Evidence map›Paper›PMID 38331731›Full record

ArticleBMC immunology2024

Systematic evaluation of B-cell clonal family inference approaches.

Daria Balashova, Barbera D C van Schaik, Maria Stratigopoulou, Jeroen E J Guikema, Tom G Caniels, Mathieu Claireaux, Marit J van Gils, Anne Musters, Dornatien C Anang, Niek de Vries and 2 more

Open access · goldAbstract read
In one paragraph

Article in BMC immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
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

3 citing papers in PubMed, 4 citations in OpenAlex.

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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

12 authors at 3 institutions in 2 countries.

Daria BalashovaAmsterdam UMC location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, Amsterdam, Netherlands.
Barbera D C van SchaikAmsterdam UMC location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, Amsterdam, Netherlands.
Maria StratigopoulouCancer Center Amsterdam, Amsterdam, The Netherlands.
Jeroen E J GuikemaCancer Center Amsterdam, Amsterdam, The Netherlands.
Tom G CanielsAmsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Meibergdreef 9, Amsterdam, Netherlands.
Mathieu ClaireauxAmsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Meibergdreef 9, Amsterdam, Netherlands.
Marit J van GilsAmsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Meibergdreef 9, Amsterdam, Netherlands.
Anne MustersAmsterdam UMC location University of Amsterdam, Experimental Immunology, Meibergdreef 9, Amsterdam, Netherlands.
Dornatien C AnangAmsterdam UMC location University of Amsterdam, Experimental Immunology, Meibergdreef 9, Amsterdam, Netherlands.
Niek de VriesAmsterdam UMC location University of Amsterdam, Experimental Immunology, Meibergdreef 9, Amsterdam, Netherlands.
Victor Greiff *Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.
Antoine H C van Kampen *Amsterdam UMC location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, Amsterdam, Netherlands. a.h.vankampen@amsterdamumc.nl.
Amsterdam University Medical Centers · NLUniversity of Amsterdam · NLUniversity of Oslo · NO

Funding

Wellcome Trust
6 · The paper itself

Abstract

The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method.

Indexed as

B-LymphocytesReceptors, Antigen, B-CellHigh-Throughput Nucleotide SequencingHumansMutationReceptors, Antigen, B-CellAIRR-seq dataAIRR-seq data simulationB-cell clonal family partitioningB-cell receptor repertoireB-cell shared clonal families

Identifiers

PMID38331731
PMCPMC11370117
OpenAlexW4391653069

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.