (search, query, data integration and exchange) What metadata to capture in a standardized format with controlled vocabularies (and formal descriptions)? 9 A useful distinction of metadata Model metadata: • Required to understand, interpret, and meaningfully integrate experimental results • Typically queryable in software systems • Important parameters to describe conclusions (data visualizations) Confounder metadata: • Non model metadata required to replicate and reproduce experimental results • Needed for “data forensics” (e.g. batches of reagents, maintenance of experimental equipment, etc.) 10 Standardized metadata Capture all (detailed descriptions, SOP) Make model metadata explicit (controlled vocabulary, standard format) But what’s really model metadata? Data and informatics use cases – Types of queries and analyses – Integration with other data sources – Information systems / UI components – Consider re-use of data for other projects LINCS Metadata Standards: Vempati et al J Biomol Screen 2014 11 Data Coordination V' '\/'l tw I Central Database I repository Stch+flesulls+Nav ~= •- lfoAv"J ••• ID@ ~ t:~ ~~ @ ~ Standards Computing Globalindex Data APls liiill! Repository -~ "-t:~-Repository I 12 Data set IDs and provenance Permanent ID via (authoritaitve) repository or data publication – DOI – PURL Capture data provenance – PROV-O: The PROV Ontology (W3C) http://www.w3.org/TR/prov-o/ – PAV (Provenance, Authoring Versioning) Ontology http://purl.org/pav/ 13 Provenance The link between source data, computation / processing and derived data / results • static verifiable record • track changes • compare / discrepancies • repeat / reproduce • Citation • version • data release PDIFF, Woodman 2011 14 RDBMS vs Semantic Web technologies RDBMS Ontologies • Closed world assumption • Open world assumption • No reasoning support • Reasoning support • Need to know schema • Provide restriction-free for highly specialized framework (formal queries semantics) • Data sharing not easy, no • Easy data/knowledge semantics sharing • Efficient RDBMS access • Triple store access • Established technology • Relatively early stage • Industry standard • Standards emerging Replicability vs Reproducibility [repeat] [replicate ] same same experiment experiment same lab different lab same different experiment experiment different set up some of same [ rep1rod uce ] reuse Drummond C Replicabil ~ y is not Re oducibility: Nor is it Good Science, online Peng RD, Reproducible Research in Computational Science Science 2 Dec 2011: 1226-1227. .
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