Introduction

What is TCT?

Translator Component Toolkit is a python library that allowing users to explore and use KGs in the Translator ecosystem. Users can check out the key function documentations here: https://ncatstranslator.github.io/Translator_component_toolkit/

TCT Github repo

Key features for TCT

Allowing users to select APIs, predicates according to the user's intention.
Parallel and fast querying of the selected APIs.
Providing reproducible results by setting constraints.
Faciliting to explore knowledge graphs from both Translator ecosystem and user defined APIs.
Connecting large language models to convert user's questions into TRAPI queries.
Find the identifier given a name using name resolver
Annotate a node using node annotator
Explore knowledge graphs in Translator
Find neighbors in the Translator KGs for a given node
Find paths between node A and node B in the Translator KG
Find a subnetwork given a list of nodes in the Translator KG
Developer-friendly wrappers for resolving labels/CURIEs, caching Translator resources, and returning parsed finder results
Connecting user's API with Translator API
Note: Visualization capabilities (pyvis, matplotlib, seaborn) can be installed separately via the vision extra.

How to use TCT

Install Requirements

To install TCT as a python library:

pip install TCT
# TCT is in development, to get the most recent update, user can install it through the github repo

This is the recommended approach for a minimal installation.

Visualization support is optional. Install it with the vision extra when you need the plotting and graph-rendering utilities:

pip install "TCT[vision]"

Development Installation

The TCT is continuously updated, if you would like to use the latest functions, you can clone this repository and install it in development mode:

Using pip: (recommended for development)

git clone https://github.com/NCATSTranslator/Translator_component_toolkit.git
cd Translator_component_toolkit
pip install -e .

Using UV :

git clone https://github.com/NCATSTranslator/Translator_component_toolkit.git
cd Translator_component_toolkit
uv sync

To include visualization support in the UV environment:

uv sync --extra vision

Building and Deployment

Using pip:

• Build: python -m build
• Install dependencies: pip install -e .

Using UV:

• Build: uv build
• Install dependencies: uv sync
• Run in UV environment: uv run python your_script.py

Please follow the example notebooks (four utilities) below to explore the Translator APIs.

KG overview

Explore different KGs KG overview

Name Resolver and Node Normalizer

Example notebook for Name Resolver and Node Normalizer

Neighborhood finder

Example notebook for NeighborhoodFinder

Path finder

Example notebook for PathFinder

Network finder

Example notebook for NetworkFinder

Developer-friendly finder APIs

The finder notebooks above include quick-start sections using the developer-friendly pathfinder and neighborhood_finder APIs, now part of TCT's main API surface (from TCT import query_TCT_pathfinder, neighborhood_finder).

Use the detailed NeighborhoodFinder, PathFinder, NetworkFinder, KG overview, and visualization notebooks when you need more fine-grained endpoint selection, predicate control, raw query construction, parser workflows, or visualization setup.

Connecting to a user's API

API should be developed following the standard from TRAPI.
An example notebook for add a user's API can be found here.
Note: It does not work if no user' API is established

Visualize the results

After each pipeline, it will generate a result file for visualization. A user can use the Visualization html file to visulaize the results.

Key Translator components

Connecting to key Translator components can be found here

Contributing

TCT is a tool that helps to explore knowledge graphs developed in the Biomedical Data Translator Consortium. Consortium members and external contributors are encouraged to submit issues and pull requests.

Contact info

Guangrong Qin, guangrong.qin@isbscience.org