Inference of gain and loss events from phyletic patterns using stochastic mapping and maximum parsimony--a simulation study.
Inference of gain and loss events from phyletic patterns using stochastic mapping and maximum parsimony--a simulation study.
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DOI：
10.1093/gbe/evr101
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发表时间：
2011
影响因子：
3.3
通讯作者：
Pupko T
中科院分区：
文献类型：
--
作者：
Cohen O;Pupko T
细菌进化的特点是基因家族的频繁获得和丢失事件。这些事件可以从系统模式数据推断出来，系统模式数据是跨多个基因组的基因家族库的紧凑表示。最大简约范式是一种经典的和流行的方法来检测基因家族的增益和损失映射到特定的分支。我们和其他人以前开发的概率模型，旨在占的增益和损失的随机动态。这些模型是一种称为随机映射的方法的关键组成部分，在该方法中，对潜在系统发育树的每个分支估计增益和损失事件的概率和期望。在这项工作中，我们提出了一个物种模式模拟器，其中的增益和损失的动态假设遵循连续时间马尔可夫链沿着树。实现了各种模型和选项，使模拟软件可用于分析二元（存在/不存在）数据的大量研究。使用这个模拟软件，我们比较了最大简约和随机映射方法的能力，以准确地检测增益和损失事件沿着树。我们的模拟涵盖了大量的基因家族的收益和损失的倾向和基因家族之间的这些倾向的变异性方面的进化情景。虽然在所有的模拟方案中，这两种方法都获得了相对较低的误报率，但随机映射在真阳性率方面优于最大简约法。我们进一步研究了影响这两种方法性能的因素。例如，我们发现，当目标是沿着系统发育树的内部分支映射增益和损失事件时，最大简约推理的准确性大大降低。此外，由于对分支长度的不可靠估计，使用较小的数据集（有限数量的基因家族）降低了随机作图的准确性。我们的模拟器和模拟结果是另外相关的分析其他类型的二进制编码的数据，如同源限制性位点，缺口和内含子的存在，仅举几例。模拟软件和推理方法都可以在用户友好的服务器上免费获得：http://gloome.tau.ac.il/。
Bacterial evolution is characterized by frequent gain and loss events of gene families. These events can be inferred from phyletic pattern data—a compact representation of gene family repertoire across multiple genomes. The maximum parsimony paradigm is a classical and prevalent approach for the detection of gene family gains and losses mapped on specific branches. We and others have previously developed probabilistic models that aim to account for the gain and loss stochastic dynamics. These models are a critical component of a methodology termed stochastic mapping, in which probabilities and expectations of gain and loss events are estimated for each branch of an underlying phylogenetic tree. In this work, we present a phyletic pattern simulator in which the gain and loss dynamics are assumed to follow a continuous-time Markov chain along the tree. Various models and options are implemented to make the simulation software useful for a large number of studies in which binary (presence/absence) data are analyzed. Using this simulation software, we compared the ability of the maximum parsimony and the stochastic mapping approaches to accurately detect gain and loss events along the tree. Our simulations cover a large array of evolutionary scenarios in terms of the propensities for gene family gains and losses and the variability of these propensities among gene families. Although in all simulation schemes, both methods obtain relatively low levels of false positive rates, stochastic mapping outperforms maximum parsimony in terms of true positive rates. We further studied the factors that influence the performance of both methods. We find, for example, that the accuracy of maximum parsimony inference is substantially reduced when the goal is to map gain and loss events along internal branches of the phylogenetic tree. Furthermore, the accuracy of stochastic mapping is reduced with smaller data sets (limited number of gene families) due to unreliable estimation of branch lengths. Our simulator and simulation results are additionally relevant for the analysis of other types of binary-coded data, such as the existence of homologues restriction sites, gaps, and introns, to name a few. Both the simulation software and the inference methodology are freely available at a user-friendly server: http://gloome.tau.ac.il/.
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DOI：
10.1073/pnas.0400975101
发表时间：
2004-06-29
影响因子：
11.1
作者：
Boussau, B;Karlberg, EO;Andersson, SGE
通讯作者：
Andersson, SGE
影响因子：
6.5
作者：
GRAYBEAL, A
通讯作者：
GRAYBEAL, A
影响因子：
5.8
作者：
Cohen, Ofir;Ashkenazy, Haim;Pupko, Tal
通讯作者：
Pupko, Tal
影响因子：
12.3
作者：
Daubin V;Lerat E;Perrière G
通讯作者：
Perrière G
影响因子：
10.7
作者：
Cohen O;Pupko T
通讯作者：
Pupko T