And that’s an example of quote mining, because in context with the italicized text comparing what someone who is not a true Christian experiences with one who is, yes, he was clueless.
It’s from the article that I cited to Rum from gpuccio at UCD a few posts above. It shows preservation of AA positions comparing the human proteins to other living organisms. These are proteins that are part of the spliceosome.
That’s not quote mining, because I quoted everything you wrote. Dale, you are the antithesis of humility.
You quoted me out of context. That would be quote mining.
And it is humility with respect to Whom that matters.
There is no context that would make your quote look humble.
Thanks for proving my point in spades, Dale:
Matthew 25:40
And the King will make answer and say to them, Truly I say to you, Because you did it to the least of these my brothers, you did it to me.
Thank you for tacitly acknowledging that you quote mined.
My quote does not need to look humble when the topic under discussion is humility before God.
And your Matthew quote is out of context with respect to whom we need to humble with as well. Jesus was not humble when he was rebuking the Pharisees, for instance.
(I wouldn’t say you were long on humility, yourself.)
I didn’t. Quote mining is about changing the meaning of the quote by removing context.
Maybe I’m the first to point this out, but you’re not Jesus, Dale. Therefore, you can’t use Jesus’s behavior to justify a massive exception for humble ol’ you.
Because YOU did it to the least of these my brothers, YOU did it to me.
No exceptions.
Did I claim to be like you did?
Yes, and fallaciously supporting your argument by taking a quote out of context. By your taking the quote out of context, you were falsely implying that I was saying he was clueless about everything in general.
Nor did I claim to be. You are excelling in taking things out of context. That was merely an example of how you are misapplying with whom humility is needed. And “…you’re not Jesus” is a nice red herring, as well.
And yet again, misunderstanding, maybe intentionally for appearances, the subject under discussion, namely with whom humility is needed in a particular instance.
OK, now what point are you making? Incidentally, how do you determine “bits” for amino acids?
What does this have to do with your “substitutability”? Please put some effort into parsing out the logic of the argument you are making.
You started by saying “PRPF8 has very little substitutability”, and you apparently meant this to imply (as in be evidence for the claim) that the size of sequence space tells us how densely packed functional sequences are in that space.
I’m looking at the graph and I don’t know what these bits are, and it isn’t clear to me what the different bars really represent. When it says “cellular slime molds”, does that include all known slime molds? Is “fungi” just one representative species, an average of all of them, and in that case what’s it an average of?
And most importantly, how does this tie in with your claim that the size of sequence space itself tells us how frequently we might find functional sequences in that space, and how well-connected they are?
Perhaps you could start a new topic and begin with taking some time to explain in more detail how your argument is supposed to work.
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This shows me that you are not looking at counter arguments. Gpuccio’s work has been discussed extensively at TSZ where Jock accused gpuccio of the Texas sharpshooter fallacy despite it being a very poor analogy of gpuccio’s methods.
The number of bits is a measure of the functional information. It is essentially the number of preserved amino acids in the string divided by the total sequence space and taking the log base 2 of that number. Demski used log base 2 to measure functional information.
Not as you have described it. It’s a measure of similarity to human sequence. Why should that be equated with functional information? And what total sequence space are you referring to? And why log base 2?
https://uncommondescent.com/intelligent-design/functional-information-defined/
Here is gpuccio’s description and equation.
From the paper
I call –log2 of the ratio Target space/Search space : Functionally Specified Information (FSI) for that function
log2 is used to measure in bits per Demski’s method.
Once again, not an answer to my questions. What is the target space here? How is it determined? What is the search space, ditto?
The target space is 20^ number of amino acids that are not preserved. The sequence space is 20^ total number of amino acids.
Bill, that’s totally, utterly, objectively, empirically false. We’ve demonstrated that ourselves. Maybe you should review our papers that I cited in the discussion with Ann Gauger.
Why would you ever think such an assumption is valid?
Bill, that made no sense. Is it what you meant to say?
This is my best understanding of his method. If the amino acid is preserved and the organisms are separated by long periods of time it is assumed critical in that position. When you observe the DNA it is mutating at a much higher rate than the AA’s which indicates that purifying selection is eliminating organisms with that specific AA mutation.
Where it is certainly possible that an AA did not move but could have changed it is also possible that where a substitution did occur the amount of workable AA’s in that position are also limited. The estimate appears conservative for that reason. Durston’s method and Axe’s method estimate substitutability at each position.
There is very little chance that a 2300 AA protein could form without a deterministic mechanism let alone one that is part of a 200 protein complex.
Bad idea. A better test would be to survey a large number of organisms and consider sites that are universally preserved, not just pairwise preservation. Now, what this shows you is that some spliceosomal proteins are highly conserved. But it doesn’t show you how those proteins evolved, or what the number of possible proteins could be that could serve that function well enough to be selected. Once again you look at the peak and conclude that the peak is 100% of the hill.
You are describing Durston’s method. It turns out that the results come out with similar estimates.
I don’t think this is a good argument. You have to explain how the protein got in this position in the first place. There is really no hill in this application. The spliceosome works properly or the organism cannot function. Removing introns is mission critical for eukaryotic cells.