MrBayes
MrBayes is a program for Bayesian inference and model choice across a wide range of phylogenetic and evolutionary models. MrBayes uses Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution of model parameters.
Finding available modules
[name@server ~]$ module spider mrbayes
Example
Sequential
1. Write the submission script
#!/bin/bash
#SBATCH --account=def-someuser # replace with your PI account
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=3G # increase as needed
#SBATCH --time=1:00:00 # increase as needed
module load mrbayes/3.2.7
cd $SCRATCH
# Copy one of the example locally on the local storage
cp -v $EBROOTMRBAYES/share/examples/mrbayes/primates.nex .
# Run using 1 core
mb primates.nex
2. Submit the sequential job
[name@server ~]$ sbatch submit-mrbayes-seq.sh
Parallel
MrBayes support running on multi-cores and multi-nodes, and GPU.
MPI
1. Write the submission script
#!/bin/bash
#SBATCH --account=def-someuser # replace with your PI account
#SBATCH --ntasks=8 # increase as needed
#SBATCH --mem-per-cpu=3G # increase as needed
#SBATCH --time=1:00:00 # increase as needed
module load mrbayes/3.2.7
cd $SCRATCH
# Copy one of the example locally on the local storage
cp -v $EBROOTMRBAYES/share/examples/mrbayes/primates.nex .
# Run using $SLURM_NTASKS
srun mb primates.nex
2. Submit the sequential job
[name@server ~]$ sbatch submit-mrbayes-parallel.sh
GPU
1. Write the submission script
#!/bin/bash
#SBATCH --account=def-someuser # replace with your PI account
#SBATCH --cpus-per-task=1
#SBATCH --gpus=1
#SBATCH --mem-per-cpu=3G # increase as needed
#SBATCH --time=1:00:00 # increase as needed
module load gcc cuda/12 mrbayes/3.2.7
cd $SCRATCH
# Copy one of the example locally on the local storage
cp -v $EBROOTMRBAYES/share/examples/mrbayes/primates.nex .
# Run using $SLURM_NTASKS
srun mb primates.nex
2. Submit the sequential job
[name@server ~]$ sbatch submit-mrbayes-gpu.sh
Checkpointing
For users needing very long runs of MrBayes, it is suggested to break up the work into several small jobs rather than one very long job. Long jobs have a higher probably of being interrupted by maintenance windows or unforeseen problems. Fortunately, MrBayes has a built in mechanism for creating checkpoints, where progress can be saved from one job and continued in a subsequent job.
1. Create the first script (job).
execute primates.nex;
mcmc ngen=10000000 nruns=2 temp=0.02 mcmcdiag=yes samplefreq=1000
stoprule=yes stopval=0.005 relburnin=yes burninfrac=0.1 printfreq=1000
checkfreq=1000;
2. Create a second script (job).
execute primates.nex;
mcmc ngen=20000000 nruns=2 temp=0.02 mcmcdiag=yes samplefreq=1000
stoprule=yes stopval=0.005 relburnin=yes burninfrac=0.1 printfreq=1000
append=yes checkfreq=1000;
3. Submit the jobs
#!/bin/bash
#SBATCH --account=def-someuser # replace with your PI account
#SBATCH --ntasks=8 # increase as needed
#SBATCH --mem-per-cpu=3G # increase as needed
#SBATCH --time=1:00:00 # increase as needed
#SBATCH --array=1-2%1 # match the number of sub-jobs, only 1 at a time
module load gcc mrbayes/3.2.7
cd $SCRATCH
# Copy one of the example locally on the local storage
cp -v $EBROOTMRBAYES/share/examples/mrbayes/primates.nex .
# Run using $SLURM_NTASKS
srun mb job${SLURM_ARRAY_TASK_ID}.nex
2. Submit the jobs
[name@server ~]$ sbatch submit-mrbayes-cp.sh