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BioAssay Ontology (BAO)
Stephan Schürer, PhD
ICBO, Buffalo, July 30 2011
One of the most important approaches to find novel entry points for drug discovery programs
Historically in pharmaceutical companies Since ~2005, massive NIH effort (MLI) to make HTS
accessible to public sector research PubChem is the major repository of HTS data More recently: EU-OpenScreen project
Background for BioAssay Ontology
High-throughput screening
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Lack of standardized assay annotations No standardized endpoint names or formats
Data is rarely re-used(!)Common queries cannot be askedAnalysis across different data sets is difficultIntegration with other databases is difficult
No knowledge model for assays and screening results
Motivation for BioAssay Ontology
Large public screening data setsPubChem, ChEMBL, PDSP, ChemBank, Binding DB
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• Identify inhibitors of kinases in biochemical assays.• Identify compounds active in multiple luciferase reporter
gene assays.• Identify compounds active in cell viability assays and
organize by cell lines and assay types.• Identify active compounds in assays related to pathway X.• …
Queries the Ontology should be able to answer
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Leverage the aggregated corpus of publically available HTS data to infer molecular mechanism of actions (MMOA) of small molecule perturbagens in biological model systems.
Schürer et al. “BioAssay Ontology Annotations Facilitate Cross-Analysis of Diverse High-throughput Screening Data Sets” J Biomol Screen 2011 (16), 415-426.
BAOSearch Software (beta):http://baosearch.ccs.miami.edu Query, explore, download BAO-annotated PubChem content Some semantic search capabilities
Project Website and Wiki with relevant materials and documentation:http://www.bioassayontology.org/http://www.bioassayontology.org/wiki
BAO Products and Resources
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Application / user focus vs. “universal” ontologies Efficiency vs. “realism” of representations Rapid application development
Orthogonal ontologies vs. Ontology mapping Universal “realism” vs. domain or application-specific
Chemical bond: 2D structure graph, 3D rule based, molecular mechanics, semi-empirical, up-initio QM
Disease Virtual world
Questions / Discussion points
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Collaborative ontology development Collaborative vs. individual effort Control over development and focus / application focus Rapid application development Quality
Aligning BAO to upper level ontology (BFO) Benefits vs. required resources Do upper level ontologies matter for specialized
applications?
Questions / Discussion points
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Aligning BAO with OBI Some level of overlap OBI: process-oriented (model the investigation) BAO: purpose of categorization and analysis of HTS data BAO model becomes more complex if based on OBI
How do we do it practically Define missing assays to OBI and MIREOT back? Quick term templates (QTT)? Define our relations as short-cut relationships (using RO)?
Questions / Discussion points
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Additional slides
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BAO-facilitated Example for Analysis(Luciferase Assays)
Details in: Schürer et al. “BioAssay Ontology Annotations Facilitate Cross-Analysis of Diverse High-throughput Screening Data Sets” J Biomol Screen 2011 (16), 415-426.
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Panel AssaySingle ConcOtherConc-responseA
ssa
y C
ou
nt
Most promiscuous reporter gene compounds
Most promiscuous reporter gene compoundsR
epor
ter D
RR
epor
ter S
CVi
abilit
y D
RVi
abilit
y SC
Enz
Activ
DR
Enz
Activ
SC
ATP
DR
ATP
SCLu
cife
rin D
RLu
cife
rin S
C
Promiscuity Index
0 10.2
Com
poun
ds
Luciferase Enzyme Inhibitors
Generally cytotoxic
Examples: Cytotoxic Series
Cluster Reporter PCIdx: 0.56Cluster Reporter Active: 58Cluster Viability PCIdx: 0.64Cluster Viability Active 27 Cluster Reporter PCIdx: 0.48
Cluster Reporter Active: 23Cluster Viability PCIdx: 0.45Cluster Viability Active 10
Cluster Reporter PCIdx: 0.41Cluster Reporter Active: 29Cluster Viability PCIdx: 0.57Cluster Viability Active 13
Daunorubicin
Emetine
Examples: Luciferase Inhibitor Series
Cluster Size: 6Cluster Reporter PCIdx: 0.61Cluster Reporter Active: 101Cluster EnzActivity PCIdx: 0.58Cluster EnzActivity: 15
Cluster Size: 4Cluster Reporter PCIdx: 0.38Cluster Reporter Active: 52Cluster EnzActivity PCIdx: 0.61Cluster EnzActivity: 11
Cluster Size: 5Cluster Reporter PCIdx: 0.46Cluster Reporter Active: 77Cluster EnzActivity PCIdx: 0.58Cluster EnzActivity: 14
Schürer et al. “BioAssay Ontology Annotations Facilitate Cross-Analysis of Diverse High-throughput Screening Data Sets” J Biomol Screen 2011 (16), 415-426.
1) Development of the Bioassay Ontology
2) Annotation of assays and assay results(content curation)
3) Development of software tools
BAO Project: Three major components
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BAO design to describe assays
Application of BAO: BAO Search Software
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http://baosearch.ccs.miami.edu/baosearch/
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BAO: Concept Search
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Biochemical Assays with IC50 < 1 mM
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Chemical structure search
BioAssay Ontology (NCBO bioportal and project site):http://bioportal.bioontology.org/ontologies/45410http://www.bioassayontology.org/visualize/
Terminology / annotations for biochemical assays: http://www.bioassayontology.org/>Assay Annotation Template
Over 1000 BAO-annotated assays from PubChem (available in BAOSearch)
BAO Products and Resources
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• Chris Mader• Amar Koleti• Nakul Datar• Sreeharsha
Venkatapuram• Felimon Gayanilo
• Mark Southern
• Saminda Abeyruwan• Uma Vempati• Magdalena Przydzial• Kunie Sakurai• Robin Smith• Yuanyuan Jia• Caty Chung
• Ubbo Visser• Vance Lemmon• Mitsunori Ogihara
• Nick Tsinoremas
http://bioassayontology.org
Acknowledgements
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