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Updated: Aug 5, 2026
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T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing
Published on: January 12, 2021
Machine Learning of Personal Repertoires From Public T Cell Receptors
Or Malca1, Alona Zilberberg1, Sol Efroni1
1Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel.
Immunological Reviews
|July 27, 2026
Summary
Public T-cell receptor (TCR) sequences, though rare, offer a shared coordinate system for comparing immune histories across individuals. Analyzing a large meta-repertoire reveals predictable patterns in public TCRβ sequences, enabling computational analysis of immune responses.
Area of Science:
- Immunology
- Bioinformatics
- Computational Biology
Background:
- T-cell receptor (TCR) repertoires encode individual immunological history.
- Most unique TCR sequences are private, limiting cross-individual comparisons.
- A subset of public TCRβ sequences recurs predictably across individuals.
Purpose of the Study:
- To review the biology and computational exploitation of public TCR sequences.
- To quantify properties of public TCR sequences using a large meta-repertoire.
- To summarize computational methods and applications leveraging public TCR sequences.
Main Methods:
- Analysis of a 1.5-billion-sequence meta-repertoire from 7943 samples.
- Quantification of public TCRβ sequence properties (universe size, rarefaction, publicness distribution, observation mass, recapture rate).
- Review and summarization of computational methods (frequency vectors, networks, transformers, GNNs) and their applications.
Main Results:
- Estimated finite universe of TCRβ CDR3 sequences (≈1.97×10⁹ amino acid, ≈8.25×10⁹ nucleotide).
- Publicness is a cohort-scale statistic with predictable heavy-tail growth.
- Super-public sequences constitute a small fraction but carry significant observation mass; high recapture rates observed.
Conclusions:
- Public TCR sequences provide a computationally legible framework for repertoire analysis.
- Computational methods exploiting public TCR sequences show promise across various immunological applications.
- Further research is needed to bridge current capabilities with universal TCR-based diagnostics.
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