FROM: https://rnr.h-its.org/about
#LIUSY ADDED
#Folk et al.SB2017使用此方法鉴定出离群种
Rogue taxa are a class of taxa with uncertain position in a phylogenetic tree. For inference methods that yield a tree set (bootstrapping, Bayesian tree searches), rogue taxa can assume different positions for each tree. Theoretically, the presence of few rogue taxa in a tree set is sufficient to render the consensus tree of this tree set devoid of any phylogenetic information (足以使得，这个树集的一致性树缺乏任何系统发育信息). Practically, in almost any tree set we can at least slightly improve the sum of branch support values in a consensus tree (by removing rogue taxa).
Citation:
If RogueNaRok has been useful for your work, please cite as follows:
A.J. Aberer, D. Krompass, A. Stamatakis: "Pruning Rogue Taxa Improves Phylogenetic Accuracy: An Efficient Algorithm and Webservice", Systematic Biology, in press.
Upload a tree set (1 Newick tree per line). Also upload a maximum likelihood estimate (MLE) tree, if you want RogueNaRok to optimize the bipartition support drawn on your MLE tree. #(例子中的MLE-tree选择的是一棵没有支持率的树；其实这样的话，是不是也可以上传一套BI的后验分布树？)
Start rogue taxon identification (in many cases default parameters do not need to be modified). Once finished, a listing of taxa is annotated with values indicating how detrimental (不利、有害的) the specific taxon is to the consensus tree. The annotation can be extended with the result of various runs. This way, you can for instance compare, what taxa are determined to be rogues depending on parameter and algorithm choice (or which rogues also decrease the bipartition support drawn on a MLE tree).
Once you have decided on a set of rogue taxa to exclude, you can remove these taxa from your tree set and obtain the consensus tree of your choice (resp. MLE tree with bipartition support). Alternatively, you may want to go through further iterations of step (2), in case taxa were determined to be rogue that are crucial for your analysis or in case you want to continue exploring the parameter space.
As explained on the first page ("submit a job"), the web browser has to be forced to display the output applet (小程序)(allowance of pop-ups (弹出小窗口)and javascript). For the applet a compatible java plugin must be installed.
Each time, you press "prune/visualize", the server creates an output file for visualization. These files then appear as tabs in the order of creation in the Archaeopteryx tree viewer. Your current selection will appear in red in previous visualizations (but obviously not in the current tab, since you excluded these taxa).
Intended use: Before exploring the effect of pruning taxa, visualize the unpruned consensus tree (i.e., do not select any taxa). This opens an "_init"-tab in the tree viewer. Now make a selection and prune taxa. In the _init-tab, the pruned taxa will appear in red, thus indicating the previous position of the taxa you pruned.
Abbreviations: tabs are named according to mode (various consensus trees, ML-tree) under which they were created. A counter n at the end of the id indicates that this tab is the n-th consensus tree you viusalized.
RogueNaRok-algorithm: a fast algorithm that iteratively determines how exactly the support of consensus bipartitions changes, if a set of n taxa is removed from the tree set. An annotation of 1.5 means, that the equivalent of 1.5 fully supported bipartitions (e.g., 3 bipartitions with support 50%) is added to the consensus tree, if this taxon and all taxa determined in previous iterations (note the sorting of the listing) are removed from the tree set.
Leaf stability index: a statistic for measuring the node stability in a tree set based on quartet frequencies by Thorley and Wilkinson (1999). Values range between 0 (unstable) and 1 (stable).
Taxonomic instability index: a statistic for measuring the node stability in a tree set based on unweighted patristic distances by Maddison and Maddison (also implemented in Mesquite). The higher the annotation value, the more unstable is the taxon.
For large datasets (e.g. 1000 trees with 1000 taxa) or expensive choices of parameters, it is advisable to download a copy of the RogueNaRok implementation and execute the programs on a local machine or a cluster.
For the most current version, visit https://github.com/aberer/RogueNaRok.
The wiki on our github site provides detailed information on program parameters and a hands-on tutorial:
software: https://github.com/aberer/RogueNaRok/wiki
The RogueNaRok algorithm is explained in detail in this technical report:
http://cme.h-its.org/exelixis/rrdr2011-10.php
The standalone version of the code also provides an implementation of the unrooted maximum agreement subtree (U-MAST) that is not part of the webserver version.
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#https://github.com/aberer/RogueNaRok/wiki/A-FAQ-like-Introduction-to-RogueNaRok
命令行版本。#
#网页版软件使用图解：
第一步，
#上传需要一些时间，上传完成后，弹出的浏览器窗口为：
第二步，
首先用所有的默认参数，即'Configure Rogue Taxon Search’这一类所有参数，'Do it'先跑一遍。
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