Evidence map›Paper›PMID 42180390›Full record

ArticleImmunoinformatics (Amsterdam, Netherlands)2025

Challenges and future directions of AIRR-seq-based diagnostics.

Ulrik Stervbo, Paraskevas Filippidis, Felix Breden, Lindsay G Cowell, Frederic Davi, Victor Greiff, Anton W Langerak, Eline T Luning Prak, Alexandra F Sharland, Enkelejda Miho and 1 more

Abstract read
In one paragraph

Article in Immunoinformatics (Amsterdam, Netherlands), 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. Machine learning in AIRR diagnostics: Advances and applications.Immunoinformatics (Amsterdam, Netherlands) · 2025
    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.

Ulrik StervboCenter for Translational Medicine and Immune Diagnostics Laboratory, Medical Department I, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany.ORCID 0000-0002-2831-8868
Paraskevas FilippidisDepartment of Pathology, Yale School of Medicine, New Haven, 06511, CT, USA.ORCID 0000-0002-6784-7420
Felix BredenDepartment of Biological Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.ORCID 0000-0001-9762-6314
Lindsay G CowellDepartment of Health Data Science and Biostatistics, O'Donnell School of Public Health and Department of Immunology, School of Biomedical Sciences, UT Southwestern Medical Center, Dallas, TX, 75390, USA.
Frederic DaviLaboratory of Molecular Hematology, Department of Hematology, Hôpital Pitié-Salpêtrière, APHP, Sorbonne Université, Paris, France.
Victor GreiffDepartment of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway.ORCID 0000-0003-2622-5032
Anton W LangerakLaboratory Medical Immunology, Department Immunology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.ORCID 0000-0002-2078-3220
Eline T Luning PrakDepartment of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA 19104, USA.ORCID 0000-0002-9478-9211
Alexandra F SharlandSydney Medical School, Faculty of Medicine and Health, University of Sydney, NSW 2006, Australia.ORCID 0000-0003-1579-5398
Enkelejda MihoInstitute of Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied University of Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland.ORCID 0000-0001-6461-0519
Pieter MeysmanAntwerp Unit for Data Analysis and Computation in Immunology and Sequencing, University of Antwerp, Antwerp, Belgium.ORCID 0000-0001-5903-633X

Funding

Regulation of B cell Responses in SLE and Other Autoimmune DiseasesU19AI110483 · NIAID · EMORY UNIVERSITY · PI Ignacio E. Sanz · 2014 to 2026
$76.6M
Human Pancreas Analysis Program for Type 1 Diabetes - HPAP-T1DU01DK112217 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI MARK A. ATKINSON, KLAUS H KAESTNER · 2021 to 2026
$46.8M
i-AKC: Integrated AIRR Knowledge CommonsU24AI177622 · NIAID · UT SOUTHWESTERN MEDICAL CENTER · PI LINDSAY G. COWELL · 2023 to 2026
$4.0M
NIAID NIH HHS U19 AI110483NIAID NIH HHS U24 AI177622NIDDK NIH HHS U01 DK112217
6 · The paper itself

Abstract

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a promising diagnostic method across various clinical conditions, yet its widespread implementation faces several challenges. This perspective examines the current landscape of AIRR-seq diagnostics and outlines key obstacles and opportunities for advancement. Critical challenges include the need for standardized quality controls, privacy protection under General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) frameworks, and the development of clinically compatible bioinformatics pipelines. Machine learning approaches offer potential solutions for interpreting complex repertoire signatures, though these models must balance accuracy with interpretability for clinical adoption. Future applications may include early disease detection, prognosis, and monitoring of treatment and vaccine responses. However, successful clinical integration will require sustained collaboration among funding bodies, regulatory agencies, researchers, diagnosticians, and clinicians to establish clear guidelines and expand existing repositories with well-characterized patient samples. The collaborative efforts of the AIRR Diagnostics Working Group and the AIRR Community's initiatives are working towards unlocking the potential of AIRR-seq in precision medicine and enhancing diagnostic capabilities.

Indexed as

AIRR-seqClinical translationDiagnosticsInterpretabilityMachine learningStandardization

Identifiers

PMID42180390
PMCPMC13193248

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.