Identifying Similar T Cell Receptor Hyper-Variable Sequences with 'ClusTCR2' R package ClusTCR2 version 1.7.3.01 Enhancing T cell receptor (TCR) sequence analysis, 'ClusTCR2', based on 'ClusTCR' python program, leverages Hamming distance to compare the complement-determining region three (CDR3) sequences for sequence similarity, variable gene (V gene) and length. The second step employs the Markov Cluster Algorithm to identify clusters within an undirected graph, providing a summary of amino acid motifs and matrix for generating network plots. Tailored for single-cell RNA-seq data with integrated TCR-seq information, 'ClusTCR2' is integrated into the Single Cell TCR and Expression Grouped Ontologies (STEGO) R application or 'STEGO.R'. See the two publications for more details. Sebastiaan Valkiers, Max Van Houcke, Kris Laukens, Pieter Meysman (2021) <doi:10.1093/bioinformatics/btab446>, Kerry A. Mullan, My Ha, Sebastiaan Valkiers, Nicky de Vrij, Benson Ogunjimi, Kris Laukens, Pieter Meysman (2023) <doi:10.1101/2023.09.27.559702>. Software Imports: DescTools, ggplot2, ggseqlogo, network, plyr, RColorBrewer, stringr, scales, sna, VLF Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Kerry A. Mullan <Kerry.Mullan@uantwerpen.be> Comprehensive R Archive Network (CRAN) Kerry A. Mullan [aut, cre], Sebastiaan Valkiers [aut, ctb], Kris Laukens [aut, ctb], Pieter Meysman [aut, ctb] GPL (>= 3) 2024-05-16 application/tgz https://CRAN.R-project.org/package=ClusTCR2 doi:10.32614/CRAN.package.ClusTCR2