Evidence map›Paper›PMID 42162238›Full record

ArticleCommunications biology2026

ANARCII enables alignment-free antigen receptor numbering using a generalised language model.

Alexander Greenshields-Watson, Parth Agarwal, Sarah A Robinson, Benjamin Heathcote Williams, Gemma L Gordon, Henriette L Capel, Yushi Li, Fabian C Spoendlin, Broncio Aguilar-Sanjuan, Fergus Boyles and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications biology, 2026. 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
–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

3 citing papers in PubMed.

  1. AVIDbase: A biologically accurate structural dataset of nanobody-antigen complexes.Protein science : a publication of the Protein Society · 2026
    Article
  2. Revised Adaptive Immune Receptor Data in the Immune Epitope Database.bioRxiv : the preprint server for biology · 2026
    Article
  3. 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.

Alexander Greenshields-Watson *Department of Statistics, Oxford Protein Informatics Group, University of Oxford, Oxford, UK.
Parth Agarwal *Department of Statistics, Oxford Protein Informatics Group, University of Oxford, Oxford, UK.
Sarah A Robinson *Department of Statistics, Oxford Protein Informatics Group, University of Oxford, Oxford, UK.
Benjamin Heathcote WilliamsDepartment of Statistics, Oxford Protein Informatics Group, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0001-9544-0890
Gemma L GordonDepartment of Statistics, Oxford Protein Informatics Group, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-8259-9111
Henriette L CapelDepartment of Statistics, Oxford Protein Informatics Group, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-3757-5313
Yushi LiDepartment of Statistics, Oxford Protein Informatics Group, University of Oxford, Oxford, UK.
Fabian C SpoendlinDepartment of Statistics, Oxford Protein Informatics Group, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-3006-6217
Broncio Aguilar-SanjuanDepartment of Statistics, Oxford Protein Informatics Group, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0001-8068-6417
Fergus BoylesDepartment of Statistics, Oxford Protein Informatics Group, University of Oxford, Oxford, UK.
Charlotte M DeaneDepartment of Statistics, Oxford Protein Informatics Group, University of Oxford, Oxford, UK. deane@stats.ox.ac.uk.ORCID http://orcid.org/0000-0003-1388-2252

Funding

RCUK | Engineering and Physical Sciences Research Council (EPSRC) EP/S024093/1
6 · The paper itself

Abstract

Antigen receptor numbering allows delineation of antigen-binding regions of antibodies and T cell receptors, from sequence alone. Numbering is currently achieved by aligning to a reference set. This approach may result in different numbering depending on reference set used or fail on sequences from rare species or formats. We present a method (ANARCII) which requires no alignment step and is based on a Seq2Seq language model. ANARCII improves upon existing methods through more consistent numbering of key regions, robustness to truncations, generalisation to unseen species, and easier user installation. The lightweight architecture allows numbering of 90,000 sequences per minute on a high-end GPU. The software is available via web app ( https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/anarcii/ ), and package ( https://github.com/oxpig/ANARCII ). Ultimately ANARCII allows numbering of more antibody-like sequences, with better recovery of full-length regions from existing databases, and enables comparative analysis of new receptors not numbered by existing tools.

Indexed as

Receptors, Antigen, T-CellSoftwareAnimalsHumansReceptors, Antigen, T-Cell

Identifiers

PMID42162238
PMCPMC13462589

What OpenQuestion holds

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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.