ArticlePLoS computational biology2020
A Bayesian phylogenetic hidden Markov model for B cell receptor sequence analysis.
Article in PLoS computational biology, 2020. 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, 24 citations in OpenAlex.
- B cells and B-cell receptor repertoire features in coronary heart disease: immunopathogenic roles, clinical relevance, and therapeutic potential.Frontiers in cardiovascular medicine · 2026Review
- Maturation of germinal center B cells after influenza virus vaccination in humans.The Journal of experimental medicine · 2024Article
- Broadly neutralizing antibodies for HIV prevention: a comprehensive review and future perspectives.Clinical microbiology reviews · 2024Review
- B cell phylogenetics in the single cell era.Trends in immunology · 2024Review
- Inference of B cell clonal families using heavy/light chain pairing information.PLoS computational biology · 2022Article
- Phylogenetic analysis of migration, differentiation, and class switching in B cells.PLoS computational biology · 2022Article
- Ecological Processes Shaping Microbiomes of Extremely Low Birthweight Infants.Frontiers in microbiology · 2022Article
- Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis.Methods in molecular biology (Clifton, N.J.) · 2022Article
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- Rationalizing Random Walks: Replicating Protective Antibody Trajectories.Trends in immunology · 2021Review
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- Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data.Nucleic acids research · 2020Article
- Using B cell receptor lineage structures to predict affinity.PLoS computational biology · 2020Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
The human body generates a diverse set of high affinity antibodies, the soluble form of B cell receptors (BCRs), that bind to and neutralize invading pathogens. The natural development of BCRs must be understood in order to design vaccines for highly mutable pathogens such as influenza and HIV. BCR diversity is induced by naturally occurring combinatorial "V(D)J" rearrangement, mutation, and selection processes. Most current methods for BCR sequence analysis focus on separately modeling the above processes. Statistical phylogenetic methods are often used to model the mutational dynamics of BCR sequence data, but these techniques do not consider all the complexities associated with B cell diversification such as the V(D)J rearrangement process. In particular, standard phylogenetic approaches assume the DNA bases of the progenitor (or "naive") sequence arise independently and according to the same distribution, ignoring the complexities of V(D)J rearrangement. In this paper, we introduce a novel approach to Bayesian phylogenetic inference for BCR sequences that is based on a phylogenetic hidden Markov model (phylo-HMM). This technique not only integrates a naive rearrangement model with a phylogenetic model for BCR sequence evolution but also naturally accounts for uncertainty in all unobserved variables, including the phylogenetic tree, via posterior distribution sampling.
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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.