Difference between revisions of "Immunology Ontologies and Their Applications in Processing Clinical Data"

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The National Center for Biomedical Ontology [http://bioontology.org (NCBO)] in collaboration with the Protein Ontology [http://pir.georgetown.edu/pro/ (PRO)] and the Infectious Disease Ontology [http://infectiousdiseaseontology.org/page/Main_Page (IDO)] will host a three-day dissemination workshop in Buffalo, NY on June 11-13, 2012.  
 
The National Center for Biomedical Ontology [http://bioontology.org (NCBO)] in collaboration with the Protein Ontology [http://pir.georgetown.edu/pro/ (PRO)] and the Infectious Disease Ontology [http://infectiousdiseaseontology.org/page/Main_Page (IDO)] will host a three-day dissemination workshop in Buffalo, NY on June 11-13, 2012.  
 
:Day 1 will provide a survey of current ontology-based research in immunology and infectious disease with a view to future coordination among ontology developers and users in this field.
 
:Day 1 will provide a survey of current ontology-based research in immunology and infectious disease with a view to future coordination among ontology developers and users in this field.
−
:Day 2 will be focused on flow cytometry.
+
:Day 2 will be focused on flow cytometry, including the question of the Cell and Protein Ontologies and of the role of surface protein expression in cell type classification.
 
:Day 3 will include a session devoted to the use of ontologies to assist clinicians working with infectious disease data, followed by a session on the Ontology for General Medical Science.
 
:Day 3 will include a session devoted to the use of ontologies to assist clinicians working with infectious disease data, followed by a session on the Ontology for General Medical Science.
 +
 +
'''Venue: [http://www.universityinn.com/ Ramada Inn, UB North Campus, Buffalo]'''
  
 
'''Goals'''
 
'''Goals'''
  
−
Provisional goals of the meeting are:
+
The goals of this meeting are: To identify and coordinate activities on-going in immunology ontology and related fields, with special attention to the use of ontologies to support clinical data analysis in flow cytometry and related fields.
−
 
 
−
To identify and coordinate activities on-going in immunology ontology and related fields, with special attention to the use of ontologies to support clinical data analysis in flow cytometry and other fields.
 
  
 
'''Registration'''
 
'''Registration'''
  
−
This meeting is free for registered participants. Space is limited and those interested in participating should contact [mailto:phismith@buffalo.edu Barry Smith] as soon as possible.  
+
'''THIS MEETING IS NOW FULL. NO FURTHER REGISTRATIONS ACCEPTED.'''
  
 
----
 
----
−
 
 
'''
 
'''
−
== Draft Schedule ==
+
== Day 1: Monday, June 11, 2012 ==
 
'''
 
'''
  
−
''Day 1: Monday, June 11, 2012: 9:00am-5:00pm''
+
08:30 Registration and Breakfast
  
 
'''<u>An Overview of Ontologies to Support Research in Immunology and Infectious Disease</u>'''
 
'''<u>An Overview of Ontologies to Support Research in Immunology and Infectious Disease</u>'''
  
−
'''Morning: The Gene Ontology, Reactome, The Immunology Ontology, The Immune Epitope Ontology and the Allergy Ontology'''
+
09:15 Barry Smith (University at Buffalo) and Cathy Wu (University of Delaware) [http://ontology.buffalo.edu/12/immunology_ontology/smith.pptx slides]
−
 
 
−
:Barry Smith (University at Buffalo)
 
 
::Bio-Ontologies for Immunology Research: An Introduction
 
::Bio-Ontologies for Immunology Research: An Introduction
 +
:::Brief survey of the goals of the meeting.
  
−
:Alexander Diehl (University at Buffalo)  
+
09:30 Alexander Diehl (University at Buffalo) [http://ontology.buffalo.edu/12/immunology_ontology/Diehl_GO_IP.pptx slides]
 
::The Gene Ontology and Immune System Processes
 
::The Gene Ontology and Immune System Processes
 +
:::The Gene Ontology contains a wealth of terms covering immune system processes for the annotation of proteins involved in the functioning of the immune system.  I will provide a overview of these terms and their use in GO annotation.
  
−
:Anna Maria Masci (Duke University)
+
10:00 Cliburn Chan (Duke University) [http://ontology.buffalo.edu/12/immunology_ontology/Chan_Networks.pptx slides]
 +
::Ontology for Cellular Immune Networks
 +
:::Will describe initial work on an ontology of cellular immune networks that is designed to capture the qualitative cytokine expression patterns and cellular phenotypes associated with specific immune activation networks (e.g. Th1 network). We will outline use of the ontology for immune assay integration and statistical enrichment analysis.
 +
 
 +
10:30 Break
 +
 
 +
11:00 Anna Maria Masci (Duke University) [http://ontology.buffalo.edu/12/immunology_ontology/Masci%20AM.pdf slides]
 
::The Immunology Ontology (with special focus on the liver)
 
::The Immunology Ontology (with special focus on the liver)
 +
:::An emerging scenario is uncovering immune response as a sophisticated biological process, which requires an intensive cross-talk between immunocytes, parenchymal and stromal cell types. These timely and anatomically restricted interactions regulate the outcome of immune response to damage induced by stress and pathogens. Due to its complexity and patho-physiological relevance, the liver represents an interesting prototype of context-dependent immune response. We will introduce the Liver Immunology Ontology (LIO), which has as primary goal the representation of the immune response induced in the context of the liver.
  
−
:Peter d'Eustacho (New York University)
+
11:30 Peter d'Eustacho (New York University) [http://ontology.buffalo.edu/12/immunology_ontology/dEustachio.pptx slides]
 
::Innate Immunity: Signaling via Toll-Like Receptors in Reactome
 
::Innate Immunity: Signaling via Toll-Like Receptors in Reactome
 +
:::The innate immune responses mediated by Toll-like receptors (TLR) provide a first line of defense against microbial pathogens in many vertebrates. In Reactome we have integrated annotations of human TLR molecular functions with those of 6800 other human proteins involved in diverse biological processes to generate a resource suitable for data mining, pathway analysis, and other systems biology approaches. These annotations allow human TLR proteins, the complexes they form, and the functions they mediate to be classified and related to those of structurally similar TLR proteins from chicken, mouse, and other species.
  
−
:Bjoern Peters (University of California at San Diego)
+
12:00 '''Lunchtime talk'''
−
::Representation of immunology experiments using OBI
+
Atul Butte (Stanford) [http://ontology.buffalo.edu/12/immunology_ontology/Butte.pptx slides]
−
::Representing epitope mapping experiments for the Immune Epitope Database (IEDB)
+
::Discovery of a novel inflammatory receptor and related drug for type 2 diabetes from integration of publicly-available microarray data
−
 
 
−
:Alexander C. Yu (University at Buffalo)
 
−
::The Allergy Ontology
 
−
 
 
−
'''Lunchtime talk'''
 
−
:Atul Butte (Stanford): Discovery of a novel inflammatory receptor and related drug for type 2 diabetes from integration of publicly-available microarray data
 
−
 
 
−
'''Afternoon: The Infectious Disease Ontology (IDO) and Its Extensions'''
 
  
−
:Lindsay Cowell (University of Texas Southwestern Medical Center)
+
14:00 Lindsay Cowell (University of Texas Southwestern Medical Center) [http://ontology.buffalo.edu/12/immunology_ontology/Cowell.pdf slides]
 
::An Introduction to the Infectious Disease Ontology
 
::An Introduction to the Infectious Disease Ontology
−
:::Update on IDO-Core; simplified definitions; new approach to MIREOTing; new terms/definitions/relations; a template for creating an IDO Extension
+
:::The IDO-Core; new approach to MIREOTing; new terms/definitions/relations; a template for creating an IDO Extension
  
−
:Albert Goldfain (Blue Highway)
+
14:30 Albert Goldfain (Blue Highway) [http://ontology.buffalo.edu/12/immunology_ontology/Goldfain_IDO-Staph.pptx slides]
 
::Staph Aureus (Sa) IDO  
 
::Staph Aureus (Sa) IDO  
  
−
:Christos (Kitsos) Louis (IMBB-FORTH, Crete)
+
15:00 Break
 +
 
 +
15:30 Christos (Kitsos) Louis (IMBB-FORTH, Crete) [http://ontology.buffalo.edu/12/immunology_ontology/Louis_IDOMAL.ppt slides]
 
::IDO Mal (Malaria Ontology)
 
::IDO Mal (Malaria Ontology)
 +
:::We will outline the Malaria Ontology, including three new sub-domains dealing with:
 +
::::a) drug resistance
 +
::::b) remedies and traditional medicinal plants
 +
::::c) vector-mediated transmission.
 +
:::We will also describe our conversion from the OBO to the OWL format.
  
−
:TBD
+
16:00 Yu Lin (University of Michigan)[http://ontology.buffalo.edu/12/immunology_ontology/YuLun.pdf slides]
−
::IDO Flu (Influenza Ontology)
+
::IDO Bru (Brucellosis Ontology)
 +
:::IDO Bru is an extension ontology of IDO. We will focus on those aspects of Brucellosis represented in IDOBru as outlined in [http://www.jbiomedsem.com/content/2/1/9]. We will also discuss IDOBru's policy on use of IDs, and its treatment of Brucella-host interaction.
  
−
:Yu Lin (University of Michigan)
+
16:30 Oliver He (University of Michigan) [http://ontology.buffalo.edu/12/immunology_ontology/He_VO.pptx slides]
−
::IDO Bru (Brucellosis Ontology)
+
::Contributions of the Vaccine Ontology (VO) to Immunology Research and Public Health
 +
:::Vaccinology is applied immunology. VO is a community-based biomedical ontology in the domain of vaccine and vaccination. We will introduce the top level of VO, and sketch applications of VO in elucidating fundamental protective immune mechanisms and improving public health.
  
 
----
 
----
 +
'''
  
−
''Day 2: Tuesday, June 12, 2012: 9:00am-5:00pm''
+
== Day 2: Tuesday, June 12, 2012 ==
 +
'''
  
 
'''<u>Ontologies and Flow Cytometry Informatics'''</u>
 
'''<u>Ontologies and Flow Cytometry Informatics'''</u>
  
 
:'''Background''' Increasingly, flow cytometry is being employed in clinical laboratories for the diagnosis, prognosis and monitoring of disease. The advent of highly multidimensional flow cytometry and automated gating algorithms for the analysis of flow cytometry data, coupled with the rise of personalized medicine, are poised to expand greatly the need for a reliable, structured framework for the representation of the types of cells present in human blood and tissues. We are currently enhancing the representation of hematopoietic and other cell types in the Cell Ontology (CL) to allow for the logical definition of cell types based on cellular attributes, and in doing so we rely on relations to terms of the Protein Ontology (PRO) as a key component of these definitions. The goal of today's session is explore how the use of clinical flow cytometry data can serve as a driver of ontology development in both the PRO and the CL by assessing current standard clinical assays and recent approaches based on automated gating of multidimensional flow cytometry.
 
:'''Background''' Increasingly, flow cytometry is being employed in clinical laboratories for the diagnosis, prognosis and monitoring of disease. The advent of highly multidimensional flow cytometry and automated gating algorithms for the analysis of flow cytometry data, coupled with the rise of personalized medicine, are poised to expand greatly the need for a reliable, structured framework for the representation of the types of cells present in human blood and tissues. We are currently enhancing the representation of hematopoietic and other cell types in the Cell Ontology (CL) to allow for the logical definition of cell types based on cellular attributes, and in doing so we rely on relations to terms of the Protein Ontology (PRO) as a key component of these definitions. The goal of today's session is explore how the use of clinical flow cytometry data can serve as a driver of ontology development in both the PRO and the CL by assessing current standard clinical assays and recent approaches based on automated gating of multidimensional flow cytometry.
 +
 
:Examples of questions to be addressed include:
 
:Examples of questions to be addressed include:
 
::Which protein isoforms and post-translationally modified forms identified by flow cytometry typing reagents need to be represented in the PRO to enable cell types defined in their terms to be represented in the CL?  
 
::Which protein isoforms and post-translationally modified forms identified by flow cytometry typing reagents need to be represented in the PRO to enable cell types defined in their terms to be represented in the CL?  
 
::How can use of the PRO and CL ontologies will promote standardization in interpretation and integration of clinical flow cytometry data?  
 
::How can use of the PRO and CL ontologies will promote standardization in interpretation and integration of clinical flow cytometry data?  
  
−
'''Morning: Flow cytometry typing of normal and malignant cell types
+
:Background Reading
 +
::[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013777/?tool=pubmed The Protein Ontology: a structured representation of protein forms and complexes]
 +
::[http://www.biomedcentral.com/1471-2105/12/6 Logical Development of the Cell Ontology]
 +
::Cytometry-Ontology Framework ([http://ontology.buffalo.edu/pro/CytometryOntologyFramework.pdf Draft])
 +
 
 +
08:30 Breakfast
 +
 
 +
'''9:00-noon: Introduction to the Protein Ontology Flow Cytometry Driving Biological Project'''
 +
 
 +
9:00 Cathy Wu (University of Delaware): Introduction to the Protein Ontology [http://ontology.buffalo.edu/12/immunology_ontology/Wu_PRO.pdf slides]
 +
 
 +
9:30 Alexander Diehl (Buffalo) [http://ontology.buffalo.edu/12/immunology_ontology/Diehl_CL.pptx slides]
 +
::Hematopoietic Cell Types in the Cell Ontology
 +
:::The Cell Ontology includes over 350 terms that represent hematopoietic cell types.  I will provide an overview of these terms and our strategy for representing key properties of cell types through logical definitions, with examples from Leukemia and Multiple Myeloma
  
−
:Alexander Diehl (Buffalo)
+
10:00 Discussion of the PRO Driving Biomedical Project
−
::An Introduction to Flow Cytometry Ontology
+
Moderator: Alexander Diehl (Buffalo)  
 +
:Presentation of key issues:
 +
::Assessing representation requirements for Flow Cytometry in PRO, CL, IEDB, OBI, ImmPort, and Immune System Modeling.
 +
::Development of a data store to collect extended cell type-protein relationships.
 +
::Defining a tool wish-list for CL-linked flow cytometry analysis and CL-assisted marker selection for cell type analysis.
  
−
:Cathy Wu (Delaware), Darren Natale (Georgetown) and Alexander Diehl (Buffalo)
+
10:45 Break
−
::The Protein Ontology and Cell Type Definitions
 
  
−
:Alexander Diehl (Buffalo)
+
11:15 Oliver He (University of Michigan) [http://ontology.buffalo.edu/12/immunology_ontology/He_Flow_Cytometry.pptx slides]
−
::Overview of Hematopoietic Cell Types in the Cell Ontology
+
::How Flow Cytometry can be used in Vaccine Research
−
::An Ontological Treatment of Protein Marker Expression on Multiple Myeloma Subtypes
+
:::To better understand fundamental protective immune mechanisms, flow cytometry has frequently been used to measure vaccine-induced innate immunity, and antigen-specific T-cell and B-cell responses. Biomedical ontologies (e.g., VO, OBI, and PRO) play important roles in data representation, integration, and automated reasoning in vaccine-related flow cytometry research.
−
::Overview of Euroflow Leukemia Typing Panels
 
  
−
:(TBD)
+
11:45 Dave Parrish ([http://www.labanswer.com/ LabAnswer])
−
::Clinical Flow Cytometry in HIV
+
::Storing and Retrieving Flow Cytometry Data
−
::Representation of cells used in experiments such as: PBMCs, splenocytes, adherent cells
+
:::The Flow Cytometry Laboratory at Roswell Park Cancer Institute has recently deployed an internally developed application managing the operational workflow of the laboratory. We will describe the use of the relational database in capturing assay results and ultimately associating with a final interpretation. Although early in the process the goal is to support the use of the Cell and Protein Ontologies in panel design and interpretation classification.  
−
::Discussion of the ontological treatment of typing panels.
 
  
−
'''Afternoon: Automated gating of Flow Cytometry results and linking to the Cell Ontology'''
+
12:30 Lunch
  
−
:Ryan Brinkman (Vancouver)
+
'''Afternoon: Automated gating of Flow Cytometry results and linking to the Cell Ontology. Flow cytometry typing of normal and malignant cell types'''
−
::1. Overview of the representation of flow cytometry assays in OBI
 
−
::2. Overview of [http://www.bioconductor.org/packages/release/bioc/html/flowMeans.html flowMeans] and [http://flowcap.flowsite.org/ flowCAP]
 
−
::3. Connecting results from automated FCM analysis systems with the Cell Ontology
 
  
−
:Cliburn Chan (Duke)
+
13:30 Cliburn Chan (Duke) [http://ontology.buffalo.edu/12/immunology_ontology/Chan_HIV.pptx slides]
−
::Flow Cytometry Analysis System
+
::Automated flow cytometry analysis in HIV studies
 +
:::Will describe recent work on automated cell subset identification and alignment across multiple HIV-related data sets with statistical mixture models. What do we need in order to be able to use ontologies for automated annotation and labeling of cell subsets?
  
−
:Nikesh Kotecha (Cytobank)
+
14:00 Nikesh Kotecha (Cytobank) [http://ontology.buffalo.edu/12/immunology_ontology/kotecha.pdf slides]
 
::Incorporating annotations into the analysis workflow - examples using Cytobank and NCBO's BioPortal
 
::Incorporating annotations into the analysis workflow - examples using Cytobank and NCBO's BioPortal
 +
:::Cytobank is a platform to manage, share and analyze flow cytometry data over the web. I will describe the challenges addressed in working with large numbers of samples as well as incorporating novel visualizations and algorithms (e.g. SPADE) for high dimensional data (e.g. 40+ parameter mass cytometry experiments). Central to much of this work is interfaces to promote and incorporate annotations into the analysis workflow. I will also  highlight some recent work in Cytobank to incorporate ontologies via NCBO's BioPortal
  
−
:Topics to be discussed will include
+
14:30 Melanie Courtot (Ryan Brinkman's group, Vancouver) [http://ontology.buffalo.edu/12/immunology_ontology/Courtot_CytometryOntologyFramework.pdf slides]
−
::Methods to automatically link flow cytometry results to cell type identification.
+
::1. Overview of the representation of flow cytometry assays in [http://purl.obolibrary.org/obo/obi OBI]
−
::How to motivate the annotation of data using the discussed ontologies (the role of interfaces)
+
::2. Connecting results from automated FCM analysis systems with the Cell Ontology
 +
 
 +
15:00 Break
 +
 
 +
15:30 General Discussion of Ontologies and Flow Cytometry (Moderator: Alan Ruttenberg, Buffalo)
 +
 
 +
16:30 End
  
 
----
 
----
 +
'''
 +
 +
== Day 3: Wednesday, June 13, 2012 ==
 +
'''
 +
 +
8:30 Breakfast
 +
 +
'''<u>9:00-noon: Immunology Ontologies (Continued)</u>'''
 +
 +
9:00 Bjoern Peters (La Jolla Institute for Allergy and Immunology) [http://ontology.buffalo.edu/12/immunology_ontology/Peters.pptx slides]
 +
::Representation of immunology experiments using OBI
 +
::Representing epitope mapping experiments for the Immune Epitope Database (IEDB)
 +
:::The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meaning to describe all aspects of how biomedical investigations are conducted. OBI builds on the Gene Ontology (GO) and related efforts that provide a formal and interoperable representation of biomedical knowledge.  OBI adds the ability to describe how this knowledge was derived. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. The presentation will describe the state of OBI and several applications that are using it. Specific focus will be on epitope mapping and characterization experiments captured in the Immune Epitope Database (IEDB) which heavily utilizes OBI. The presentation will also point out gaps in coverage of immunological terms that are currently in OBI but poorly defined and outside the scope of OBI, and which deserve a better home.
  
−
''Day 3: Wednesday, June 13, 2012:9:00am-6:00pm''
+
10: Alex Yu (Buffalo) [http://ontology.buffalo.edu/12/immunology_ontology/Alex_Yu.pdf slides]
 +
:The Allergy Ontology
  
−
'''<u>The Role of Ontologies in Clinical Medicine</u>'''
+
10:30 Break
  
−
'''9am-noon: Next Steps in Protein Ontology Flow Cytometry Driving Biological Project'''
+
11:00 Alexander Diehl (Buffalo) [http://ontology.buffalo.edu/12/immunology_ontology/Diehl_Autoimmune.pptx slides]
 +
:Towards an Auto-Immune Disease Ontology
 +
::I will discuss the construction of an auto-immune disease ontology through use of the Ontology of General Medical Sciences as a general framework for ontology development.
  
−
:TBD
 
  
−
'''noon-3pm SESSION OPEN TO THE PUBLIC: Practical Applications of Ontologies in Clinical Research''' (includes lunch)
+
'''<u>The Role of Ontologies in Clinical Medicine</u>'''
  
−
:Albert Goldfain (Blue Highway / Syracuse)
+
12:00: Lunch
 +
 
 +
1:00pm :Albert Goldfain (Blue Highway / Syracuse)
 
::Creating Personalized Infectious Disease Ontologies  
 
::Creating Personalized Infectious Disease Ontologies  
  
−
:Alan Ruttenberg (Buffalo)
+
1:30 Christos (Kitsos) Louis (IMBB-FORTH, Crete)
−
::The Protein Ontology and the treatment of protein isoforms, mutations, and aggregates of relevance to Alzheimer's Disease 
+
::Ontologies and Vector-Borne Diseases
  
−
:TBD
+
2:00 Werner Ceusters (Buffalo) [http://www.referent-tracking.com/RTU/sendfile/?file=20120613.ppt slides]
−
::The HIV Ontology
+
::Assessment instruments and biomedical reality: examples in the pain domain
  
−
:Werner Ceusters (Buffalo)
+
2:30 Refreshment Break
−
::Assessment instruments and biomedical reality: examples in the pain domain
 
  
−
'''3pm-6pm: Working Session on the Ontology for General Medical Science (OGMS)'''  
+
'''<u>3pm-6pm: Working Session on the Ontology for General Medical Science (OGMS)</u>'''  
  
 
:Moderator: Albert Goldfain (Blue Highway / Syracuse)
 
:Moderator: Albert Goldfain (Blue Highway / Syracuse)
 
:Topics to be treated will include:
 
:Topics to be treated will include:
−
::An update on OGMS
+
::Current status of OGMS and the OGMS Reference
−
::Relations in OGMS
+
::Linking diseases to their underlying disorders using basis relations
−
+
::Defining 'relapse' and 'remission' processes.
 +
::Updates on the Vital Sign Ontology
 +
::Recipes for OGMS-conformant extension ontologies
 
:''Close: 6:00pm''
 
:''Close: 6:00pm''
 
----
 
----
 
'''Relevant ontology efforts'''
 
'''Relevant ontology efforts'''
  
−
:GO-IP Gene Ontology -- Immunological Process (Alex Diehl)
+
:GO-IP Gene Ontology -- Immunological Process (Alexander Diehl)
−
:CL Cell ontology immune branches (e.g. for dendritic cells)
+
:CL Cell ontology immune branches
 
:PRO Protein Ontology  
 
:PRO Protein Ontology  
−
:IO Immunology Ontology (Lindsay Cowell and Alex Diehl)
+
:IO Immunology Ontology (Lindsay Cowell and Alexander Diehl)
 
:IEO Immune Epitope Ontology (Bjoern Peters)     
 
:IEO Immune Epitope Ontology (Bjoern Peters)     
 
:MHC Major Histocompatibility Complex Ontology (Bjoern Peters)
 
:MHC Major Histocompatibility Complex Ontology (Bjoern Peters)
Line 157: Line 205:
 
:Vaccine Ontology (Oliver He)
 
:Vaccine Ontology (Oliver He)
 
:AO Allergy Ontology (Alex C. Yu)                           
 
:AO Allergy Ontology (Alex C. Yu)                           
−
:ND Neurological Disease Ontology (Alex Diehl)                                                                                                                                                                                                                                                                                                                     
+
:ND Neurological Disease Ontology (Alexander Diehl)                                                                                                                                                                                                                                                                                                                     
 
----
 
----
−
'''Participants will include'''
+
'''
 +
 
 +
== Participants ==
  
−
:Ryan Brinkman (University of British Columbia, June 11-12)
+
:Alex Benns (Frontier Science, Amherst, NY)
 +
:Anthony Bloom (Frontier Science, Amherst, NY)
 +
:Kenneth Braun (Frontier Science, Amherst, NY)
 +
:Ryan Brinkman (University of British Columbia, Vancouver)
 
:Atul Butte (Stanford University)
 
:Atul Butte (Stanford University)
 +
:James S. Cavenaugh (University of Rochester Medical Center)
 
:Werner Ceusters (University at Buffalo)
 
:Werner Ceusters (University at Buffalo)
−
:Cliburn Chan (Duke University, June 11-12)  
+
:Cliburn Chan (Duke University)  
 
:Quan Chen (NIH/NIAID)
 
:Quan Chen (NIH/NIAID)
 +
:Melanie Courtot (BCCRC, Vancouver)
 +
:Alexander Cox (University at Buffalo)
 
:Lindsay Cowell (University of Texas Southwestern Medical Center)
 
:Lindsay Cowell (University of Texas Southwestern Medical Center)
 +
:Oliver Crespo (BD Biosciences, San Jose, CA)
 
:Paresh Dandona (Diabetes and Endocrinology Center of Western New York / University at Buffalo)
 
:Paresh Dandona (Diabetes and Endocrinology Center of Western New York / University at Buffalo)
 
:Peter d'Eustachio (New York University)
 
:Peter d'Eustachio (New York University)
−
:Alex Diehl (University at Buffalo)
+
:Alexander Diehl (University at Buffalo)
 +
:William Duncan (University at Buffalo)
 
:Chester Fox (University at Buffalo)
 
:Chester Fox (University at Buffalo)
 +
:Lee Ann Garrett-Sinha (University at Buffalo)
 +
:Carmelo Gaudioso (Roswell Park Cancer Institute, Buffalo)
 
:Albert Goldfain (University at Buffalo, Syracuse University and Blue Highway, Inc.)
 
:Albert Goldfain (University at Buffalo, Syracuse University and Blue Highway, Inc.)
 
:Oliver He (University of Michigan)
 
:Oliver He (University of Michigan)
 
:Leonard Jacuzzo (University at Buffalo)
 
:Leonard Jacuzzo (University at Buffalo)
 +
:Mark Jensen (University at Buffalo)
 
:Christos (Kitsos) Louis (IMBB-FORTH, Crete)
 
:Christos (Kitsos) Louis (IMBB-FORTH, Crete)
 
:Nikesh Kotecha (Cytobank)
 
:Nikesh Kotecha (Cytobank)
 
:Yu Lin (University of Michigan)
 
:Yu Lin (University of Michigan)
 +
:Wei Luo (University at Buffalo)
 +
:Supriya Mahajan (University at Buffalo)
 
:Anna Maria Masci (Duke University)
 
:Anna Maria Masci (Duke University)
 
:Darren Natale (Georgetown University)
 
:Darren Natale (Georgetown University)
 
:Dave Parrish (Digital Infuzion)
 
:Dave Parrish (Digital Infuzion)
−
:Bjoern Peters, (La Jolla Institute for Allergy and Immunology)
+
:Bjoern Peters (La Jolla Institute for Allergy and Immunology)
 +
:Mark Ressler (University at Buffalo)
 +
:Jessica L. Reynolds (University at Buffalo)
 
:Alan Ruttenberg (University at Buffalo)
 
:Alan Ruttenberg (University at Buffalo)
 
:Stanley A. Schwartz (University at Buffalo)
 
:Stanley A. Schwartz (University at Buffalo)
 +
:Prontip Saelee (University at Buffalo)
 +
:Veronica Shamovsky (NYU School of Medicine)
 
:Barry Smith (University at Buffalo)
 
:Barry Smith (University at Buffalo)
 
:Cathy Wu (University of Delaware, Georgetown University)
 
:Cathy Wu (University of Delaware, Georgetown University)
 
:Alex C. Yu (University at Buffalo)
 
:Alex C. Yu (University at Buffalo)

Latest revision as of 12:45, 12 April 2013

The National Center for Biomedical Ontology (NCBO) in collaboration with the Protein Ontology (PRO) and the Infectious Disease Ontology (IDO) will host a three-day dissemination workshop in Buffalo, NY on June 11-13, 2012.

Day 1 will provide a survey of current ontology-based research in immunology and infectious disease with a view to future coordination among ontology developers and users in this field.
Day 2 will be focused on flow cytometry, including the question of the Cell and Protein Ontologies and of the role of surface protein expression in cell type classification.
Day 3 will include a session devoted to the use of ontologies to assist clinicians working with infectious disease data, followed by a session on the Ontology for General Medical Science.

Venue: Ramada Inn, UB North Campus, Buffalo

Goals

The goals of this meeting are: To identify and coordinate activities on-going in immunology ontology and related fields, with special attention to the use of ontologies to support clinical data analysis in flow cytometry and related fields.

Registration

THIS MEETING IS NOW FULL. NO FURTHER REGISTRATIONS ACCEPTED.

Day 1: Monday, June 11, 2012

08:30 Registration and Breakfast

An Overview of Ontologies to Support Research in Immunology and Infectious Disease

09:15 Barry Smith (University at Buffalo) and Cathy Wu (University of Delaware) slides

Bio-Ontologies for Immunology Research: An Introduction
Brief survey of the goals of the meeting.

09:30 Alexander Diehl (University at Buffalo) slides

The Gene Ontology and Immune System Processes
The Gene Ontology contains a wealth of terms covering immune system processes for the annotation of proteins involved in the functioning of the immune system. I will provide a overview of these terms and their use in GO annotation.

10:00 Cliburn Chan (Duke University) slides

Ontology for Cellular Immune Networks
Will describe initial work on an ontology of cellular immune networks that is designed to capture the qualitative cytokine expression patterns and cellular phenotypes associated with specific immune activation networks (e.g. Th1 network). We will outline use of the ontology for immune assay integration and statistical enrichment analysis.

10:30 Break

11:00 Anna Maria Masci (Duke University) slides

The Immunology Ontology (with special focus on the liver)
An emerging scenario is uncovering immune response as a sophisticated biological process, which requires an intensive cross-talk between immunocytes, parenchymal and stromal cell types. These timely and anatomically restricted interactions regulate the outcome of immune response to damage induced by stress and pathogens. Due to its complexity and patho-physiological relevance, the liver represents an interesting prototype of context-dependent immune response. We will introduce the Liver Immunology Ontology (LIO), which has as primary goal the representation of the immune response induced in the context of the liver.

11:30 Peter d'Eustacho (New York University) slides

Innate Immunity: Signaling via Toll-Like Receptors in Reactome
The innate immune responses mediated by Toll-like receptors (TLR) provide a first line of defense against microbial pathogens in many vertebrates. In Reactome we have integrated annotations of human TLR molecular functions with those of 6800 other human proteins involved in diverse biological processes to generate a resource suitable for data mining, pathway analysis, and other systems biology approaches. These annotations allow human TLR proteins, the complexes they form, and the functions they mediate to be classified and related to those of structurally similar TLR proteins from chicken, mouse, and other species.

12:00 Lunchtime talk Atul Butte (Stanford) slides

Discovery of a novel inflammatory receptor and related drug for type 2 diabetes from integration of publicly-available microarray data

14:00 Lindsay Cowell (University of Texas Southwestern Medical Center) slides

An Introduction to the Infectious Disease Ontology
The IDO-Core; new approach to MIREOTing; new terms/definitions/relations; a template for creating an IDO Extension

14:30 Albert Goldfain (Blue Highway) slides

Staph Aureus (Sa) IDO

15:00 Break

15:30 Christos (Kitsos) Louis (IMBB-FORTH, Crete) slides

IDO Mal (Malaria Ontology)
We will outline the Malaria Ontology, including three new sub-domains dealing with:
a) drug resistance
b) remedies and traditional medicinal plants
c) vector-mediated transmission.
We will also describe our conversion from the OBO to the OWL format.

16:00 Yu Lin (University of Michigan)slides

IDO Bru (Brucellosis Ontology)
IDO Bru is an extension ontology of IDO. We will focus on those aspects of Brucellosis represented in IDOBru as outlined in [1]. We will also discuss IDOBru's policy on use of IDs, and its treatment of Brucella-host interaction.

16:30 Oliver He (University of Michigan) slides

Contributions of the Vaccine Ontology (VO) to Immunology Research and Public Health
Vaccinology is applied immunology. VO is a community-based biomedical ontology in the domain of vaccine and vaccination. We will introduce the top level of VO, and sketch applications of VO in elucidating fundamental protective immune mechanisms and improving public health.

Day 2: Tuesday, June 12, 2012

Ontologies and Flow Cytometry Informatics

Background Increasingly, flow cytometry is being employed in clinical laboratories for the diagnosis, prognosis and monitoring of disease. The advent of highly multidimensional flow cytometry and automated gating algorithms for the analysis of flow cytometry data, coupled with the rise of personalized medicine, are poised to expand greatly the need for a reliable, structured framework for the representation of the types of cells present in human blood and tissues. We are currently enhancing the representation of hematopoietic and other cell types in the Cell Ontology (CL) to allow for the logical definition of cell types based on cellular attributes, and in doing so we rely on relations to terms of the Protein Ontology (PRO) as a key component of these definitions. The goal of today's session is explore how the use of clinical flow cytometry data can serve as a driver of ontology development in both the PRO and the CL by assessing current standard clinical assays and recent approaches based on automated gating of multidimensional flow cytometry.
Examples of questions to be addressed include:
Which protein isoforms and post-translationally modified forms identified by flow cytometry typing reagents need to be represented in the PRO to enable cell types defined in their terms to be represented in the CL?
How can use of the PRO and CL ontologies will promote standardization in interpretation and integration of clinical flow cytometry data?
Background Reading
The Protein Ontology: a structured representation of protein forms and complexes
Logical Development of the Cell Ontology
Cytometry-Ontology Framework (Draft)

08:30 Breakfast

9:00-noon: Introduction to the Protein Ontology Flow Cytometry Driving Biological Project

9:00 Cathy Wu (University of Delaware): Introduction to the Protein Ontology slides

9:30 Alexander Diehl (Buffalo) slides

Hematopoietic Cell Types in the Cell Ontology
The Cell Ontology includes over 350 terms that represent hematopoietic cell types. I will provide an overview of these terms and our strategy for representing key properties of cell types through logical definitions, with examples from Leukemia and Multiple Myeloma

10:00 Discussion of the PRO Driving Biomedical Project Moderator: Alexander Diehl (Buffalo)

Presentation of key issues:
Assessing representation requirements for Flow Cytometry in PRO, CL, IEDB, OBI, ImmPort, and Immune System Modeling.
Development of a data store to collect extended cell type-protein relationships.
Defining a tool wish-list for CL-linked flow cytometry analysis and CL-assisted marker selection for cell type analysis.

10:45 Break

11:15 Oliver He (University of Michigan) slides

How Flow Cytometry can be used in Vaccine Research
To better understand fundamental protective immune mechanisms, flow cytometry has frequently been used to measure vaccine-induced innate immunity, and antigen-specific T-cell and B-cell responses. Biomedical ontologies (e.g., VO, OBI, and PRO) play important roles in data representation, integration, and automated reasoning in vaccine-related flow cytometry research.

11:45 Dave Parrish (LabAnswer)

Storing and Retrieving Flow Cytometry Data
The Flow Cytometry Laboratory at Roswell Park Cancer Institute has recently deployed an internally developed application managing the operational workflow of the laboratory. We will describe the use of the relational database in capturing assay results and ultimately associating with a final interpretation. Although early in the process the goal is to support the use of the Cell and Protein Ontologies in panel design and interpretation classification.

12:30 Lunch

Afternoon: Automated gating of Flow Cytometry results and linking to the Cell Ontology. Flow cytometry typing of normal and malignant cell types

13:30 Cliburn Chan (Duke) slides

Automated flow cytometry analysis in HIV studies
Will describe recent work on automated cell subset identification and alignment across multiple HIV-related data sets with statistical mixture models. What do we need in order to be able to use ontologies for automated annotation and labeling of cell subsets?

14:00 Nikesh Kotecha (Cytobank) slides

Incorporating annotations into the analysis workflow - examples using Cytobank and NCBO's BioPortal
Cytobank is a platform to manage, share and analyze flow cytometry data over the web. I will describe the challenges addressed in working with large numbers of samples as well as incorporating novel visualizations and algorithms (e.g. SPADE) for high dimensional data (e.g. 40+ parameter mass cytometry experiments). Central to much of this work is interfaces to promote and incorporate annotations into the analysis workflow. I will also highlight some recent work in Cytobank to incorporate ontologies via NCBO's BioPortal

14:30 Melanie Courtot (Ryan Brinkman's group, Vancouver) slides

1. Overview of the representation of flow cytometry assays in OBI
2. Connecting results from automated FCM analysis systems with the Cell Ontology

15:00 Break

15:30 General Discussion of Ontologies and Flow Cytometry (Moderator: Alan Ruttenberg, Buffalo)

16:30 End

Day 3: Wednesday, June 13, 2012

8:30 Breakfast

9:00-noon: Immunology Ontologies (Continued)

9:00 Bjoern Peters (La Jolla Institute for Allergy and Immunology) slides

Representation of immunology experiments using OBI
Representing epitope mapping experiments for the Immune Epitope Database (IEDB)
The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meaning to describe all aspects of how biomedical investigations are conducted. OBI builds on the Gene Ontology (GO) and related efforts that provide a formal and interoperable representation of biomedical knowledge. OBI adds the ability to describe how this knowledge was derived. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. The presentation will describe the state of OBI and several applications that are using it. Specific focus will be on epitope mapping and characterization experiments captured in the Immune Epitope Database (IEDB) which heavily utilizes OBI. The presentation will also point out gaps in coverage of immunological terms that are currently in OBI but poorly defined and outside the scope of OBI, and which deserve a better home.

10: Alex Yu (Buffalo) slides

The Allergy Ontology

10:30 Break

11:00 Alexander Diehl (Buffalo) slides

Towards an Auto-Immune Disease Ontology
I will discuss the construction of an auto-immune disease ontology through use of the Ontology of General Medical Sciences as a general framework for ontology development.


The Role of Ontologies in Clinical Medicine

12:00: Lunch

1:00pm :Albert Goldfain (Blue Highway / Syracuse)

Creating Personalized Infectious Disease Ontologies

1:30 Christos (Kitsos) Louis (IMBB-FORTH, Crete)

Ontologies and Vector-Borne Diseases

2:00 Werner Ceusters (Buffalo) slides

Assessment instruments and biomedical reality: examples in the pain domain

2:30 Refreshment Break

3pm-6pm: Working Session on the Ontology for General Medical Science (OGMS)

Moderator: Albert Goldfain (Blue Highway / Syracuse)
Topics to be treated will include:
Current status of OGMS and the OGMS Reference
Linking diseases to their underlying disorders using basis relations
Defining 'relapse' and 'remission' processes.
Updates on the Vital Sign Ontology
Recipes for OGMS-conformant extension ontologies
Close: 6:00pm

Relevant ontology efforts

GO-IP Gene Ontology -- Immunological Process (Alexander Diehl)
CL Cell ontology immune branches
PRO Protein Ontology
IO Immunology Ontology (Lindsay Cowell and Alexander Diehl)
IEO Immune Epitope Ontology (Bjoern Peters)
MHC Major Histocompatibility Complex Ontology (Bjoern Peters)
OGMS Ontology for General Medical Science (Albert Goldfain)
IDO Infectious Disease Ontology (Lindsay Cowell)
Vaccine Ontology (Oliver He)
AO Allergy Ontology (Alex C. Yu)
ND Neurological Disease Ontology (Alexander Diehl)

Participants

Alex Benns (Frontier Science, Amherst, NY)
Anthony Bloom (Frontier Science, Amherst, NY)
Kenneth Braun (Frontier Science, Amherst, NY)
Ryan Brinkman (University of British Columbia, Vancouver)
Atul Butte (Stanford University)
James S. Cavenaugh (University of Rochester Medical Center)
Werner Ceusters (University at Buffalo)
Cliburn Chan (Duke University)
Quan Chen (NIH/NIAID)
Melanie Courtot (BCCRC, Vancouver)
Alexander Cox (University at Buffalo)
Lindsay Cowell (University of Texas Southwestern Medical Center)
Oliver Crespo (BD Biosciences, San Jose, CA)
Paresh Dandona (Diabetes and Endocrinology Center of Western New York / University at Buffalo)
Peter d'Eustachio (New York University)
Alexander Diehl (University at Buffalo)
William Duncan (University at Buffalo)
Chester Fox (University at Buffalo)
Lee Ann Garrett-Sinha (University at Buffalo)
Carmelo Gaudioso (Roswell Park Cancer Institute, Buffalo)
Albert Goldfain (University at Buffalo, Syracuse University and Blue Highway, Inc.)
Oliver He (University of Michigan)
Leonard Jacuzzo (University at Buffalo)
Mark Jensen (University at Buffalo)
Christos (Kitsos) Louis (IMBB-FORTH, Crete)
Nikesh Kotecha (Cytobank)
Yu Lin (University of Michigan)
Wei Luo (University at Buffalo)
Supriya Mahajan (University at Buffalo)
Anna Maria Masci (Duke University)
Darren Natale (Georgetown University)
Dave Parrish (Digital Infuzion)
Bjoern Peters (La Jolla Institute for Allergy and Immunology)
Mark Ressler (University at Buffalo)
Jessica L. Reynolds (University at Buffalo)
Alan Ruttenberg (University at Buffalo)
Stanley A. Schwartz (University at Buffalo)
Prontip Saelee (University at Buffalo)
Veronica Shamovsky (NYU School of Medicine)
Barry Smith (University at Buffalo)
Cathy Wu (University of Delaware, Georgetown University)
Alex C. Yu (University at Buffalo)
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