Difference between revisions of "Pharmacoracle"
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The Pharmacoracle was implemented as an attempt to understand the field of pharmacogenomics, based on data from PharmGKB, and solving the same problem as [https://github.com/PharmGKB/PharmCAT/wiki PharmGKBs PharmCAT software], but in the context of the technology and clinical recommendations of our clinic. It may be sensible to integrate PharmCAT in the Pharmacoracle, as soon as PharmCAT becomes the gold standard for VCF to PGx recommendations. | The Pharmacoracle was implemented as an attempt to understand the field of pharmacogenomics, based on data from PharmGKB, and solving the same problem as [https://github.com/PharmGKB/PharmCAT/wiki PharmGKBs PharmCAT software], but in the context of the technology and clinical recommendations of our clinic. It may be sensible to integrate PharmCAT in the Pharmacoracle, as soon as PharmCAT becomes the gold standard for VCF to PGx recommendations. | ||
− | Queries to Pharmacoracle are written in the [https://www.w3.org/TR/owl2-manchester-syntax/ Manchester syntax] in the same way as for the [https://protegewiki.stanford.edu/wiki/DLQueryTab DL Query Tab] of [https://protege.stanford.edu/ Stanford's desktop Protégé]. An advantage of this approach is that the queries replaces the need of a separate rules engine (rules are to some extent built into the OWL semantics). A disadvantage is that the performance in a production environment may be suboptimal (slow). I believe that the standard way to use OWL is to make a triplet store database (RDF database, e.g. JENA) based on the OWL ontology. The rules can then be written as SPARQL queries to this database. It is possible that there are more lessons to be learned from the PGx clinical decision support | + | Queries to Pharmacoracle are written in the [https://www.w3.org/TR/owl2-manchester-syntax/ Manchester syntax] in the same way as for the [https://protegewiki.stanford.edu/wiki/DLQueryTab DL Query Tab] of [https://protege.stanford.edu/ Stanford's desktop Protégé]. An advantage of this approach is that the queries replaces the need of a separate rules engine (rules are to some extent built into the OWL semantics). A disadvantage is that the performance in a production environment may be suboptimal (slow). I believe that the standard way to use OWL is to make a triplet store database (RDF database, e.g. JENA) based on the OWL ontology. The rules can then be written as SPARQL queries to this database. It is possible that there are more lessons to be learned from the PGx clinical decision support [https://bioportal.bioontology.org/ontologies/GENE-CDS ontology of Mathias Samwald], which is at the core of the [https://doi.org/10.1371/journal.pone.0093769 An Ontology-Based, Mobile-Optimized System for Pharmacogenomic Decision Support at the Point-of-Care] of the [http://upgx.eu UPGx] project. |
The Pharmacoracle infrastructure is written in Python. But the [http://owlcs.github.io/owlapi/ OWLAPI] is mainly JAVA technology, and making the Pharmacoracle totally JAVA, might speed up things. A link between OWL and Python is provided by [http://www.lesfleursdunormal.fr/static/_downloads/article_owlready_aim_2017.pdf Jean Baptiste Lamy's Owlready2], which we use to translate PharmGKB's JSON-LD to OWL, but it seems that the OWLAPI should be run separately. | The Pharmacoracle infrastructure is written in Python. But the [http://owlcs.github.io/owlapi/ OWLAPI] is mainly JAVA technology, and making the Pharmacoracle totally JAVA, might speed up things. A link between OWL and Python is provided by [http://www.lesfleursdunormal.fr/static/_downloads/article_owlready_aim_2017.pdf Jean Baptiste Lamy's Owlready2], which we use to translate PharmGKB's JSON-LD to OWL, but it seems that the OWLAPI should be run separately. |
Revision as of 12:09, 28 August 2018
Pharmacoracle - a REST API based on the OWLAPI
The Pharmacoracle contains OWL ontologies that are based on CPIC recommendations provided by the PharmGKB API, and which have been further adapted to the needs of the Department of Pharmacology at Oslo University Hospital.
The Pharmacoracle was implemented as an attempt to understand the field of pharmacogenomics, based on data from PharmGKB, and solving the same problem as PharmGKBs PharmCAT software, but in the context of the technology and clinical recommendations of our clinic. It may be sensible to integrate PharmCAT in the Pharmacoracle, as soon as PharmCAT becomes the gold standard for VCF to PGx recommendations.
Queries to Pharmacoracle are written in the Manchester syntax in the same way as for the DL Query Tab of Stanford's desktop Protégé. An advantage of this approach is that the queries replaces the need of a separate rules engine (rules are to some extent built into the OWL semantics). A disadvantage is that the performance in a production environment may be suboptimal (slow). I believe that the standard way to use OWL is to make a triplet store database (RDF database, e.g. JENA) based on the OWL ontology. The rules can then be written as SPARQL queries to this database. It is possible that there are more lessons to be learned from the PGx clinical decision support ontology of Mathias Samwald, which is at the core of the An Ontology-Based, Mobile-Optimized System for Pharmacogenomic Decision Support at the Point-of-Care of the UPGx project.
The Pharmacoracle infrastructure is written in Python. But the OWLAPI is mainly JAVA technology, and making the Pharmacoracle totally JAVA, might speed up things. A link between OWL and Python is provided by Jean Baptiste Lamy's Owlready2, which we use to translate PharmGKB's JSON-LD to OWL, but it seems that the OWLAPI should be run separately.
The Pharmacoracle is used in our PGx analysis script Pharmacolyzer, but can also be used directly.
Hints on how to use it (see also example section below)
In order to write queries to an OWL ontology we need to know the names of the OWL elements, notably OWL Properties (e.g. has_haplotype) and and OWL Classes (e.g. TPMTstar3A). The Oslo University Hospital PGx OWL ontology is still work in progress, and therefore not publicly accessible, but PharmGKB nomenclature is kept where possible. In addition we need to know the Manchester expressions of cardinality that are used to connect OWL Properties and OWL Classes, in particular:
Cardinality word | Cardinality | Explanation |
---|---|---|
some | Connects a property to at least one class, e.g. has_haplotype some TPMTstar3A | |
exactly | number | Connects a property to an exact number of classes, e.g. has_haplotype exactly 1 TPMTstar3A |
min | number | Connects a property to a minimum number of classes, e.g. has_haplotype min 1 TPMTstar3A |
For each query we add a code word to indicate the relationship between our query and the desired answer (cf. the DL Query tab in the desktop version of Stanford's Protégé), as explained in this table:
Code word | Relationship |
---|---|
SubClasses | Get elements that contain the query (but not equivalent to the query) |
DirectSubClasses | Get only the SubClasses that are closest to the query |
EquivalentClasses | Get only elements equivalent to the query |
SuperClasses | Get elements that contain parts of the query (but not the entire query and not equivalent) |
DirectSuperClasses | Get only the SuperClasses that are closest to the query |
In order to query the Pharmacoracle automatically, we have made a Python script Pharmacolyzer that returns dosing recommendation based on a pharmacotyped patient VCF.
Examples of use
Find the PGx alleles that contain the variant snpC>T at chr6:18139228
curl https://www.pgx.no/api/pharmacoracle -d "query=SubClasses:snpCtoT_at some chr6_18139228"
Find the PGx allele that is identical to the variants snpC>T at chr6:18139228 and snpT>C at chr6:18130918
curl https://www.pgx.no/api/pharmacoracle -d "query=EquivalentClasses:(snpCtoT_at some chr6_18139228) and (snpTtoC_at some chr6_18130918)"
Find the PGx alleles that contain either snpC>T at chr6:18139228 or snpT>C at chr6:18130918
curl https://www.pgx.no/api/pharmacoracle -d "query=DirectSuperClasses:(snpCtoT_at some chr6_18139228) and (snpTtoC_at some chr6_18130918)"
Find the Azathioprine TPMT functional status of the TPMT*3B allele:
curl https://www.pgx.no/api/pharmacoracle -d "drugs=azathioprine&query=SuperClasses:has_haplotype some TPMTstar3B"
Find the Azathioprine dosing recommendations for a patient with two Azathioprine TPMT No Function alleles:
curl https://www.pgx.no/api/pharmacoracle -d "drugs=azathioprine&query=EquivalentClasses:has_phenotype some (has_function exactly 2 AzathioprineTPMT_NoFunction)"