{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Tehran University of Medical Sciences, Tehran, Iran. Mandanalee13@gmail.com.",
    "Tehran University of Medical Sciences, Tehran, Iran."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mandana AmeliMojarad"
    },
    {
      "affs": [
        1
      ],
      "name": "Melika AmeliMojarad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9vn86x6kzf60r.bib",
  "cited_by": 0,
  "doi": "10.1186/s44342-026-00084-5",
  "fulltext_url": "https://europepmc.org/articles/PMC13681751",
  "id": "9vn86x6kzf60r",
  "issns": [
    "1598-866X",
    "2234-0742"
  ],
  "landing_url": "https://europepmc.org/article/MED/42897688",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QH426-470"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7222980210",
  "pdf_url": "https://europepmc.org/articles/PMC13681751?pdf=render",
  "pmcid": "PMC13681751",
  "pmid": "42897688",
  "published": "2026-10-19",
  "publisher": "BioMed Central",
  "ris": "https://research.draconitic.com/paper/9vn86x6kzf60r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:32Z",
      "native_id": "MED:42897688",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42897688"
    },
    {
      "first_seen": "2026-10-30T13:45:34Z",
      "native_id": "73bce658efe84269894ad1c048460274",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s44342-026-00084-5"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Recovering CDR3 sequences from RNA-seq data: a critical assessment of computational and experimental strategies.",
  "title_text": "Recovering CDR3 sequences from RNA-seq data: a critical assessment of computational and experimental strategies.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:34Z",
  "url": "https://research.draconitic.com/paper/9vn86x6kzf60r",
  "venue": "Genomics & informatics",
  "versions": []
}