此前介绍利用Kaks_calculator计算ka/ks 值，本次对paml 进行计算kaks做一简单介绍。
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:betaγ
* 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