> In the past, I used Bayesian methods for tree inference, and I now,
> for the first time, use RAxML. I would appreciate if anyone can answer
> the following questions for me:
>
> 1) Does RAxML, similar to most other ML and Bayesian tree inference
> programs, also provide branch lengths as expected substitutions per
> site?
as mean number of substitutions per site.
> For example, consider an alignment of length 100, and RAxML
> estimates a particular branch length to be 0.1. If I wanted to know
> the total number of substitutions on that branch, it should be a valid
> procedure to multiply 0.1 by 100, shouldn't it?
yes that seems okay.
> 2) For a particular sequence, how are sites with a gap or missing
> information handled?
This has already been answered in a pervious thread, all gap or
missing data
is treated as undetermined chqaracter.
> How do they influence the branch lengths? For
> example, if 50 out of the 100 sites are missing information for a
> particular sequence, will the missing sites be assumed to be identical
> with the ancestral state, or how exactly does it work? I am asking
> because in my case, the outgroup sequence has a 70 sites gap at the
> end of the alignment (and none of the other sequences have any gaps),
> and I want to understand how that may influence the branch lengths.
This will mainly increase the branch length of the outgroup, since the
missing part
that is treated as undetermined characters will appear to be very
distant from anything else.
> 3) I read in this forum that it may be better to not specify an
> outgroup; instead, the trees can be rooted using a common tree drawer
> program such as FigTree. Will that decision haver any influence on the
> branch lengths, in particular for the outgroup sequence (I read a post
> where someone said this is or was a known bug)?
There was a bug allright, but in general and in principle, the
specification
of an outgroup is just a drawing option, hence whether you specify and
outgroup or not does
not have any influence on the computations or the model per se.
Alexis