ArticlePLoS computational biology2016
Likelihood-Based Inference of B Cell Clonal Families.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
- Priming of Multiple HIV Neutralizing B Cell Precursors in Humans.medRxiv : the preprint server for health sciences · 2026Article
- From Single Cells to Silicon: Emerging Technologies Transforming Monoclonal Antibody Discovery.Antibodies (Basel, Switzerland) · 2026Review
- Large-scale paired chain BCR analysis reveals antibody clonal family inference bias and enhances resolution with machine learning.PLoS computational biology · 2026Article
- Nucleotide context models outperform protein language models for predicting antibody affinity maturation.PLoS computational biology · 2025Article
- Nucleotide context models outperform protein language models for predicting antibody affinity maturation.bioRxiv : the preprint server for biology · 2025Article
- deepNGS navigator: exploring antibody NGS datasets using deep contrastive learning.Bioinformatics (Oxford, England) · 2025Article
- Article
- A Sitewise Model of Natural Selection on Individual Antibodies via a Transformer-Encoder.Molecular biology and evolution · 2025Article
- Delineating the functional activity of antibodies with cross-reactivity to SARS-CoV-2, SARS-CoV-1 and related sarbecoviruses.PLoS pathogens · 2024Article
- Computational detection of antigen-specific B cell receptors following immunization.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Combining mutation and recombination statistics to infer clonal families in antibody repertoires.eLife · 2024Article
- The S2 subunit of spike encodes diverse targets for functional antibody responses to SARS-CoV-2.PLoS pathogens · 2024Article
- Protocol for fast clonal family inference and analysis from large-scale B cell receptor repertoire sequencing data.STAR protocols · 2024Article
- Evaluating methods for B-cell clonal family assignment.bioRxiv : the preprint server for biology · 2024Article
- Systematic evaluation of B-cell clonal family inference approaches.BMC immunology · 2024Article
- Vaccine induction of CD4-mimicking HIV-1 broadly neutralizing antibody precursors in macaques.Cell · 2024Article
- Applying phylogenetic methods for species delimitation to distinguish B-cell clonal families.Frontiers in immunology · 2024Article
- B cell phylogenetics in the single cell era.Trends in immunology · 2024Review
- Fast clonal family inference from large-scale B cell repertoire sequencing data.Cell reports methods · 2023Article
- Germline-encoded specificities and the predictability of the B cell response.PLoS pathogens · 2023Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called "rearrangement" forming progenitor B cells, then a Darwinian process of lineage diversification and selection called "affinity maturation." The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem "clonal family inference." In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM) framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets.
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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.