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V(D)J Recombination

Genetics and Genomics · NLM MeSH · updated 2026-10-29 · version 1

Citations verified against PubMedNot yet clinician-reviewed10 referencesApproximately 4 min read

The process by which the V (variable), D (diversity), and J (joining) segments of immunoglobulin genes or t-cell receptor genes are assembled during the development of lymphoid cells using nonhomologous dna end-joining.

Table of Contents

1. Definition2. Research landscape3. Reading the evidence4. Open-access studies5. Key reviews6. References7. About this article

Definition and research guide

This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.

Content available: 4 complete abstracts (748 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.

Definition

The process by which the V (variable), D (diversity), and J (joining) segments of immunoglobulin genes or t-cell receptor genes are assembled during the development of lymphoid cells using nonhomologous dna end-joining.

Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.

Also known as: VDJ Recombination; Recombinations, VDJ; Recombination, VDJ; VDJ Recombinations.

Where it fits in the MeSH classification

Broader topics
Gene Rearrangement; Recombination, Genetic
See also
VDJ Recombinases; VDJ Exons

Related concepts

  • Immunoglobulin Genes
  • T-Cell Receptor Genes
  • Lymphocytes
  • Nonhomologous Dna End-Joining

Research landscape

PubMed indexes 684 records tagged with the MeSH heading “V(D)J Recombination” (counts retrieved 2026-10-29). 232 of them (33.9%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 455 records.

Reviews account for 117 records (17.1%) and meta-analyses for 0. Randomized controlled trials number 2 (0.3%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.

PubMed records by publication periodthrough 1999: 0 records; 2000-2009: 0 records; 2010-2019: 455 records; 2020 onward: 232 records0through 199902000-20094552010-20192322020 onward
Records in PubMed indexed with this heading, by publication period. Real counts from NCBI E-utilities; the final period covers only part of the current year. Volume reflects how much has been published, not how strong the evidence is.
Show the data
PeriodRecords
through 19990
2000-20090
2010-2019455
2020 onward232
Share of records by publication typeReviews: 17.1 percent; Meta-analyses: 0.0 percent; Randomized controlled trials: 0.3 percentReviews17.1% (117)Meta-analyses0.0% (0)Randomized controlled trials0.3% (2)
Share of all PubMed records on this heading that carry each publication type. Types overlap with other records, so the shares do not sum to 100%.
Publication-type chart data
Publication types among 684 PubMed records
TypeRecordsShare
Reviews11717.1%
Meta-analyses00.0%
Randomized controlled trials20.3%

Method: PubMed query "V(D)J Recombination"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.

Reading the evidence in genetics and genomics

Genetic findings vary widely in strength. A variant linked to a condition in one study may not replicate in other ancestries, and most common variants have small effects that are meaningful only in aggregate. Studies of rare, high-impact variants can support strong causal claims about a gene, while association studies describe statistical links that need functional and independent replication. Check the sample size, the ancestry of participants, whether results were replicated, and whether a laboratory or clinical outcome was actually measured.

Evidence from open-access studies

These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.

Study comparison — selected abstracts, not a systematic review
Study / authorsYearSource label / designEurope PMC citationsLicence / source
VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium
Bagaev DV, Vroomans RMA, Samir J, Stervbo U, Rius C, Dolton G, et al.
2020Research article
Design not recorded in structured metadata
368cc by
PMID 31588507 · DOI · PMC6943061
High-throughput immune repertoire analysis with IGoR
Marcou Q, Mora T, Walczak AM
2018Research article
Design not recorded in structured metadata
234cc by
PMID 29422654 · DOI · PMC5805751
Chromosome dynamics near the sol-gel phase transition dictate the timing of remote genomic interactions
Khanna N, Zhang Y, Lucas JS, Dudko OK, Murre C
2019Research article
Design not recorded in structured metadata
121cc by
PMID 31235807 · DOI · PMC6591236
Linear RAG scanning mediates editing of Igκ variable region repertoires
Li X, Hu H, Zhang Y, Zhu T, Guan Y, Xu K, et al.
2026Research article
Design not recorded in structured metadata
1cc by
PMID 41986707 · DOI · PMC13190342

VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium [1]

Research article Bagaev DV, Vroomans RMA, Samir J, Stervbo U, Rius C, Dolton G, et al. · · 2020 · cited 368 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Here, we report an update of the VDJdb database with a substantial increase in the number of T-cell receptor (TCR) sequences and their cognate antigens.
Closing excerpt
net.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (155 words)

Here, we report an update of the VDJdb database with a substantial increase in the number of T-cell receptor (TCR) sequences and their cognate antigens. The update further provides a new database infrastructure featuring two additional analysis modes that facilitate database querying and real-world data analysis. The increased yield of TCR specificity identification methods and the overall increase in the number of studies in the field has allowed us to expand the database more than 5-fold. Furthermore, several new analysis methods are included. For example, batch annotation of TCR repertoire sequencing samples allows for annotating large datasets on-line. Using recently developed bioinformatic methods for TCR motif mining, we have built a reduced set of high-quality TCR motifs that can be used for both training TCR specificity predictors and matching against TCRs of interest. These additions enhance the versatility of the VDJdb in the task of exploring T-cell antigen specificities. The database is available at https://vdjdb.cdr3.net.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 31588507 · DOI · PMC6943061

High-throughput immune repertoire analysis with IGoR [2]

Research article Marcou Q, Mora T, Walczak AM · · 2018 · cited 234 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
High-throughput immune repertoire sequencing is promising to lead to new statistical diagnostic tools for medicine and biology.
Closing excerpt
We demonstrate that IGoR outperforms existing tools in accuracy and estimate the sample sizes needed for reliable repertoire characterization.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (124 words)

High-throughput immune repertoire sequencing is promising to lead to new statistical diagnostic tools for medicine and biology. Successful implementations of these methods require a correct characterization, analysis, and interpretation of these data sets. We present IGoR (Inference and Generation Of Repertoires)-a comprehensive tool that takes B or T cell receptor sequence reads and quantitatively characterizes the statistics of receptor generation from both cDNA and gDNA. It probabilistically annotates sequences and its modular structure can be used to investigate models of increasing biological complexity for different organisms. For B cells, IGoR returns the hypermutation statistics, which we use to reveal co-localization of hypermutations along the sequence. We demonstrate that IGoR outperforms existing tools in accuracy and estimate the sample sizes needed for reliable repertoire characterization.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 29422654 · DOI · PMC5805751

Chromosome dynamics near the sol-gel phase transition dictate the timing of remote genomic interactions [3]

Research article Khanna N, Zhang Y, Lucas JS, Dudko OK, Murre C · · 2019 · cited 121 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
Diverse antibody repertoires are generated through remote genomic interactions involving immunoglobulin variable (VH), diversity (DH) and joining (JH) gene segments.
Closing excerpt
These results suggest that chromosome organization near the sol-gel phase transition dictates the timing of genomic interactions to orchestrate gene expression and somatic recombination.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (149 words)

Diverse antibody repertoires are generated through remote genomic interactions involving immunoglobulin variable (VH), diversity (DH) and joining (JH) gene segments. How such interactions are orchestrated remains unknown. Here we develop a strategy to track VH-DHJH motion in B-lymphocytes. We find that VH and DHJH segments are trapped in configurations that allow only local motion, such that spatially proximal segments remain in proximity, while spatially remote segments remain remote. Within a subset of cells, however, abrupt changes in VH-DHJH motion are observed, plausibly caused by temporal alterations in chromatin configurations. Comparison of experimental and simulated data suggests that constrained motion is imposed by a network of cross-linked chromatin chains characteristic of a gel phase, yet poised near the sol phase, a solution of independent chromatin chains. These results suggest that chromosome organization near the sol-gel phase transition dictates the timing of genomic interactions to orchestrate gene expression and somatic recombination.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 31235807 · DOI · PMC6591236

Linear RAG scanning mediates editing of Igκ variable region repertoires [4]

Research article Li X, Hu H, Zhang Y, Zhu T, Guan Y, Xu K, et al. · · 2026 · cited 1 times (Europe PMC)

Abstract reading summary — source excerpts
Opening excerpt
V(D)J recombination-mediated Igκ light chain variable region exon assembly in precursor (pre)-B cells involves recombination activating gene (RAG) endonuclease-orchestrated cleavage between and joining of paired Vκ and Jκ gene segments and flanking RAG-targeting recombination signal sequences (RSSs)1-3.
Closing excerpt
Our findings implicate Cer/Sis deletion and/or displacement as a developmental switch that converts the two-loop-based diffusional primary Igk rearrangement mechanism into a one-loop-based linear scanning secondary rearrangement mechanism.
Interpretation limits
Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (320 words)

V(D)J recombination-mediated Igκ light chain variable region exon assembly in precursor (pre)-B cells involves recombination activating gene (RAG) endonuclease-orchestrated cleavage between and joining of paired Vκ and Jκ gene segments and flanking RAG-targeting recombination signal sequences (RSSs)1-3. The 3.1-megabase Igk contains 4 Jκs (Jκ1, 2, 4, 5) and 100-plus Vκs in clusters oriented for deletional or inversional joining2. Vκ-to-Jκ joining is ordered, with primary Vκ-to-Jκ1 rearrangements occurring first, followed by secondary rearrangements of upstream Vκs that replace primary VκJκ1s by joining to Jκ2-5 (refs. 4,5). Loop extrusion moves deletional-oriented and inversional-oriented, locus-wide Vκs past the Cer/Sis CTCF-binding element-based diffusion platform for short-range diffusional presentation to Jκ1-bound RAG in the primary recombination centre (RC). To achieve diffusion-mediated Vκ-to-Jκ1 joining, Igk evolved powerful Vκ-associated and Jκ-associated RSSs3. Secondary Igk rearrangements replace non-functional or autoreactive primary VκJκ1 rearrangements, expanding the Igκ repertoire and mediating central tolerance by means of receptor editing4,6-11. Here we describe studies that elucidate the physiologically critical secondary Igk recombination mechanism. Primary deletional and inversional VκJκ1 joins, respectively, delete or displace Cer/Sis, creating a pre-B cell population that harbours secondary VκJκ1-based RCs across the Vκ locus and leaves most unrearranged Vκs immediately upstream of secondary RCs in deletional orientation. High-throughput assays demonstrated that RAG scanning from secondary VκJκ1-based RCs, collectively, extends linearly across the Vκ locus in primary pre-B cell populations. Correspondingly, studies of induced pluripotent stem (iPS) cell-generated mouse models or cell lines with physiological VκJκ-rearrangements further revealed that deletional and, originally, inversional Vκs are mostly captured by Jκ2-5-based secondary RCs in deletional orientation by means of linear RAG scanning. Strong Vκ-RSSs contribute to restricting secondary rearrangements, including potential editing rearrangements, to Vκs immediately upstream of a given secondary RC and support, at a lower level, linear scanning-based inversional Vκ-to-Jκ rearrangements. Our findings implicate Cer/Sis deletion and/or displacement as a developmental switch that converts the two-loop-based diffusional primary Igk rearrangement mechanism into a one-loop-based linear scanning secondary rearrangement mechanism.

Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41986707 · DOI · PMC13190342

Key reviews and meta-analyses

The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.

  • Research article V(D)J recombination: mechanisms of initiation [5]
    Schatz DG, Swanson PC · Annu Rev Genet · 2011 · cited 453 times (Europe PMC)
  • Research article Mechanisms of programmed DNA lesions and genomic instability in the immune system [6]
    Alt FW, Zhang Y, Meng FL, Guo C, Schwer B · Cell · 2013 · cited 403 times (Europe PMC)
  • Research article V(D)J recombination, somatic hypermutation and class switch recombination of immunoglobulins: mechanism and regulation [7]
    Chi X, Li Y, Qiu X · Immunology · 2020 · cited 198 times (Europe PMC)
  • Research article Human RAG mutations: biochemistry and clinical implications [8]
    Notarangelo LD, Kim MS, Walter JE, Lee YN · Nat Rev Immunol · 2016 · cited 186 times (Europe PMC)
  • Research article The response to and repair of RAG-mediated DNA double-strand breaks [9]
    Helmink BA, Sleckman BP · Annu Rev Immunol · 2012 · cited 144 times (Europe PMC)
  • Research article Predicting the spectrum of TCR repertoire sharing with a data-driven model of recombination [10]
    Elhanati Y, Sethna Z, Callan CG, Mora T, Walczak AM · Immunol Rev · 2018 · cited 123 times (Europe PMC)

References

  1. Bagaev DV, Vroomans RMA, Samir J, Stervbo U, Rius C, Dolton G, et al.. VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium. . 2020. PMID 31588507 · DOI · PMC6943061
  2. Marcou Q, Mora T, Walczak AM. High-throughput immune repertoire analysis with IGoR. . 2018. PMID 29422654 · DOI · PMC5805751
  3. Khanna N, Zhang Y, Lucas JS, Dudko OK, Murre C. Chromosome dynamics near the sol-gel phase transition dictate the timing of remote genomic interactions. . 2019. PMID 31235807 · DOI · PMC6591236
  4. Li X, Hu H, Zhang Y, Zhu T, Guan Y, Xu K, et al.. Linear RAG scanning mediates editing of Igκ variable region repertoires. . 2026. PMID 41986707 · DOI · PMC13190342
  5. Schatz DG, Swanson PC. V(D)J recombination: mechanisms of initiation. Annu Rev Genet. 2011. PMID 21854230 · DOI
  6. Alt FW, Zhang Y, Meng FL, Guo C, Schwer B. Mechanisms of programmed DNA lesions and genomic instability in the immune system. Cell. 2013. PMID 23374339 · DOI · PMC4382911
  7. Chi X, Li Y, Qiu X. V(D)J recombination, somatic hypermutation and class switch recombination of immunoglobulins: mechanism and regulation. Immunology. 2020. PMID 32031242 · DOI
  8. Notarangelo LD, Kim MS, Walter JE, Lee YN. Human RAG mutations: biochemistry and clinical implications. Nat Rev Immunol. 2016. PMID 26996199 · DOI · PMC5757527
  9. Helmink BA, Sleckman BP. The response to and repair of RAG-mediated DNA double-strand breaks. Annu Rev Immunol. 2012. PMID 22224778 · DOI · PMC4038028
  10. Elhanati Y, Sethna Z, Callan CG, Mora T, Walczak AM. Predicting the spectrum of TCR repertoire sharing with a data-driven model of recombination. Immunol Rev. 2018. PMID 29944757 · DOI · PMC6033145

About this article

Sources
NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
Verification
Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
Review state
automated source verified; no human expert review has been recorded yet.
Version
1, last updated 2026-10-29.

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At a glance

684
PubMed records on this heading
34%
published since 2020
117
review articles
2
randomized trials
10
verified references below

Counts come from PubMed on 2026-10-29; they show how much has been written, not how strong the evidence is.

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