Copy this input file into the file "codeml.ctl":

-------------------------------------------------------------

seqfile = test.phy * sequence data file name

treefile = test.tree * tree structure file name

outfile = test.mlc * main result file name

noisy = 9 * 0,1,2,3,9: how much rubbish on the screen

verbose = 1 * 1: detailed output, 0: concise output

runmode = 0 * 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

clock = 0 * 0: no clock, unrooted tree, 1: clock, rooted tree

aaDist = 0 * 0:equal, +:geometric; -:linear, {1-5:G1974,Miyata,c,p,v}

model = 0

NSsites = 0

* 0:one w; 1:NearlyNeutral; 2:PositiveSelection; 3:discrete;

* 4:freqs; 5:gamma;6:2gamma;7:beta;8:beta&w;9:betaγ10:3normal

icode = 0 * 0:standard genetic code; 1:mammalian mt; 2-10:see below

Mgene = 0 * 0:rates, 1:separate; 2:pi, 3:kappa, 4:all

fix_kappa = 0 * 1: kappa fixed, 0: kappa to be estimated

kappa = 1 * initial or fixed kappa

fix_omega = 0 * 1: omega or omega_1 fixed, 0: estimate

omega = 1 * initial or fixed omega, for codons or codon-based AAs

ncatG = 10 * # of categories in the dG or AdG models of rates

getSE = 0 * 0: don't want them, 1: want S.E.s of estimates

RateAncestor = 0 * (0,1,2): rates (alpha>0) or ancestral states (1 or 2)

Small_Diff = .45e-6

cleandata = 1 * remove sites with ambiguity data (1:yes, 0:no)?

fix_blength = 0 * 0: ignore, -1: random, 1: initial, 2: fixed

-------------------------------------------------------------

(Change test.phy and test.tree to your files).

Execute CodeML by:

-------------------------------------------------------------

codeml codeml.ctl

-------------------------------------------------------------

Then, in your .mlc file, you should have a few lines like:

-------------------------------------------------------------

Detailed output identifying parameters

kappa (ts/tv) = 1.00000

omega (dN/dS) = 1.00000

-------------------------------------------------------------

But your sequences are much too similar. I think CodeML is not appropriate to test selection in such cases.

Hi Stacy,

Do you use a tree as input ? CodeML will require a tree to estimate one global single dN/dS with the null model (model = 0, NSsites = 0).

Could copy and paste your MLC input file please?

Romain

Yes, i used a tree for the input. Below is a sample of the input sequence and tree file:

4 1149

pf3d7_0400 atggataaaaataatttgatacccaaaggtagtaattataatgagaatgtggaggtaatatctcgagtggatacatttaataataaatatggattatctataaaaaattattcatggatagttaaagaggtgattgctattataatattagtacatggtttaggttcaagtttacgttttggttttttgagacatagcgtaaatatagtagataatatgcatgctacattaattgatagtgataatttttatatttataaaaatagttggattgaagaatttaataagaatggttattctgtatatggaatagatttacaaggtcatggagaatctgatgggatagctaatttaagacttcatataaatgattttgatgatttttctgatgatgttatagatcatattaaaaaaatacatcattcgattatgttcgaaaatgataagaaaggtaatttatataataataataataataataataataataataataataaaatggaaagtatggaaaaaattcctatgtatttaattggttattctatgggaggaaatatagttttaagaactttagaaatattaggaaaa---aaagatcgaatttcaaaatataatattaaaggaattatatgtatttctgctatggtttcagtacaattagtagggtttacagattcttttaaatatcgatacttttatttaccagcaacatggttgcttgctaccttatttccaacatatagacaaaaaacaggaagtgttgaatttaagaaatttccatatgttaatgatattatatcattagatagtggtagatttaagggtgataaaacaaataaatttacatatggaattgttaaatctttagatactttacataaatatatagatgatattcctagaaatatacctatactgtttatccattcaagaaatgatagtctttgttattatcccggcttagaaatattttataatagattagtcagtgataataaagaattagtgaccttagaaaataacgaacatcttgttattatggaaccaggaaatgaacaaattctacaaaaaattcttgattggatttttaacatttataaaaaagaagatgaacgtaataatgtaaaagaggaaatcaccctgaag

1 atggataaaaataatttgatacccaaaggtagtaattataatgagaatgtggaggtaatatctcgagtggatacatttaataataaatatggattatctataaaaaattattcatggatagttaaagaggtgattgctattataatattagtacatggtttaggttcaagtttacgttttggttttttgagacatagcgtaaatatagtagataatatgcatgctacattaattgatagtgataatttttatatttataaaaatagttggattgaagaatttaataagaatggttattctgtatatggaatagatttacaaggtcatggagaatctgatgggatagctaatttaagacttcatataaatgattttgatgatttttctgatgatgttatagatcatattaaaaaaatacatcattcgattatgttcgaaaatgataagaaaggtaatttatataataataataataataataataataataataataataaaatggaaagtatggaaaaaattcctatgtatttaattggttattctatgggaggaaatatagttttaagaactttagaaatattaggaaaa---aaagatcgaatttcaaaatataatattaaaggaattatatgtatttctgctatggtttcagtacaattagtagggtttacagattcttttaaatatcgatacttttatttaccagcaacatggttgcttgctaccttatttccaacatatagacaaaaaacaggaagtgttgaatttaagaaatttccatatgttaatgatattatatcattagatagtggtagatttaagggtgataaaacaaataaatttacatatggaattgttaaatctttagatactttacataaatatatagatgatattcctagaaatatacctatactgtttatccattcaagaaatgatagtctttgttattatcccggcttagaaatattttataatagattagtcagtgataataaagaattagtgaccttagaaaataacgaacatcttgttattatggaaccaggaaatgaacaaattctacaaaaaattcttgattggatttttaacatttataaaaaagaagatgaacgtaataatgtaaaagaggaaatcaccctgaag

2 atggataaaaataatttgatacccaaaggtagtaattataatgagaatgtggaggtaatatctcgagtggatacatttaataataaatatggattatctataaaaaattattcatggatagttaaagaggtgattgctattataatattagtacatggtttaggttcaagtttacgttttggttttttgagacatagcgtaaatatagtagataatatgcatgctacattaattgatagtgataatttttatatttataaaaatagttggattgaagaatttaataagaatggttattctgtatatggaatagatttacaaggtcatggagaatctgatgggatagctaatttaagacttcatataaatgattttgatgatttttctgatgatgttatagatcatattaaaaaaatacatcattcgattatgttcgaaaatgataagaaaggtaatttatataataataataataataataataataataataataataaaatggaaagtatggaaaaaattcctatgtatttaattggttattctatgggaggaaatatagttttaagaactttagaaatattaggaaaatcaaaagatcgaatttcaaaatataatattaaaggaattatatgtatttctgctatggtttcagtacaattagtagggtttacagattcttttaaatatcgatacttttatttaccagcaacatggttgcttgctaccttatttccaacatatagacaaaaaacaggaagtgttgaatttaagaaatttccatatgttaatgatattatatcattagatagtggtagatttaagggtgataaaacaaataaatttacatatggaattgttaaatctttagatactttacataaatatatagatgatattcctagaaatatacctatactgtttatccattcaagaaatgatagtctttgttattatcccggcttagaaatattttataatagattagtcagtgataataaagaattagtgaccttagaaaataacgaacatcttgttattatggaaccaggaaatgaacaaattctacaaaaaattcttgattggatttttaacatttataaaaaagaagatgaacgtaataatgtaaaagaggaaatcaccctgaag

3 atggataaaaataatttgatacccaaaggtagtaattataatgagaatgtggaggtaatatctcgagtggatacatttaataataaatatggattatctataaaaaattattcatggatagttaaagaggtgattgctattataatattagtacatggtttaggttcaagtttacgttttggttttttgagacatagcgtaaatatagtagataatatgcatgctacattaattgatagtgataatttttatatttataaaaatagttggattgaagaatttaataagaatggttattctgtatatggaatagatttacaaggtcatggagaatctgatgggatagctaatttaagacttcatataaatgattttgatgatttttctgatgatgttatagatcatattaaaaaaatacatcattcgattatgttcgaaaatgataagaaaggtaatttatataataataataataataataataataataataataataaaatggaaagtatggaaaaaattcctatgtatttaattggttattctatgggaggaaatatagttttaagaactttagaaatattaggaaaa---aaagatcgaatttcaaaatataatattaaaggaattatatgtatttctgctatggtttcagtacaattagtagggtttacagattcttttaaatatcgatacttttatttaccagcaacatggttgcttgctaccttatttccaacatatagacaaaaaacaggaagtgttgaatttaagaaatttccatatgttaatgatattatatcattagatagtggtagatttaagggtgataaaacaaataaatttacatatggaattgttaaatctttagatactttacataaatatatagatgatattcctagaaatatacctatactgtttatccattcaagaaatgatagtctttgttattatcccggcttagaaatattttataatagattagtcagtgataataaagaattagtgaccttagaaaataacgaacatcttgttattatggaaccaggaaatgaacaaattctacaaaaaattcttgattggatttttaacatttataaaaaagaagatgaacgtaataatgtaaaagaggaaatcaccctgaag

((3:0.00005,2:0.00005):0.00000,1:0.00002,pf3d7_0400:0.00000);

Copy this input file into the file "codeml.ctl":

-------------------------------------------------------------

seqfile = test.phy * sequence data file name

treefile = test.tree * tree structure file name

outfile = test.mlc * main result file name

noisy = 9 * 0,1,2,3,9: how much rubbish on the screen

verbose = 1 * 1: detailed output, 0: concise output

runmode = 0 * 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

clock = 0 * 0: no clock, unrooted tree, 1: clock, rooted tree

aaDist = 0 * 0:equal, +:geometric; -:linear, {1-5:G1974,Miyata,c,p,v}

model = 0

NSsites = 0

* 0:one w; 1:NearlyNeutral; 2:PositiveSelection; 3:discrete;

* 4:freqs; 5:gamma;6:2gamma;7:beta;8:beta&w;9:betaγ10:3normal

icode = 0 * 0:standard genetic code; 1:mammalian mt; 2-10:see below

Mgene = 0 * 0:rates, 1:separate; 2:pi, 3:kappa, 4:all

fix_kappa = 0 * 1: kappa fixed, 0: kappa to be estimated

kappa = 1 * initial or fixed kappa

fix_omega = 0 * 1: omega or omega_1 fixed, 0: estimate

omega = 1 * initial or fixed omega, for codons or codon-based AAs

ncatG = 10 * # of categories in the dG or AdG models of rates

getSE = 0 * 0: don't want them, 1: want S.E.s of estimates

RateAncestor = 0 * (0,1,2): rates (alpha>0) or ancestral states (1 or 2)

Small_Diff = .45e-6

cleandata = 1 * remove sites with ambiguity data (1:yes, 0:no)?

fix_blength = 0 * 0: ignore, -1: random, 1: initial, 2: fixed

-------------------------------------------------------------

(Change test.phy and test.tree to your files).

Execute CodeML by:

-------------------------------------------------------------

codeml codeml.ctl

-------------------------------------------------------------

Then, in your .mlc file, you should have a few lines like:

-------------------------------------------------------------

Detailed output identifying parameters

kappa (ts/tv) = 1.00000

omega (dN/dS) = 1.00000

-------------------------------------------------------------

But your sequences are much too similar. I think CodeML is not appropriate to test selection in such cases.

Which program should we use for similar sequences?

I am Gaelle and i m trying to understand how to interprete outputs when using Paml. How to calculate the degree of significance? as in model 0 we have W value, but how to know if it is statistically significant or not? thank you in advance.

Hi, just extending this thread with a question. Could someone please see the attached image here and explain what is the omega value here?