Maximum likelihood phylogenetic tree building with RAxML-ng (via UNIX/conda)

• In this worksheet you will learn how create a Maximum Likelihood phylogenetic tree using RAxML-NG

Suggested prerequisites

• It is recommended that you have followed the Concepts in Computer Programming and UNIX tutorial (basics) tutorials before starting.
• A knowledge of the model of evolution parameters (if you wish to change the defaults)
• A knowledge of RAxML-NG is useful. You can read the RAxML-NG paper here and the manual and other documents can be found here.
• Installing RAxML-NG through conda is easiest so its suggested you have followed the Setting up and using conda tutorial.

Dataset

• This demonstration uses 16S_Staph_example_aligned.fasta as produced by the Aligning sequences using MAFFT (via UNIX/conda) worksheet.
  • RAxML-NG requires sequences to be aligned so ensure you have performed multiple sequence alignment or, if doing a whole genome alignment, you have created a SNP alignment, e.g. using Snippy-core

Steps

1. Create a directory to store the analysis and then change directory into that directory

mkdir raxmlng_demo
cd raxmlng_demo

1. Download the dataset from 16S_Staph_example_aligned.fasta
   • You can save this directly to your terminal current working directory by using the wget command (wget can be installed via conda).

wget https://conmeehan.github.io/PathogenDataCourse/Datasets/16S_Staph_example_aligned.fasta

1. Install RAxML-NG using conda
   • It is recommended to always install packages in their own environments so here will we create an enironment and install RAxML-NG in one step.

     mamba create -n raxmlng -c bioconda raxml-ng -y
     mamba activate raxmlng
     

2. Run RAxML-NG on the downloaded sequences

• --all indicates that you want to both build the ML tree and also perform extensive bootstrap analyses.
• --msa is the name of the input aligned set of sequences file
• --model is the evolutionary model you wish to use. You can learn about evolutionary models in this set of slides
  • For RAxML-NG it is almost always ok to use the GTR+G model
• --prefix is the identifying string you wish to have at the start of every output file
• --threads is the number of threads to dedicate to the program. I have 8 threads on my computer so am dedicating 7 for this

  raxml-ng --all --msa 16S_Staph_example_aligned.fasta --model GTR+G --prefix 16S_Staph_example --threads 7
  

1. RAxML-NG outputs the following files:
   • 16S_Staph_example.raxml.bestModel: The model of evolution used by RAxML-NG, which is estimated during the run
   • 16S_Staph_example.raxml.bestTree: The maximum likelihood tree without the bootstrap values
   • 16S_Staph_example.raxml.bootstraps: The individual trees produced at every bootstrap replicate
   • 16S_Staph_example.raxml.log: The log file outlining the individual steps undertaken by RAxML-NG
   • 16S_Staph_example.raxml.mlTrees: RAxML-NG by default runs the ML algorithm 20 times and selects the best run (to try and avoid local maxima). This file stores the trees from all 20 runs.
   • 16S_Staph_example.raxml.rba: The input alignment stored in a binary format
   • 16S_Staph_example.raxml.startTree: RAxML-NG by default runs the ML algorithm 20 times and selects the best run (to try and avoid local maxima). Each of these has a starting tree built with either parsimony or random on which the ML algorithm begins. This file stores the starting trees from all 20 runs
   • 16S_Staph_example.raxml.support: The maximum likelihood tree with the bootstrap values
   • This is the tree you usually want to use for further analyses
2. Deactivate your mamba environment when finished

   mamba deactivate