谷歌浏览器插件
订阅小程序
在清言上使用

DeTox: a Pipeline for the Detection of Toxins in Venomous Organisms.

BRIEFINGS IN BIOINFORMATICS(2024)

引用 0|浏览14
暂无评分
摘要
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.
更多
查看译文
关键词
bioinformatic pipeline,venom,toxin detection,transcriptomics
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要