Ontology Support for Influenza and Surveillance

© 2006 The MITRE Corporation. All rights reserved

Ontology Support for Influenza Research and Surveillance

Joanne Luciano, PhD, Lynette Hirschman, PhD, Marc Colosimo, PhD

Approved for Public Release; Distribution Unlimited.

28 April 2008 Case Number 08-0738

© 2008 The MITRE Corporation. All rights reserved Approved for Public Release; Distribution Unlimited. 28 April 2008 Case Number 08-0738

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Case Study 1: Indonesia ■  Possible Human to Human transmission of H5N1 (May 2006) ■  Samples were collected and epidemiological data obtained

–  Know who got sick and their relationship to each other –  Know when they got sick and if they died –  Have some public sequence data from that time

■  It is not known if these sample are from these people!

Public Sequence Data

30 Aug 2006

A/Indonesia/CDC595/2006 (2006-05-09)

A/Indonesia/CDC594/2006 (2006-05-10)

….

A/Indonesia/CDC625L/2006 (2006-05-22)

A/Indonesia/CDC644/2006 (2006-05-30)

WHO Nature

Same person ?

GenBank

isolation_source="gender:M; age:32; Lung Aspirate"

Metadata 23 May 2006

Metadata 12 Jul 2006

Butler “Family tragedy spotlights flu mutations “ Nature 442, 114 - 115 (12 Jul 2006) News


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Case Study 2: UK ■  Outbreak of H5N1 in the UK at a turkey farm Feb 1, 2007 ■  What is the source of the outbreak?

–  Contact with infected wild birds? ■  But turkeys were in an enclosed “biosecure” unit ■  No H5N1 detected in the region in the 2 previous months

–  Govt. veterinarian suggested turkey meat from Hungary might be source of infection ■  Turkey farm is adjacent to a poultry packing plant that had processed

poultry products from Hungary ■  Hungary had reported an H5N1 outbreak 2 weeks earlier

■  Sequence data showed that strain infecting the turkeys was 99.96% identical to strain that had infected Hungarian birds

■  Conclusion: Infected Hungarian poultry was source of H5N1 infection –  Open question (relevant to food defense):

how did H5N1 spread from processing plant to live turkeys?


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Research Agenda

■ Reference Database (Sequences & Metadata) – What metadata to collect? – Where to find data and how to connect different sources

(bridging the gap)?


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Genomic Sequence Data

Systems Biology

Demographic data Clinical data

Research Question: Bridging the Gap - Connecting Genomics and Epidemiology

Geospatial data Temporal data Pathogenicity

Host Epidemiology: Occurrence of

Disease in Host

Genomics: Genes of Pathogen

Influenza Ontology


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■ Identify the right collaborators ■ Collect metadata terms ■ Identify resources for that include these terms ■ Regularize metadata

– Generate a controlled vocabulary (terms)

■ Validate subset with BioHealthBase CEIRS data ■ Iterate, review with community, publish ■ Integrate Influenza ontology into workflow

Influenza Ontology: Development


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Influenza Ontology First Draft: Community

■  BioHealthBase: NIAID Influenza Database Point of Contact for

–  Centers of Excellence for Influenza Research and Surveillance (CEIRS) ■  Research: Emory, Mt Sinai, St. Jude, Univ. of Rochester ■  Surveillance: St. Jude, UCLA, Univ. of Minnesota

–  Los Alamos National Laboratory (LANL)

■  Gemina: Category A-C Pathogen Database Point of contact for

– Children’s Hospital Boston –  Johns Hopkins University

■  MITRE

Collaboration with BioHealthBase and Gemina


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Influenza Ontology First Draft: Identify metadata

200 controlled vocabulary terms covering several fields


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Reuse of existing ontologies & metadata standards

OBI – Ontology of Biomedical Investigations EnvO – Environmental Ontology (habitat of pathogen) GAZ – Gazeteer (geographic locations) FMA – Foundational Model of Anatomy DC – Dublin Core (publication metadata) PATO – Phenotype SO – Sequence Ontology (sequence features) Cell – Cell Ontology (types of cells) DO – Disease Ontology IDO – Infectious Disease Ontology

Influenza Ontology First Draft: Metadata resources


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Influenza Ontology First Draft

Excel Spreadsheet

Initial steps: • Collect metadata terms • Map and align terms • Group related information • Identify and define relationships • Identify external ontologies

Formalize

OBO-Edit: Ontology Editing Tool

Formalize: • Normalize terms into a CV • Issue unique identifiers • Instantiate class hierarchy • Define properties and values • Link to external ontology terms

Status: We have just started the formalization step.


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Future Work

Ontology development ■ Complete formalization process ■ Validate subset with data from BioHealthBase

– Circulate for review and comments

■ Use ontology to annotate influenza data


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Team

■  BioHealthBase (UT Southwestern Medical Center) –  Burke Squires –  Richard Scheuermann

■  Institute of Genome Sciences/Gemina (U. Maryland Baltimore) –  Lynn Schriml

■  MITRE –  Joanne Luciano –  Lynette Hirschman –  Marc Colosimo

Technology

Ontology Support for Influenza and Surveillance