How to make ImmPort data fit for secondary use

Barry Smithhttp://ontology.buffalo.edu/smith

Goals of ImmPort• Accelerate a more collaborative and coordinated research

environment

• Create an integrated database that broadens the usefulness of scientific data

• Advance the pace and quality of scientific discovery

• Integrate relevant data sets from participating laboratories, public and government databases, and private data sources

• Promote rapid availability of important findings

• Provide analysis tools to advance immunological research

Improve immunology research through enhanced

• Collaboration• Coordination• Discoverability• Integration• Analyzability

Hypothesis: all of these ends will be promoted by describing ImmPort data using terms from shared high quality ontologies

ImmPort data is already being tagged with ontology terms

For example• where data is prepared to meet FDA requirements• where data is published to meet NIH mandates for

reusability• in the post-submission phase, where data is analyzed by

third parties

But this tagging is • partial• uncoordinated• uses ontologies and analysis tools of varying quality

SDY 165: Characterization of in vitro Stimulated B Cells from Human Subjects shared to Semi-

Public Workspace (SPW) Project

SDY 165: Characterization of in vitro Stimulated B Cells from Human Subjects shared to Semi-

Public Workspace (SPW) Project

During the human B cell (Bc) recall response, rapid cell division results in multiple Bc subpopulations. RNA microarray and functional analyses showed that proliferating CD27lo cells are a transient pre-plasmablast population, expressing genes associated with Bc receptor editing. Undivided cells had an active transcriptional program of non-ASC B cell functions, including cytokine secretion and costimulation, suggesting a link between innate and adaptive Bc responses. Transcriptome analysis suggested a gene regulatory network for CD27lo and CD27hi Bc differentiation.

• In vitro stimulated B cells from human subjects • B cell receptor editing

SDY 165: Characterization of in vitro Stimulated B Cells from Human Subjects shared to Semi-

Public Workspace (SPW) Project

Pubmed 22468229

Discoverability: examples

• Find [ImmPort] data pertaining to in vitro stimulated B cells from human subjects

• Find studies of genes associated with B cell receptor editing in human subjects

• Find all data in public and government databases relating to B cell receptor editing

Discoverability through literature search

Two queries: – In vitro stimulated B cells from human subjects– B cell receptor editingon• Pubmed• MeSH (Medical Subject Headings)• Google

Pubmed 22468229

PubMed retrieves 144 results for “In vitro stimulated B cells from human Subjects” –

Zand paper not found

PubMed retrieves 0 results for “Zand[Author] AND In vitro stimulated B cells from human subjects”

Pubmed retrieves 179 results for “B cell receptor editing” – Zand paper not found

MeSH results for “In vitro stimulated B cells from human subjects”

MeSH results for “in vitro stimulated B cells from human subjects”

MeSH results for “B Cell receptor editing”

Google retrieves 180 results for “In vitro stimulated B cells from human subjects” –

Zand paper not found

Jackpot

How to make this [ImmPort data]SDY 165: Characterization of in vitro Stimulated B Cells from Human Subjects shared to Semi-Public Workspace (SPW) ProjectDuring the human B cell (Bc) recall response, rapid cell division results in multiple Bc subpopulations. RNA microarray and functional analyses showed that proliferating CD27lo cells are a transient pre-plasmablast population, expressing genes associated with Bc receptor editing. Undivided cells had an active transcriptional program of non-ASC B cell functions, including cytokine secretion and costimulation, suggesting a link between innate and adaptive Bc responses. Transcriptome analysis suggested a gene regulatory network for CD27lo and CD27hi Bc differentiation.

discoverable?

B cell receptor editing

GO:0002452

GO definition

GO provides a definition

and position in GO hierarchy

-- hierarchy allows logical reasoning

GOPubMed: 179 results for “B cell receptor editing”

(B cell receptor editing Zand) AND ("Zand"[au])

why are zero documents retrieved?

Proposal1. Tag ImmPort SDY abstracts with GO URIs2. Publish the results to the GO Annotation database

During the human B cell recall response, rapid cell division results in multiple B cell subpopulations. RNA microarray and functional analyses showed that proliferating CD27lo cells are a transient pre-plasmablast population, expressing genes associated with B cell receptor editing. Undivided cells had an active transcriptional program of non-ASC B cell functions, including cytokine secretion and costimulation, suggesting a link between innate and adaptive Bc responses. Transcriptome analysis suggested a gene regulatory network for CD27lo and CD27hi Bc differentiation.

But GO is not enough

See http://ncorwiki.buffalo.edu/index.php/Immunology_Ontologies

immune disordersinfectious diseasesallergiesimmune epitopes, etc. etc.

For special case of Flow Cytometry and CyTOF:ImmPort Ontology Meeting, Stanford, September 4-5, 2013: http://x.co/1W1Om

Files in SDY 165

lk_race.txt

American Indian or Alaska NativeAsianBlack or African AmericanNative Hawaiian or Other Pacific IslanderNot_SpecifiedOtherUnknownWhite

ImmPort Templates

https://immport.niaid.nih.gov/immportWeb/experimental/displaySubmitTemplates.do

ImmPort Templates: Race

https://immport.niaid.nih.gov/immportWeb/experimental/displaySubmitTemplates.do

ImmPort Templates

How specify Race if Race = ‘Other’?

ImmPort Templates

How specify “Subject Phenotype”?

NG / BISC proposal

create controlled vocabularies (ontology drop down lists) for fields currently populated by submitters with free text

Files in SDY 165

lk_sample_type

proposal: where controlled vocabularies exist, provide definitions for all terms

Two kinds of definitions• human readable definitions support consistency

of data entry• logical definitions – allow logical analysis of data– support aggregation of data– allow automatic validation of consistent data entry

Definitions can often be taken over from already existing public domain ontologies such as GO • use of ready-made definitions supports discoverability,

and creates automatic linkage to huge bodies of public domain data

ImmPort Antibody Registry (Diehl, et al)

from BD Lyoplate Screening Panels Human Surface Markers

Discoverability

Where did this lk_sample_type list come from?

CDISC

• Clinical Data Interchange Standards Consortium

• http://www.cdisc.org/

CDISC Glossary

SDTM• Study Data Tabulation Model developed by

FDA as part of CDISC– for Race, Gender, Ethnicity, … – no human readable definitions – no logical definitions

Jan 2013: release of CDISC SDTM Model by CDISC2RDF (Kerstin Forsberg of AstraZeneca)

PHUSE (EU, Roche, AstraZeneca, FDA, …) project to incorporate ontology technology

into CDISC

BRIDG

• http://bridgmodel.nci.nih.gov/files/BRIDG_Model_3.2_html/index.htm

• Biomedical Research Integrated Domain Group (BRIDG) Project

BRIDG 3.2 Domain Analysis Model

Other strategies to simplify creation of structured data for submission into ImmPort

• ELN: Electronic Lab Notebooks – PRIME: “Contur ELN has been automating the process

of data deposition into ImmPort, making it much easier for our researchers to submit data to ImmPort”

• CTMS: Clinical Trial Management Systems• EHR: Electronic Health Records– experiments to prepopulate EHR data into CTMS and

from there into case report forms (and into ImmPort?)• Minimal Information Checklists

MIFLOWCYT: Minimal Information for a Flow Cytometry Experiment

Checklist strategy for creating public data repositories via journals

• 75% of articles in Cytometry A are MiFlowCyt compliant

• Result: a growing repository of flow cytometry data (flowrepository.org)

• OBI = Ontology for Biomedical Investigations, an ontology to support creation of structured data about clinical and biological experiments

http://mibbi.sourceforge.net/portal.shtml

Proposal

advertise on ImmPort website best (= most successful) practices from• ELN: Electronic Lab Notebooks • CTMS: Clinical Trial Management Systems• EHR: Electronic Health Records• Minimal Information Checklists

NIAID Sample Data Sharing Plan (Last Reviewed February 12, 2013)

• Sharing of data generated by this project is an essential part of our proposed activities and will be carried out in several different ways.

• Presentations at national scientific meetings. … it is expected that approximately four presentations at national meetings would be appropriate. …

• Annual lectureship. A lectureship has brought to the University distinguished scientists and clinicians …

• Newsletter. The [disease interest group] publishes a newsletter …• Web site of the Interest Group. The [interest group] currently maintains a Web

site where information [about the disease] is posted …• Annual [Disease] Awareness week….• SAGE Library Data. It is our explicit intention that these [Serial analysis of gene

expression] data will be placed in a readily accessible public database. …

NIAID Sample Data Sharing Plan

• SAGE Library Data. It is our explicit intention that these [Serial analysis of gene expression] data will be placed in a readily accessible public database. …

–but how will these data be described?

Proposal

All data sharing plans for NIAID-funded research should require:• paper abstracts and SDY summaries be tagged

with ontology terms• tables and figures in papers be tagged with

ontology terms

See http://ncorwiki.buffalo.edu/index.php/Immunology_Ontologies

ImmPort Ontology Meeting, Stanford, September 4-5, 2013: http://x.co/1W1Om

Further information from phismith@buffalo.edu