MetaPhlAn
MetaPhlAn is a "computational tool for profiling the composition of microbial communities (Bacteria, Archaea and Eukaryotes) from metagenomic shotgun sequencing data (i.e. not 16S) with species-level. With StrainPhlAn, it is possible to perform accurate strain-level microbial profiling", according to its GitHub repository. While the software stack on our clusters does contain modules for a couple of older versions (2.2.0 and 2.8) of this software, we now expect users to install recent versions using a Python virtual environment.
Pour plus d'information, voir le site wiki de MetaPhlan.
Wheels disponibles
Pour connaître les wheels disponibles, utilisez la commande avail_wheels.
[name@server ~]$ avail_wheels metaphlan --all-versions
name version python arch
--------- --------- -------- -------
MetaPhlAn 4.0.3 py3 generic
MetaPhlAn 3.0.7 py3 generic
Télécharger les bases de données
MetaPhlAn exige qu'un ensemble de bases de données soit téléchargé dans $SCRATCH.
Important : La b ase de données doit se trouver dans $SCRATCH.
Téléchargez les bases de données à partir deSegatalab FTP.
1. À partir d'un nœud de connexion, créez le répertoire pour les données.
[name@server ~]$ export DB_DIR=$SCRATCH/metaphlan_databases
[name@server ~]$ mkdir -p $DB_DIR
[name@server ~]$ cd $DB_DIR
2. Download the data:
[name@server ~]$ parallel wget ::: http://cmprod1.cibio.unitn.it/biobakery4/metaphlan_databases/mpa_vJan21_CHOCOPhlAnSGB_202103.tar http://cmprod1.cibio.unitn.it/biobakery4/metaphlan_databases/mpa_vJan21_CHOCOPhlAnSGB_202103_marker_info.txt.bz2 http://cmprod1.cibio.unitn.it/biobakery4/metaphlan_databases/mpa_vJan21_CHOCOPhlAnSGB_202103_species.txt.bz2
Note that this step cannot be done from a compute node but must be done from a login node.
3. Extract the downloaded data, for example using an interactive job:
[name@server ~]$ salloc --account=<your account> --cpus-per-task=2 --mem=10G
Untar and unzip the databases:
[name@server ~]$ tar -xf mpa_vJan21_CHOCOPhlAnSGB_202103.tar
[name@server ~]$ parallel bunzip2 ::: *.bz2
Running MetaPhlAn
Once the database files have been downloaded and extracted, you can submit a job. You may edit the following job submission script according to your needs:
#!/bin/bash
#SBATCH --account=def-someuser
#SBATCH --time=01:00:00
#SBATCH --cpus-per-task=4 # Number of cores
#SBATCH --mem=15G # requires at least 15 GB of memory
# Load the required modules
module load gcc blast samtools bedtools bowtie2 python/3.10
# Allez au scratch
cd $SCRATCH
DB_DIR=$SCRATCH/metaphlan_databases
# Générez votre enironnement virtuel dans $SLURM_TMPDIR
virtualenv --no-download ${SLURM_TMPDIR}/env
source ${SLURM_TMPDIR}/env/bin/activate
# Installez metaphlan et ses déepndances.
pip install --no-index --upgrade pip
pip install --no-index metaphlan==X.Y.Z # EDIT: the required version here, e.g. 4.0.3
# Reuse the number of core allocated to our job from `--cpus-per-task=4`
# It is important to use --index and --bowtie2db so that MetaPhlAn can run inside the job
metaphlan metagenome.fastq --input_type fastq -o profiled_metagenome.txt --nproc $SLURM_CPUS_PER_TASK --index mpa_vJan21_CHOCOPhlAnSGB_202103 --bowtie2db $DB_DIR --bowtie2out metagenome.bowtie2.bz2
Soumettez ensuite la tâche à l'ordonnanceur.
[name@server ~]$ sbatch metaphlan-job.sh