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Add interactive tool for Mgnify Jupyter lab
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Bérénice Batut
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<tool id="interactive_tool_mgnify_notebook" tool_type="interactive" name="Interactive MGnify Notebook" version="0.0.1" profile="22.01">
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<requirements>
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<container type="docker">quay.io/microbiome-informatics/emg-notebooks.dev:latest</container>
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</requirements>
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<entry_points>
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<entry_point name="MGnify Interactive Tool" requires_domain="True">
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<port>8888</port>
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<url>lab</url>
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</entry_point>
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</entry_points>
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<environment_variables>
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<environment_variable name="HISTORY_ID">$__history_id__</environment_variable>
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<environment_variable name="REMOTE_HOST">$__galaxy_url__</environment_variable>
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<environment_variable name="GALAXY_WEB_PORT">8080</environment_variable>
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<environment_variable name="GALAXY_URL">$__galaxy_url__</environment_variable>
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<environment_variable name="DISABLE_AUTH">true</environment_variable>
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<environment_variable name="API_KEY" inject="api_key" />
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</environment_variables>
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<command><![CDATA[
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#import re
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export GALAXY_WORKING_DIR=`pwd` &&
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mkdir -p ./jupyter/outputs/ &&
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mkdir -p ./jupyter/data &&
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mkdir -p ./jupyter/notebooks &&
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#for $count, $file in enumerate($input):
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#set $cleaned_name = str($count + 1) + '_' + re.sub('[^\w\-\.\s]', '_', str($file.element_identifier))
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ln -sf '$file' './jupyter/data/${cleaned_name}.${file.ext}' &&
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#end for
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## change into the directory where the notebooks are located
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cd ./jupyter/ &&
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export HOME=/home/jovyan/ &&
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export PATH=/home/jovyan/.local/bin:\$PATH &&
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#if $mode.mode == 'scratch'
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## copy all notebooks, workflows and data
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cp /home/jovyan/mgnify-examples/home.ipynb ./ &&
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cp -r /home/jovyan/mgnify-examples/* ./notebooks/ &&
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## provide all rights to copied files
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jupyter trust ./home.ipynb &&
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jupyter trust ./notebooks/*/*.ipynb &&
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jupyter lab --allow-root --no-browser --NotebookApp.token=''
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#else
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#set $noteboook_name = re.sub('[^\w\-\.\s]', '_', str($mode.ipynb.element_identifier))
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cp '$mode.ipynb' './${noteboook_name}.ipynb' &&
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jupyter trust './${noteboook_name}.ipynb' &&
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#if $mode.run_it
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jupyter nbconvert --to notebook --execute --output ./ipython_galaxy_notebook.ipynb --allow-errors ./*.ipynb &&
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#set $noteboook_name = 'ipython_galaxy_notebook'
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#else
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jupyter lab --allow-root --no-browser --NotebookApp.token='' &&
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#end if
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cp './${noteboook_name}.ipynb' '$jupyter_notebook'
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#end if
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]]>
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</command>
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<inputs>
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<conditional name="mode">
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<param name="mode" type="select" label="Do you already have a notebook?" help="If not, no problem we will provide you with a default one.">
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<option value="scratch">Start with a fresh notebook</option>
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<option value="previous">Load a previous notebook</option>
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</param>
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<when value="scratch">
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<!--<conditional name="lang">
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<param name="lang" type="select" label="Programming language to use">
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<option value="R Examples">R</option>
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<option value="Python Examples">Python</option>
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</param>
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<when value="R Examples">
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<param name="notebook" type="select" label="Notebook to use">
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<option value="Comparative Metagenomics">Comparative Metagenomics</option>
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<option value="Fetch Analyses metadata for a Study">Fetch Analyses metadata for a Study</option>
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<option value="Search for Samples or Studies">Search for Samples or Studies</option>
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</param>
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</when>
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<when value="Python Examples">
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<param name="notebook" type="select" label="Notebook to use">
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<option value="Download paginated API data to a CSV">Download paginated API data to a CSV</option>
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<option value="Load Analyses for a MGnify Study">Load Analyses for a MGnify Study</option>
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</param>
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</when>
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</conditional>-->
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</when>
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<when value="previous">
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<param name="ipynb" type="data" format="ipynb" label="IPython Notebook"/>
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<param name="run_it" type="boolean" truevalue="true" falsevalue="false" label="Execute notebook and return a new one."
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help="This option is useful in workflows when you just want to execute a notebook and not dive into the webfrontend."/>
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</when>
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</conditional>
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<!--<param name="input" multiple="true" type="data" optional="true" label="Include data into the environment"/>-->
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</inputs>
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<outputs>
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<data name="jupyter_notebook" format="ipynb" label="Executed Mgnify Notebook"></data>
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</outputs>
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<tests>
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<test expect_num_outputs="1">
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<param name="mode" value="previous" />
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<param name="ipynb" value="test.ipynb" />
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<param name="run_it" value="true" />
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<output name="jupyter_notebook" file="test.ipynb" ftype="ipynb"/>
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</test>
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</tests>
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<help><![CDATA[
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The notebooks in this Jupyter Lab environment allows to explore programmatic access to `MGnify <https://www.ebi.ac.uk/metagenomics/>`_'s datasets using Python or with R using the `MGnifyR <https://github.com/beadyallen/MGnifyR>`_ package.
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Why such notebooks?
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-------------------
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The quantity and richness of `metagenomics-derived data <https://www.ebi.ac.uk/metagenomics/search>`_ in MGnify grows every day. The `MGnify website <https://www.ebi.ac.uk/metagenomics/>`_ is the best place to start exploring and searching the MGnify database, and allows users to download modest query results as CSV tables.
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For larger queries, or more complex requirements like fetching metadata from samples across multiple studies, a programmatic access approach is far better.
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Programmatic access - fetching data from MGnify using a terminal command or code script - uses the `MGnify API <https://docs.mgnify.org/en/latest/api.html>`_ (`Application Programming Interface <https://en.wikipedia.org/wiki/API>`_). The API provides access to every data type in MGnify: `Studies <https://www.ebi.ac.uk/metagenomics/api/v1/studies>`_, `Samples <https://www.ebi.ac.uk/metagenomics/api/v1/samples>`_, `Analyses <https://www.ebi.ac.uk/metagenomics/api/v1/analyses>`_, `Annotations <https://www.ebi.ac.uk/metagenomics/api/v1/annotations/interpro-identifiers>`_, `MAGs <https://www.ebi.ac.uk/metagenomics/api/v1/genome-catalogues>`_ etc: it is what lies behind the MGnify website. Using the API means you can fetch more data than is possible via the website, and can help you write reproducible analysis scripts.
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The API can be explored interactively online, using the `API Browser <https://www.ebi.ac.uk/metagenomics/api/v1/>`_. But actually using the API first requires knowledge and/or installation of tools on your computer. This might range from a command line tool like `cURL <https://curl.se/>`_, to learning R and setting up the `R Studio <https://www.rstudio.com/>`_ application, to setting up a `Python <https://www.python.org/>`_ environment and installing a suite of `packages used for data analysis <https://pandas.pydata.org/>`_. Second, the API returns most data in `JSON format <https://developer.mozilla.org/en-US/docs/Learn/JavaScript/Objects/JSON>`_: this is standard on the web, but less familiar for bioinformaticians used to TSVs and dataframes.
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The `MGnify Notebook Server <http://notebooks.mgnify.org/>`_ and `MGnifyR package <https://github.com/beadyallen/MGnifyR>`_ are designed to bridge these gaps. Users can launch an online R and Python coding environment in their browser, without installing anything. The environment is hosted by EMBL's `Cell Biology and Biophysics Computational Support team <https://www.embl.org/research/units/cell-biology-biophysics/cbbcs/>`_, who support computational projects across EMBL. It already includes the main libraries needed for communicating with the MGnify API, analysing data, and making plots. It uses the popular `Jupyter Lab <https://jupyter.org/>`_ software, which means you can code inside `Notebooks <https://jupyter-notebook.readthedocs.io/en/latest/>`_: interactive code documents.
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There are example Notebooks written in both R and Python, so users can pick whichever they're more familiar with.
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]]></help>
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</tool>
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