Web of venom: exploration of big data resources in animal toxin research.

Details

Serval ID
serval:BIB_DD857727C02C
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Web of venom: exploration of big data resources in animal toxin research.
Journal
GigaScience
Author(s)
Zancolli G., von Reumont B.M., Anderluh G., Caliskan F., Chiusano M.L., Fröhlich J., Hapeshi E., Hempel B.F., Ikonomopoulou M.P., Jungo F., Marchot P., de Farias T.M., Modica M.V., Moran Y., Nalbantsoy A., Procházka J., Tarallo A., Tonello F., Vitorino R., Zammit M.L., Antunes A.
ISSN
2047-217X (Electronic)
ISSN-L
2047-217X
Publication state
Published
Issued date
02/01/2024
Peer-reviewed
Oui
Volume
13
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
Research on animal venoms and their components spans multiple disciplines, including biology, biochemistry, bioinformatics, pharmacology, medicine, and more. Manipulating and analyzing the diverse array of data required for venom research can be challenging, and relevant tools and resources are often dispersed across different online platforms, making them less accessible to nonexperts. In this article, we address the multifaceted needs of the scientific community involved in venom and toxin-related research by identifying and discussing web resources, databases, and tools commonly used in this field. We have compiled these resources into a comprehensive table available on the VenomZone website (https://venomzone.expasy.org/10897). Furthermore, we highlight the challenges currently faced by researchers in accessing and using these resources and emphasize the importance of community-driven interdisciplinary approaches. We conclude by underscoring the significance of enhancing standards, promoting interoperability, and encouraging data and method sharing within the venom research community.
Keywords
Animals, Venoms, Internet, Computational Biology/methods, Big Data, Databases, Factual, antivenom, drug discovery, genomics, machine learning, peptidomics, proteomics, toxin databases, transcriptomics, venom resources
Pubmed
Open Access
Yes
Create date
13/09/2024 15:17
Last modification date
27/09/2024 15:45
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