Use Case C, Worflow 2

Workflow used to obtain data for the research paper ‘The application of the Open Pharmacological Concepts Triple Store (Open PHACTS) to support Drug Discovery Research’, currently submitted to PLOS ONE.  

Requirements:
- Knime v2.9
- Open PHACTS Knime nodes version 1.0.0 (DON'T use any later version!): https://github.com/openphacts/OPS-Knime

Installation:
- Download "org.openphacts.utils.json_1.0.0.zip" and unzip it in the plugins folder of your KNIME installation
- Download the workflow (Use Case C_Workflow2.zip)
- Start your Knime environment (you should see already a couple of new nodes at the ‘Node Repository’, like OPS_KNIME)
- Import the workflow (file>>import-workflow...>>select archive file)

Running:
- Double click on the desired workflow
- Reset the OPS nodes (in orange)
- Execute the nodes. Because of a bug found, the OPS nodes have to be run one by one to avoid the retrieved columns of the metanodes get mixed up.

Description of the Workflow:
This workflow starts with a Wikipathway URI. The URI of the pathway has been copied from Wikipathways (http://wikipathways.org) (e.g.  http://wikipathways.org/index.php/Pathway:WP1531) after choosing the pathway of interest.

Using the above URI as input:
1. We use ‘Pathway Information’ API call to get information about the pathway such as the version of the data, the title and its description.
 2. A list of proteins and genes in the pathway can be retrieved directly with ‘Pathway Information: Get Target’ API call. We use those targets as input for ‘Target Pharmacology: List’ API call (3)
3. We use the ‘Target Pharmacology: List’ API call to retrieve pharmacology data from ChEMBL. In this case we return the data using ‘_pageSize=250’ in combination with a loop overall result sets with the ‘_page’ parameter. Then we apply filters (3.1) to remove: Unspecified Activity value, Activity comment = inactive, inconclusive, Not determined and/or Not active and Potential data errors (values larger than 1E08 are removed) using ‘Row filter’ and ‘Row Splitter’ nodes.
4. At this stage, we choose a target of interest (Cytochrome P450 24A1). Since we want to know which compounds hitting ‘Cytochrome P450 24A1’ are exclusive for the target of interest, we retrieve bioactivities using the ‘Joiner’ node and get those compounds that hit only one target. To do this we have used 3 nodes: ‘Pivoting’ node: we have grouped rows by ‘Compound name’ and pivoted by ‘Target name’ getting the minimum value for each target/compound.; ‘Column aggregator’ node: we count how many times a compound hit a target and ‘Numeric Row Splitter’ note: we split those compounds with activity at only one target.  
5. Once we have compounds that hit only one target, using the ‘joiner’ node we retrieve the bioactivities for those compounds and we keep ChEMBL uri to use it as input for Compound pharmacology.
6. We use the ‘Compound Pharmacology: List’ API to retrieve pharmacology from compounds and apply filters same as in point 3.
7. We exclude the target chosen in point 4 and we use ChEMBL target uri as input for ‘Pathways for Target: List’ API.
8. Other Pathways where a certain target is present are retrieved using Pathways for Target: List API call