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Inform."],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    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\u20135% 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 \u03b1\/\u03b2 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\n                    <jats:bold>.<\/jats:bold>\n                    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.\n                  <\/jats:p>","DOI":"10.1186\/s44342-026-00084-5","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T13:45:56Z","timestamp":1792417556000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Recovering CDR3 sequences from RNA-seq data: a critical assessment of computational and experimental strategies"],"prefix":"10.1186","volume":"24","author":[{"given":"Mandana","family":"AmeliMojarad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melika","family":"AmeliMojarad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,19]]},"reference":[{"key":"84_CR1","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1186\/S40364-025-00807-W","volume":"13","author":"J Pu","year":"2025","unstructured":"Pu J, Liu T, Zhou Y, Chen M, Fu X, Wan Y, et al. 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