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>.

Getting started

Package details

AuthorKerry A. Mullan [aut, cre], Sebastiaan Valkiers [aut, ctb], Kris Laukens [aut, ctb], Pieter Meysman [aut, ctb]
Bioconductor views GeneTarget SingleCell
MaintainerKerry A. Mullan <>
LicenseGPL (>= 3)
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the ClusTCR2 package in your browser

Any scripts or data that you put into this service are public.

ClusTCR2 documentation built on May 29, 2024, 9:32 a.m.