Evidence map›Paper›PMID 35622332›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Adaptive Immune Receptor Repertoire (AIRR) Community Guide to TR and IG Gene Annotation.

Lmar Babrak, Susanna Marquez, Christian E Busse, William D Lees, Enkelejda Miho, Mats Ohlin, Aaron M Rosenfeld, Ulrik Stervbo, Corey T Watson, Chaim A Schramm and 1 more

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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Revised Adaptive Immune Receptor Data in the Immune Epitope Database.bioRxiv : the preprint server for biology · 2026
    Article
  4. Review
  5. 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

11 authors.

Lmar BabrakInstitute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland.
Susanna MarquezDepartment of Pathology, Yale School of Medicine, New Haven, CT, USA.
Christian E BusseDivision of B Cell Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
William D LeesInstitute of Structural and Molecular Biology, Birkbeck College, University of London, London, UK.
Enkelejda MihoInstitute of Biomedical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland.
Mats OhlinDepartment of Immunotechnology, Lund University, Lund, Sweden.
Aaron M RosenfeldDepartment of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Ulrik StervboCenter for Translational Medicine, Immunology, and Transplantation, Medical Department I, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany.
Corey T WatsonDepartment of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, USA.
Chaim A SchrammVaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. chaim.schramm@nih.gov.
AIRR Community

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR) has revolutionized the ability to carry out large-scale experiments to study the adaptive immune response. Since the method was first introduced in 2009, AIRR sequencing (AIRR-Seq) has been applied to survey the immune state of individuals, identify antigen-specific or immune-state-associated signatures of immune responses, study the development of the antibody immune response, and guide the development of vaccines and antibody therapies. Recent advancements in the technology include sequencing at the single-cell level and in parallel with gene expression, which allows the introduction of multi-omics approaches to understand in detail the adaptive immune response. Analyzing AIRR-seq data can prove challenging even with high-quality sequencing, in part due to the many steps involved and the need to parameterize each step. In this chapter, we outline key factors to consider when preprocessing raw AIRR-Seq data and annotating the genetic origins of the rearranged receptors. We also highlight a number of common difficulties with common AIRR-seq data processing and provide strategies to address them.

Indexed as

Genes, ImmunoglobulinHigh-Throughput Nucleotide SequencingAntibodiesHumansMolecular Sequence AnnotationReceptors, ImmunologicAntibodiesReceptors, ImmunologicAIRR-SeqB-cell receptorGene annotationGermline databasePreprocessingSingle-cell sequencingT-cell receptor

Identifiers

PMID35622332
PMCPMC9761530

What OpenQuestion holds

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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.