Suppr超能文献

一种用于进化序列比对的“长插入缺失”模型。

A "Long Indel" model for evolutionary sequence alignment.

作者信息

Miklós I, Lunter G A, Holmes I

机构信息

Department of Statistics, University of Oxford, Oxford, UK.

出版信息

Mol Biol Evol. 2004 Mar;21(3):529-40. doi: 10.1093/molbev/msh043. Epub 2003 Dec 23.

Abstract

We present a new probabilistic model of sequence evolution, allowing indels of arbitrary length, and give sequence alignment algorithms for our model. Previously implemented evolutionary models have allowed (at most) single-residue indels or have introduced artifacts such as the existence of indivisible "fragments." We compare our algorithm to these previous methods by applying it to the structural homology dataset HOMSTRAD, evaluating the accuracy of (1) alignments and (2) evolutionary time estimates. With our method, it is possible (for the first time) to integrate probabilistic sequence alignment, with reliability indicators and arbitrary gap penalties, in the same framework as phylogenetic reconstruction. Our alignment algorithm requires that we evaluate the likelihood of any specific path of mutation events in a continuous-time Markov model, with the event times integrated out. To this effect, we introduce a "trajectory likelihood" algorithm (Appendix A). We anticipate that this algorithm will be useful in more general contexts, such as Markov Chain Monte Carlo simulations.

摘要

我们提出了一种新的序列进化概率模型,该模型允许任意长度的插入和缺失,并给出了适用于我们模型的序列比对算法。以前实现的进化模型最多允许单残基插入和缺失,或者引入了一些人为因素,如不可分割的“片段”的存在。我们将我们的算法应用于结构同源性数据集HOMSTRAD,通过评估(1)比对的准确性和(2)进化时间估计,将我们的算法与这些先前的方法进行比较。使用我们的方法,首次有可能在与系统发育重建相同的框架中集成具有可靠性指标和任意空位罚分的概率序列比对。我们的比对算法要求我们在连续时间马尔可夫模型中评估任何特定突变事件路径的似然性,同时将事件时间积分掉。为此,我们引入了一种“轨迹似然性”算法(附录A)。我们预计该算法在更一般的情况下将是有用的,例如马尔可夫链蒙特卡罗模拟。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验