ArticleJournal of immunology (Baltimore, Md. : 1950)2014
Integrating B cell lineage information into statistical tests for detecting selection in Ig sequences.
Article in Journal of immunology (Baltimore, Md. : 1950), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- New generalized metric based on branch length distance to compare B cell lineage trees.Algorithms for molecular biology : AMB · 2024Article
- Combining mutation and recombination statistics to infer clonal families in antibody repertoires.eLife · 2024Article
- B cell phylogenetics in the single cell era.Trends in immunology · 2024Review
- Autoimmunity and immunodeficiency associated with monoallelic LIG4 mutations via haploinsufficiency.The Journal of allergy and clinical immunology · 2023Article
- Gastric DLBCL clonal evolution as function of patient age.Frontiers in immunology · 2022Article
- From Antibody Repertoires to Cell-Cell Interactions to Molecular Networks: Bridging Scales in the Germinal Center.Frontiers in immunology · 2022Review
- Article
- Using B cell receptor lineage structures to predict affinity.PLoS computational biology · 2020Article
- GLaMST: grow lineages along minimum spanning tree for b cell receptor sequencing data.BMC genomics · 2020Article
- Fierce Selection and Interference in B-Cell Repertoire Response to Chronic HIV-1.Molecular biology and evolution · 2019Article
- Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning.BMC bioinformatics · 2019Article
- The Bayesian optimist's guide to adaptive immune receptor repertoire analysis.Immunological reviews · 2018Review
- Beyond Hot Spots: Biases in Antibody Somatic Hypermutation and Implications for Vaccine Design.Frontiers in immunology · 2018Review
- Gene-Specific Substitution Profiles Describe the Types and Frequencies of Amino Acid Changes during Antibody Somatic Hypermutation.Frontiers in immunology · 2017Article
- Effects of Darwinian Selection and Mutability on Rate of Broadly Neutralizing Antibody Evolution during HIV-1 Infection.PLoS computational biology · 2016Article
- The Diversity and Molecular Evolution of B-Cell Receptors during Infection.Molecular biology and evolution · 2016Review
- Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting.Science advances · 2016Article
- T-cell receptor profiling in cancer.Molecular oncology · 2015Review
- Practical guidelines for B-cell receptor repertoire sequencing analysis.Genome medicine · 2015Review
- Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data.Bioinformatics (Oxford, England) · 2015Article
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Authors and funding
5 authors.
Funding
Abstract
Detecting selection in B cell Ig sequences is critical to understanding affinity maturation and can provide insights into Ag-driven selection in normal and pathologic immune responses. The most common sequence-based methods for detecting selection analyze the ratio of replacement and silent mutations using a binomial statistical analysis. However, these approaches have been criticized for low sensitivity. An alternative method is based on the analysis of lineage trees constructed from sets of clonally related Ig sequences. Several tree shape measures have been proposed as indicators of selection that can be statistically compared across cohorts. However, we show that tree shape analysis is confounded by underlying experimental factors that are difficult to control for in practice, including the sequencing depth and number of generations in each clone. Thus, although lineage tree shapes may reflect selection, their analysis alone is an unreliable measure of in vivo selection. To usefully capture the information provided by lineage trees, we propose a new method that applies the binomial statistical framework to mutations identified based on lineage tree structure. This hybrid method is able to detect selection with increased sensitivity in both simulated and experimental data sets. We anticipate that this approach will be especially useful in the analysis of large-scale Ig sequencing data sets generated by high-throughput sequencing technologies.
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