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专业背景:计算机科学 研究方向与兴趣: JavaEE-Web软件开发, 生物信息学, 数据挖掘与机器学习, 智能信息系统 目前工作: 基因组, 转录组, NGS高通量数据分析, 生物数据挖掘, 植物系统发育和比较进化基因组学

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ModelTest3.7 for Win or Linux  

2012-08-01 12:58:32|  分类: 生信分析软件 |  标签: |举报 |字号 订阅

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Medeltest3.7

准备工作:

下载modeltest软件windows版本,解压到系统盘,这里为C盘,我们这里将其置于根目录。

http://www.rhizobia.co.nz/phylogenetics/modeltest.html该网站下载文件modelblockPAUPb10.txt和文件ML-search.txt。下载Paup4.0

1.  打开paup,打开你自己的filename.nex文件,然后再打开并执行所下载的modelblockPAUPb10.txt文件。Paup将运行并产生一个名为model.scores的文件。

2.  将产生的model.scores文件与modeltest执行程序放置在相同的文件夹内,这里为C:\modeltest3.7\Modeltest3.7 folder\bin\modeltest3.7.win

3.  在开始-运行中输入cmd命令,确认,弹出一个dos界面的窗口,将其默认目录改为model.scores文件所在文件夹,修改方式为在dos窗口输入:cd 盘符:\filename\filename,确认,这里我们输入

cd C:\modeltest3.7\” Modeltest3.7 folder” \bin\。带有空格的文件夹名要用双引号括起来。

4.  修改好后,在窗口内输入:Modeltest3.7.win.exe < model.scores > test.outfile,确认。在model.scores所在文件夹内产生test.outfile文件,即我们所需要的文件。文件test.outfile的文件名可以修改。

5.  用记事本打开test.outfile文件,里面列出了两种检验标准LRTAIC分别选出的最优DNA进化模型。

6.  如果你要在paup中利用最大似然法建树,那么只需将其中一种标准的对应命令拷贝到下载的ML-serch.txt文件中标记的位置,然后用paup打开你的nex文件之后执行该文件即可。或者将命令拷贝到你自己的ML批处理文件内执行。命令既是begin paup;end;之间的部分。

7 如果是用于mrbayes分析,不能直接拷贝上述命令。

其中baseBase frequencies,代表各碱基出现的频率,顺序为A-C-G-T,其中T的频率没有列出来,不过这些值都可以在test.outfile中找到。在mrbayes中,对应于Statefreqpr选项,该选项默认状态为Dirichlet(1.0,1.0,1.0,1.0),依次为A-C-G-T的频率,即各碱基的出现频率相同。在此需将该选项状态修改为fixed( ),括号中依次填入base中对应的值,中间逗号隔开,如Statefreqpr=fixed(0.1649,0.3340,0.3209,0.1801)。其中各值相加为1,但在mrbayes中,各值范围不定,各选项的值可同时乘或除以某值,如1001000等,只要比例不变。如在此可修改为Statefreqpr=fixed(1649,3340,3209,1801)

   其中rmatrate matrix,是碱基位点的变异频率或者说异质率(rate heterogeneity,共6个值,R(a) [A-C]R(b) [A-G]R(c) [A-T]R(d) [C-G]R(e) [C-T]R(f) [G-T]rmat命令中只列出前五个,最后一个R(f) [G-T]未列出,不过这些值都可以在test.outfile中找到。在mrbayes中,该命令对应于revmatpr选项,该选项默认状态为Revmatpr= Dirichlet(1.0,1.0,1.0,1.0,1.0,1.0),括号中对应于rmat的各值,顺序不变,依次为[A-C] [A-G] [A-T] [C-G] [C-T] [G-T],各数值间用逗号隔开;在此,我们需将该选项状态修改为fixed,即recmatprfixed( ),括号中依次填入rmat中对应的各值,如revmatprfixed(1.0000,3.7410,1.0000,1.0000,2.0672,1.0000)

  其中pinvarProportion of invariable sites,是不变位点的比率或称among-site rate variation,在mrbayes中对应于Pinvarpr选项,该命令默认为uniform状态,即pinvarpr = uniform(0,1),取0-1内的一个值,0-1也是该选项的有效范围,在此,我们将该选项修改为fixed固定值选项,即pinvarpr=fixed( ),括号内填入pinvar中对应的数值,如pinvarpr=fixed(0.3076)

  其中ShapeGamma distribution shape parameter,确定伽马分布的形状参数α,在mrbayes中对应于Shapepr选项,该选项默认状态为α范围内的均一分布,shapepr = uniform(0, α) ,在此修改为shapepr =fixed(1.5422)

  现在假设原命令是:

  lset Base=(0.1649 0.3340 0.3209)  Nst=6  Rmat=(1.0000 3.7410 1.0000 1.0000 2.0672)  Rates=gamma  Shape=1.5422  Pinvar=0.3076;

 修改后对应的mrbayes命令为:

Lset nst=6 rates=gamma;

Prset Statefreqpr=fixed(0.1649,0.3340,0.3209,0.1801)

revmatprfixed(1.0000,3.7410,1.0000,1.0000,2.0672,1.0000) shapepr =fixed(1.5422) pinvarpr=fixed(0.3076);

statefreqprrevmatpr中,fixed一般设置为dirichlet,不知道两者有没有明显的差别?

另,statefreqpr以及shapeprpinvarpr参数在mrbayes中似乎没见别人用过,不晓得为何?

Medeltest3.7

Note: This program has been superceded by jModelTest. Modeltest 3.7 (Posada and Crandall 1998) is a program that, in conjunction with PAUP*, selects the best-fit nucleotide substitution model for a set of aligned sequences. This model can then be implemented in maximum-likelihood and Bayesian phylogenetic analyses. The aim of this software is to facilitate comparisons between 56 alternative models using different criteria.

Model selection can be conducted on the basis of hierarchical likelihood ratio tests (hLRT), Akaike Information Criterion (AIC = -2 lnL + 2K; Akaike 1974), corrected AIC (AICc = AIC + 2K(K+1)/(N-K-1); Hurvich and Tsai 1989, Sugiura 1978) or Bayesian Information Criterion (BIC = -2lnL + KlogN; Schwarz 1978) [L = model likelihood, K = number of estimatable parameters, N = sample size]. AIC can be interpreted as the amount of information lost when we use a particular model to approximate the real process of nucleotide substitution; thus, the model with the smallest AIC is favored. Given equal priors for each of the competing models, the model with the smallest BIC is equivalent to the model with the maximum posterior probability.

ModelTest home page

Documentation: 

For further information about Modeltest 3.7 look at the manual or go to the Modeltest web page. For a discussion on the advantages and disadvantages of different model selection approaches in phylogenetics, see Posada and Buckley (2004).

If you are interested in selection of best-fit models of evolution for protein sequence alignments, see Abascal et al. (2005).

Input Format: 
Instructions for All:

Install:
cd source
make
cp ./modeltest3.7 ../bin

Running Modeltest through a terminal window

  1. Format your data into a NEXUS file. You can use this example dataset (download).
  2. Execute the NEXUS file in PAUP*.
  3. Execute the modelblock file within PAUP* by typing:
    execute modelblock.txt;

    This file tells PAUP* to compute likelihood scores for each of 56 models on the same neighbor-joining tree. When the computations are over you will see an output file named model.scores in your home directory.

  4. Save this file under a different name which is specific to your project; otherwise, Modeltest will not work the next time you run it.
  5. To run the computed tree scores in Modeltest, type:

    modeltest3.7 < infile > outfile1

    infile is the name of your input file — remember to change it from model.scores to something specific — and outfile1 is the name of your output file.

  6. By default, Modeltest will select the best-fit nucleotide substitution model using the likelihood ratio test and the AIC. Modeltest 3.7 also allows model selection based on the AICc and BIC. To do this, you must specify this option and also specify the sample size. Sample size for an alignment of DNA sequences is a difficult concept as it will depend on the number of characters, the number of taxa, and their correlation. You could specify the number of characters or the number of characters times the number of taxa, but probably none of these options is correct most of the time.

  7. To run the computed tree scores in Modeltest implementing AICc model selection, type:

    modeltest3.7 -n100 [replace 100 by your sample size] < infile > outfile2
  8. To run the computed tree scores in Modeltest implementing BIC model selection, type:

    modeltest3.7 -b -n100 [replace 100 by your sample size] < infile > outfile3

Although Modeltest will automatically create command blocks that can be pasted directly into PAUP* to set the parameters for maximum-likelihood analyses, it is best to first carefully interpret the results generated by the program. Note that hLRT, AIC, AICc and BIC may select different models; choosing among them is up to the user.

An important additional issue is taking into account the uncertainty in model selection. The output of Modeltest allows examining uncertainty on the basis of the AIC differences (deltas, or rescaled AICs), and the normalized relative AIC for each model (AIC weights). For cases in which support for a particular model is not overwhelming, users may want to consider the implementation of model averaging, a procedure that allows drawing inferences from several models simultaneously. By default, Modeltest 3.7 calculates model averaged estimates of parameters. This is accomplished by estimating parameters for each model and then averaging the estimates according to how likely each model is (i.e., based on Akaike weights).

Instructions for Windows: 

There is a tutorial available that has detailed instructions for running the Windows version of Modeltest.

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