In order to aid in making bioinformatics pipelines more reproducible, I have put together a docker image which houses OBITools. OBITools is a software bundle which helps you analyze NGS data. See their website for more information.
Having obitools housed in a docker image enables us to easily migrate or communicate our workflow. Here is a short representation of how one can call functions from the bundle using Python's docker package.
First, prepare some boilerplate stuff to have the container ready as a Python variable.
import docker
from docker.errors import NotFound
client = docker.from_env()
# Create or start docker container named 'obitools'.
try:
obt = client.containers.get('obitools')
if obt.status == 'exited':
print('Starting container obitools.')
obt.start()
else:
print('Container obitools ready.')
except NotFound:
print('Creating container obitools.')
obt = client.containers.run(
image='romunov/obitools:1.2.13',
name='obitools',
# This maps my local wolfdata to /data in container
volumes={'/home/romunov/wolfdata': {'bind': '/data', 'mode': 'rw'}},
tty=True, detach=True
)
And then run the command
score_min = 40
reads_r = "wolf_R.fastq"
reads_f = "wolf_F.fastq"
output = "wolf.fastq"
wolf = obt.exec_run(tty=True, workdir='/data', cmd=f'illuminapairedend --score-min={score_min} -r {reads_r} {reads_f} > {output}')
Command illuminapairedend --score-min=40 -r wolf_R.fastq wolf_F.fastq > wolf.fastq would be sent to the docker container and output would be located in /home/romunov/wolfdata (see the first code chunk, specifically study the argument volumes).