ArticleFrontiers in immunology2019
sumrep: A Summary Statistic Framework for Immune Receptor Repertoire Comparison and Model Validation.
Article in Frontiers in immunology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Deciphering small sequence differences in T cell receptor-antigen pairing.Nature communications · 2026Article
- nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework.PLoS computational biology · 2024Article
- Detecting T-cell clonal expansions and quantifying clone survival using deep profiling of immune repertoires.Frontiers in immunology · 2024Article
- AIRRSHIP: simulating human B cell receptor repertoire sequences.Bioinformatics (Oxford, England) · 2023Article
- ViCloD, an interactive web tool for visualizing B cell repertoires and analyzing intraclonal diversities: application to human B-cell tumors.NAR genomics and bioinformatics · 2023Article
- Dynamic kernel matching for non-conforming data: A case study of T cell receptor datasets.PloS one · 2023Article
- Comparing T cell receptor repertoires using optimal transport.PLoS computational biology · 2022Article
- B-cell receptor repertoire sequencing: Deeper digging into the mechanisms and clinical aspects of immune-mediated diseases.iScience · 2022Review
- AIRRscape: An interactive tool for exploring B-cell receptor repertoires and antibody responses.PLoS computational biology · 2022Article
- Reference-based comparison of adaptive immune receptor repertoires.Cell reports methods · 2022Article
- Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis.Methods in molecular biology (Clifton, N.J.) · 2022Article
- TCRCell · 2021Article
- Methods for sequence and structural analysis of B and T cell receptor repertoires.Computational and structural biotechnology journal · 2020Review
- Article
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Authors and funding
10 authors.
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Abstract
The adaptive immune system generates an incredible diversity of antigen receptors for B and T cells to keep dangerous pathogens at bay. The DNA sequences coding for these receptors arise by a complex recombination process followed by a series of productivity-based filters, as well as affinity maturation for B cells, giving considerable diversity to the circulating pool of receptor sequences. Although these datasets hold considerable promise for medical and public health applications, the complex structure of the resulting adaptive immune receptor repertoire sequencing (AIRR-seq) datasets makes analysis difficult. In this paper we introduce sumrep, an R package that efficiently performs a wide variety of repertoire summaries and comparisons, and show how sumrep can be used to perform model validation. We find that summaries vary in their ability to differentiate between datasets, although many are able to distinguish between covariates such as donor, timepoint, and cell type for BCR and TCR repertoires. We show that deletion and insertion lengths resulting from V(D)J recombination tend to be more discriminative characterizations of a repertoire than summaries that describe the amino acid composition of the CDR3 region. We also find that state-of-the-art generative models excel at recapitulating gene usage and recombination statistics in a given experimental repertoire, but struggle to capture many physiochemical properties of real repertoires.
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