It is the presuppositions that are built into the program that I question. Once those are hardwired, no one questions them... they just look at the output and say it was "computer generated" and scientific.
Take a look at the Editorial in today's WSJ called, Climate of Fear. It takes this same issue out of the emotional (around here) context of evolution and clearly demonstrates it in the context of global warming - which also uses computers.
Further, it shows how a belief system can go out in search of proof - seeing "facts" through "global warming colored glasses" - and criticizing any disagreement by anyone who questions the underlying assumptions.
I urge everyone who has an interest in these types of issues to read this editorial. The author, Dr. Richard Lindzen, is a professor at MIT. Note especially the climate of fear that has been created by "objective" scientists against any scientist who dares question the same set of data and see a different rubric. How funding is withheld from those who see data differently. How academic promotions are withheld. In short, how everyone in an entire department can end up believing the same thing and advocating the same thing - even if it is not proven or simply not true. And yet at the same time, they can do all this under the guise of "science". It is a way to stifle all dissent and independent thought. And it happens every day in most fields of endeavor.
Scientists are simply humans - subject to all the emotional vaguaries of all humans.
Don't think for a moment that everything described by Dr. Lindzen doesn't equally apply to those humans who work with biological data and devote themselves to proving evolution.
There is a reason, however, that established theories can only be overturned by more inclusive theories.
An assertion that the unsolved problems of biology cannot be solved is not a theory.
That is total and complete bull, and you don't know what you're talking about.
I do quantum chemistry. There are multiple competing programs in the field, and people are constantly checking and measuring them against each other, as well as against experiment. The same is true for bioinformatics.
I urge everyone who has an interest in these types of issues to read this editorial. The author, Dr. Richard Lindzen, is a professor at MIT. Note especially the climate of fear that has been created by "objective" scientists against any scientist who dares question the same set of data and see a different rubric. How funding is withheld from those who see data differently. How academic promotions are withheld. In short, how everyone in an entire department can end up believing the same thing and advocating the same thing - even if it is not proven or simply not true. And yet at the same time, they can do all this under the guise of "science". It is a way to stifle all dissent and independent thought. And it happens every day in most fields of endeavor.
It's paranoid nonsense. Sorry, an MIT prof. can be a paranoid nutter just like anyone else.
Don't think for a moment that everything described by Dr. Lindzen doesn't equally apply to those humans who work with biological data and devote themselves to proving evolution.
No one in biology is concerned much with 'proving' evolution. That's a done deal. But if you doubt their work, by all means get the same data they have - it's all publicly available on the National Library of Medicine database, write a program - none of the algorithms are particularly mathematically sophisticated - and run it yourself. You said you write software. It should be a piece of cake.
Going at least by the history of the discipline, this is simply not true, regardless of what may (or may not) happen in climate science.
Look at the various programs that have been used to deduce phylogenies from genetic sequence data. My Lord, the debate went on and on and on (and still does) about rooting trees, seeding trees, what constitutes a least probability tree and the probability of it being found, the relation between the models and what actually happens in evolution, etc, etc. Every aspect of these programs has been examined in the most excruciating detail, and continues to be.
The inference of ancestral sequences is a closely related problem. To arrogantly and sweepingly assert it isn't/won't receive the same treatment is bigoted and unjustified.
Besides, just a quick glance at the literature indicates (unsurprisingly, at least to anyone who follows science) that the presuppositions involved in infering ancestral sequences are being examined. Here's just one example. It's not chosen for any particular reason, just the first one I happened to google up:
Ancestral sequence alignment under optimal conditions
BMC Bioinformatics 2005, 6:273
http://www.biomedcentral.com/1471-2105/6/273
Abstract (full text available at link)
Background
Multiple genome alignment is an important problem in bioinformatics. An important subproblem used by many multiple alignment approaches is that of aligning two multiple alignments. Many popular alignment algorithms for DNA use the sum-of-pairs heuristic, where the score of a multiple alignment is the sum of its induced pairwise alignment scores. However, the biological meaning of the sum-of-pairs of pairs heuristic is not obvious. Additionally, many algorithms based on the sum-of-pairs heuristic are complicated and slow, compared to pairwise alignment algorithms.
An alternative approach to aligning alignments is to first infer ancestral sequences for each alignment, and then align the two ancestral sequences. In addition to being fast, this method has a clear biological basis that takes into account the evolution implied by an underlying phylogenetic tree.
In this study we explore the accuracy of aligning alignments by ancestral sequence alignment. We examine the use of both maximum likelihood and parsimony to infer ancestral sequences. Additionally, we investigate the effect on accuracy of allowing ambiguity in our ancestral sequences.
Results
We use synthetic sequence data that we generate by simulating evolution on a phylogenetic tree. We use two different types of phylogenetic trees: trees with a period of rapid growth followed by a period of slow growth, and trees with a period of slow growth followed by a period of rapid growth.
We examine the alignment accuracy of four ancestral sequence reconstruction and alignment methods: parsimony, maximum likelihood, ambiguous parsimony, and ambiguous maximum likelihood. Additionally, we compare against the alignment accuracy of two sum-of-pairs algorithms: ClustalW and the heuristic of Ma, Zhang, and Wang.
Conclusion
We find that allowing ambiguity in ancestral sequences does not lead to better multiple alignments. Regardless of whether we use parsimony or maximum likelihood, the success of aligning ancestral sequences containing ambiguity is very sensitive to the choice of gap open cost. Surprisingly, we find that using maximum likelihood to infer ancestral sequences results in less accurate alignments than when using parsimony to infer ancestral sequences. Finally, we find that the sum-of-pairs methods produce better alignments than all of the ancestral alignment methods.