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Spo:An Ontology for Describing Host-pathogen Interactions Inherent to Streptococcus Pneumoniae Infections
Talk by Cátia Vaz
INESC-ID / ISEL-IPL
Joint work with Alexandre Francisco, Susana Vinga, Pedro Reis, Ana Teresa Freitas
and Pneumopath Consorcium
host-pathogen interactions
Over the past twenty years, the study of infection has tended to consider individual virulence factors or host factors.
For finding new targets for diagnosis and treatment it is important to take into account more that one factor It is important to study the host-pathogen interactions
during infection of Streptococcus pneumonaie. This is one of the main goals of the Pneumopath
project.
host-pathogen interactions
The infection can be determined by multiple attributes of both host and pathogen. It is important to take into account:
epidemiological and genomic characterization of pneumococcal strains;
the results from experiments that evaluate host or pneumococcal responses to infection or different environmental challenges;
the results from experiments that identify host genetic susceptibility factors.
Data
The data to be considered to describe the host-pathogen interactions during infection of Streptococcus pneumonaie includes: characterization of pneumococcal strains, typing information data of in vitro and in vivo experiments with animals and
cell models. Some of these data are scattered across numerous
information systems and repositories, each with its own terminologies, identifier schemes, and data formats.
Data Integration
Thererefore, it is needed to have a common understanding of the concepts that describes host-pneumococcal interactions and thus it is need to: Define a vocabulary
The concepts and relations The semantic interconections
Relations between concepts and relations :. Define an Ontology!
Modeling Information
We have defined a model based on the information We have defined the concepts and their relations It allows more sharing of data and interoperability
We express data using knowledge representation languages We define a less coupled model to technology, making simpler
data integration
The model is very adaptive With the appearance of new concepts and new relations, it is
only necessary to add that new information avoid processes of data migration
Modeling Information
Thus, semantic annotation and interoperability become an absolute necessity for the integration of such diverse biomolecular data.
ExperimentsWorkpackage
Experiment
AnimalExperiment
Assay
GrowthExperiment
Participant
Institution
GeographicInformation
hasWorkpackage hasParticipant
“is-a”“is-a”
“is-a”belongsToInstitution
hasGeographicInformation
hasParticipant
ExperimentsWorkpackage
Experiment
AnimalExperiment
Assay
GrowthExperiment
Participant
Institution
GeographicInformation
hasWorkpackage hasParticipant
“is-a”“is-a”
“is-a”belongsToInstitution
hasGeographicInformation
hasParticipant
Name
DateRawFile Name
Name
AddressCountryCountryISOCodeRegion
Common Understanding...For instance, in experiences with animals:
And in experiences of Growth:
Two different Time Series?
Common Understanding...And with the same measurements in the same kind of experience:
different partners...
Experiment Measurements
AssayMeasurement
TemporalMeasurement
PainScore
GrowthCount
Measurement
TimeOfDeath Age BacteriaBatchDose
“is-a” “is-a”“is-a”“is-a”
“is-a”
“is-a”
“is-a”
Experiment Measurements
AssayMeasurement
TemporalMeasurement
PainScore
GrowthCount
Measurement
TimeOfDeath Age BacteriaBatchDose
“is-a” “is-a”“is-a”“is-a”
“is-a”
“is-a”
“is-a”
ValueValueUnit
MediumTimeTimeUnit
NameStandardDeviation
MethodType
Organisms
Bacteria
TimeOfDeath
Age
Animal Microorganism
Organism
Species
belongsToSpecies
hasAge
hasTimeOfDeath
“is-a” “is-a”
“is-a”“is-a”
GeographicInformation
hasGeographicInformation
Human Mouse
Organisms
Bacteria
TimeOfDeath
Age
Animal Microorganism
Organism
Species
belongsToSpecies
hasAge
hasTimeOfDeath
“is-a” “is-a”
“is-a”“is-a”
GeographicInformation
hasGeographicInformation
Human Mouse
NameStrain
Name
Gender
CarriageClinicalDiseaseRiskFactors
Isolate
Isolate
Origin
Environment Host Animal
TypingInformation
GeographicInformation
Species
hasGeographicInformation
hasGeographicInformation
belongsToSpecies
hasTypingInformation
http://www.phyloviz.net/typon/
Typon concepts
Isolate
Isolate
Origin
Environment Host Animal
TypingInformation
GeographicInformation
Species
hasGeographicInformation
hasGeographicInformation
belongsToSpecies
hasTypingInformation
http://www.phyloviz.net/typon/
ContigsNameGenesFileOtherNameProteinsFileStrainWholeGenomeSequenced
Experiment with Animals
AnimalExperiment
GrowthCount
PainScore
BacteriaBatchDose
Bacteria
Animal
AnimalGroup
hasBacteria
hasAnimalGroup
hasAnimal
hasGrowthCount
hasPainScore
hasBacteriaBatchDose
Experiment with Animals
AnimalExperiment
GrowthCount
PainScore
BacteriaBatchDose
Bacteria
Animal
AnimalGroup
hasBacteria
hasAnimalGroup
hasAnimal
hasGrowthCount
hasPainScore
hasBacteriaBatchDose
RouteSurvivalRoute
Strain
Experiments of Growth
GrowthExperiment
GrowthCount
Bacteria
hasGrowthCount
hasBacteriaSugarConcentrationTypeOfSugar
How this worked in practice?We collected data files for the partners in the project
We have meetings with partners to understand the concepts involved in their data
Their data were among several spreadsheets, with different formats
How this worked in practice?We collected data files for the partners in the project
We have meetings with partners to understand the concepts involved in their data
Their data were among several spreadsheets, with different formats
But...
How this worked in practice?
But:Partners were not familiar with knowledge representation
Moreover:they have difficulty in understanding how the columuns and rows of their spreadsheets transform into triples
How this worked in practice?
But:Partners were not familiar with knowledge representation
Moreover:they have difficulty in understanding how the columuns and rows of their spreadsheets transform into triples
? How did we work on this?
How this worked in practice?We have meetings with partners to understand the concepts involved in their data
. . .We discussed the common concepts among them and the relations between the
We developed and ontology
We show to the partners a “tabular” view of the ontology
We refined the ontology until we reached a general agreement among partners
We transformed the data according to this new model and we integrated it for further analysis
Final Remarks
SPO was developed in the context of a large research project It describes knowledge in this field; Allows validation and aggregation of existing
knowledge which is essential for data integration.• We are continuing improving and generalizing
this ontology for describing more aspects of host-pathogen interactions