Benchmarking Tree and Ancestral Sequence Inference for B Cell Receptor Sequences

Davidsen, Kristian; Matsen, F. A.

doi:10.3389/fimmu.2018.02451

Cited by 35 publications

(79 citation statements)

References 84 publications

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“…Davidsen and Matsen [308] compared maximum likelihood and maximum parsimony methods for BCR lineage reconstruction. They concluded that an improved and informed maximum parsimony method outperforms classic maximum likelihood.…”

Section: Lineage Reconstructionmentioning

confidence: 99%

Quantitative Immunology for Physicists

Altan‐Bonnet

Mora

Walczak

2019

Preprint

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The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field. CONTENTS Dissociation ratesAs discussed below, immunological interactions span the range of very short lived interactions, k off > 10 s −1 or τ off = (k off ) −1 < 0.1 s for antibody binding to its target in the initial phase of an immune response, to extremely long-lived interactions, k off < 10 −4 s −1 or τ off > 3 hours for cytokines interacting with their receptors. These estimates set a huge range of time scales the immune system must deal with, even before taking into consideration delays in cellular responses and how these affect the ligand environment on time scales ranging from hours to days. These considerations form the crux of the matter for quantitative immunology at the cellular scale: while the physical chemistry of ligand-receptor interactions is straightforward, the immune system builds a response of devilish complexity from such elementary interactions. Numbers of receptors per cellIn some situations, such as in T cell antigen recognition, where both the T cell receptor and the antigen exist in a membrane-bound form. All dynamics of interactions must be estimated taking into account the surface concentrations of molecules, with proper adjustment for slower diffusion in the association rates.

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Section: Lineage Reconstructionmentioning

confidence: 99%

Quantitative Immunology for Physicists

Altan‐Bonnet

Mora

Walczak

2019

Preprint

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“…Furthermore, there exist several methods to construct phylogenetic trees, including distance-based metrics, maximum likelihood (ML), maximum parsimony (MP), and Bayesian inference (Stamatakis, 2006;Gascuel, 2006;Bouckaert et al, 2014). While there exist multiple simulation tools capable of exploring how inference method impacts the resulting phylogenetic trees derived from simulated B cell data (Yermanos et al, 2017;Davidsen and Matsen IV, 2018;Safonova et al, 2015;Weber et al, 2019), the extent of this influence on the evolutionary conclusions on experimental data remains largely unexplored. It remains unknown, for example, the extent of which the phylogenetic inference strategy impacts the biological conclusions pertaining to the evolutionary landscape across various infection cohorts.…”

Section: Introductionmentioning

confidence: 99%

The influence of the phylogenetic inference pipeline on murine antibody repertoire sequencing data following viral infection

Yermanos

Greiff

Stadler

et al. 2020

Preprint

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Understanding B cell evolution following vaccination or infection is crucial for instructing targeted immunotherapies when searching for potential therapeutic or virus-neutralizing antibodies. Antibody phylogenetics holds the potential to quantify both clonal selection and somatic hypermutation, two key players shaping B cell evolution. A wide range of bioinformatic pipelines and phylogenetic inference methods have been utilized on antibody repertoire sequencing datasets to delineate B cell evolution. Although the majority of B cell repertoire studies incorporate some aspect of antibody evolution, how the chosen computational methods affect the results is largely ignored. Therefore, we performed an extensive computational analysis on time-resolved antibody repertoire sequencing data to better characterize how commonly employed bioinformatic practices influence conclusions regarding antibody selection and evolution. Our findings reveal that different combinations of clonal lineage assignment strategies, phylogenetic inference methods, and biological sampling affect the inferred size, mutation rates, and topologies of B cell lineages in response to virus infection.

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“…Emergence of Rep-seq technologies triggered developments of various clonal tree reconstruction approaches (Barak et al, 2008, Davidsen andMatsen, 2018) such as likelihood-based ImmuniTree (Sok et al, 2013) and IgPhyML (Hoehn et al, 2017) algorithms. Horns et al, 2016 proposed a minimum spanning tree (MST) algorithm for clonal tree reconstruction and applied it to barcoded Rep-seq datasets that were error-corrected using unique molecular identifiers (a similar MST-based BRILIA tool was developed by Lee et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

IgEvolution: clonal analysis of antibody repertoires

Safonova

Pevzner

2019

Preprint

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Constructing antibody repertoires is an important error-correcting step in analyzing immunosequencing datasets that is important for reconstructing evolutionary (clonal) development of antibodies. However, the state-of-the-art repertoire construction tools typically miss low-abundance antibodies that often represent internal nodes in clonal trees and are crucially important for clonal tree reconstruction. Thus, although repertoire construction is a prerequisite for follow up clonal tree reconstruction, the existing repertoire reconstruction algorithms are not well suited for this task. Since clonal analysis has the potential to reveal errors in the constructed repertoires and contribute to constructing more accurate repertoires, we advocate a tree-guided construction of antibody repertoires that combines error correction and clonal reconstruction as interconnected (rather than independent) tasks. We developed the IgEvolution algorithm for simultaneous repertoire and clonal tree reconstruction and applied it for analyzing multiple immunosequencing datasets representing antigen-specific immune responses. We demonstrate that analysis of clonal trees reveals highly mutable positions that correlate with antigen-binding sites and light-chain contacts in crystallized antibody-antigen complexes. We further demonstrate that this analysis leads to a new approach for identifying complementarity determining regions (CDRs) in antibodies.

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Benchmarking Tree and Ancestral Sequence Inference for B Cell Receptor Sequences

Cited by 35 publications

References 84 publications

Quantitative Immunology for Physicists

Quantitative Immunology for Physicists

The influence of the phylogenetic inference pipeline on murine antibody repertoire sequencing data following viral infection

IgEvolution: clonal analysis of antibody repertoires

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