Shaadi Mehr, Todd Castoe, Marymegan Daly, Florence Jungo, Kim N Kirchhoff, Ivan Koludarov, Stephen P Mackessy, Jason Macrander, Praveena Naidu, Maria Vittoria Modica, Elda E Sanchez, Giulia Zancolli, Mandë Holford
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引用次数: 0
Abstract
Venomous animal research is hampered by fragmented, specialized, and non-interoperable databases (isolated genomic, proteomic, and ecological data). Despite the immense promise of venomous organisms to yield novel bioactive compounds for pharmacological and evolutionary applications, the informatics landscape for such taxa has remained patchy, lacking macro-scale integration across species. We present VenomsBase, an integrated, modular resource that synthesizes multi-omics data, ecological metadata, and functional annotations for venom-bearing organisms. Following the FAIR guidelines, VenomsBase combines an ontology-driven architecture with big-data cloud workflows for sequence integration, motif clustering, 3D display, and linking ecological metadata. Standardized tools and training modules facilitate worldwide access to resources for both researchers in developed countries and in resource-limited areas. Its plug-and-play design allows for integration of additional analytical modules and extension to other species. One can also examine evolutionary trends and connect venom chemistry to ecological niches. VenomsBase would (i) accelerate the pace of venom discovery, whether for therapeutic purposes or evolutionary significance, by providing validated, cross-referenced data sets and community-driven curation, and (ii) foster an open, just, and innovation-ready venom research ecosystem.
期刊介绍:
GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.