Evidence map›Paper›PMID 40691818›Full record

ArticleBMC bioinformatics2025

clonevdjseq: A workflow and bioinformatics management system for sequencing, archiving, and analysis of VDJ sequences from clonal libraries.

Keith Mitchell, Samuel Hunter, Lutz Froenicke, Karl Murray, Matthew Settles, James S Trimmer

Abstract read
In one paragraph

Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. mAbs · 2026
    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

6 authors.

Keith MitchellDepartment of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA, USA. kgmitchell@ucdavis.edu.
Samuel HunterBioinformatics Core, Genome Center, University of California Davis, Davis, CA, USA.
Lutz FroenickeDNA Technology Core, Genome Center, University of California Davis, Davis, CA, USA.
Karl MurrayDepartment of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA, USA.
Matthew SettlesBioinformatics Core, Genome Center, University of California Davis, Davis, CA, USA.
James S TrimmerDepartment of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA, USA.

Funding

Recombinant Immunolabels for Nanoprecise Brain Mapping Across ScalesU24NS109113 · NINDS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KARL Daniel MURRAY · 2018 to 2026
$9.9M
NIH HHS U24 NS109113NINDS NIH HHS U24 NS109113
6 · The paper itself

Abstract

backgroundAdvances in next-generation sequencing technologies have facilitated extensive analysis of B cell and T cell receptor (BCR/TCR, respectively) sequences from monoclonal hybridoma libraries, single B cells, and single T cells, generating vast amounts of important data pertaining to antigen recognition. However, existing workflows and bioinformatics tools often lack the flexibility and scalability needed to handle large clonal level datasets effectively. An initial system and hybridoma dependent version of this code was distributed as part of the NeuroMabSeq publication, but clonevdjseq aims to be a technical addendum for broader system compatibility and enhanced modeling.

resultsWe present clonevdjseq, an integrated and accessible software solution leveraging nextflow and Django. Developed primarily for large hybridoma libraries, the workflow and pipeline is amenable to BCR/TCR sequence analysis of homogenous populations or clones of B and T cells, respectively. The clonevdjseq pipeline includes modules for read processing, amplicon denoising, and quality control of paired variable light/heavy chains of BCRs from B cells and hybridomas, or alpha(ɑ)/beta(β) and delta(δ)/gamma(γ) chains of TCRs in the case of T cell applications. The pipeline is built upon a robust, high-throughput library prep protocol, upon which processed data has been verified across thousands of monoclonal antibodies. The results of this effort has yielded sequences used to develop functional recombinant monoclonal antibodies and single chain variable fragments as a part of the NeuroMabSeq initiative where thousands of hybridoma samples were processed (Mitchell et al. in Sci Rep 13(1):16200, 2023) as well as provide additional modeling and extensibility to other modalities. The clonevdjseq software is accessible via Nextflow and also offers a database and web app as a final optional step in the processing for dissemination of results and data exploration.

conclusionsclonevdjseq offers a comprehensive and scalable solution for the processing and analysis of large monoclonal and oligoclonal VDJ datasets. Its modular design, dynamic pipeline, and robust database integration facilitate efficient data management and analysis. The platform is publicly available and aims to support the research community by providing an accessible and flexible tool for archiving and dissemination of BCR sequences from hybridomas, with applicability for other applications such as TCR sequences from single-cell T cell populations.

Indexed as

Computational BiologyHigh-Throughput Nucleotide SequencingSequence Analysis, DNASoftwareAnimalsGene LibraryHumansReceptors, Antigen, B-CellReceptors, Antigen, T-CellWorkflowReceptors, Antigen, B-CellReceptors, Antigen, T-CellBCR sequencingBioinformaticsCloneClonevdjseqDatabaseDjangoHybridomaMonoclonal antibodiesNextflowNf-coreNGSSingle-cellTCR sequencingVDJ

Identifiers

PMID40691818
PMCPMC12278597

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.