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Next-generation transcriptome assembly and analysis: Impact of ploidy

机译:下一代转录体组装和分析:倍增性的影响

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Whole genome duplications (WGD) occur widely in plants, but the effects of these events impact all branches of life. WGD events have major evolutionary impacts, often leading to major structural changes within the chromosomes and massive changes in gene expression that facilitate rapid speciation and gene diversification. Even for species that currently have diploid genomes, the impact of ancestral duplication events is still present in the genomes, especially in the context of highly similar gene families that are retained from WGD. However, the impact of these ploidies on various bioinformatics workflows has not been studied well. In this review, we overview biological significance of polyploidy in different organisms. We describe the impact of having polyploid transcriptomes on bioinformatics analyses, especially focusing on transcriptome assembly and transcript quantification. We discuss the benefits of using simulated benchmarking data when we examine the performance of various methods. We also present an example strategy to generate simulated allopolyploid transcriptomes and RNAseq datasets and how these benchmark datasets can be used to assess the performance of transcript assembly and quantification methods. Our benchmarking study shows that all transcriptome assembly methods are affected by having polyploid genomes. Quantification accuracy is also impacted by polyploidy depending on the method. These simulated datasets can be adapted for testing, such as, read mapping, variant calling, and differential expression using biologically realistic conditions.
机译:全基因组重复(WGD)在植物中广泛发生,但这些事件的影响会影响所有生命分支。 WGD事件具有重大的进化影响,往往导致染色体内的主要结构变化和基因表达的大规模变化,促进了快速的形态和基因多样化。即使对于目前有二倍体基因组的物种,祖先复制事件的影响仍然存在于基因组中,特别是在高度相似的基因家族中保留从WGD的基因。然而,这些族对各种生物信息学工作流程的影响尚未得到很好的研究。在本综述中,我们概述不同生物中多倍体的生物意义。我们描述了在生物信息学分析中具有多倍体转录om的影响,特别是关注转录组合组件和转录物定量。我们讨论在检查各种方法的性能时使用模拟基准数据的好处。我们还展示了一个示例策略来生成模拟的AllopolyPloid转录om和RNASEQ数据集以及如何使用这些基准数据集来评估转录程序组件和定量方法的性能。我们的基准测试表明,所有转录组合方法都受到多倍体基因组的影响。根据该方法,定量精度也受到多倍体的影响。这些模拟数据集可以适用于使用生物学现实条件测试,例如使用生物学上的读取映射,变体调用和差异表达。

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