CoV2-TCR

CoV2-TCR classifies and screens T cell receptor (TCR) CDR3 sequences for recognition of SARS-CoV-2 epitopes to identify epitope-specific TCRs from RNA-seq immune repertoires.

Key Features:

• Data extraction: Extracts TCR repertoires from RNA-seq data using MiXCR for comparative analysis between COVID-19 patients and healthy donors.
• Diversity and clonality analysis: Assesses repertoire diversity and clonality and relates higher clonality and lower diversity to active and severe COVID-19 cases.
• Classifier development: Implements the CoV2-TCR classifier to predict TCR CDR3 recognition of SARS-CoV-2 epitopes, achieving AUC 0.967 on independent datasets and outperforming existing tools.
• Epitope recognition profiling: Identifies TCR CDR3 sequences recognizing peptides, including 28 MHC-I epitopes from the original virus and 22 immunogenic peptides from variants, and reports high-abundance, low-abundance, and rare clonotype distributions per peptide.

Scientific Applications:

• Immune repertoire profiling: Characterizes TCR repertoire changes associated with COVID-19 disease progression.
• Epitope identification for vaccine research: Prioritizes antigenic MHC-I epitopes and variant-derived immunogenic peptides for vaccine and immunogen selection.
• TCR-epitope interaction screening: Enables computational screening of candidate TCR CDR3 sequences that recognize SARS-CoV-2 epitopes for immunological research and therapeutic studies.

Methodology:

Uses RNA-seq data processed with MiXCR to extract TCR CDR3 repertoires, performs diversity and clonality analyses, identifies TCRs recognizing specified MHC-I and variant peptides, and develops/evaluates the CoV2-TCR classifier with reported AUC 0.967 on independent datasets.