Python module to query the Target Central Resource Database (TCRD)

This is an (unofficial) python module to help query the Target Central Resource Database (TCRD). Find a detailed view of the TCRD schema in TCRDv6_ERD.pdf.

System Requirements

Hardware requirements

The scripts require only a standard computer with enough RAM to support the in-memory operations.

Software requirements

OS Requirements

This package is supported for macOS. The package has been tested on the following systems:

• macOS: Sequoia (15.0.1)

Python Dependencies

pymysql # 1.0.2
pandas  # 2.1.0

Execution Guide

1. Download and install the latest SQL version of the TCRD from http://juniper.health.unm.edu/tcrd/download/.
2. Edit your database details in openConnection() function in code/tcrd.py script.
3. Import the tcrd module in your script and start playing around!

What is the TCRD?

(Adapted from http://juniper.health.unm.edu/tcrd/)

The Target Central Resource Database (TCRD) is the central resource behind the Illuminating the Druggable Genome Knowledge Management Center (IDG-KMC). TCRD contains information about human targets, with special emphasis on four families of targets that are central to the NIH IDG initiative: GPCRs, kinases and ion channels (note that olfactory GPCRs are treated as a separate family. A key aim of the KMC is to classify the development/druggability level of targets. We currently categorize targets into four target development/druggability levels (TDLs) defined as follows:

• Tclin, these targets have activities in DrugCentral (ie. approved drugs) with known mechanism of action.
• Tchem, these targets have activities in ChEMBL, Guide to Pharmacology or DrugCentral that satisfy IDG activity thresholds.
• Tbio, non-Tchem/Tclin targets that satisfy one or more of the following criteria: target is above the cutoff criteria for Tdark and is annotated with a Gene Ontology Molecular Function or Biological Process leaf term(s) with an Experimental Evidence code.
• Tdark, these are targets about which virtually nothing is known and satisfy two or more of the following criteria: a PubMed text-mining score from Jensen Lab < 5, <= 3 Gene RIFs, <= 50 Antibodies available according to antibodypedia.com.