Recovering CDR3 sequences from RNA-seq data: a critical assessment of computational and experimental strategies.
- 1Tehran University of Medical Sciences, Tehran, Iran. [email protected].
- 2Tehran University of Medical Sciences, Tehran, Iran.
Abstract
The adaptive immune response depends on T cell receptor (TCR) diversity to counter cancers, autoimmunity, and infections. This diversity is encoded within CDR3, which serves as a unique barcode for each T cell clone. Characterizing CDR3 sequences is essential for tracking clonal dynamics, identifying neoantigen-reactive T cells, and designing personalized immunotherapy. While TCR-seq remains the benchmark, existing RNA-seq datasets offer a cost-effective retrospective alternative. However, tools like MiXCR and TRUST4, optimized for TCR-enriched samples, are limited in bulk and single-cell RNA-seq due to low transcript abundance and sparse coverage. In this review, we synthesize quantitative evidence from published head-to-head benchmarking studies that performed matched RNA-seq and TCR-seq on the same biological samples. These studies demonstrate that standard RNA-seq typically recovers only 3-5% of unique clonotypes identified by TCR-seq, with substantial underrepresentation of low-frequency clones; however, in T cell-rich tissues and when high sequencing depth is combined with optimized computational pipelines, RNA-seq can capture the majority of high-frequency clonotypes, albeit with limited accuracy in clonotype frequency estimation and poor α/β pairing recovery. We review computational efforts to recover CDR3 sequences from RNA-seq data, highlighting recurrent obstacles: transcript scarcity, fragmentation, short reads, and assembly errors. We critically assess whether artificial intelligence can overcome these barriers. We also examine emerging wet-lab strategies including probe-based enrichment, FUME-TCRseq, and rhTCRseq. We conclude that RNA-seq cannot replace TCR-seq, but AI-enhanced tools with targeted library improvements could expand the utility of existing datasets and degraded specimens.
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BibTeX
@article{amelimojarad2026recovering,
title = {{Recovering CDR3 sequences from RNA-seq data: a critical assessment of computational and experimental strategies.}},
author = {Mandana AmeliMojarad and Melika AmeliMojarad},
journal = {Genomics \& informatics},
year = {2026},
month = oct,
doi = {10.1186/s44342-026-00084-5},
url = {https://europepmc.org/article/MED/42897688},
note = {CC BY},
}
RIS
TY - JOUR
TI - Recovering CDR3 sequences from RNA-seq data: a critical assessment of computational and experimental strategies.
AU - Mandana AmeliMojarad
AU - Melika AmeliMojarad
PY - 2026
DA - 2026/10/19
JO - Genomics & informatics
DO - 10.1186/s44342-026-00084-5
UR - https://europepmc.org/article/MED/42897688
AB - The adaptive immune response depends on T cell receptor (TCR) diversity to counter cancers, autoimmunity, and infections. This diversity is encoded within CDR3, which serves as a unique barcode for each T cell clone. Characterizing CDR3 sequences is essential for tracking clonal dynamics, identifying neoantigen-reactive T cells, and designing personalized immunotherapy. While TCR-seq remains the benchmark, existing RNA-seq datasets offer a cost-effective retrospective alternative. However, tools like MiXCR and TRUST4, optimized for TCR-enriched samples, are limited in bulk and single-cell RNA-seq due to low transcript abundance and sparse coverage. In this review, we synthesize quantitative evidence from published head-to-head benchmarking studies that performed matched RNA-seq and TCR-seq on the same biological samples. These studies demonstrate that standard RNA-seq typically recovers only 3-5% of unique clonotypes identified by TCR-seq, with substantial underrepresentation of low-frequency clones; however, in T cell-rich tissues and when high sequencing depth is combined with optimized computational pipelines, RNA-seq can capture the majority of high-frequency clonotypes, albeit with limited accuracy in clonotype frequency estimation and poor α/β pairing recovery. We review computational efforts to recover CDR3 sequences from RNA-seq data, highlighting recurrent obstacles: transcript scarcity, fragmentation, short reads, and assembly errors. We critically assess whether artificial intelligence can overcome these barriers. We also examine emerging wet-lab strategies including probe-based enrichment, FUME-TCRseq, and rhTCRseq. We conclude that RNA-seq cannot replace TCR-seq, but AI-enhanced tools with targeted library improvements could expand the utility of existing datasets and degraded specimens.
SN - 1598-866X
SN - 2234-0742
ER -