Evidence map›Paper›PMID 39703430›Full record

ArticleNAR genomics and bioinformatics2024

AntiBody Sequence Database.

Simon Malesys, Rachel Torchet, Bertrand Saunier, Nicolas Maillet

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2024. 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. 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

4 authors.

Simon MalesysInstitut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, 28 rue du Dr Roux, F-75015 Paris, France.ORCID https://orcid.org/0000-0003-1863-1970
Rachel TorchetInstitut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, 28 rue du Dr Roux, F-75015 Paris, France.ORCID https://orcid.org/0000-0002-2306-5566
Bertrand SaunierUnité de Virologie Structurale, Institut Pasteur, Université Paris Cité, CNRS UMR 3569, 28 rue du Dr Roux, 75015 Paris, France.ORCID https://orcid.org/0000-0002-0700-3082
Nicolas MailletInstitut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, 28 rue du Dr Roux, F-75015 Paris, France.ORCID https://orcid.org/0000-0003-1611-5243

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antibodies play a crucial role in the humoral immune response against health threats, such as viral infections. Although the theoretical number of human immunoglobulins is well over a trillion, the total number of unique antibody protein sequences accessible in databases is much lower than the number found in a single individual. Training AI (Artificial Intelligence) models, for example to assist in developing serodiagnoses or antibody-based therapies, requires building datasets according to strict criteria to include as many standardized antibody sequences as possible. However, the available sequences are scattered across partially redundant databases, making it difficult to compile them into single non-redundant datasets. Here, we introduce ABSD (AntiBody Sequence Database, https://absd.pasteur.cloud), which contains data from major publicly available resources, creating the largest standardized, automatically updated and non-redundant source of public antibody sequences. This user-friendly and open website enables users to generate lists of antibodies based on selected criteria and download the unique sequence pairs of their variable regions.

Identifiers

PMID39703430
PMCPMC11655285

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.