Dear Paillou,
well, consensus of random starting trees is definitely not a good substitute for the ML tree.
To make ML tree inference faster, you can specify a (taxonomic) constraint tree, and/or use the
adaptive version of raxml-ng:
https://github.com/amkozlov/raxml-ng/wiki/Installation#building-adaptive-branch
Also, please have a look at this tutorial:
https://github.com/amkozlov/ng-tutorial/wiki/evomics2024
Best,
Oleksiy
On 02.04.24 10:55, Pailler Vincent wrote:
> Dear Oleksiy,
>
> I work on a 16S sequencing project. I did first a DADA2 analysis to get my taxa. Now, I would like
> to get a tree on these data.
>
> I did my sequences alignment with DECIPHER. Then I try to deal with raxml-ng. The computing time is
> very long when I try :
>
> ./raxml-ng --msa ./output/alignement.fasta--model GTR+G--prefix test --threads 128
>
> That's why I tried to get a consensus tree based on 100 starting trees (--start option) :
>
> ./raxml-ng --msa./output/alignement.fasta--model GTR+G--prefix starting --tree pars{50},rand{50}
> --start --threads 128
>
> ./raxml-ng --consense MRE --tree starting.raxml.startTree--prefix consensus.MRE --threads 128
>
> Then, I tried to get the bootstrap values of that tree :
>
> ./raxml-ng --bootstrap --msa ./output/alignement.fasta--prefix bootstrap --model GTR+G --seed 2
> --threads 128
>
> It is still running. I will finish by mapping these values on the consensus tree with :
>
> raxml-ng --support --tree consensus.MRE.consensusTreeMRE --bs-trees allbootstraps --prefix
> bootstrap_tree --threads 32
>
> Is that a correct way to do? Is there any parameter to trick with?
>
> Best regards,
> Paillou
>
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