ArticlePLoS computational biology2019
Per-sample immunoglobulin germline inference from B cell receptor deep sequencing data.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed, 88 citations in OpenAlex.
- Article
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- Nucleotide context models outperform protein language models for predicting antibody affinity maturation.bioRxiv : the preprint server for biology · 2025Article
- Detection of PCR chimeras in adaptive immune receptor repertoire sequences.Bioinformatics (Oxford, England) · 2025Article
- Genotyping of selected germline adaptive immune system loci using short-read sequencing data.Genome research · 2025Article
- Challenges and future directions of AIRR-seq-based diagnostics.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- Article
- A Sitewise Model of Natural Selection on Individual Antibodies via a Transformer-Encoder.Molecular biology and evolution · 2025Article
- Ultrasensitive allele inference from immune repertoire sequencing data with MiXCR.Genome research · 2024Article
- Full-length single-cell BCR sequencing paired with RNA sequencing reveals convergent responses to pneumococcal vaccination.Communications biology · 2024Article
- The S2 subunit of spike encodes diverse targets for functional antibody responses to SARS-CoV-2.PLoS pathogens · 2024Article
- nanoBERT: a deep learning model for gene agnostic navigation of the nanobody mutational space.Bioinformatics advances · 2024Article
- IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data.Nucleic acids research · 2023Article
- Germline-encoded specificities and the predictability of the B cell response.PLoS pathogens · 2023Article
- AIRR community curation and standardised representation for immunoglobulin and T cell receptor germline sets.Immunoinformatics (Amsterdam, Netherlands) · 2023Article
- Characterization of the immunoglobulin lambda chain locus from diverse populations reveals extensive genetic variation.Genes and immunity · 2023Article
- AIRR-C IG Reference Sets: curated sets of immunoglobulin heavy and light chain germline genes.Frontiers in immunology · 2023Article
- Adaptive immune receptor genotyping using theFrontiers in immunology · 2023Article
- Analysis of IGH allele content in a sample group of rheumatoid arthritis patients demonstrates unrevealed population heterogeneity.Frontiers in immunology · 2023Article
- T cell receptor beta germline variability is revealed by inference from repertoire data.Genome medicine · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The collection of immunoglobulin genes in an individual's germline, which gives rise to B cell receptors via recombination, is known to vary significantly across individuals. In humans, for example, each individual has only a fraction of the several hundred known V alleles. Furthermore, the currently-accepted set of known V alleles is both incomplete (particularly for non-European samples), and contains a significant number of spurious alleles. The resulting uncertainty as to which immunoglobulin alleles are present in any given sample results in inaccurate B cell receptor sequence annotations, and in particular inaccurate inferred naive ancestors. In this paper we first show that the currently widespread practice of aligning each sequence to its closest match in the full set of IMGT alleles results in a very large number of spurious alleles that are not in the sample's true set of germline V alleles. We then describe a new method for inferring each individual's germline gene set from deep sequencing data, and show that it improves upon existing methods by making a detailed comparison on a variety of simulated and real data samples. This new method has been integrated into the partis annotation and clonal family inference package, available at https://github.com/psathyrella/partis, and is run by default without affecting overall run time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.