>David, what criteria do your programs use to find a "better"
network?<
Criteria include maximizing a probabilistic model (aka
maximum likelihood), minimizing the number of
evolutionary changes (aka maximum parsimony), and
minimizing a distance measure. There are other methods
that combine network generation and selection into one
step, but this makes comparing different networks
problematic. I am mostly using the program PAUP*, which
stands for Phylogenetic Analysis Using Parsimony *and
other methods, but there are numerous programs out there
implementing similar algorithms.
>Our local group works with viruses, and stresses them.
Then they let them reproduce for several weeks in a
chemostat under stress, and they sequence them several
times a day. So at the end of the experiment, they know
the detailed sequence history, as well as the beginning
and end points.<
>Then our computer guy takes the beginning and final
sequence, and lets the computer figure the optimum tree to
get from here to there. But the computer's "best" pathway
has always been far from the actual path.<
My analysis are similar except that I would need a time
machine to get the starting and intermediate sequences,
so I can only look for similarity between the final
sequences. However, many of these methods have been
tested against bacterial and viral studies and found to be
reasonably reliable. It depends on how well the pattern of
evolution matches the assumptions of the methods, and
various independent tests can examine that question in
many cases. (e.g., extremely high rates of evolution are a
problem for any method but also will produce nearly
random sequences).
Dr. David Campbell
Old Seashells
University of Alabama
Biodiversity & Systematics
Dept. Biological Sciences
Box 870345
Tuscaloosa, AL 35487 USA
bivalve@mail.davidson.alumlink.com
That is Uncle Joe, taken in the masonic regalia of a Grand
Exalted Periwinkle of the Mystic Order of Whelks-P.G.
Wodehouse, Romance at Droitgate Spa
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