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TIR-Learner, a New Ensemble Method for TIR Transposable Element Annotation, Provides Evidence for Abundant New Transposable Elements in the Maize Genome
Molecular Plant ( IF 17.1 ) Pub Date : 2019-02-23 , DOI: 10.1016/j.molp.2019.02.008
Weijia Su , Xun Gu , Thomas Peterson

Transposable elements (TEs) make up a large and rapidly evolving proportion of plant genomes. Among Class II DNA TEs, TIR elements are flanked by characteristic terminal inverted repeat sequences (TIRs). TIR TEs may play important roles in genome evolution, including generating allelic diversity, inducing structural variation, and regulating gene expression. However, TIR TE identification and annotation has been hampered by the lack of effective tools, resulting in erroneous TE annotations and a significant underestimation of the proportion of TIR elements in the maize genome. This problem has largely limited our understanding of the impact of TIR elements on plant genome structure and evolution. In this paper, we propose a new method of TIR element detection and annotation. This new pipeline combines the advantages of current homology-based annotation methods with powerful de novo machine-learning approaches, resulting in greatly increased efficiency and accuracy of TIR element annotation. The results show that the copy number and genome proportion of TIR elements in maize is much larger than that of current annotations. In addition, the distribution of some TIR superfamily elements is reduced in centromeric and pericentromeric positions, while others do not show a similar bias. Finally, the incorporation of machine-learning techniques has enabled the identification of large numbers of new DTA (hAT) family elements, which have all the hallmarks of bona fide TEs yet which lack high homology with currently known DTA elements. Together, these results provide new tools for TE research and new insight into the impact of TIR elements on maize genome diversity.



中文翻译:

TIR-Learner,一种用于TIR转座因子注释的新集成方法,为玉米基因组中大量新的转座因子提供了证据

转座因子(TEs)构成了植物基因组中很大且迅速发展的部分。在II类DNA TE中,TIR元件的侧面是特征性末端反向重复序列(TIR)。TIR TEs在基因组进化中可能起重要作用,包括产生等位基因多样性,诱导结构变异和调节基因表达。然而,由于缺乏有效的工具,TIR TE的鉴定和注释受到阻碍,导致错误的TE注释和玉米基因组中TIR元素比例的严重低估。这个问题在很大程度上限制了我们对TIR元素对植物基因组结构和进化的影响的理解。在本文中,我们提出了一种新的TIR元素检测和标注方法。从头学习机器方法,从而大大提高了TIR元素注释的效率和准确性。结果表明,玉米中TIR元件的拷贝数和基因组比例远大于目前的注释。此外,某些TIR超家族元素在着丝粒和着丝粒附近的位置减少了,而另一些则没有相似的偏差。最后,机器学习技术的引入使得能够识别大量新的DTA(hAT)家族元素,这些家族元素具有真正的TE的所有特征,但与目前已知的DTA缺乏高度同源性元素。这些结果共同为TE研究提供了新工具,并为TIR元素对玉米基因组多样性的影响提供了新的见解。

更新日期:2019-02-23
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