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首页> 外文期刊>Nucleic Acids Research >TEMP: a computational method for analyzing transposable element polymorphism in populations
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TEMP: a computational method for analyzing transposable element polymorphism in populations

机译:TEMP:一种分析种群中转座子多态性的计算方法

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Insertions and excisions of transposable elements (TEs) affect both the stability and variability of the genome. Studying the dynamics of transposition at the population level can provide crucial insights into the processes and mechanisms of genome evolution. Pooling genomic materials from multiple individuals followed by high-throughput sequencing is an efficient way of characterizing genomic polymorphisms in a population. Here we describe a novel method named TEMP, specifically designed to detect TE movements present with a wide range of frequencies in a population. By combining the information provided by pair-end reads and split reads, TEMP is able to identify both the presence and absence of TE insertions in genomic DNA sequences derived from heterogeneous samples; accurately estimate the frequencies of transposition events in the population and pinpoint junctions of high frequency transposition events at nucleotide resolution. Simulation data indicate that TEMP outperforms other algorithms such as PoPoolationTE, RetroSeq, VariationHunter and GASVPro. TEMP also performs well on whole-genome human data derived from the 1000 Genomes Project. We applied TEMP to characterize the TE frequencies in a wild Drosophila melanogaster population and study the inheritance patterns of TEs during hybrid dysgenesis. We also identified sequence signatures of TE insertion and possible molecular effects of TE movements, such as altered gene expression and piRNA production. TEMP is freely available at github: https://github.com/JialiUMassWengLab/TEMP.git
机译:转座因子(TEs)的插入和切除会影响基因组的稳定性和变异性。在种群水平上研究转座的动力学可以提供对基因组进化过程和机制的重要见解。汇集来自多个个体的基因组材料,然后进行高通量测序,是表征人群中基因组多态性的有效方法。在这里,我们描述了一种称为TEMP的新颖方法,该方法专门设计用于检测在群体中出现的频率范围很广的TE运动。通过结合双端阅读和分开阅读提供的信息,TEMP能够识别来源于异质样品的基因组DNA序列中TE插入的存在与否。准确估计种群中转座事件的频率,并以核苷酸分辨率精确定位高频转座事件的连接点。仿真数据表明,TEMP的性能优于其他算法,例如PoPoolationTE,RetroSeq,VariationHunter和GASVPro。 TEMP在源自1000个基因组计划的全基因组人类数据上也表现出色。我们应用TEMP来表征野生果蝇种群中的TE频率,并研究杂种不成因期间TE的遗传模式。我们还确定了TE插入的序列特征以及TE运动的可能分子效应,例如改变的基因表达和piRNA产生。可以在github上免费获得TEMP:https://github.com/JialiUMassWengLab/TEMP.git

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