for the same cp gene, why the omega values sometimes high, sometimes low

[Pingchuan Li]

I am doing the analysis for chloroplast genes, a total number of 84 genes were analyzied for the omega value, the other 79 genes looks like normal with lower omega values (<1).

however, 5 of them were pretty werid, I used model 0 and rooted tree without clock, (I know, it triggered a warning)

		Rp1	Rp2	Rp3	Rp4	Rp5	Rp6	Rp7	Rp8
cp053	psbJ	279.98933	1.10585	222.64738	95.60406	53.8783	76.60037	190.97245	1.00387
cp062	rpl33	999	999	807.40005	999	52.86094	999	12.24692	999
cp065	rps12	999	0.5779	0.0001	0.0001	59.05469	0.0001	0.49524	6.69742
cp075	rpl36	1.78207	113.93493	0.0001	998.99999	1.13032	3.28315	1.32871	1.39289
cp200	rps12	0.0001	6.75007	0.56881	6.93176	201.37658	0.0001	0.0001	38.25311

any idea how to deal with this problem? will it be caused by the tree or by the model?

thanks,

Pingchuan

# -------------- codeml-M0.ctl --------------------

      seqfile = individual_gene.paml     * sequence data filename

     treefile = tree.txt      * tree structure file name

      outfile = mlc           * main result file name

      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

        ndata = 1 * number of gene alignments to be analysed

        clock = 0  * 0:no clock, 1:clock; 2:local clock; 3:CombinedAnalysis

        model = 0  * models for codons: 0:one, 1:b, 2:2 or more dN/dS ratios for branches

      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

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

        omega = .4 * initial or fixed omega, for codons or codon-based AAs

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

* 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.

[Ziheng]

first you can run the same analysis twice to confirm stability across runs.  

here are some possible reasons for extreme estimates of omega.  if you have no synonymous differences, you may get a large value (infinity).  if you have no nonsynonymous differences, you may get 0.  extreme estimates can also occur if the sequences are too similar or too divergent of if you have serious alignment problems.  

ziheng