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.