ClusTCR2:Identifying Similar T Cell Receptor Hyper-Variable Sequences
with 'ClusTCR2'
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>.
