大规模非参数跨站点费率模型的可辨识性与推断
Identifiability and inference of non-parametric rates-across-sites models on large-scale phylogenies
摘要：
基因座间的突变率变化是众所周知的，在从分子序列重建进化树时会引起困难，特别是可识别性问题。在这里，我们介绍了一种新的方法来估计一般率跨站点模型。我们的研究结果意味着，特别是，大的adhergenies通常是可识别的速率变化。我们还推导出高概率重建的序列长度要求。我们的主要贡献是一种新的算法，集群网站根据其突变率。在该位点聚类步骤之后，可以使用标准重建技术来恢复同源性。我们的研究结果依赖于一个基本的见解：即，对于大树，某些网站的统计经验集中的测量现象。
英文摘要：
Mutation rate variation across loci is well known to cause difficulties, notably identifiability issues, in the reconstruction of evolutionary trees from molecular sequences. Here we introduce a new approach for estimating general rates-across-sites models. Our results imply, in particular, that large phylogenies are typically identifiable under rate variation. We also derive sequence-length requirements for high-probability reconstruction. Our main contribution is a novel algorithm that clusters sites according to their mutation rate. Following this site clustering step, standard reconstruction techniques can be used to recover the phylogeny. Our results rely on a basic insight: that, for large trees, certain site statistics experience concentration-of-measure phenomena.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
