Difference between revisions of "Applications/Beast"
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[username@c230 ~]$ module add beast/1.8.3 | [username@c230 ~]$ module add beast/1.8.3 | ||
[username@c230 ~]$ module add libbeagle/2.1.2 | [username@c230 ~]$ module add libbeagle/2.1.2 | ||
[username@c230 ~]$ module add java/jdk1.8.0_102 | [username@c230 ~]$ module add java/jdk1.8.0_102 | ||
[username@c230 ~]$ beast Random_Cratopus_BEAST.xml | [username@c230 ~]$ beast Random_Cratopus_BEAST.xml | ||
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| + | * Then submitting this to [[General/Slurm|SLURM]]. | ||
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| + | <pre style="background-color: black; color: white; border: 2px solid black; font-family: monospace, sans-serif;"> | ||
| + | sbatch BeastDEMO.job | ||
| + | Submitted job 345663 | ||
| + | </pre> | ||
Revision as of 13:15, 18 April 2017
Contents
Application Details
- Description: BEAST is a cross-platform program for Bayesian analysis of molecular sequences using MCMC.
- Version: 1.8.3
- Modules: beast/1.8.3
- Licence: Free, open-source
Usage Examples
BEAST is a cross-platform program for Bayesian analysis of molecular sequences using MCMC. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability. We include a simple to use user-interface program for setting up standard analyses and a suit of programs for analysing the results.
It can make use of highly-parallel processors such as those in 3D graphics boards (referred to as Graphics Processing Units or GPUs) found in many PCs. In general using it (even if not using a GPU) will improve the performance of BEAST. However, installing it for BEAST to use is not a simple operation presently (but we hope to fix this shortly) and it is not necessarily going to benefit all data sets. In particular, for the use of a GPU (and currently only NVidia ones are supported) to be efficient, long partitions are required (perhaps >500 unique site patterns).
Interactive
While logged into a reserved high memory node (c230):
[username@c230 ~]$ module add beast/1.8.3 [username@c230 ~]$ module add libbeagle/2.1.2 [username@c230 ~]$ module add java/jdk1.8.0_102 [username@c230 ~]$ beast Random_Cratopus_BEAST.xml
Non Interactive
Using a SLURM script to run an array based processing beast processing script across the compute nodes:
#!/bin/bash
#SBATCH -J Random_BEAST
#SBATCH -N 1
#SBATCH -o %A-%a.log
#SBATCH -e %A-%a.err
#SBATCH -p compute
module add beast/1.8.3
module add libbeagle/2.1.2
module add java/jdk1.8.0_102
for i in ${SLURM_ARRAY_TASK_ID}
do
cp Random_Cratopus_BEAST.xml Random_Cratopus_BEAST_${SLURM_ARRAY_TASK_ID}.xml
sed -i -e "s/Random_Cratopus_BEAST/Random_Cratopus_BEAST_${SLURM_ARRAY_TASK_ID}/g" Random_Cratopus_BEAST_${SLURM_ARRAY_TASK_ID}.xml
done
beast "Random_Cratopus_BEAST_${SLURM_ARRAY_TASK_ID}".xml
- Then submitting this to SLURM.
sbatch BeastDEMO.job Submitted job 345663
Further Information
- http://beast.bio.ed.ac.uk/
- http://beast.bio.ed.ac.uk/Tutorial_6
- http://beast.bio.ed.ac.uk/Tutorial_8
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