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Godon tutorial

Godon is a software for codon model optimization written in Go. This is short demonstration of Godon features.

Installing godon

If you are using GNU/Linux, go to the website and grab a precompiled binary from the Downloads section.

$ wget https://bitbucket.org/Davydov/godon/downloads/godon-master-linux-gnu-x86_64 -O godon

Now make this binary executable:

$ chmod +x godon

You could always check find documentation by calling godon --help:

$ ./godon --help
usage: godon [<flags>] <command> [<args> ...]

codon models optmizer and sampler

Flags:
  -h, --help                     Show context-sensitive help (also try --help-long and --help-man).
  -v, --version                  Show application version.
      --gcode=1                  NCBI genetic code id, standard by default
      --fg-branch=-1             foreground branch number
      --max-branch-length=100    maximum branch length
  -n, --no-branch-length         don't optimize branch lengths
      --codon-frequency="F3X4"   codon frequecny (F0 or F3X4)
      --codon-frequency-file=CODON-FREQUENCY-FILE  
                                 codon frequencies file (overrides --codon-frequency)
      --ncat-site-rate=1         number of categories for the site rate variation (no variation by default)
      --ncat-codon-rate=1        number of categories for the codon rate variation (no variation by default)
      --proportional             use three rates and three proportions instead of gamma distribution
      --ncat-beta=4              number of the categories for the beta distribution (models M7&M8)
  -s, --start=START              read start position from the trajectory or JSON file
      --randomize-start          use uniformly distributed random starting point; by default random starting point is distributed around realistic parameter values
      --iter=10000               number of iterations
      --report=1                 report every N iterations
  -m, --method="lbfgsb"          optimization method to use (lbfgsb: limited-memory Broyden–Fletcher–Goldfarb–Shanno with bounding constraints, simplex: downhill simplex, annealing:
                                 simullated annealing, mh: Metropolis-Hastings, n_lbfgs: LBFGS from nlopt, n_simplex: downhill simplex from nlopt, n_cobyla: COBYLA from nlopt,
                                 n_bobyqa: BOBYQA from nlopt, n_sqp: SQP from nlopt, n_mlsl: MLSL from nlopt (BOBYQA local optimizer), none: just compute likelihood, no optimization)
      --final                    perform final extra computations, i.e. NEB and BEB site posterior (default on, use --no-final to disable)
      --neb                      perform naive empirical bayes of positive selection (default on, use --no-neb to disable)
      --beb                      perform bayes empirical bayes of positive selection (default on, use --no-beb to disable)
      --codon-rates              perform NEB analysis of codon rates
      --site-rates               perform NEB analysis of site rates
      --codon-omega              perform NEB analysis of codon omega
      --report-acceptance=200    report acceptance rate every N iterations
      --adaptive                 use adaptive MCMC or sumulated annealing
      --skip-adaptive=-1         number of iterations to skip for adaptive mcmc (5% by default)
      --maximum-adaptive=-1      stop adapting after iteration (20% by default)
      --aggregate=none           state aggregation mode: observed (all positions, keep observed states), observed_new (new implementation of observed), fixed (absolutely conserved
                                 positions, keep observed), random (like observed, but non-aggregated states are shuffled between the positions)
  -p, --procs=PROCS              number of threads to use
  -S, --seed=-1                  random generator seed, default time based
      --cpu-profile=CPU-PROFILE  write cpu profile to file
  -o, --out=OUT                  write log to a file
  -t, --trajectory=TRAJECTORY    write optimization trajectory to a file
  -l, --log-level=notice         set loglevel (critical, error, warning, notice, info, debug)
  -j, --json=JSON                write json output to a file

Commands:
  help [<command>...]
    Show help.

  optimize* [<flags>] <model> <alignment> <tree>
    Run model optimization or sampling

  test [<flags>] <model> <alignment> <tree>
    Run test for positive selection

There are also two commands in godon: optimize and test. To get help for specific commands use: ./godon command --help.

Gene sequence and a tree

We will use Drosohpila gene VPS13 and its orthologs from 12 Drosophila species. Vacuolar protein sorting 13 (Vps13) encodes a protein located to endosomal membrane that is involved in protein homeostasis.

The sequence aligmnent and the phylogenetic tree are already available in this repository. When creating your own files for analysis make sure to have identical sequence identifiers used in a tree and in a sequence alignment.

There are a couple of caveats when analyzing your own sequence. First, make sure to provide a good-quality codon-based sequence alignment. Misalignment could lead to false positive selection events identified. One way to create a high-quality sequence alignment is to use GUIDANCE2 in codon mode and mask (replace with X) residues with scores below certain threshold. In Selectome the threshold is set to 0.93.

Second, in certain species one of the stop codons could encode selenocystein (a non-standard amino acid). As selenocystein is not supported by majority of codon models, you need to mask this codon. Finally, make sure to create a high-quality phylogenetic tree. E.g., using software such are PhyML or RAxML, or a software package based on Bayesian approach. If branch support values are low, consider removing some sequences or trying multiple topologies, and choosing the most conservative estimates of positive selection.

It is important to keep in mind that branch-length estimates provided by software such as PhyML is expressed in the number of nucleotide substitutions per unit of pseudotime. In codon models the branch lengths are describing the number of codon substitutions. So it is important to allow Godon or other codon model software to estimate branch lengths.

The simplest model

We first use Godon with the classical codon model, also known as Goldman Yang 1994 or M0.

By using this model we assume there is a single dN/dS ratio over all the sites of the alignment and all the branches of the tree. This assumption is almost never satisfied. However, this model allows to detect an extremely strong positive selection affecting majority of the positions over long period of time. This model has a low computational cost, therefore it is frequently used to estimate branch lengths for the codon alignment. These branch lengths could be then used without further optimization for more complex codon models. Usage of fixed branch length (estimated with M0) improve computational performance without compromising statistical properties too much (see supplementary materials of this paper).

Now let's run the model:

$ ./godon M0 --out-tree M0_tree.nwk EMGT00050000000025.Drosophila.001.fst EMGT00050000000025.Drosophila.001.nwk
Tree is rooted. Will unroot.
Starting lnL=-55216.574
Maximum likelihood: -48511.34704457653
omega=0.08243971396522087
kappa=1.7393328157675232
Running time: 44.50772981s

By default Godon will try to use all the available CPUs, you can control it with --procs N argument. We use --out-tree to save the tree for further use.

From the output you see that omega (ratio between non-synonymous and synonymous mutations) is very close to zero. This means that on average the gene is under purifying selection.

Now let's run the same model in PAML. For this we first need to create a .ctl file. In this case it would look like this:

     seqfile = EMGT00050000000025.Drosophila.001.phy * sequence data file name
    treefile = EMGT00050000000025.Drosophila.001.nwk * tree structure file name
     outfile = m0.mlc * main result file name

       noisy = 1 * 0,1,2,3,9: how much rubbish on the screen
     verbose = 0 * 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
       ndata = 1
       clock = 0 * 0:no clock, 1:clock; 2:local clock

      aaDist = 0 * 0:equal, +:geometric; -:linear, 1-6:G1974,Miyata,c,p,v,a
                 * 7:AAClasses

       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, 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; 2-11:see below
       Mgene = 0 * 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

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

Now let's run codeml, i.e. codon model optimization program from PAML:

$ codeml m0.ctl

CODONML in paml version 4.9f, October 2017

<lots of output>

np =    20
lnL0 = -61516.513165
Out..
lnL  = -48511.346805
1243 lfun, 1243 eigenQcodon, 22374 P(t)
end of tree file.

Time used:  1:46

Codeml only knows how to use a single CPU, that's part of the reason the analysis took longer. But as you see, maximum likelihood values are very similar.

It is possible to have a shorter config file, see paml/m0/m0_minimal.ctl.

The M8

The model M8 is more flexible than M0 and allows omega (dN/dS) to vary over the sites of the sequence aligmnent. Omega rates can follow the beta distribution, also a small subset of sites is allowed to evolve under positive selection (omega>1) or neutral evolution (omega=1). In the M8, positive selection strength for sites is not allowed to change between branches.

To detect positive selection we need to run two models: M8 (model allowing for positive selection) and M8a (model allowing only for neutral evolution). We then use likelihood-ratio test to reject the null-hypothesis (that is M8a). Likelihood-ratio allows us to get a p-value.

We can tell godon do fit both models in one run. Not only this is less code to type, but also this way godon detect likelihood underestimation and rerun optimization if needed.

$ ./godon test M8 --m0-tree EMGT00050000000025.Drosophila.001.fst EMGT00050000000025.Drosophila.001.nwk
Tree is rooted. Will unroot.
Optimizing branch lengths using M0
Starting lnL=-55216.574
Maximum likelihood: -48511.34704457653
Running H0
Starting lnL=-48129.156
Maximum likelihood: -47500.40133746173
Running H1
Starting lnL=-50587.547
Maximum likelihood: -47500.401334032475
Starting with D=6.858506822027266e-06
Rerunning H0, trying to reduce LR (D=6.858506822027266e-06)
Starting lnL=-47500.401
Maximum likelihood: -47500.401333194204
Rerunning H1 because of negative LR (D=-1.6765407053753734e-06)
Starting lnL=-47500.401
Maximum likelihood: -47500.40133318673
Final D=1.4944816939532757e-08
lnL0=-47500.401333, lnL1=-47500.401333
Running time: 1m43.597952792s

In this run godon estimated three models. First, it used M0 to estimate branch lengths. Then it estimated M8a (H0, null hypothesis) and M8 (H1, alternative hypothesis). In the final three lines we find D (the statistics of likelihood-ratio test), and both likelihoods. As D is close to zero, p-value is non-significant.

The branch-site model

In the branch-site model omega can vary both between tree branches and between sites. This model is also more sensitive than M8 (see supplementary materials of this paper).

Notice: parametrization of the branch-site model is slightly different (but equivalent) to the one used in PAML. Instead of having p0 and p1 as parameters, godon uses p01sum = p0 + p1 and p0prop = p0 / (p0 + p1). There are two advanteges of this parametrization. First, this way both parameters have a predefined range (0,1). Second, there is less dependency between the two. Both properties are useful when it comes to likelihood optimization or MCMC. It goes without saying that it is straightforward to go back to the classical parametrization whenever needed: p0 = p01sum * p0prop and p1 = p01sum - p0.

When running the branch-site model you need to specify which branch or set of branches to test. It is often useful to test multiple branches. In this scenario we are going to test all non-leaf of the tree. The reason we exclude leaf branches is because a sequencing error can easily lead to a false positive in positive selection analysis.

 ./godon test BS --m0-tree --all-branches --no-leaves EMGT00050000000025.Drosophila.001.fst EMGT00050000000025.Drosophila.001.nwk
<...>
Testing branch 1/7
Foreground branch: (((((a001,a002),a003),a004),(a005,a006))#1,a007,((a008,a009),a010));
<...>
Final D=14.945732179141487
NEB analysis
pos	codon	aa	p
552	AAA	K	0.980
1197	ACT	T	0.719
1410	CAC	H	0.721
1414	AGC	S	0.955
2266	GTG	V	0.697
2443	CAG	Q	0.981
BEB analysis
pos	codon	aa	p
552	AAA	K	0.978
1197	ACT	T	0.810
1410	CAC	H	0.833
1414	AGC	S	0.829
2266	GTG	V	0.828
2443	CAG	Q	0.924
lnL0=-47910.976201, lnL1=-47903.503335
Testing branch 2/7
<...>
Running time: 9m31.011400287s

In this output godon shows the branch it is testing (note #1 in the tree). Since the value of D could be significant (D=14.9), godon performs posterior analysis to detect sites under positive selection (see here). Remember, you still need to compute p-value and correct for multiple testing. R-code computing p-value could look like this:

p.value <- pchisq(d, df=1, lower.tail=F)/2

Notice that there was no positive selection detect using M8, this is probably due to lower power of M8 in detecting positive selection compared to the branch-site model.

Codon gamma rate variation

We recently showed that the assumption of constant synonymous rate could dramatically increase the proportion of false positives in positive selection analysis. Luckily, in godon it is very easy to enable codon gamma rate variation. Be aware, this mode is more computationally intensive.

In this demonstration we will show how to use codon rate variation with M8. The same approach will work for the branch-site model.

./godon test M8 --m0-tree --ncat-codon-rate 4 EMGT00050000000025.Drosophila.001.fst EMGT00050000000025.Drosophila.001.nwk
<...>
Final D=0.00028724932053592056
lnL0=-47412.211816, lnL1=-47412.211672
Running time: 8m31.825944886s

In both models (M8 and M8+codon rate variation) no positive selection was detected.

Additional notes

  • It is easy to export results to a machine-readable form (JSON). Use --json for this.
  • Here every time we first estimated branch lengths using M0. We could do it only once and the reuse the tree. The tree can be exported using --out-tree or --json export.

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a demonstration of Godon usage; Godon is implementing codon model optimization written in Go

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