Reproducible evaluation of transposable element detectors with McClintock 2 guides accurate inference of Ty insertion patterns in yeast.
Reproducible evaluation of transposable element detectors with McClintock 2 guides accurate inference of Ty insertion patterns in yeast.
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DOI：
10.1186/s13100-023-00296-4
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发表时间：
2023-07-14
期刊：
影响因子：
4.9
通讯作者：
Bergman, Casey M.
中科院分区：
文献类型：
--
作者：
Chen, Jingxuan;Basting, Preston J.;Han, Shunhua;Garfinkel, David J.;Bergman, Casey M.
关键词：
已经开发了许多计算方法来使用短读全基因组测序数据检测非参考转座因子（TE）插入。这种方法的多样性和复杂性通常对寻求可重复地安装、执行或评估多个TE插入检测器的新用户提出挑战。我们之前开发了McClintock元管道，以促进六个第一代短读TE探测器的安装，执行和评估。在这里，我们报告了一个使用Snakemake和Conda用Python编写的完全重新实现的McClintock版本，它改进了安装，错误处理，速度，稳定性和可扩展性。McClintock 2现在包括12个短读TE探测器、辅助预处理和分析模块、交互式HTML报告以及可重复评估组件TE探测器准确性的模拟框架。当应用于模型微生物真核生物酿酒酵母时，我们发现McClintock 2组件识别非参考TE插入的精确位置的能力存在很大差异，RelocaTE 2在模拟数据中显示出最高的召回率和精度。我们发现，RelocaTE 2，TEMP，TEMP 2和TEBreak提供了一致的估计50个非参考TE插入每个菌株和Ty 2具有最高数量的非参考TE插入在一个物种的面板1000酵母基因组。最后，我们表明，最好的酵母应用于重测序数据的预测有足够的分辨率，揭示了Ty 1，Ty 2和Ty 4的酵母tRNA基因上游的核小体结合区域的二联体模式的整合，使我们能够扩展知识的精细规模的目标偏好，揭示以前实验诱导的Ty 1插入自发插入其他copia超家族反转录转座子在酵母中。McClintock（https：//github.com/bergmanlab/mcclintock/）提供了一个用户友好的管道，用于使用多个TE检测器在短读WGS数据中识别TE，这将有利于研究各种不同生物体中TE插入变异的研究人员。应用改进的McClintock系统模拟和经验的酵母基因组数据揭示了最好的同类方法和新的生物学见解的一个最广泛研究的模型真核生物，并提供了一个范例，用于评估和选择非参考TE检测器在其他物种。在线版本包含补充材料，可通过10.1186/s13100-023-00296-4获取。
Many computational methods have been developed to detect non-reference transposable element (TE) insertions using short-read whole genome sequencing data. The diversity and complexity of such methods often present challenges to new users seeking to reproducibly install, execute, or evaluate multiple TE insertion detectors. We previously developed the McClintock meta-pipeline to facilitate the installation, execution, and evaluation of six first-generation short-read TE detectors. Here, we report a completely re-implemented version of McClintock written in Python using Snakemake and Conda that improves its installation, error handling, speed, stability, and extensibility. McClintock 2 now includes 12 short-read TE detectors, auxiliary pre-processing and analysis modules, interactive HTML reports, and a simulation framework to reproducibly evaluate the accuracy of component TE detectors. When applied to the model microbial eukaryote Saccharomyces cerevisiae, we find substantial variation in the ability of McClintock 2 components to identify the precise locations of non-reference TE insertions, with RelocaTE2 showing the highest recall and precision in simulated data. We find that RelocaTE2, TEMP, TEMP2 and TEBreak provide consistent estimates of 50 non-reference TE insertions per strain and that Ty2 has the highest number of non-reference TE insertions in a species-wide panel of 1000 yeast genomes. Finally, we show that best-in-class predictors for yeast applied to resequencing data have sufficient resolution to reveal a dyad pattern of integration in nucleosome-bound regions upstream of yeast tRNA genes for Ty1, Ty2, and Ty4, allowing us to extend knowledge about fine-scale target preferences revealed previously for experimentally-induced Ty1 insertions to spontaneous insertions for other copia-superfamily retrotransposons in yeast. McClintock (https://github.com/bergmanlab/mcclintock/) provides a user-friendly pipeline for the identification of TEs in short-read WGS data using multiple TE detectors, which should benefit researchers studying TE insertion variation in a wide range of different organisms. Application of the improved McClintock system to simulated and empirical yeast genome data reveals best-in-class methods and novel biological insights for one of the most widely-studied model eukaryotes and provides a paradigm for evaluating and selecting non-reference TE detectors in other species. The online version contains supplementary material available at 10.1186/s13100-023-00296-4.
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DOI：
10.1038/s41576-021-00367-3
发表时间：
2021-09
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
De Coster W;Weissensteiner MH;Sedlazeck FJ
通讯作者：
Sedlazeck FJ
影响因子：
3.3
作者：
Han S;Basting PJ;Dias GB;Luhur A;Zelhof AC;Bergman CM
通讯作者：
Bergman CM
影响因子：
7
作者：
Gardner EJ;Lam VK;Harris DN;Chuang NT;Scott EC;Pittard WS;Mills RE;1000 Genomes Project Consortium;Devine SE
通讯作者：
Devine SE
影响因子：
7
作者：
Baller, Joshua A.;Gao, Jiquan;Voytas, Daniel F.
通讯作者：
Voytas, Daniel F.
影响因子：
--
作者：
Gruening, Bjorn;Sallou, Olivier;Perez-Riverol, Yasset
通讯作者：
Perez-Riverol, Yasset