caspase activity PubChem nomenclature

fluorescence viability

cheminformatics semantic

domain

binding based programming knowledge search technology end point

thesauri article object PDSP XML enzyme substrate based versioning natural language high-thoughput screening (HTS) software classification polysemes Data Standards for Data Integration

biological pathways Beta-Lactamase Induction dehydrogenase activity specificity

subject headings cyclic AMP redistribution RDF calcium redistribution OWL novel chemical tools

library pharmaceutical

individuals tags

semantic web structural biology

Stephan Schrer University of Miami

indexing

homographs energy transfer

authorized terms

data sets screening

chemical biology

activity enzyme reporter

standards

servers search tool

small molecule

biological assay

mical probes ChemBank

disease networks biomedical knowledge

Fluorogenic substrate GFP induction

controlled vocabulary

subject indexing schemes

taxonomies

synonyms

concepts structure meta-data

information exchange properties annotation

clas

cheATP Lucises ferin Coupled

SPARC Workshop, NIH, Feb 25-26 2015

sschurer@med.miami.edu

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

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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.

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D No Publlc1tlon [J Hl!IS Publication

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AMIS Article Minimum Information Standard

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ARRIVE Animals in Research: Reporting In Vivo ..

REPORTING GUIOELINE

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BioDBCore Core Attributes of Biological Databases

REPORTING GUIOELINE

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ABOUT

BioSharing Standards (http://www.biosharing.org)

3

http:http://www.biosharing.org

a mibbi Bioscience reporting guidelines and tools @ Portal

@ Foundry

@ About

Minimum Information guidelines from diverse bioscience communities

If you want to register your checklist to MIBBI, please contact the BioSharing team Excel spreadsheet and XML document (schema) describing all registered projects

Bioscience projects registered with MIBBI

CIMR Core Information for Metabolomics Reporting

GIATE Guidelines for Information A bout Therapy Experiments

MIABE Minimal Information About a Bioactive Entity

MIABiE Minimum Information About. a Biofilm Experiment

MIACA Minimal Information About a Cellular Assay

MIAME Minimum Information About. a Microarray Experiment

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

MIASE Minimum Information About. a Simulation Experiment

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

I !. x

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I !. x I !. x

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Checklists Minimum Information Guidelines

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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

http:http://regenbase.org

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

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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

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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)?

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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 whats 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

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Repository

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Central Database I repository

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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/

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http://purl.org/pavhttp://www.w3.org/TR/prov-o

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

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RDBMS Ontologies Closed world assumption Open world assumption No reasoning support Reasoning support Need to know schema

for highly specialized queries

Provide restriction-free framework (formal semantics)

Data sharing not easy, no semantics

Easy data/knowledge sharing

Efficient RDBMS access Triple store access Established technology Relatively early stage Industry standard Standards emerging

RDBMS vs Semantic Web technologies

Replicability vs Reproducibility

[repeat] same

experiment same lab

same experiment

different set up

[ rep1rod u ce]

[replicate] same

experiment different lab

different experiment

some of same

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.

Data Standards for Data IntegrationSlide Number 2Slide Number 3Slide Number 4Slide Number 5Slide Number 6Slide Number 7Slide Number 8Slide Number 9Slide Number 10Slide Number 11Slide Number 12Slide Number 13Slide Number 14RDBMS vs Semantic Web technologiesReplicability vs Reproducibility