ArticleFrontiers in immunology2023
NAIR: Network Analysis of Immune Repertoire.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Molecular identification ofOpen veterinary journal · 2025Article
- PepTCR-Net: prediction of multi-class antigen peptides by T-cell receptor sequences with deep learning.Briefings in bioinformatics · 2025Article
- The role of bioinformatics algorithms in modern biopharmaceutical design: Progress, challenges, and future perspectives.BioImpacts : BI · 2025Article
- Dysregulation of CD4Journal for immunotherapy of cancer · 2024Article
- Neoadjuvant CD40 Agonism Remodels the Tumor Immune Microenvironment in Locally Advanced Esophageal/Gastroesophageal Junction Cancer.Cancer research communications · 2024Article
- TCR-NP: a novel approach to prioritize T-cell Receptor repertoire network properties.Statistics innovation · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
T cells represent a crucial component of the adaptive immune system and mediate anti-tumoral immunity as well as protection against infections, including respiratory viruses such as SARS-CoV-2. Next-generation sequencing of the T-cell receptors (TCRs) can be used to profile the T-cell repertoire. We developed a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods to characterize and investigate changes in the landscape of TCR sequences. We first performed network analysis on the TCR sequence data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified (1) disease-specific/associated clusters and (2) shared clusters across samples based on our customized search algorithms and assessed their relationship with clinical outcomes such as recovery from COVID-19 infection. Furthermore, to identify disease-specific TCRs, we introduced a new metric that incorporates the clonal generation probability and the clonal abundance by using the Bayes factor to filter out the false positives. TCR-seq data from COVID-19 subjects and healthy donors were used to illustrate that the proposed approach to analyzing the network architecture of the immune repertoire can reveal potential disease-specific TCRs responsible for the immune response to infection.
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Registered trials
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