T cell receptor sequence clustering and antigen specificity.

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• T cells play vital roles in cancer, autoimmune, and infectious diseases.
• Understanding T cell receptor (TCR) epitope recognition at a repertoire level is crucial but not fully elucidated.
• Advances in sequencing technology provide abundant TCR sequence data.

Purpose of the Study:

• To review current T cell receptor (TCR) sequence clustering methods and their similarity measures.
• To discuss the performance and potential improvements of these clustering techniques.
• To guide non-specialists in using TCR repertoire sequencing for disease tracking, patient stratification, and therapy prediction.

Main Methods:

• Review of existing literature on TCR sequence clustering algorithms.
• Analysis of various similarity metrics used for TCR sequence comparison.
• Discussion of the strengths and limitations of different clustering approaches.

Main Results:

• Several TCR clustering methods exist, utilizing diverse similarity measures.
• The choice of method and similarity metric impacts the biological interpretation of TCR repertoire data.
• Performance evaluation highlights areas for improvement in TCR sequence annotation.

Conclusions:

• Effective TCR clustering is essential for linking immune repertoires to disease states.
• This review provides a foundational understanding for applying TCR sequencing in clinical settings.
• Further development of novel clustering techniques can enhance TCR annotation and its clinical utility.