ReviewToxins2015
Bioinformatics-Aided Venomics.
Review in Toxins, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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.
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.
Who cites it
22 citing papers in PubMed.
- From marine predator to pharmacology: Conotoxin diversity, discovery, and therapeutic potential.Zoological research · 2026Review
- Transcriptomic Insights Into the Evolution of Snake Venom: Mechanisms, Diversity, and Adaptation.Scientifica · 2026Review
- Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence.Nature communications · 2025Article
- A review of the venom microbiome and its utility in ecology and evolution including future directions for emerging research.Symbiosis (Philadelphia, Pa.) · 2025Article
- A Vision for VenomsBase: An Integrated Knowledgebase for the Study of Venoms and Their Applications.Integrative organismal biology (Oxford, England) · 2025Article
- In Silico Conotoxin Studies: Progress and Prospects.Molecules (Basel, Switzerland) · 2024Review
- MultiToxPred 1.0: a novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approach.BMC bioinformatics · 2024Article
- Peeking into the Stingers: A Comprehensive SWATH-MS Study of the European HornetInternational journal of molecular sciences · 2024Article
- Bibliometric Review of the Literature on Cone Snail Peptide Toxins from 2000 to 2022.Marine drugs · 2023Review
- Article
- Comprehensive Transcriptome Sequencing Analysis ofFrontiers in physiology · 2022Article
- Molecular diversity and evolutionary trends of cysteine-rich peptides from the venom glands of Chinese spider Heteropoda venatoria.Scientific reports · 2021Article
- Advances in venomics: Modern separation techniques and mass spectrometry.Journal of chromatography. B, Analytical technologies in the biomedical and life sciences · 2020Review
- Neuropeptide signalling systems - An underexplored target for venom drug discovery.Biochemical pharmacology · 2020Review
- European Medicinal Leeches-New Roles in Modern Medicine.Biomedicines · 2020Review
- Venomics of the ectoparasitoid wasp Bracon nigricans.BMC genomics · 2020Article
- A Multiomics Approach Unravels New Toxins With PossibleFrontiers in pharmacology · 2020Article
- Snails In Silico: A Review of Computational Studies on the Conopeptides.Marine drugs · 2019Review
- Discovery Methodology of Novel Conotoxins fromMarine drugs · 2018Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Venomics is a modern approach that combines transcriptomics and proteomics to explore the toxin content of venoms. This review will give an overview of computational approaches that have been created to classify and consolidate venomics data, as well as algorithms that have helped discovery and analysis of toxin nucleic acid and protein sequences, toxin three-dimensional structures and toxin functions. Bioinformatics is used to tackle specific challenges associated with the identification and annotations of toxins. Recognizing toxin transcript sequences among second generation sequencing data cannot rely only on basic sequence similarity because toxins are highly divergent. Mass spectrometry sequencing of mature toxins is challenging because toxins can display a large number of post-translational modifications. Identifying the mature toxin region in toxin precursor sequences requires the prediction of the cleavage sites of proprotein convertases, most of which are unknown or not well characterized. Tracing the evolutionary relationships between toxins should consider specific mechanisms of rapid evolution as well as interactions between predatory animals and prey. Rapidly determining the activity of toxins is the main bottleneck in venomics discovery, but some recent bioinformatics and molecular modeling approaches give hope that accurate predictions of toxin specificity could be made in the near future.
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Identifiers
What OpenQuestion holds
Registered trials
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.