{
  "abstract": "T cell receptors (TCRs) are central to adaptive immunity, yet their vast sequence and structural diversity present a significant challenge to fully understand immune responses. The application of high-throughput sequencing technologies, including bulk and single-cell approaches, generates vast datasets of TCR repertoire information, requiring advanced computational tools for meaningful analysis. Here, we provide a comprehensive overview of the state-of-the-art in silico tools developed to enable diverse TCR repertoire analyses. We categorize over 40 computational tools into six primary analytical stages creating a workflow for TCR analysis in the context of cancer immunotherapy: (1) data acquisition, including differences between TCR sequencing technologies and databases; (2) TCR reconstruction and inference, which focuses on accurately extracting from raw sequencing data the V(D)J gene usage, including complementarity-determining region sequences, and the α/β pairing; (3) TCR clustering, which groups receptors based on similarity, helping characterize repertoire shifts, therapy responses and identify cancer-associated TCR clones; (4) structural modeling of TCRs and TCR–peptide-major histocompatibility complex (MHC), which is used to predict the three-dimensional structures of TCRs with or without their targets; (5) TCR specificity prediction, which predicts whether a given TCR can bind to a given peptide-MHC complex; and finally (6) functional and clinical integration, addressing the breakthroughs and bottlenecks for wider clinical application of these methods. For each category, we discuss the underlying methodologies, representative tools and their key applications, details about usability and accessibility, and comments on their strengths and limitations. With this overview, we offer a critical perspective on the current state of the field, providing an overall framework and guidance for new users and developers of these technologies. We also highlight open challenges and key future directions, particularly regarding the integration of multi-omics data and next-generation artificial intelligence approaches to unlock the full potential of TCR repertoire analysis for clinical immunotherapy applications.",
  "authors": [
    {
      "affiliations": [
        "Biology and Biochemistry, University of Houston, Houston, Texas, USA"
      ],
      "name": "Pâmella Borges"
    },
    {
      "affiliations": [
        "Biology and Biochemistry, University of Houston, Houston, Texas, USA"
      ],
      "name": "Martiela Vaz de Freitas"
    },
    {
      "affiliations": [
        "Mathematics, University of Houston, Houston, Texas, USA"
      ],
      "name": "Jinkyung Yoo"
    },
    {
      "affiliations": [
        "Biology and Biochemistry, University of Houston, Houston, Texas, USA"
      ],
      "name": "Finn Beruldsen"
    },
    {
      "affiliations": [
        "Biology and Biochemistry, University of Houston, Houston, Texas, USA"
      ],
      "name": "Jaila Lewis"
    },
    {
      "affiliations": [
        "Biochemistry and Molecular Biology, Universidade Federal do Ceará, Fortaleza, Brazil"
      ],
      "name": "Francisca Joseli Freitas de Sousa"
    },
    {
      "affiliations": [
        "Biology and Biochemistry, University of Houston, Houston, Texas, USA"
      ],
      "name": "Sae Hee Choi"
    },
    {
      "affiliations": [
        "Computer Science, University of Houston, Houston, Texas, USA"
      ],
      "name": "Duy Bao Nguyen"
    },
    {
      "affiliations": [
        "Postgraduate program in Biochemistry, Universidade Federal do Ceará, Fortaleza, Brazil",
        "Biophysics, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil"
      ],
      "name": "Geancarlo Zanatta"
    },
    {
      "affiliations": [
        "Biostatistics and Data Science, The University of Texas Medical Branch at Galveston, Galveston, Texas, USA"
      ],
      "name": "Jeong Hoon Jang"
    },
    {
      "affiliations": [
        "Medicine, Universidade de São Paulo, São Paulo, Brazil"
      ],
      "name": "Eduardo Donadi"
    },
    {
      "affiliations": [
        "Clinical Pharmacy, University of Southern California, Los Angeles, California, USA"
      ],
      "name": "Houda Alachkar"
    },
    {
      "affiliations": [
        "Department of Medicine, The University of Chicago, Chicago, Illinois, USA",
        "David and Etta Jonas Center for Cellular Therapy, The University of Chicago, Chicago, Illinois, USA"
      ],
      "name": "Steven P Wolf"
    },
    {
      "affiliations": [
        "Department of Molecular Biology and Biotechnology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil"
      ],
      "name": "Maurício Menegatti Rigo"
    },
    {
      "affiliations": [
        "Mathematics, University of Houston, Houston, Texas, USA",
        "Health Systems and Population Health Sciences, University of Houston, Houston, Texas, USA"
      ],
      "name": "Hyeongseon Jeon"
    },
    {
      "affiliations": [
        "Biology and Biochemistry, University of Houston, Houston, Texas, USA"
      ],
      "name": "Dinler Amaral Antunes"
    }
  ],
  "title": "Mapping the TCR landscape: computational tools empowering translational immunology and therapy design",
  "uid": "d5114760-e59b-5e2b-8980-c8b68eab92e9"
}