Let's say I want to construct a phylogenetic tree based on orthologous nucleotide sequences; I do not want to use protein sequences to have a better resolution. These species have different GC-content.

If we use a straightforward approach like maximum likelihood with JC69 or any other classical nucleotide model, conserved protein coding sequences of distant species with similar GC-content will artificially cluster together. This will happen because GC-content will mainly affect wobbling codon positions, and they will look similar on the nucleotide level.

What are possible ways to overcome this? I considered the following options so far:

  1. Using protein sequence. This is possible of course, but we lose a lot of information on the short distance. Not applicable to non-coding sequences.

  2. Recoding. In this approach C and T can be combined into a single pyrimidine state Y (G and A could be also combined in some implementations). This sounds interesting, but, first, we also lose some information here. Mathematical properties of the resulting process are not clear. As a result, this approach is not widely used.

  3. Excluding third codon position from the analysis. Losing some short-distance information again. Also, not all synonymous substitution are specific to the third codon positions, so we still expect to have some bias. Not applicable to non-coding sequence.

It should be possible in theory to have a model which allows shifts in GC-content. This will be a non time-reversible Markov process. As far as I understand there are some computational difficulties estimating likelihood for such models.

  • $\begingroup$ I would just add that I think there's a key assumption in the setup here: "I do not want to use protein sequences to have a better resolution". We can decompose 'better' here - it's likely to be more precise but also more biased, the latter for all the reasons you outline. $\endgroup$ – roblanf May 19 '17 at 7:24
  • 2
    $\begingroup$ In case you might be interested, I tested some of the approaches you mention, plus a few other recoding schemes (dx.doi.org/10.6084/m9.figshare.732758) in the following papers: arxiv.org/abs/1307.1586 and dx.doi.org/10.1093/molbev/msu105 $\endgroup$ – bli May 19 '17 at 9:05

There are models that take into account compositional heterogeneity both under the maximum likelihood and Bayesian frameworks. Although the substitution process is not time-reversible, the computations are simplified by assuming that the instantaneous rate matrix can be decomposed into an "equilibrium frequency vector" (non-homogeneous) and a symmetric, constant exchange rate matrix.

I guess all your suggestions are also valid, and I remember recoding being used successfully to reduce the GC-content bias (examples in the references above and here).

| improve this answer | |

The following 2004 paper describes a way to model compositional changes across the tree, in a Bayesian framework: https://doi.org/10.1080/10635150490445779

A python package implementing this ("p4"), and improvements added along the years, is available here: https://github.com/pgfoster/p4-phylogenetics

To get started, you may find useful examples here: http://p4.nhm.ac.uk/scripts.html

This has been used in a few large-scale phylogenetic analyses.

| improve this answer | |

The answer is the logDet algorithm was constructed to overcome GC% clustering.

At the time it was devised only a distance method was available/implemented, so it wasn't very powerful. The posts here imply that a Bayesian or ML approach are available and these do tightly stick to the model.

Original publication here

| improve this answer | |
  • $\begingroup$ Do you have a publication or webpage in mind? Can you link it? $\endgroup$ – nicolallias Apr 9 '19 at 9:54
  • 1
    $\begingroup$ Link provided above. Goes back a long way ... to 1996 $\endgroup$ – Michael Apr 9 '19 at 11:21

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.