Evidence map›Paper›PMID 30429847›Full record

ArticleFrontiers in immunology2018

Benchmarking Tree and Ancestral Sequence Inference for B Cell Receptor Sequences.

Kristian Davidsen, Frederick A Matsen

Abstract read
In one paragraph

Article in Frontiers in immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Leveraging DAGs to improve context-sensitive and abundance-aware tree estimation.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. TRIBAL: Tree Inference of B cell Clonal Lineages.bioRxiv : the preprint server for biology · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Antibody repertoire sequencing analysis.Acta biochimica et biophysica Sinica · 2022
    Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. iScience · 2021
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Kristian DavidsenComputational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, United States.
Frederick A MatsenComputational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, United States.

Funding

Modeling the dynamics and evolution of immune responses to influenza virusesU19AI117891 · NIAID · EMORY UNIVERSITY · PI AHMED, RAFI, ANTIA, RUSTOM NOSHIR · 2015 to 2019
$8.4M
DEFINING THE INFANT IMMUNE RESPONSE TO HIVR01AI120961 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI OVERBAUGH, JULIE M. · 2016 to 2020
$4.1M
Leveraing deep sequencing data to understand antibody maturationR01GM113246 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MATSEN, FREDERICK ALBERT · 2014 to 2018
$1.9M
FHCRC High-Performance Computing ClusterS10OD020069 · OD · FRED HUTCHINSON CANCER RESEARCH CENTER · PI KOOPERBERG, CHARLES L · 2015 to 2015
$600k
Howard Hughes Medical InstituteNIAID NIH HHS R01 AI120961NIAID NIH HHS U19 AI117891NIGMS NIH HHS R01 GM113246NIH HHS S10 OD020069
6 · The paper itself

Abstract

B cell receptor sequences evolve during affinity maturation according to a Darwinian process of mutation and selection. Phylogenetic tools are used extensively to reconstruct ancestral sequences and phylogenetic trees from affinity-matured sequences. In addition to using general-purpose phylogenetic methods, researchers have developed new tools to accommodate the special features of B cell sequence evolution. However, the performance of classical phylogenetic techniques in the presence of B cell-specific features is not well understood, nor how much the newer generation of B cell specific tools represent an improvement over classical methods. In this paper we benchmark the performance of classical phylogenetic and new B cell-specific tools when applied to B cell receptor sequences simulated from a forward-time model of B cell receptor affinity maturation toward a mature receptor. We show that the currently used tools vary substantially in terms of tree structure and ancestral sequence inference accuracy. Furthermore, we show that there are still large performance gains to be achieved by modeling the special mutation process of B cell receptors. These conclusions are further strengthened with real data using the rules of isotype switching to count possible violations within each inferred phylogeny.

Indexed as

Computer SimulationAlgorithmsBenchmarkingB-LymphocytesEvolution, MolecularHumansImmunoglobulin Class SwitchingModels, GeneticMutationPhylogenyReceptors, Antigen, B-CellReceptors, Antigen, B-Cellancestral sequence reconstructionantibodiesB cell receptor repertoirebenchmarkingphylogeny

Identifiers

PMID30429847
PMCPMC6220437

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.