Seriously: it’s the easiest thing in the world. Just shout, “THEY LAUGHED AT GALILEO!” and leave.
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Your misquote of Coyne, which you have not only never corrected, but continued to use after it was shown to be a misquote.[1]
See above. An example of where you were told exactly why you were wrong, which you ignored by (i) blaming some-one else, (ii) continuing to lie about your sources and (iii) using the same misquote again as if you’d never been shown it was a misquote.
We know how you responded to this criticism - you repeated your original claim as if it had never been challenged.
Nor is this an isolated instance. There are hundreds more.
But go ahead. Explain how you think you responded satisfactorily to this objection.
You don’t read the works you cite yourself, so why should anyone believe you are willing to read the works cited by others? AFAICR you’ve never answered this.
Which brings me back to Theobald. Your response to his article being cited was to copy part of the index and not address the contents at all. You show no sign of even reading that article, let alone trying to understand it or discuss it. This isn’t an isolated incident either.
Which, along with your continued ‘quoting’ from sources you haven’t read - which has been repeatedly criticised and which you cannot possibly not know you are doing - renders this:
… just another lie.
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This is an exercise we have both done in the past. Go on uniprot select two different proteins. Select several different animals and compare the trees. Also compare the percentage identity matrix. This is not consistent among different proteins.
So you’re talking about comparing amino acid sequences. How does uniprot produce trees? Why should we expect percent identity matrices to be the same for different proteins? What does any of this actually mean?
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You have produced a tree in uniprot yourself. It takes one click. So does the percentage identify matrix. I think there are two possible causes of the different trees and sequence dissimilarity in the identify matrix.
In the case of p53 the starting designed sequence may me different for each animal or the design may be the same and the differences are due to a high mutational tolerance for this gene.
In the case of beta catenin it looks like the design is the the same and due to functional constraint there is very little hierarchal structure to the tree.
I’m asking you to tell me how uniprot produces a tree from the data. “One click” isn’t an answer. Do you know the algorithm?
What makes you think there’s a starting designed sequence at all?
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I don’t.
I think the only explanation that makes sense for complex functional sequences like beta catenin is design. How would you explain that the WNT beta catenin pathway evolved?
Then maybe you shouldn’t mess with things you don’t understand. Now, if you just pushed a button, what you get is neighbor-joining using uncorrected simple-matching distances. This is not what a real phylogenetic analysis would do. And the alignment is probably pretty bad too.
Dunno. I’ve never looked into it. The way to look into it would be to search the literature. Have you ever tried that? And what you think makes sense is no guide to what actually makes sense.
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I have looked at papers for the origin of the pathway that beta catenin is part of but they are speculative, assume it evolved and do not address the sequence problem.
What corrections are necessary? Would these corrections allow P53 and beta catenin trees to match?
Discordant gene trees is consistent with reproduction and natural variation.
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Note that beta catenin is present in protists, as are several other wnt-associated components. What you mean by “speculative” is “at variance with your beliefs”. And there is no such thing as “the sequence problem”.
That you don’t know the answers to these questions suggests that you are far out of your depth and further that you have no interest in or capacity for learning.
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So far we don’t even know that the gene trees are discordant.
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Well, sure. But he thinks they are, and he thinks the discordance is meaningful. Yet another claim without support.
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I suspect that the trees he produced (by a method he isn’t acquainted with) are mostly just signal-free.
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I suspect everything he produces is signal-free.
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Not to interrupt an interesting discussion, but, in fairness, I don’t know the answers to these questions either, and would freely admit to being out of my depth, were I to try and comment on them. Yet, in @colewd’s defense, the man is trying to read the literature (or claims to, anyway), trying to use the tools (incompetent though he may be in choosing them, or in interpreting their output), directly asks questions on a casual board frequented by qualified people… Isn’t it a bit hostile to berate him for that, too? Sure, he drew premature conclusions from data he - like most every other lay person - wouldn’t know how to interpret even if they were comprehensive; conclusions possibly drawn far in advance of any attempts at looking at the data, too, and yet he started an inquiry anyway, even if prompted to by critics. To call him a lost cause for that may just end up being a self-fulfilling prophesy, discouraging him from cultivating what ever kernel of curiosity there may be at the root of them going online in search of a challenge in the first place; and to never begin becoming the sort of investigator of whom diligence is expected enough for failure in observing any to be a serious blemish.
Imagine, if you will, that these things have been explained to him repeatedly. And yet, in spite of these specific issues being explained, he persists in intentional ignorance.
I suspect that if you were to examine his history of posting here and elsewhere, you would not find this difficult to imagine at all.
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No reason you should. But unlike Bill, you don’t assume competence in this area. My response might seem unwarranted, but remember that I’ve been talking to Bill for quite a few years now and am well acquainted with what he knows and is capable of knowing. Bill doesn’t actually ask questions, even when his sentences end with question marks. Those are assertions in the form of questions.
We could discuss the matter. The short answer would be that I couldn’t tell without seeing the data, but my guess is that the conflicts are likely to be due to noise rather than real signal incongruence.
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From Chat GPT 3.5
One of the evolutionary problems with very large sequence spaces is that it can be extremely difficult for organisms to find optimal solutions or adaptations within these spaces.
In evolutionary biology, a sequence space refers to the collection of all possible sequences of a particular length and composition. The size of a sequence space can vary widely depending on the length of the sequence and the number of possible components, such as nucleotides in DNA or amino acids in proteins.
In very large sequence spaces, the number of possible sequences is so vast that it becomes increasingly difficult for organisms to find optimal solutions through random mutation and natural selection. This is because the search space is so large that even small changes in the genetic sequence can lead to a vast number of possible outcomes, most of which are likely to be non-functional or deleterious