Docker Kraken Template

Start BiBiGrid Cluster

First, we need to start a bibigrid cluster in the cloud. If you have not done this already, edit the properties file downloaded from the gcb-tutorial repository and add your credentials and path to your SSH key file.

Start bibigrid:

bibigrid -u $USER -c -o bibigrid.properties

ssh -i ~/.ssh/SSH_CREDENTIALS.pem ssh -i id_rsa ubuntu@$BIBIGRID_MASTER

Download Github repository

Now your are logged on to the master node of your cloud-based SGE cluster! We will clone the docker-kraken-template-gcb github repository to the master node and continue working on the master node.

Clone the Docker Kraken Pipeline from Github:

cd /vol/spool
git clone https://github.com/BiBiServ/docker-kraken-template-gcb.git
cd docker-kraken-template-gcb

Set environment variables

The command line calls on this page assume that you have several environment variables set for your cloud environment. This makes it easier to copy & paste the commands.

Bielefeld Setup:

export NUM_NODES=4
export NUM_CORES=4
export HOST_SPOOLDIR=/vol/spool
export HOST_SCRATCHDIR=/vol/scratch
export DOCKER_USERNAME=<DOCKERHUB ACCOUNT NAME>

Giessen Setup:

export NUM_NODES=4
export NUM_CORES=4
export HOST_DBDIR=/volume/krakendb
export HOST_DATADIR=/volume/data
export HOST_SPOOLDIR=/vol/spool
export DOCKER_USERNAME=<DOCKERHUB ACCOUNT NAME>

Kraken Docker Image

The Dockerfile includes all information about the Docker image. Place scripts you want to have accessible in the Docker image into the container_scripts directory. These scripts will be called to download the database to the hosts and run the analyses.

FROM bibiserv/gcb-ubuntu

# the following required packages from the base ubuntu installation
# have already been installed in the bibiserv/ubuntu-gcb image
# to avoid high download traffic during the tutorial

#RUN apt-get update && \
#    apt-get install -y -f perl-modules libgomp1 python-swiftclient && \
#    rm -rf /var/lib/apt/lists/*

# create directories where the host file system can be mounted
RUN mkdir /vol

# copy the required scripts that run the pipeline from your machine to the
# Docker image and make them executable
ADD ./kraken/ /vol/kraken/
RUN chmod 755 /vol/kraken/*
ADD ./container_scripts/ /vol/scripts/

Login to DockerHub

We need to pull the updated image to each of the hosts before we can start the analysis scripts. Before pushing to the DockerHub, you need to login:

docker login -u $DOCKER_USERNAME

Building and Pushing the Docker Image

Now every time you made a changes to the container scripts, you need to push the image to DockerHub:

docker build -t "$DOCKER_USERNAME/kraken-docker" .
docker push $DOCKER_USERNAME/kraken-docker

Running Kraken containers on the cluster nodes

Let's start by creating wrapper script for the docker run command to make it easier for us to define the environment of the cluster when running a container. The script will call a COMMAND while simultaneously defining which SCRATCHDIR (local disk) and SPOOLDIR (NFS shared between the master and all slaves) of the host will be mounted to the container.

Bielefeld Setup

docker_run.sh CONTAINER SCRATCHDIR SPOOLDIR COMMAND

Giessen Setup

docker_run.sh CONTAINER DBDIR DATADIR SPOOLDIR COMMAND

The docker run command inside the script should look like this (add mounts, container and command):

docker run -e "NSLOTS=$NSLOTS" ....

Edit the docker_run.sh script in the scripts folder and define the mount points inside your container.

Download Kraken Database

Note: Donwloading the database to the local SCRATCHDIR is only necessary in the Bielefeld Setup. In the Giessen Setup, the database and data are provided via volumes and already mounted to the cluster nodes during startup.

Now we can work on the Kraken pipeline which will run inside the container.

First we need to download the Kraken database to each of the hosts. You need to work on the kraken_download_db.sh file. The Kraken Database is located in the SWIFT object store container gcb. To download it using the swift client, you simply call:

swift -U gcb:swift -K ssbBisjNkXmwgSXbvyAN6CtQJJcW2moMHEAdQVN0 -A http://swift:7480/auth \
download gcb minikraken.tgz --output <CONTAINER SCRATCHDIR>/minikraken.tgz

Write a script kraken_download_db.sh which will download the Kraken DB to the container-local scratch disk. Untar the file using tar xvzf minikraken.tgz. Save the script in the container_scripts directory.

Note: you need to run docker build and docker push after each change you made to the container scripts. If you start a remote job, make sure you pull the new version of the container. You can test the container locally using your docker_run.sh wrapper.

If you want to distribute the jobs on the cluster, use qsub to sumit the job to the SGE queue. The -pe option ensures, that we only download the database once on each host

Bielefeld Setup:

qsub -N DB_Download -t 1-$NUM_NODES -pe multislot $NUM_CORES -cwd \
/vol/spool/docker-kraken-gcb/scripts/docker_run.sh \
$DOCKER_USERNAME/kraken-docker $HOST_SCRATCHDIR $HOST_SPOOLDIR \
/vol/scripts/kraken_download_db.sh

Run Kraken Analysis

Next, we need to write a wrapper script for the kraken call. In the Bielefeld setup you need to download the FASTQ file from SWIFT first:

swift -U gcb:swift -K ssbBisjNkXmwgSXbvyAN6CtQJJcW2moMHEAdQVN0 \
-A http://swift:7480/auth download gcb INFILE --output <SCRATCHDIR/INFILE>

In the Giessen Setup the FASTQ is already mounted to the host HOST_DATADIR.

Note: The list of input file names can be found in samples.txt.

Now you can run Kraken on the INFILE:

/vol/kraken/kraken --preload --threads $NSLOTS --db <PATH TO KRAKEN DB> \
--fastq-input --gzip-compressed --output <SPOOLDIR/OUTFILE> <INFILE>

Note: Every time you make changes to your script, to need to build and push your Docker container before testing it using the docker_run.sh wrapper.

Start the pipeline for just one input file:

Bielefeld Setup:

qsub -N kraken_SRR935726 -pe multislot $NUM_CORES -cwd \
/vol/spool/docker-kraken-gcb/scripts/docker_run.sh \
$DOCKER_USERNAME/kraken-docker $HOST_SCRATCHDIR $HOST_SPOOLDIR \
"/vol/scripts/kraken_pipeline.sh SRR935726.fastq.gz SRR935726"

Giessen Setup:

qsub -N kraken_SRR935726 -pe multislot $NUM_CORES -cwd \
/vol/spool/docker-kraken-gcb/scripts/docker_run.sh \
$DOCKER_USERNAME/kraken-docker $HOST_DBDIR $HOST_DATADIR $HOST_SPOOLDIR \
"/vol/scripts/kraken_pipeline.sh SRR935726.fastq.gz SRR935726"

You will find the output files in /vol/spool.

If your pipeline is working, analyze all FASTQ files:

Bielefeld Setup:

for i in `cat samples.txt | sed 's/.fastq.gz//g'`
do 
qsub -N kraken_$i -pe multislot $NUM_CORES -cwd \
/vol/spool/docker-kraken-gcb/scripts/docker_run.sh \
$DOCKER_USERNAME/kraken-docker $HOST_SCRATCHDIR $HOST_SPOOLDIR \
"/vol/scripts/kraken_pipeline.sh $i.fastq.gz $i"
done

Giessen Setup:

for i in `cat samples.txt | sed 's/.fastq.gz//g'`
do 
qsub -N kraken_$i -pe multislot $NUM_CORES -cwd \
/vol/spool/docker-kraken-gcb/scripts/docker_run.sh \
$DOCKER_USERNAME/kraken-docker $HOST_DBDIR $HOST_DATADIR $HOST_SPOOLDIR \
"/vol/scripts/kraken_pipeline.sh $i.fastq.gz $i"
done

Generate Krona plot

cd /vol/spool
for i in *out; do cut -f2,3 $i > $i.krona; done
ktImportTaxonomy *krona -o krona.html
cp -r krona.html* ~/public_html

You can use your browser to look at the Krona output. Go to: http://<BIBIGRID_MASTER>/~ubuntu/

Clean up

After logout, terminate the BiBiGrid cluster:

bibigrid -o bibigrid.properties -l
bibigrid -o bibigrid.properties -t CLUSTERID

bibiserv / docker-kraken-template-gcb Goto Github PK