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ABOUT.rst

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BioBlend is a Python library for interacting with CloudMan and Galaxy's API.

BioBlend is supported and tested on:

  • Python 2.6, 2.7, 3.3 and 3.4
  • Galaxy release_14.02 and later.

Conceptually, it makes it possible to script and automate the process of cloud infrastructure provisioning and scaling via CloudMan, and running of analyses via Galaxy. In reality, it makes it possible to do things like this:

  • Create a CloudMan compute cluster, via an API and directly from your local machine:

    from bioblend.cloudman import CloudManConfig
from bioblend.cloudman import CloudManInstance
cfg = CloudManConfig('<your cloud access key>', '<your cloud secret key>', 'My CloudMan',  'ami-<ID>', 'm1.small', '<password>')
cmi = CloudManInstance.launch_instance(cfg)
cmi.get_status()

  • Reconnect to an existing CloudMan instance and manipulate it:

    from bioblend.cloudman import CloudManInstance
cmi = CloudManInstance("<instance IP>", "<password>")
cmi.add_nodes(3)
cluster_status = cmi.get_status()
cmi.remove_nodes(2)

  • Interact with Galaxy via a straightforward API:

    from bioblend.galaxy import GalaxyInstance
gi = GalaxyInstance('<Galaxy IP>', key='your API key')
libs = gi.libraries.get_libraries()
gi.workflows.show_workflow('workflow ID')
gi.workflows.run_workflow('workflow ID', input_dataset_map)

  • Interact with Galaxy via an object-oriented API:

    from bioblend.galaxy.objects import GalaxyInstance
gi = GalaxyInstance("URL", "API_KEY")
wf = gi.workflows.list()[0]
hist = gi.histories.list()[0]
inputs = hist.get_datasets()[:2]
input_map = dict(zip(wf.input_labels, inputs))
params = {"Paste1": {"delimiter": "U"}}
wf.run(input_map, "wf_output", params=params)

Note

Although this library allows you to blend these two services into a cohesive unit, the library itself can be used with either service irrespective of the other. For example, you can use it to just manipulate CloudMan clusters or to script the interactions with an instance of Galaxy running on your laptop.