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Hi, |
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@bqminh will know the answer to your actual question, but I just want to chime in here to register my (strong) disagreement with the FAQs here. In my opinion there is no threshold above which one can 'rely' on a clade. UFBOOT is a measure of sampling variance (as is any bootstrap in phylogenetics), that's it. So datasets with more information (e.g. longer alignments) will have higher bootstrap values regardless of whether a clade is reliable. The aLRT should behave similarly, but for different reasons (it's a measure of whether a branch length is distinguishable from zero). This is covered extensively in the literature, but my favourite paper on it is this one: https://academic.oup.com/mbe/article/29/2/457/1024815 On that note, I think we should remove these mentions of arbitrary thresholds from the FAQs, but I'll open another discussion for that. (And apologies for not actually answering the question!) |
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The answers:
Here's how you can figure it out (and how I figured it out...). If you take a small example file you can run command lines to distinguish what's going on, including whether changing the order on the command line changes the output. I increased the sample sizes just to get more reproducible estimates (small differences are still expected, because I didn't fix the random number seed):
Now you can look at the output trees by:
Without printing all the trees, I'll just focus on one node where the two values were quite different, so easy to distinguish (i put in the branch length so you can see it's the same branch!):
Hope that helps! R |
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The answers:
73.3/96
isaLRT/UFBOOT
Here's how you can figure it out (and how I figured it out...). If you take a small example file you can run command lines to distinguish what's going on, including whether changing the order on the command line changes the output. I increased the sample sizes just to get more reproducible estimates (small differences are still expected, because I didn't fix the random number seed):