What happened
Camelid heavy-chain antibodies, and particularly their variable domains known as nanobodies or VHHs, combine full antigen-binding capacity with a compact and highly stable scaffold, which makes them attractive for both fundamental immunology and therapeutic development. High-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) allows nanobody repertoires to be profiled at great depth, but the analyses applied to VHH data are typically assembled ad hoc from standalone scripts, which limits standardisation and reproducibility across laboratories. Here we present nanorepertoire, an end-to-end Nextflow DSL2 pipeline dedicated to camelid VHH repertoires. It takes paired-end AIRR-seq FASTQ files through quality control, adapter trimming, read merging, in-silico translation, CD-HIT clonotyping and deep-learning CDR3 annotation with nanoCDR-X (Bagordo et al., 2026), and returns an interactive HTML report describing clonal architecture, CDR3 length and amino-acid composition, intra-clonal homogeneity and repertoire diversity, together with the computational carbon footprint of the run. Applied to two publicly available SARS-CoV-2 RBD-selected llama libraries sampled before and after phage-display enrichment (4.8 million paired-end reads in total), the pipeline completed in 59 min on a 16-vCPU cloud instance and recovered 41,363 distinct CDR3 paratopes, reproducing the expected contraction of clonal diversity upon selection. nanorepertoire is open source under the MIT licence at https://github.com/lescailab/nanorepertoire, is archived on Zenodo, and runs unchanged on local, HPC and cloud infrastructures. Update (2026-09-18): An Artificial Intelligence Model for Longitudinal Assessment of TCR Repertoires in SARS-CoV-2 Vaccine Recipients . T cells play a crucial role in reducing disease severity during SARS-CoV-2 infection and in shaping long-term immune memory. However, the precise molecular immune responses, particularly involving T-cell receptor (TCR) repertoire changes after full vaccination, and the use of TCR analysis to evaluate vaccine efficacy, remain incompletely understood. In this study, we developed the DeepAir-Cov19 model (AUC=0.94), a large language model tailored to identify SARS-CoV-2-specific TCRs, and observed significant differences in the TCR profiles between antibody-negative and antibody-positive populations before and after vaccination, indicating that the immune status of pre-vaccine recipients can directly assess the efficacy of vaccination. Notably, SARS-CoV-2-specific TCRs expanded to peak levels after the second dose and remained detectable in most subjects up to 10 months post-vaccination. Meanwhile, we also identified three specific V genes, 20 V-J combinations, and 3 epitopes associated with these responses. Finally, by leveraging vaccine-specifi