GLOOME: gain loss mapping engine
GLOOME: gain loss mapping engine
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DOI：
10.1093/bioinformatics/btq549
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发表时间：
2010-11-01
期刊：
影响因子：
5.8
通讯作者：
Pupko, Tal
中科院分区：
文献类型：
--
作者：
Cohen, Ofir;Ashkenazy, Haim;Pupko, Tal
存在和不存在概况（系统模式）的进化分析在生物学中被广泛使用。假设观察到的系统发育模式是系统发育树沿着的获得和损失动态的结果。由谱系模式表示的特征的实例包括限制性位点、基因家族、内含子和插入缺失，仅举几例。在这里，我们提出了一个用户友好的Web服务器，准确地推断分支机构特定的和网站特定的收益和损失事件。新的推理方法是基于一个随机映射的方法，利用模型，可靠地捕捉底层的进化过程。各种功能，包括分析数据的能力与各种进化模型，推断增益和损失事件使用随机映射或最大简约，并估计增益和损失率为每个字符分析。
The evolutionary analysis of presence and absence profiles ( phyletic patterns) is widely used in biology. It is assumed that the observed phyletic pattern is the result of gain and loss dynamics along a phylogenetic tree. Examples of characters that are represented by phyletic patterns include restriction sites, gene families, introns and indels, to name a few. Here, we present a user-friendly web server that accurately infers branch-specific and site-specific gain and loss events. The novel inference methodology is based on a stochastic mapping approach utilizing models that reliably capture the underlying evolutionary processes. A variety of features are available including the ability to analyze the data with various evolutionary models, to infer gain and loss events using either stochastic mapping or maximum parsimony, and to estimate gain and loss rates for each character analyzed.