Estimating transcriptome complexities across eukaryotes.
Estimating transcriptome complexities across eukaryotes.
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DOI：
10.1186/s12864-023-09326-0
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发表时间：
2023-05-11
期刊：
影响因子：
4.4
通讯作者：
中科院分区：
文献类型：
--
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基因组的复杂性是一个不断增长的领域的演变，在模型和新兴的非模型系统的比较进化分析的案例研究。了解基因组的复杂性和功能成分是一个尚未开发的知识财富，可以进行探索。由于基因组大小和复杂性之间“明显缺乏对应性”，需要一种方法来量化生物体的复杂性。在这项研究中，我们使用了一组复杂性指标，允许使用TranD评估复杂性的变化。我们确定复杂性是否在转录组中增加或减少，以及随着复杂性的变化，在什么样的结构水平上增加或减少。在这项研究中，我们定义了三个指标- TpG，EpT和EpG-来量化转录组的复杂性，封装的动态选择性剪接。在这里，我们比较了1）全基因组注释，2）直系同源物的过滤子集和3）新基因的复杂性度量，以阐明直系同源物和新基因在转录模型分析中的影响。发布有效外显子数（EEN），以比较转录本内外显子大小的分布与均匀外显子位置的随机预期。EEN解释了外显子大小的差异，这很重要，因为直系同源物和全转录组分析的复杂性的新基因差异偏向于具有很少外显子和很少替代转录物的低复杂性基因。通过我们的度量分析，我们能够以比以前的跨物种比较更高的精度和准确度量化不同谱系的复杂性变化。这些分析代表了在非模型进化基因组学的新兴领域中向全转录组分析迈出的一步，其关键见解是在生命之树的深时间尺度上进化推断复杂性变化。我们提出了一种方法来量化直系同源调用和正确的复杂性分析谱系特异性的影响产生的偏见。有了这些指标，我们直接分析新形成的谱系特异性基因的定量特性，因为它们降低了复杂性。在线版本包含补充材料，可通过10.1186/s12864-023-09326-0获取。
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 evaluating changes in complexity using TranD. We ascertain if complexity is increasing or decreasing across transcriptomes and at what structural level, as complexity varies. In this study, we define three metrics – TpG, EpT, and EpG- to quantify the transcriptome's complexity that encapsulates 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 orthologs and novel genes in transcript model analysis. Effective Exon Number (EEN) issued 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 gene 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 quantify changes in complexity across diverse lineages with greater precision and accuracy than previous cross-species comparisons under ortholog conditioning. These analyses represent a step 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. The online version contains supplementary material available at 10.1186/s12864-023-09326-0.
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