Provided by: python3-bioblend_1.0.0-1_all 
NAME
bioblend - BioBlend Documentation
ABOUT
BioBlend is a Python library for interacting with the Galaxy API.
BioBlend is supported and tested on:
• Python 3.7, 3.8, 3.9 and 3.10
• Galaxy release 17.09 and later.
BioBlend's goal is to make it easier to script and automate the running of Galaxy analyses
and administering of a Galaxy server. In practice, it makes it possible to do things like
this:
• 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')
wf_invocation = gi.workflows.invoke_workflow('workflow ID', inputs)
• 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_invocation = wf.invoke(input_map, params=params)
About the library name
The library was originally called just Blend but we renamed it to reflect more of its
domain and a make it bit more unique so it can be easier to find. The name was intended
to be short and easily pronounceable. In its original implementation, the goal was to
provide a lot more support for CloudMan and other integration capabilities, allowing them
to be blended together via code. BioBlend fitted the bill.
INSTALLATION
Stable releases of BioBlend are best installed via pip from PyPI:
$ python3 -m pip install bioblend
Alternatively, the most current source code from our Git repository can be installed with:
$ python3 -m pip install git+https://github.com/galaxyproject/bioblend
After installing the library, you will be able to simply import it into your Python
environment with import bioblend. For details on the available functionality, see the API
documentation.
BioBlend requires a number of Python libraries. These libraries are installed
automatically when BioBlend itself is installed, regardless whether it is installed via
PyPi or by running python3 setup.py install command. The current list of required
libraries is always available from setup.py in the source code repository.
If you also want to run tests locally, some extra libraries are required. To install them,
run:
$ python3 setup.py test
USAGE
To get started using BioBlend, install the library as described above. Once the library
becomes available on the given system, it can be developed against. The developed scripts
do not need to reside in any particular location on the system.
It is probably best to take a look at the example scripts in docs/examples source
directory and browse the API documentation. Beyond that, it's up to your creativity :).
DEVELOPMENT
Anyone interested in contributing or tweaking the library is more then welcome to do so.
To start, simply fork the Git repository on Github and start playing with it. Then, issue
pull requests.
API DOCUMENTATION
BioBlend's API focuses around and matches the services it wraps. Thus, there are two
top-level sets of APIs, each corresponding to a separate service and a corresponding step
in the automation process. Note that each of the service APIs can be used completely
independently of one another.
Effort has been made to keep the structure and naming of those API's consistent across the
library but because they do bridge different services, some discrepancies may exist. Feel
free to point those out and/or provide fixes.
For Galaxy, an alternative object-oriented API is also available. This API provides an
explicit modeling of server-side Galaxy instances and their relationships, providing
higher-level methods to perform operations such as retrieving all datasets for a given
history, etc. Note that, at the moment, the oo API is still incomplete, providing access
to a more restricted set of Galaxy modules with respect to the standard one.
Galaxy API
API used to manipulate genomic analyses within Galaxy, including data management and
workflow execution.
API documentation for interacting with Galaxy
GalaxyInstance
----
Config
Contains possible interaction dealing with Galaxy configuration.
class bioblend.galaxy.config.ConfigClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL
composition (e.g., http://<galaxy_instance>/api/libraries): self.module =
'workflows' | 'libraries' | 'histories' | ...
get_config() -> dict
Get a list of attributes about the Galaxy instance. More attributes will be
present if the user is an admin.
Return type
list
Returns
A list of attributes. For example:
{'allow_library_path_paste': False,
'allow_user_creation': True,
'allow_user_dataset_purge': True,
'allow_user_deletion': False,
'enable_unique_workflow_defaults': False,
'ftp_upload_dir': '/SOMEWHERE/galaxy/ftp_dir',
'ftp_upload_site': 'galaxy.com',
'library_import_dir': 'None',
'logo_url': None,
'support_url': 'https://galaxyproject.org/support',
'terms_url': None,
'user_library_import_dir': None,
'wiki_url': 'https://galaxyproject.org/'}
get_version() -> dict
Get the current version of the Galaxy instance.
Return type
dict
Returns
Version of the Galaxy instance For example:
{'extra': {}, 'version_major': '17.01'}
module: str = 'configuration'
whoami() -> dict
Return information about the current authenticated user.
Return type
dict
Returns
Information about current authenticated user For example:
{'active': True,
'deleted': False,
'email': 'user@example.org',
'id': '4aaaaa85aacc9caa',
'last_password_change': '2021-07-29T05:34:54.632345',
'model_class': 'User',
'username': 'julia'}
----
Datasets
----
Dataset collections
----
Datatypes
----
Folders
----
Forms
----
FTP files
----
Genomes
Groups
----
Histories
----
Invocations
----
Jobs
----
Libraries
----
Quotas
----
Roles
----
Tools
----
Tool data tables
----
Tool dependencies
----
ToolShed
----
Users
----
Visual
----
Workflows
Object-oriented Galaxy API
Client
Wrappers
Usage documentation
This page describes some sample use cases for the Galaxy API and provides examples for
these API calls. In addition to this page, there are functional examples of complete
scripts in the docs/examples directory of the BioBlend source code repository.
Connect to a Galaxy server
To connect to a running Galaxy server, you will need an account on that Galaxy instance
and an API key for the account. Instructions on getting an API key can be found at
https://galaxyproject.org/develop/api/ .
To open a connection call:
from bioblend.galaxy import GalaxyInstance
gi = GalaxyInstance(url='http://example.galaxy.url', key='your-API-key')
We now have a GalaxyInstance object which allows us to interact with the Galaxy server
under our account, and access our data. If the account is a Galaxy admin account we also
will be able to use this connection to carry out admin actions.
View Histories and Datasets
Methods for accessing histories and datasets are grouped under GalaxyInstance.histories.*
and GalaxyInstance.datasets.* respectively.
To get information on the Histories currently in your account, call:
>>> gi.histories.get_histories()
[{'id': 'f3c2b0f3ecac9f02',
'name': 'RNAseq_DGE_BASIC_Prep',
'url': '/api/histories/f3c2b0f3ecac9f02'},
{'id': '8a91dcf1866a80c2',
'name': 'June demo',
'url': '/api/histories/8a91dcf1866a80c2'}]
This returns a list of dictionaries containing basic metadata, including the id and name
of each History. In this case, we have two existing Histories in our account,
'RNAseq_DGE_BASIC_Prep' and 'June demo'. To get more detailed information about a History
we can pass its id to the show_history method:
>>> gi.histories.show_history('f3c2b0f3ecac9f02', contents=False)
{'annotation': '',
'contents_url': '/api/histories/f3c2b0f3ecac9f02/contents',
'id': 'f3c2b0f3ecac9f02',
'name': 'RNAseq_DGE_BASIC_Prep',
'nice_size': '93.5 MB',
'state': 'ok',
'state_details': {'discarded': 0,
'empty': 0,
'error': 0,
'failed_metadata': 0,
'new': 0,
'ok': 7,
'paused': 0,
'queued': 0,
'running': 0,
'setting_metadata': 0,
'upload': 0},
'state_ids': {'discarded': [],
'empty': [],
'error': [],
'failed_metadata': [],
'new': [],
'ok': ['d6842fb08a76e351',
'10a4b652da44e82a',
'81c601a2549966a0',
'a154f05e3bcee26b',
'1352fe19ddce0400',
'06d549c52d753e53',
'9ec54455d6279cc7'],
'paused': [],
'queued': [],
'running': [],
'setting_metadata': [],
'upload': []}}
This gives us a dictionary containing the History's metadata. With contents=False (the
default), we only get a list of ids of the datasets contained within the History; with
contents=True we would get metadata on each dataset. We can also directly access more
detailed information on a particular dataset by passing its id to the show_dataset method:
>>> gi.datasets.show_dataset('10a4b652da44e82a')
{'data_type': 'fastqsanger',
'deleted': False,
'file_size': 16527060,
'genome_build': 'dm3',
'id': 17499,
'metadata_data_lines': None,
'metadata_dbkey': 'dm3',
'metadata_sequences': None,
'misc_blurb': '15.8 MB',
'misc_info': 'Noneuploaded fastqsanger file',
'model_class': 'HistoryDatasetAssociation',
'name': 'C1_R2_1.chr4.fq',
'purged': False,
'state': 'ok',
'visible': True}
Uploading Datasets to a History
To upload a local file to a Galaxy server, you can run the upload_file method, supplying
the path to a local file:
>>> gi.tools.upload_file('test.txt', 'f3c2b0f3ecac9f02')
{'implicit_collections': [],
'jobs': [{'create_time': '2015-07-28T17:52:39.756488',
'exit_code': None,
'id': '9752b387803d3e1e',
'model_class': 'Job',
'state': 'new',
'tool_id': 'upload1',
'update_time': '2015-07-28T17:52:39.987509'}],
'output_collections': [],
'outputs': [{'create_time': '2015-07-28T17:52:39.331176',
'data_type': 'galaxy.datatypes.data.Text',
'deleted': False,
'file_ext': 'auto',
'file_size': 0,
'genome_build': '?',
'hda_ldda': 'hda',
'hid': 16,
'history_content_type': 'dataset',
'history_id': 'f3c2b0f3ecac9f02',
'id': '59c76a119581e190',
'metadata_data_lines': None,
'metadata_dbkey': '?',
'misc_blurb': None,
'misc_info': None,
'model_class': 'HistoryDatasetAssociation',
'name': 'test.txt',
'output_name': 'output0',
'peek': '<table cellspacing="0" cellpadding="3"></table>',
'purged': False,
'state': 'queued',
'tags': [],
'update_time': '2015-07-28T17:52:39.611887',
'uuid': 'ff0ee99b-7542-4125-802d-7a193f388e7e',
'visible': True}]}
If files are greater than 2GB in size, they will need to be uploaded via FTP. Importing
files from the user's FTP folder can be done via running the upload tool again:
>>> gi.tools.upload_from_ftp('test.txt', 'f3c2b0f3ecac9f02')
{'implicit_collections': [],
'jobs': [{'create_time': '2015-07-28T17:57:43.704394',
'exit_code': None,
'id': '82b264d8c3d11790',
'model_class': 'Job',
'state': 'new',
'tool_id': 'upload1',
'update_time': '2015-07-28T17:57:43.910958'}],
'output_collections': [],
'outputs': [{'create_time': '2015-07-28T17:57:43.209041',
'data_type': 'galaxy.datatypes.data.Text',
'deleted': False,
'file_ext': 'auto',
'file_size': 0,
'genome_build': '?',
'hda_ldda': 'hda',
'hid': 17,
'history_content_type': 'dataset',
'history_id': 'f3c2b0f3ecac9f02',
'id': 'a676e8f07209a3be',
'metadata_data_lines': None,
'metadata_dbkey': '?',
'misc_blurb': None,
'misc_info': None,
'model_class': 'HistoryDatasetAssociation',
'name': 'test.txt',
'output_name': 'output0',
'peek': '<table cellspacing="0" cellpadding="3"></table>',
'purged': False,
'state': 'queued',
'tags': [],
'update_time': '2015-07-28T17:57:43.544407',
'uuid': '2cbe8f0a-4019-47c4-87e2-005ce35b8449',
'visible': True}]}
View Data Libraries
Methods for accessing Data Libraries are grouped under GalaxyInstance.libraries.*. Most
Data Library methods are available to all users, but as only administrators can create new
Data Libraries within Galaxy, the create_folder and create_library methods can only be
called using an API key belonging to an admin account.
We can view the Data Libraries available to our account using:
>>> gi.libraries.get_libraries()
[{'id': '8e6f930d00d123ea',
'name': 'RNA-seq workshop data',
'url': '/api/libraries/8e6f930d00d123ea'},
{'id': 'f740ab636b360a70',
'name': '1000 genomes',
'url': '/api/libraries/f740ab636b360a70'}]
This gives a list of metadata dictionaries with basic information on each library. We can
get more information on a particular Data Library by passing its id to the show_library
method:
>>> gi.libraries.show_library('8e6f930d00d123ea')
{'contents_url': '/api/libraries/8e6f930d00d123ea/contents',
'description': 'RNA-Seq workshop data',
'name': 'RNA-Seq',
'synopsis': 'Data for the RNA-Seq tutorial'}
Upload files to a Data Library
We can get files into Data Libraries in several ways: by uploading from our local machine,
by retrieving from a URL, by passing the new file content directly into the method, or by
importing a file from the filesystem on the Galaxy server.
For instance, to upload a file from our machine we might call:
>>> gi.libraries.upload_file_from_local_path('8e6f930d00d123ea', '/local/path/to/mydata.fastq', file_type='fastqsanger')
Note that we have provided the id of the destination Data Library, and in this case we
have specified the type that Galaxy should assign to the new dataset. The default value
for file_type is 'auto', in which case Galaxy will attempt to guess the dataset type.
View Workflows
Methods for accessing workflows are grouped under GalaxyInstance.workflows.*.
To get information on the Workflows currently in your account, use:
>>> gi.workflows.get_workflows()
[{'id': 'e8b85ad72aefca86',
'name': 'TopHat + cufflinks part 1',
'url': '/api/workflows/e8b85ad72aefca86'},
{'id': 'b0631c44aa74526d',
'name': 'CuffDiff',
'url': '/api/workflows/b0631c44aa74526d'}]
This returns a list of metadata dictionaries. We can get the details of a particular
Workflow, including its steps, by passing its id to the show_workflow method:
>>> gi.workflows.show_workflow('e8b85ad72aefca86')
{'id': 'e8b85ad72aefca86',
'inputs': {'252': {'label': 'Input RNA-seq fastq', 'value': ''}},
'name': 'TopHat + cufflinks part 1',
'steps': {'250': {'id': 250,
'input_steps': {'input1': {'source_step': 252,
'step_output': 'output'}},
'tool_id': 'tophat',
'type': 'tool'},
'251': {'id': 251,
'input_steps': {'input': {'source_step': 250,
'step_output': 'accepted_hits'}},
'tool_id': 'cufflinks',
'type': 'tool'},
'252': {'id': 252,
'input_steps': {},
'tool_id': None,
'type': 'data_input'}},
'url': '/api/workflows/e8b85ad72aefca86'}
Export or import a workflow
Workflows can be exported from or imported into Galaxy. This makes it possible to archive
workflows, or to move them between Galaxy instances.
To export a workflow, we can call:
>>> workflow_dict = gi.workflows.export_workflow_dict('e8b85ad72aefca86')
This gives us a complex dictionary representing the workflow. We can import this
dictionary as a new workflow with:
>>> gi.workflows.import_workflow_dict(workflow_dict)
{'id': 'c0bacafdfe211f9a',
'name': 'TopHat + cufflinks part 1 (imported from API)',
'url': '/api/workflows/c0bacafdfe211f9a'}
This call returns a dictionary containing basic metadata on the new workflow. Since in
this case we have imported the dictionary into the original Galaxy instance, we now have a
duplicate of the original workflow in our account:
>>> gi.workflows.get_workflows()
[{'id': 'c0bacafdfe211f9a',
'name': 'TopHat + cufflinks part 1 (imported from API)',
'url': '/api/workflows/c0bacafdfe211f9a'},
{'id': 'e8b85ad72aefca86',
'name': 'TopHat + cufflinks part 1',
'url': '/api/workflows/e8b85ad72aefca86'},
{'id': 'b0631c44aa74526d',
'name': 'CuffDiff',
'url': '/api/workflows/b0631c44aa74526d'}]
Instead of using dictionaries directly, workflows can be exported to or imported from
files on the local disk using the export_workflow_to_local_path and
import_workflow_from_local_path methods. See the API reference for details.
NOTE:
If we export a workflow from one Galaxy instance and import it into another, Galaxy
will only run it without modification if it has the same versions of the tool wrappers
installed. This is to ensure reproducibility. Otherwise, we will need to manually
update the workflow to use the new tool versions.
Invoke a workflow
To invoke a workflow, we need to tell Galaxy which datasets to use for which workflow
inputs. We can use datasets from histories or data libraries.
Examine the workflow above. We can see that it takes only one input file. That is:
>>> wf = gi.workflows.show_workflow('e8b85ad72aefca86')
>>> wf['inputs']
{'252': {'label': 'Input RNA-seq fastq', 'value': ''}}
There is one input, labelled 'Input RNA-seq fastq'. This input is passed to the Tophat
tool and should be a fastq file. We will use the dataset we examined above, under View
Histories and Datasets, which had name 'C1_R2_1.chr4.fq' and id '10a4b652da44e82a'.
To specify the inputs, we build a data map and pass this to the invoke_workflow method.
This data map is a nested dictionary object which maps inputs to datasets. We call:
>>> datamap = {'252': {'src':'hda', 'id':'10a4b652da44e82a'}}
>>> gi.workflows.invoke_workflow('e8b85ad72aefca86', inputs=datamap, history_name='New output history')
{'history': '0a7b7992a7cabaec',
'outputs': ['33be8ad9917d9207',
'fbee1c2dc793c114',
'85866441984f9e28',
'1c51aa78d3742386',
'a68e8770e52d03b4',
'c54baf809e3036ac',
'ba0db8ce6cd1fe8f',
'c019e4cf08b2ac94']}
In this case the only input id is '252' and the corresponding dataset id is
'10a4b652da44e82a'. We have specified the dataset source to be 'hda'
(HistoryDatasetAssociation) since the dataset is stored in a History. See the API
reference for allowed dataset specifications. We have also requested that a new History be
created and used to store the results of the run, by setting history_name='New output
history'.
The invoke_workflow call submits all the jobs which need to be run to the Galaxy workflow
engine, with the appropriate dependencies so that they will run in order. The call returns
immediately, so we can continue to submit new jobs while waiting for this workflow to
execute. invoke_workflow returns the a dictionary describing the workflow invocation.
If we view the output History immediately after calling invoke_workflow, we will see
something like:
>>> gi.histories.show_history('0a7b7992a7cabaec')
{'annotation': '',
'contents_url': '/api/histories/0a7b7992a7cabaec/contents',
'id': '0a7b7992a7cabaec',
'name': 'New output history',
'nice_size': '0 bytes',
'state': 'queued',
'state_details': {'discarded': 0,
'empty': 0,
'error': 0,
'failed_metadata': 0,
'new': 0,
'ok': 0,
'paused': 0,
'queued': 8,
'running': 0,
'setting_metadata': 0,
'upload': 0},
'state_ids': {'discarded': [],
'empty': [],
'error': [],
'failed_metadata': [],
'new': [],
'ok': [],
'paused': [],
'queued': ['33be8ad9917d9207',
'fbee1c2dc793c114',
'85866441984f9e28',
'1c51aa78d3742386',
'a68e8770e52d03b4',
'c54baf809e3036ac',
'ba0db8ce6cd1fe8f',
'c019e4cf08b2ac94'],
'running': [],
'setting_metadata': [],
'upload': []}}
In this case, because the submitted jobs have not had time to run, the output History
contains 8 datasets in the 'queued' state and has a total size of 0 bytes. If we make this
call again later we should instead see completed output files.
View Users
Methods for managing users are grouped under GalaxyInstance.users.*. User management is
only available to Galaxy administrators, that is, the API key used to connect to Galaxy
must be that of an admin account.
To get a list of users, call:
>>> gi.users.get_users()
[{'email': 'userA@example.org',
'id': '975a9ce09b49502a',
'quota_percent': None,
'url': '/api/users/975a9ce09b49502a'},
{'email': 'userB@example.org',
'id': '0193a95acf427d2c',
'quota_percent': None,
'url': '/api/users/0193a95acf427d2c'}]
Using BioBlend for raw API calls
BioBlend can be used to make HTTP requests to the Galaxy API in a more convenient way than
using e.g. the requests Python library. There are 5 available methods corresponding to the
most common HTTP methods: make_get_request, make_post_request, make_put_request,
make_delete_request and make_patch_request. One advantage of using these methods is that
the API keys stored in the GalaxyInstance object is automatically added to the request.
To make a GET request to the Galaxy API with BioBlend, call:
>>> gi.make_get_request(gi.base_url + "/api/version").json()
{'version_major': '19.05',
'extra': {}}
To make a POST request to the Galaxy API with BioBlend, call:
>>> gi.make_post_request(gi.base_url + "/api/histories", payload={"name": "test history"})
{'importable': False,
'create_time': '2019-07-05T20:10:04.823716',
'contents_url': '/api/histories/a77b3f95070d689a/contents',
'id': 'a77b3f95070d689a',
'size': 0, 'user_id': '5b732999121d4593',
'username_and_slug': None,
'annotation': None,
'state_details': {'discarded': 0,
'ok': 0,
'failed_metadata': 0,
'upload': 0,
'paused': 0,
'running': 0,
'setting_metadata': 0,
'error': 0,
'new': 0,
'queued': 0,
'empty': 0},
'state': 'new',
'empty': True,
'update_time': '2019-07-05T20:10:04.823742',
'tags': [],
'deleted': False,
'genome_build': None,
'slug': None,
'name': 'test history',
'url': '/api/histories/a77b3f95070d689a',
'state_ids': {'discarded': [],
'ok': [],
'failed_metadata': [],
'upload': [],
'paused': [],
'running': [],
'setting_metadata': [],
'error': [],
'new': [],
'queued': [],
'empty': []},
'published': False,
'model_class': 'History',
'purged': False}
Toolshed API
API used to interact with the Galaxy Toolshed, including repository management.
API documentation for interacting with the Galaxy Toolshed
ToolShedInstance
Categories
Repositories
Tools
CONFIGURATION
BioBlend allows library-wide configuration to be set in external files. These
configuration files can be used to specify access keys, for example.
Configuration documents for BioBlend
BioBlend
exception bioblend.ConnectionError(message: str, body: Optional[Union[bytes, str]] = None,
status_code: Optional[int] = None)
An exception class that is raised when unexpected HTTP responses come back.
Should make it easier to debug when strange HTTP things happen such as a proxy
server getting in the way of the request etc. @see: body attribute to see the
content of the http response
class bioblend.NullHandler(level=0)
Initializes the instance - basically setting the formatter to None and the filter
list to empty.
emit(record: logging.LogRecord) -> None
Do whatever it takes to actually log the specified logging record.
This version is intended to be implemented by subclasses and so raises a
NotImplementedError.
exception bioblend.TimeoutException
bioblend.get_version() -> str
Returns a string with the current version of the library (e.g., "0.2.0")
bioblend.init_logging() -> None
Initialize BioBlend's logging from a configuration file.
bioblend.set_file_logger(name: str, filepath: str, level: Union[int, str] = 20,
format_string: Optional[str] = None) -> None
bioblend.set_stream_logger(name: str, level: Union[int, str] = 10, format_string:
Optional[str] = None) -> None
Config
class bioblend.config.Config(path: Optional[str] = None, fp: Optional[IO[str]] = None,
do_load: bool = True)
BioBlend allows library-wide configuration to be set in external files. These
configuration files can be used to specify access keys, for example. By default we
use two locations for the BioBlend configurations:
• System wide: /etc/bioblend.cfg
• Individual user: ~/.bioblend (which works on both Windows and Unix)
TESTING
If you would like to do more than just a mock test, you need to point BioBlend to an
instance of Galaxy. Do so by exporting the following two variables:
$ export BIOBLEND_GALAXY_URL=http://127.0.0.1:8080
$ export BIOBLEND_GALAXY_API_KEY=<API key>
The unit tests, stored in the tests folder, can be run using pytest. From the project
root:
$ pytest
GETTING HELP
If you have run into issues, found a bug, or can't seem to find an answer to your question
regarding the use and functionality of BioBlend, please use the Github Issues page to ask
your question.
RELATED DOCUMENTATION
Links to other documentation and libraries relevant to this library:
• Galaxy API documentation
• Blend4j: Galaxy API wrapper for Java
• clj-blend: Galaxy API wrapper for Clojure
INDICES AND TABLES
• Index
• Module Index
• Search Page
AUTHOR
Enis Afgan
COPYRIGHT
2012-2022, Enis Afgan