论文标题
真核生物的转录组复杂性
Transcriptome Complexities Across Eukaryotes
论文作者
论文摘要
基因组复杂性是一个不断增长的进化领域，案例研究用于模型和新兴非模型系统中的比较进化分析。理解复杂性和基因组的功能成分是尚未开发的知识丰富的探索知识。由于基因组大小和复杂性之间的“显着缺乏对应关系”，需要有一种量化生物体复杂性的方法。在这项研究中，我们使用一组复杂性指标，可以评估使用trand的复杂性变化。我们确定随着复杂性的变化，复杂性是否在转录组之间以及在哪些结构水平上的增加或下降。我们在这项研究中定义了三个指标-TPG，EPT和EPG，以量化封装替代剪接动力学的转录组的复杂性。在这里，我们比较1）整个基因组注释，2）直系同源物的过滤子集和3）新型基因，以阐明直系同源物和新基因在转录组分析中的影响。我们还得出了Hong等人，2006年的有效外显子数（EEN）的指标，以比较转录本中外显子大小的分布与均匀外显子放置的随机期望。 EEN考虑了外显子大小的差异，这很重要，因为直系同源物和整个转录组分析的复杂性的新基因差异偏向于低复杂性基因，具有很少的外显子，替代转录本很少。通过我们的度量分析，我们能够以与直系同源条件下的以前的跨物种比较相比，以更高的精度和准确性来实施各种谱系的复杂性变化。这些分析代表了在非模型进化基因组学的新兴领域中向整个转录组分析迈出的一步，并在整个生命之树深层时期对复杂性变化的进化推断进行了关键见解。我们提出了一种量化直系同源呼叫中产生的偏差并正确复杂性分析的方法。使用这些指标，我们直接分析新形成的谱系特异性基因的定量特性，因为它们在转录组中的复杂性降低。
Genomic complexity is a growing field of evolution, with case studies for comparative evolutionary analyses in model and emerging non-model systems. Understanding complexity and the functional components of the genome is an untapped wealth of knowledge ripe for exploration. With the "remarkable lack of correspondence" between genome size and complexity, there needs to be a way to quantify complexity across organisms. In this study we use a set of complexity metrics that allow for evaluation of changes in complexity using TranD. We ascertain if complexity is increasing or decreasing across transcriptomes and at what structural level, as complexity is varied. We define three metrics -- TpG, EpT, and EpG in this study to quantify the complexity of the transcriptome that encapsulate the dynamics of alternative splicing. Here we compare complexity metrics across 1) whole genome annotations, 2) a filtered subset of orthologs, and 3) novel genes to elucidate the impacts of ortholog and novel genes in transcriptome analysis. We also derive a metric from Hong et al., 2006, Effective Exon Number (EEN), to compare the distribution of exon sizes within transcripts against random expectations of uniform exon placement. EEN accounts for differences in exon size, which is important because novel genes differences in complexity for orthologs and whole transcriptome analyses are biased towards low complexity genes with few exons and few alternative transcripts. With our metric analyses, we are able to implement changes in complexity across diverse lineages with greater precision and accuracy than previous cross-species comparisons under ortholog conditioning. These analyses represent a step forward toward whole transcriptome analysis in the emerging field of non-model evolutionary genomics, with key insights for evolutionary inference of complexity changes on deep timescales across the tree of life. We suggest a means to quantify biases generated in ortholog calling and correct complexity analysis for lineage-specific effects. With these metrics, we directly assay the quantitative properties of newly formed lineage-specific genes as they lower complexity in transcriptomes.
