Evidence map›Paper›PMID 42216956›Full record

ArticleDatabase : the journal of biological databases and curation2026

Quantitative analysis of growth and diversification in venom data using database metrics.

Kim N Kirchhoff, Tobias Senoner, Selin Tuerkoglu, Ivan Koludarov, Björn M von Reumont, Mandë Holford

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Kim N KirchhoffDepartment of Organismic and Evolutionary Biology, Harvard University, 16 Divinity Avenue, 02138, Cambridge, MA, United States.ORCID 0000-0002-0004-1529
Tobias SenonerDepartment of Informatics, Bioinformatics & Computational Biology, Technical University of Munich, Boltzmannstr. 3, 85748, Munich, Germany.ORCID 0000-0002-7900-5896
Selin TuerkogluDepartment of Informatics, Bioinformatics & Computational Biology, Technical University of Munich, Boltzmannstr. 3, 85748, Munich, Germany.ORCID 0009-0002-2296-994X
Ivan KoludarovDepartment of Informatics, Bioinformatics & Computational Biology, Technical University of Munich, Boltzmannstr. 3, 85748, Munich, Germany.ORCID 0000-0002-9073-4758
Björn M von ReumontDepartment of Entomology, Natural History Museum Karlsruhe, Karlsruhe, Erbprinzenstr. 13, 76133, Germany.ORCID 0000-0002-7462-8226
Mandë HolfordDepartment of Organismic and Evolutionary Biology, Harvard University, 16 Divinity Avenue, 02138, Cambridge, MA, United States.ORCID 0000-0001-9476-2687

Funding

Charting the evolutionary development of novel genes and the molecular mechanisms of gland tissue organizationin cephalopodsDP1AT012812 · NCCIH · HUNTER COLLEGE · PI Mande Holford · 2023 to 2026
$4.5M
Federal Ministry of Research, Technology and Space 16DKWN136ANCCIH NIH HHS DP1 AT012812NIH HHS 5DP1AT012812
6 · The paper itself

Abstract

Animal venomics is a growing field of research with evolutionary and biotechnological significance. Yet, fundamental questions regarding the origin, diversification, and bioactivity of venoms remain unresolved. Here, we analysed venom tissue-related data curated in Tox-Prot, currently the most comprehensive database for animal venoms, across three snapshots spanning two decades (2005, 2015, 2025). We assessed the taxonomic landscape related to Tox-Prot entries, sequence length distribution, protein family abundances, and habitat-specific venom patterns. Our results consistently show that snakes, spiders, cone snails, and scorpions, along with their associated protein families such as Phospholipase A2, Snake Three Finger Toxin and Long 4(C-C) scorpion toxin family, dominate across Tox-Prot. Nevertheless, the taxonomic and protein family diversity has been steadily increasing, with 503 new species and 188 new protein families added by 2025 compared to the 2005 dataset. Marine species account for 16%-20% of total species, of which 63%-85% are Neogastropoda reflecting limited marine species diversity coverage; likewise, terrestrial taxa are disproportionately represented by Squamata (39%) and Hymenoptera (20%) relative to their natural diversities of 2.3% and 50.26%, respectively. At the molecular level, half of all entries correspond to mature peptide sequences of 26-75 amino acids, featuring three to four disulfide bridges and C-terminal amidations as the most frequently recorded post-translational modifications. Further, protein language model embeddings infer taxonomical diverse peptide and delimited large enzyme clusters. Our study maps two decades of venom data diversification, reflecting both the field's rapid expansion and the need for robust integrated datasets to propel and disseminate that knowledge.

Indexed as

Databases, ProteinVenomsAnimalsVenoms

Identifiers

PMID42216956
PMCPMC13221835

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Registered trials

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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.