Welcome to the Kinetics RDF model page!

Are you looking for a way to parameterize your pathway models with kinetic data? This project allows you to do so, using an Excel-based template (located here). As a start, our data model is compatible with pathways hosted in WikiPathways [ref] build using PathVisio [ref].

1. Use the WikiPathways Academy to learn how to build a pathway model representing a set of metabolic interactions
2. Add UniProt (SwissProt/gold star) [ref] annotations for the enzymes catalyzing the reactions.
3. Include Rhea [ref] identifiers (IDs) for the interactions between a substrate and target metabolite.
4. Use the ChEBI IDs listed in Rhea to annotate the substrate and product of each reaction.
5. Download the template kinetic data file.
6. Add kinetic data for each separate substrate-enzyme-reaction in your model.
7. The model support these sources for provenance (request another resource through the issue tracker):
   1. Brenda (Chang et al., 2021),
   2. Strenda (Swainston et al. 2018),
   3. Sabio-RK (Wittig et al., 2018),
   4. IUPHAR/BPS Guide to PHARMACOLOGY (Harding et al., 2022),
   5. UniProt (The UniProt Consortium, 2021),
   6. PubMed (White, 2020).
8. tba

KinRDF

Conversion of kinetic data to an RDF model, compatible with WikiPathways PathWayModels.

Setup this project in Rstudio:

### Install Python on your local machine to run this script:
### See https://support.posit.co/hc/en-us/articles/360023654474-Installing-and-Configuring-Python-with-RStudio
## Update PIP (package manager for Python packages/modules):
sudo pip install --upgrade pip
## Add virtual environment package:
sudo pip install virtualenv
## Move to correct project directory (in this case, the GitHub folder calles KinRDF)
cd /home/../KinRDF
## Create a new virtual environment in a folder called my_env:
sudo virtualenv my_env
## Activate the virtual environment:
source my_env/bin/activate
## Check if python is activated:
which python
## Check version of Python
python3 --version
##Install library to read xlsx files:
pip install openpyxl

Run RDF

Run the RDF locally (with Virtuoso Docker, on Linux): Documentation curtosy of Marvin Martens [https://github.com/marvinm2/AOPWikiRDF]

Set up a local Virtuoso SPARQL endpoint for the Kinetics RDF data (on Linux):

Step 1 - Create folder to mount

Open the terminal and create a local folder to map to the docker container. Note the path to the folder to enter it at step 3. In this example, the folder '/kinRdf' was created and entered it by using:

mkdir -p kinRdf

Step 2 - Move the RDF (.ttl) files into the newly created folder

cp Output/RDF_Kin_Data_2022-Dec.ttl kinRdf/KINRDF.ttl

Step 3 - Run the Docker image

Be sure to use ports 8890:8890 and 1111:1111. In this case, the container was named "KinRDF". Also, this step configures the mapped local folder with the data, which is in this example "/kinRdf". The Docker image used is openlink/virtuoso-opensource-7. Run the Docker image by entering:

sudo docker run -d --env DBA_PASSWORD=dba -p 8890:8890 -p 1111:1111 --name KinRDF --volume `pwd`/kinRdf/:/database/data/  openlink/virtuoso-opensource-7

Step 4 - Enter the running container

The SPARQL endpoint should already be accessible through localhost:8890/sparql/. However, while the Docker image is running, the data is not yet loaded. Therefore you need to enter the it by using:

sudo docker exec -it KinRDF  bash

Step 5 - Move the .ttl files

First, enter the "/data" folder and move the Turtle file(s) to the folder upstream by using:

mv data/KINRDF.ttl .
exit

Step 6 - Enter the container SQL and reset

Enter the running docker container SQL by using:

sudo docker exec -i KinRDF isql 1111

In case the service is already active and contains older RDF, be sure to perform a global reset and delete the old RDF files from the load_list, using the following commands:

RDF_GLOBAL_RESET();
DELETE FROM load_list WHERE ll_graph = 'KinRDF.org';

The presence of files in the load_list can be viewed using the following command:

select * from DB.DBA.load_list;

Step 7 - Load the RDF

Use the following commands to complete the loading of prefixes in the SPARQL endpoint. If errors occur, try again within a few seconds (which often works), or look at http://docs.openlinksw.com/virtuoso/errorcodes/ to find out what they mean. Add more pre-defined PREFIXES if needed:

log_enable(2);
DB.DBA.XML_SET_NS_DECL ('SEP', 'http://vocabularies.wikipathways.org/kin#',2);
DB.DBA.XML_SET_NS_DECL ('dc', 'http://purl.org/dc/elements/1.1/',2);
DB.DBA.XML_SET_NS_DECL ('rdfs', 'http://www.w3.org/2000/01/rdf-schema#',2);
DB.DBA.XML_SET_NS_DECL ('wp', 'http://vocabularies.wikipathways.org/wp#',2);
DB.DBA.XML_SET_NS_DECL ('rh', 'http://rdf.rhea-db.org/',2);
DB.DBA.XML_SET_NS_DECL ('dcterms', 'http://purl.org/dc/terms/#',2);
DB.DBA.XML_SET_NS_DECL ('xsd', 'http://www.w3.org/2001/XMLSchema#',2);
DB.DBA.XML_SET_NS_DECL ('S_id', 'http://identifiers.org/uniprot/',2);
DB.DBA.XML_SET_NS_DECL ('ECcode', 'https://identifiers.org/ec-code/',2);
DB.DBA.XML_SET_NS_DECL ('En_id', 'http://identifiers.org/ensembl/',2);
DB.DBA.XML_SET_NS_DECL ('PMID', 'http://identifiers.org/pubmed/',2);
DB.DBA.XML_SET_NS_DECL ('RHEA', 'https://www.rhea-db.org/reaction?id=',2);
DB.DBA.XML_SET_NS_DECL ('wd', 'http://www.wikidata.org/entity/',2);
DB.DBA.XML_SET_NS_DECL ('wdt', 'http://www.wikidata.org/prop/direct/',2);
log_enable(1);
grant select on "DB.DBA.SPARQL_SINV_2" to "SPARQL";
grant execute on "DB.DBA.SPARQL_SINV_IMP" to "SPARQL";

Load the data:

ld_dir('.', 'KINRDF.ttl', 'KinRDF.org');

To finalize the loading of data, use:

rdf_loader_run();

Check the status and look if the all.ttl file is loaded by entering:

select * from DB.DBA.load_list;

If the "il_state" = 2, the loading is complete. If issues occurred in this step, have a look at http://vos.openlinksw.com/owiki/wiki/VOS/VirtBulkRDFLoader. Quit the SQL by entering:

quit;

Step 8 - Enter the Virtuoso service with loaded AOP-Wiki RDF

The container is running with loaded RDF, available through http://localhost:8890, or enter the SPARQL endpoint directly through http://localhost:8890/sparql/. You can check if the data is loaded correctly, by executing the following SPARQL querie:

select distinct ?Concept where {[] dc:identifier ?Concept} LIMIT 100

Step 9 - Stop and remove the Docker container when done

sudo docker stop KinRDF
sudo docker rm KinRDF