ToxCodAn: a new toxin annotator and guide to venom gland transcriptomics
ToxCodAn: a new toxin annotator and guide to venom gland transcriptomics
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ToxCodAn：一种新的毒素注释器和毒腺转录组学指南
DOI：
10.1093/bib/bbab095
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发表时间：
2021
影响因子：
9.5
通讯作者：
Junqueira-de-Azevedo, Inácio L. M.
中科院分区：
文献类型：
--
作者：
Nachtigall, Pedro G.;Rautsaw, Rhett M.;Ellsworth, Schyler A.;Mason, Andrew J.;Rokyta, Darin R.;Parkinson, Christopher L.;Junqueira-de-Azevedo, Inácio L. M.
下一代测序已经变得非常普遍，并改变了我们探索非模型系统的能力。特别是，转录组学促进了毒液和有毒谱系中毒素进化的研究;然而，仍然存在许多挑战。首先，转录组中毒素的注释是一项费力且耗时的任务。目前的注释软件往往无法预测正确的编码序列，高估了转录组中存在的毒素数量。在这里，我们提出了ToxCodAn，一个Python脚本，旨在执行蛇毒腺转录组的精确注释。我们用一组先前策划的转录组测试ToxCodAn，并将结果与其他注释器进行比较。此外，我们提供了一个指南毒腺转录组学，以方便今后的研究和useBothrops alternatusas的案例研究ToxCodAn和我们的guide.ResultsOur分析表明，ToxCodAn提供了精确的注释毒素存在于蛇的毒腺转录组。与其他注释器的比较表明，ToxCodAn在运行时间（更快），编码序列预测（更准确）和预测的毒素数量（产生更少的假阳性）方面具有更好的性能。从这个意义上说，ToxCodAn是毒素注释的宝贵资源。ToxCodAn框架可以在未来扩展到与其他有毒谱系合作并检测新毒素。
MotivationNext-generation sequencing has become exceedingly common and has transformed our ability to explore nonmodel systems. In particular, transcriptomics has facilitated the study of venom and evolution of toxins in venomous lineages; however, many challenges remain. Primarily, annotation of toxins in the transcriptome is a laborious and time-consuming task. Current annotation software often fails to predict the correct coding sequence and overestimates the number of toxins present in the transcriptome. Here, we present ToxCodAn, a python script designed to perform precise annotation of snake venom gland transcriptomes. We test ToxCodAn with a set of previously curated transcriptomes and compare the results to other annotators. In addition, we provide a guide for venom gland transcriptomics to facilitate future research and useBothrops alternatusas a case study for ToxCodAn and our guide.ResultsOur analysis reveals that ToxCodAn provides precise annotation of toxins present in the transcriptome of venom glands of snakes. Comparison with other annotators demonstrates that ToxCodAn has better performance with regard to run time (faster), coding sequence prediction (more accurate) and the number of toxins predicted (generatingless false positives). In this sense, ToxCodAn is a valuable resource for toxin annotation. The ToxCodAn framework can be expanded in the future to work with other venomous lineages and detect novel toxins.
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影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
影响因子：
2.8
作者：
Jungo, Florence;Bougueleret, Lydie;Xenarios, Ioannis;Poux, Sylvain
通讯作者：
Poux, Sylvain
DOI：
10.1093/bioinformatics/bts565
发表时间：
2012-12-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Fu L;Niu B;Zhu Z;Wu S;Li W
通讯作者：
Li W
虎响尾蛇基因组揭示了简单毒液表型背后的复杂基因型
DOI：
10.1073/pnas.2014634118
发表时间：
2021
期刊：
Proceedings of the National Academy of Sciences
影响因子：
--
作者：
Margres, Mark J.;Rautsaw, Rhett M.;Strickland, Jason L.;Mason, Andrew J.;Schramer, Tristan D.;Hofmann, Erich P.;Stiers, Erin;Ellsworth, Schyler A.;Nystrom, Gunnar S.;Hogan, Michael P.
通讯作者：
Hogan, Michael P.
棕蝮蛇的性状分化和模块化毒素表达
影响因子：
4.4
作者：
Mason, Andrew J.;Margres, Mark J.;Strickland, Jason L.;Rokyta, Darin R.;Sasa, Mahmood;Parkinson, Christopher L.
通讯作者：
Parkinson, Christopher L.