软件安装

PAML 可实现系统发育树的构建，祖先序列估计,进化模拟和 KaKs 计算等功能。其中分支及位点 KaKs 的计算是本软件包的特色功能。

wget http://abacus.gene.ucl.ac.uk/software/paml4.9j.tgz
tar xf paml4.9j.tgz
cd  paml4.9j
rm bin/*.exe 
cd src 
make -f Makefile 
rm *.o 
mv baseml basemlg codeml pamp evolver yn00 chi2 ../bin

此次用到的是codeml

简单使用

所需文件：

同源基因对儿
对应基因cds，pep序列
paml输入文件（有上述2文件得到）
树文件（只有2物种，可自行制作，若有多物种，可进行构建树即可phlylip）

1 paml 输入序列文件

本次使用我最近的数据，来源于2个物种的同源基因对，具体如何得到基因对请挪步python版的MCScan绘图。

使用

ParaAT.pl -h test.homologs -n test.cds -a test.pep -p proc -m muscle -f paml  -o paml_result

上述脚本有疑问请挪步Kaks_calculator计算ka/ks 值

上述得到一paml_result文件夹，每个同源基因对儿形成一个单独的以*.paml结尾的文件

可获得共有27个同源基因对

ls *.paml |wc -l
27

将所有*paml文件合并为paml的输入文件

cat *.paml >>test.cod

2 树文件

关于树文件，可参考paml安装目录下*.trees格式

  3  4

(1,2,3);
((1,2),3);
((1,3),2);
((2,3),1);

其中3表示，3个物种，4表示树的个数；

在本次我只有两个物种，所以得到如下树的输入文件

vi test.trees
  2  1

(1,2);

3 配置文件

可将paml安装目录下baseml.ctl 拷贝到自己所需目录下即可进行修改

seqfile = test.cod * sequence data filename
     treefile = test.trees      * tree structure file name
      outfile = test.rlt           * main result file name

        noisy = 0  * 0,1,2,3,9: how much rubbish on the screen
      verbose = 0  * 0: concise; 1: detailed, 2: too much
      runmode = -2  * 0: user tree;  1: semi-automatic;  2: automatic
                   * 3: StepwiseAddition; (4,5):PerturbationNNI; -2: pairwise

      seqtype = 1  * 1:codons; 2:AAs; 3:codons-->AAs
    CodonFreq = 2  * 0:1/61 each, 1:F1X4, 2:F3X4, 3:codon table

        ndata = 27
        clock = 0  * 0:no clock, 1:clock; 2:local clock; 3:CombinedAnalysis
       aaDist = 0  * 0:equal, +:geometric; -:linear, 1-6:G1974,Miyata,c,p,v,a
   aaRatefile = dat/jones.dat  * only used for aa seqs with model=empirical(_F)
                   * dayhoff.dat, jones.dat, wag.dat, mtmam.dat, or your own

        model = 0
                   * models for codons:
                       * 0:one, 1:b, 2:2 or more dN/dS ratios for branches
                   * models for AAs or codon-translated AAs:
                       * 0:poisson, 1:proportional, 2:Empirical, 3:Empirical+F
                       * 6:FromCodon, 7:AAClasses, 8:REVaa_0, 9:REVaa(nr=189)

      NSsites = 0  * 0:one w;1:neutral;2:selection; 3:discrete;4:freqs;
                   * 5:gamma;6:2gamma;7:beta;8:beta&w;9:beta&gamma;
                   * 10:beta&gamma+1; 11:beta&normal>1; 12:0&2normal>1;
                   * 13:3normal>0

        icode = 0  * 0:universal code; 1:mammalian mt; 2-10:see below
        Mgene = 0
                   * codon: 0:rates, 1:separate; 2:diff pi, 3:diff kapa, 4:all diff
                   * AA: 0:rates, 1:separate
fix_kappa = 0  * 1: kappa fixed, 0: kappa to be estimated
        kappa = 2  * initial or fixed kappa
    fix_omega = 0  * 1: omega or omega_1 fixed, 0: estimate
        omega = .4 * initial or fixed omega, for codons or codon-based AAs

    fix_alpha = 1  * 0: estimate gamma shape parameter; 1: fix it at alpha
        alpha = 0. * initial or fixed alpha, 0:infinity (constant rate)
       Malpha = 0  * different alphas for genes
        ncatG = 8  * # of categories in dG of NSsites models

        getSE = 0  * 0: don't want them, 1: want S.E.s of estimates
 RateAncestor = 1  * (0,1,2): rates (alpha>0) or ancestral states (1 or 2)

   Small_Diff = .5e-6
    cleandata = 1  * remove sites with ambiguity data (1:yes, 0:no)?
*  fix_blength = 1  * 0: ignore, -1: random, 1: initial, 2: fixed, 3: proportional
       method = 0  * Optimization method 0: simultaneous; 1: one branch a time

* Genetic codes: 0:universal, 1:mammalian mt., 2:yeast mt., 3:mold mt.,
* 4: invertebrate mt., 5: ciliate nuclear, 6: echinoderm mt.,
* 7: euplotid mt., 8: alternative yeast nu. 9: ascidian mt.,
* 10: blepharisma nu.
* These codes correspond to transl_table 1 to 11 of GENEBANK.

运行脚本

codeml codeml.ctl

即可得到相应的Ka，Ks

最后编辑于：2020.09.09 08:59:17