tidytcells: standardizer for TR/MH nomenclature

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• T cell receptors (TR) are key to adaptive immunity, providing diversity and specificity.
• TR repertoire data serves as an adaptive immune biomarker and aids in identifying therapeutic TR candidates.
• Computational analysis of TR repertoires requires standardized, computer-readable data.

Purpose of the Study:

• To address the challenge of non-standard nomenclature in TR repertoire datasets.
• To introduce tidytcells, a Python package for standardizing TR nomenclature.
• To facilitate easier and more reliable computational analysis of TR repertoire data.

Main Methods:

• Development of a lightweight, platform-independent Python package named tidytcells.
• Implementation of tools specifically designed for standardizing TR nomenclature.
• Open-sourcing the software under the MIT license and making it available via PyPI.

Main Results:

• tidytcells provides easy-to-use standardization tools for TR nomenclature.
• The package simplifies the pre-processing of TR repertoire data, which is often non-trivial.
• tidytcells is available on PyPI and is open-source.

Conclusions:

• Standardized TR nomenclature is vital for accurate computational analysis of immune repertoire data.
• The tidytcells package offers a practical solution for standardizing TR nomenclature.
• tidytcells enhances the usability of TR repertoire data for biomarker discovery and therapeutic development.