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Ontology and the Future of Laboratory Information

Could it be any Clearer?

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General Game Plan for Discussion LIMS –

Changing nature Industry trends Industry slight of hand etc..

Protocol Driven Research Early experiences at ITNShaping approach at IRF

Ontologies as the mediation wrapperWhere Why

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LIMS Landscape Types LIMS, LIS, ELN, CTMS, SDMS, LES ..

Changing but not as fast as one would thinkBig dollar investment (Lab or Org .. CIO or

COO )Increasing domain specificity, or narrowing

functional objectives (Genelogics, SDMS ) Moving toward distributed science or is it

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Specification, Configuration, Execution and Delivery

Specification – The operational description of entities that are created, consumed or altered as part the supported laboratory workflows.

Configuration – The conversion of the operational description into the technical paradigm of the system

Execution – the automated conduct of operational workflows leveraging data capture and reuse of data and data structures defined in the system

Delivery - The extraction of the system data in support of the operational management and or data delivery.

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Where is the Specification Mapping and to what value ?

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Specify From What Perspective ?

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Life is Good……

Don’t dig here

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E-Science…(old news ??)

2006 National Science and Technology Council report by the Interagency Working Group on Digital Data (IWGDD), Charles Humphreys

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Scientific Data Lifecycle- Operationalized

Data Use and Dissemination

Data Structure and Storage

Data Collection and Annotation

Hypothesis Refinement

Process Refinement

ProtocolDesign

Data Collection

Data Processing

Data Access

Data Discovery

Data Analysis

Data Archiving

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Immune Tolerance Network

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ITN Early Days: Epoch Ontologies -

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Attempt 1 … Data Operations vs. Knowledge Management

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The Study Designer at the ITN

Knowledge Representation

Knowledge Acquisition

Expert

KnowledgeSpecification

Data Representation

Data Value Set Specification CaDSR Metadata

Repository

Local Metadata Repository

BRIDG

Entity Framework

Local Library

Knowledge Acquisition Data

Concepts Structure

Content

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NIAID Integrated Research Facility

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Operations

Protocol Design and Development

Research Support

Preliminary Data

Workspace

Document Managemen

t

Regulatory Review and Approval

Animal Facility Management

Animal Inventory

Animal Housing

Animal Procedures

Study Conduct

Study Design Study Activity

Laboratory Management

Anatomical Pathology

Histology VirologyClinical

Pathology Immunology

Support and Housekeeping Functionality Experiment Design and Development

ExperimentDefinition

ExperimentConduct

Methods Annotation

Document Results

Electronic Lab Notebook

SharePoint

InStem Provantis

LIMS

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Study Lifecycle

Full Proposal

Pipeline Development and Program Commitment

Initial Review

Project Approval

Project Review Define Study

Review Impact

Optimize

Configure

Study Definition and Management

Study Conduct

ROMS Systems

Study Monitoring

Review , Analysis and Archive

Complied Data

Data Review

Data Archive Schedule

SharePoint Document Workflow

Study Planning and Definition

Interactive Reporting

Data Processing Provantis

LabWare SharePoint Portal

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Groups

Study Definition

Activity Sample Type(s)

Study

Subjects

Timeline

Assay

Process

Observation

Intervention

Study Definition

Data Package

PV

LIMS

Core Services Requests

Study Conduct

Operational Reporting

Study Reports

Manuscript(s)Product(s)

Study Close

Letter of Intent

Study Plan

Concept Proposal

Full Proposal

MTACRADA

Approval

Review and Approval

StrategicStudy

Calendar

Cost Model

Return of Investment

Analysis

Specification Structure

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Project Review

and Approv

al

Create Study Plan

Review and

Approval Study

Plan

Study Conduct

Close and

Archive Study

Project approved and SharePoint project sub-site established

Study Plan template instantiated producing a physical document (the plan) and a computable representation of study activities

Optimize and align with IRF resources and priorities with study design

Configure Provantis and LabWare based on the study plan.

Extract and create the data deliverables and upload to study portal based on milestones or structure defined in the study plan

SharePoint Project MER-01

• Study MER-01-001

• Docs

SharePoint Project MER-01

• Study MER-01-001

• Study Doc

• Study Data

• Methods• Reports

Base Site

Study Site (structure no content)

SharePoint Project MER-01

• Study MER-01-001

• Study Doc

• Study Data

• Methods• Reports

Study Site (instantiated )

Study Plan

Template

Study Plan

Study Data

Provantis

LabWare

Study Definition

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Study Definition

We are manually creating the data management plan which includes the sequence of activities (SOA) and sampling plan tables that will ultimately drive LIMS configuration and specimen management• Data management plan is an integral

part of creating the study plan

• Data management plan submitted as part of the study plan for IRF approval

Establish a single computable representation of the study structure; including required activities and timing of those activities

Project components:

• Controlled list of activities

• Application to define study structure and assign activities to study day

Activity Day -2 Day 0 Day 2 Day 4 Day 6 Day 8 Day 10 Day 12 Day 14At

Necropsy

Clinical Observations Innoculation Blood Collection Swab Collection Bronchial Lavage Euthanasia ABG

Chest CT

Clinical ChemistryHematologyHemostasis

Flow Cytometry Truecount TBNKLymphoid Meyloid

TaqMan qPCRNP Swab BAL Plaque assay

Blood Culture Tissue

Necropsy Histology EM

Pathology

Immunology

Comparative Medicine

Imaging

Clinical Laboratory

Virology

Bacteriology

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The IRF ROMS Process Automation

Protocol Design

Clinical Operations

Study Execution

Sample Collection and Processing

Inventory and Location Management

Electronic Laboratory Notebook

Process Documentation SOP and Methodology

Instrument / Assay QC

Data Auditability

Scheduling and Resource Management

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LIMS Data 594 Tables

192 have data 6 define the core elements

Project PatientSample Orders Test Result

Auditing and Workflows No understanding of the what

Sample Test

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Leveraging information models in ROMS configuration

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IRF Unified Model Enough to:

Define study structure Define activity types and

association Establish data

expectations

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IRF Unified Model Enough to:

Define study structure Define activity types and

association Establish data

expectations

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IRF Unified Model Enough to:

Define study structure Define activity types and

association Establish data

expectations

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IRF Unified Model Enough to:

Define study structure Define activity types and

association Establish data

expectations

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IRF Unified Model Enough to:

Define study structure Define activity types and

association Establish data

expectations

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IRF Unified Model Enough to:

Define study structure Define activity types and

association Establish data

expectations

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Activity Day -2 Day 0 Day 2 Day 4 Day 6 Day 8 Day 10 Day 12 Day 14At

Necropsy

Clinical Observations Innoculation Blood Collection Swab Collection Bronchial Lavage Euthanasia ABG

Chest CT

Clinical ChemistryHematologyHemostasis

Flow Cytometry Truecount TBNKLymphoid Meyloid

TaqMan qPCRNP Swab BAL Plaque assay

Blood Culture Tissue

Necropsy Histology EM

Pathology

Immunology

Comparative Medicine

Imaging

Clinical Laboratory

Virology

Bacteriology

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Configuration

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Configuration

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Ontologies as a Mediation Layer

When viewed at an industry or organization wide, these types of semantic based system integration and knowledge management efforts can be overwhelming and often fail because of the prevalence of organizations to respond in one of two ways;

Do nothing. Data silos continue to exist with no metadata, no basic dictionary, no context, no “mapability” and, hence, no practical capability for flexible reuse… or

Do everything. Implement a perfect enterprise dictionary with agreed-upon definitions, and model a perfect ontology to describe the complex relationships between concepts for every aspect of the organization —an ideal that when started is rarely completed.

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Incremental Adoption

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Representation Of Biomedical Investigations Using OBI

Jie ZhengUniversity of Pennsylvannia

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Summary The Study Designer + 8 years

Knowledge Representation

Knowledge Acquisition

Expert

KnowledgeSpecification

Data Value Set Specification ISO 21090?

Local Metadata Repository

OBI

LSDAM

Local Library

Knowledge Acquisition Data

Concepts Structure

Content

Configuration Specification

Study Data Set