stamatak
commented
5 years ago I believe it would be better to post this on the raxml google group, as it is not directly related with the actual software,
Alexis
On 31.01.19 19:38, Carol-Symbiomics wrote:
hi everyone! I was curious about the implementation of the ascertainment bias correction within RAxML-ng.
I found RAxML-ng way more user-friendly than the raxmlHPC. So I decided to use RAxML-ng to process my RADseq data. I have a phylip matrix (which I got from the conversion of a vcf file). I was following the tutorial steps to proceed but after running the "Tree Inference" step I found out I have too many different tree topologies. I'm only interested in getting a clustering analysis, just cause I might have cryptic species within my data set. I've read that the ascertainment bias correction (recommended when using only variable sites as is the case of SNPs) is only important if one wants to correct for the branch length. At this point, I'm not sure on how to proceed as my tree inference assessment shows my trees do not converge to a single topology Reading input trees from file: mltrees Loaded 21 trees with 323 taxa.
Average absolute RF distance in this tree set: 160.200000 Average relative RF distance in this tree set: 0.250312 Number of unique topologies in this tree set: 20
Any advice will be greatly appreciated
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-- Alexandros (Alexis) Stamatakis
Research Group Leader, Heidelberg Institute for Theoretical Studies Full Professor, Dept. of Informatics, Karlsruhe Institute of Technology
www.exelixis-lab.org
amkozlov
hi everyone! I was curious about the implementation of the ascertainment bias correction within RAxML-ng.
I found RAxML-ng way more user-friendly than the raxmlHPC. So I decided to use RAxML-ng to process my RADseq data. I have a phylip matrix (which I got from the conversion of a vcf file). I was following the tutorial steps to proceed but after running the "Tree Inference" step I found out I have too many different tree topologies. I'm only interested in getting a clustering analysis, just cause I might have cryptic species within my data set. I've read that the ascertainment bias correction (recommended when using only variable sites as is the case of SNPs) is only important if one wants to correct for the branch length. At this point, I'm not sure on how to proceed as my tree inference assessment shows my trees do not converge to a single topology Reading input trees from file: mltrees Loaded 21 trees with 323 taxa.
Average absolute RF distance in this tree set: 160.200000 Average relative RF distance in this tree set: 0.250312 Number of unique topologies in this tree set: 20
Any advice will be greatly appreciated