Evidence map›Paper›PMID 38493344›Full record

ArticleBriefings in bioinformatics2024

DeTox: a pipeline for the detection of toxins in venomous organisms.

Allan Ringeval, Sarah Farhat, Alexander Fedosov, Marco Gerdol, Samuele Greco, Lou Mary, Maria Vittoria Modica, Nicolas Puillandre

Open access · goldAbstract read
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
6.1field-weighted citation impact, top 4% of its field
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

9 citing papers in PubMed, 11 citations in OpenAlex.

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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

8 authors at 3 institutions in 3 countries.

Allan RingevalInstitut Systématique Evolution Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, 57 rue Cuvier, 75005 Paris, France.ORCID 0009-0008-9300-6298
Sarah FarhatInstitut Systématique Evolution Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, 57 rue Cuvier, 75005 Paris, France.ORCID 0000-0001-7725-2589
Alexander FedosovInstitut Systématique Evolution Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, 57 rue Cuvier, 75005 Paris, France.
Marco GerdolDepartment of Life Sciences, University of Trieste, Trieste, Italy.
Samuele GrecoDepartment of Life Sciences, University of Trieste, Trieste, Italy.
Lou MaryInstitut Systématique Evolution Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, 57 rue Cuvier, 75005 Paris, France.
Maria Vittoria ModicaDepartment of Biology and Evolution of Marine Organisms (BEOM), Stazione Zoologica Anton Dohrn, Roma, Italy.
Nicolas PuillandreInstitut Systématique Evolution Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, 57 rue Cuvier, 75005 Paris, France.
Centre National de la Recherche Scientifique · FRUniversity of Trieste · ITStazione Zoologica Anton Dohrn · IT

Funding

European Research Council 865101
6 · The paper itself

Abstract

Venomous organisms have independently evolved the ability to produce toxins 101 times during their evolutionary history, resulting in over 200 000 venomous species. Collectively, these species produce millions of toxins, making them a valuable resource for bioprospecting and understanding the evolutionary mechanisms underlying genetic diversification. RNA-seq is the preferred method for characterizing toxin repertoires, but the analysis of the resulting data remains challenging. While early approaches relied on similarity-based mapping to known toxin databases, recent studies have highlighted the importance of structural features for toxin detection. The few existing pipelines lack an integration between these complementary approaches, and tend to be difficult to run for non-experienced users. To address these issues, we developed DeTox, a comprehensive and user-friendly tool for toxin research. It combines fast execution, parallelization and customization of parameters. DeTox was tested on published transcriptomes from gastropod mollusks, cnidarians and snakes, retrieving most putative toxins from the original articles and identifying additional peptides as potential toxins to be confirmed through manual annotation and eventually proteomic analysis. By integrating a structure-based search with similarity-based approaches, DeTox allows the comprehensive characterization of toxin repertoire in poorly-known taxa. The effect of the taxonomic bias in existing databases is minimized in DeTox, as mirrored in the detection of unique and divergent toxins that would have been overlooked by similarity-based methods. DeTox streamlines toxin annotation, providing a valuable tool for efficient identification of venom components that will enhance venom research in neglected taxa.

Indexed as

Toxins, BiologicalVenomsAnimalsPeptidesProteomicsSnakesTranscriptomePeptidesToxins, BiologicalVenomsbioinformatic pipelinetoxin detectiontranscriptomicsvenom

Identifiers

PMID38493344
PMCPMC10944572
OpenAlexW4392899360

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.