Frequently Asked Questions

Analysis

Results may vary

Results may vary

Your results may be slightly different from the ones presented in this tutorial due to differing versions of tools, reference data, external databases, or because of stochastic processes in the algorithms.



Collections

Adding a tag to a collection

Tip: Adding a tag to a collection

  • Click on the collection
  • Add a tag starting with # in the Add tags field Tags starting with # will be automatically propagated to the outputs of tools using this dataset.
  • Press Enter
  • Check that the tag is appearing below the collection name

Creating a dataset collection

Tip: Creating a dataset collection

  • Click on Operations on multiple datasets (check box icon) at the top of the history panel Operations on multiple datasets button
  • Check all the datasets in your history you would like to include
  • Click For all selected.. and choose Build dataset listbuild list collection menu item
  • Enter a name for your collection
  • Click Create List to build your collection
  • Click on the checkmark icon at the top of your history again

Creating a paired collection

Tip: Creating a paired collection

  • Click on Operations on multiple datasets (check box icon) at the top of the history panel Operations on multiple datasets button
  • Check all the datasets in your history you would like to include
  • Click For all selected.. and choose Build List of Dataset Pairs
  • Change the text of unpaired forward to a common selector for the forward reads
  • Change the text of unpaired reverse to a common selector for the reverse reads
  • Click Pair these datasets for each valid forward and reverse pair.
  • Enter a name for your collection
  • Click Create List to build your collection
  • Click on the checkmark icon at the top of your history again

Renaming a collection

Tip: Renaming a collection

  1. Click on the collection
  2. Click on the name of the collection at the top
  3. Change the name
  4. Press Enter


Data upload

Importing data from a data library

Tip: Importing data from a data library

As an alternative to uploading the data from a URL or your computer, the files may also have been made available from a shared data library:

  • Go into Shared data (top panel) then Data libraries
  • Find the correct folder (ask your instructor)
  • Select the desired files
  • Click on the To History button near the top and select as Datasets from the dropdown menu
  • In the pop-up window, select the history you want to import the files to (or create a new one)
  • Click on Import

Importing via links

Tip: Importing via links

  • Copy the link location
  • Open the Galaxy Upload Manager ( on the top-right of the tool panel)
  • Select Paste/Fetch Data
  • Paste the link into the text field
  • Press Start
  • Close the windowBy default, Galaxy uses the URL as the name, so rename the files with a more useful name.


Datasets

Adding a custom database/build (dbkey)

Galaxy may have several reference genomes built-in, but you can also create your own.

Tip: Adding a custom database/build (dbkey)

  • In the top menu bar, go to the User, and select Custom Builds
  • Choose a name for your reference build
  • Choose a dbkey for your reference build
  • Under Definition, select the option FASTA-file from history
  • Under FASTA-file, select your fasta file
  • Click the Save button

Adding a tag

Tags can help you to better organize your history and track datasets.

Tip: Adding a tag

  • Click on the dataset
  • Click on Edit dataset tags
  • Add a tag starting with # Tags starting with # will be automatically propagated to the outputs of tools using this dataset.
  • Check that the tag is appearing below the dataset name

Changing the datatype

Galaxy will try to autodetect the datatype of your files, but you may need to manually set this occasionally.

Tip: Changing the datatype

  • Click on the pencil icon for the dataset to edit its attributes
  • In the central panel, click on the Datatypes tab on the top
  • Select your desired datatype
  • Click the Change datatype button

Changing database/build (dbkey)

You can tell Galaxy which dbkey (e.g. reference genome) your dataset is associated with. This may be used by tools to automatically use the correct settings.

Tip: Changing database/build (dbkey)

  • Click on the pencil icon for the dataset to edit its attributes
  • In the central panel, change the Database/Build field
  • Select your desired database key from the dropdown list
  • Click the Save button

Converting the file format

Some datasets can be transformed into a different format. Galaxy has some built-in file conversion options depending on the type of data you have.

Tip: Converting the file format

  • Click on the pencil icon for the dataset to edit its attributes
  • In the central panel, click on the Convert tab on the top
  • Select the appropriate datatype from the list
  • Click the Convert datatype button

Creating a new file

Galaxy allows you to create new files from the upload menu. You can supply the contents of the file.

Tip: Creating a new file

  • Open the Galaxy Upload Manager
  • Select Paste/Fetch Data
  • Paste the file contents into the text field
  • Press Start and Close the window

Detecting the datatype (file format)

Tip: Detecting the datatype (file format)

  • Click on the pencil icon for the dataset to edit its attributes
  • In the central panel, click on the Datatypes tab on the top
  • Click the Detect datatype button to have Galaxy try to autodetect it.

Renaming a dataset

Tip: Renaming a dataset

  • Click on the pencil icon for the dataset to edit its attributes
  • In the central panel, change the Name field
  • Click the Save button


Features

Using the Scratchbook to view multiple datasets

Tip: Using the Scratchbook to view multiple datasets

If you would like to view two or more datasets at once, you can use the Scratchbook feature in Galaxy:

  1. Click on the Scratchbook icon on the top menu bar.
    • You should see a little checkmark on the icon now
  2. View a dataset by clicking on the eye icon to view the output
    • You should see the output in a window overlayed over Galaxy
    • You can resize this window by dragging the bottom-right corner
  3. Click outside the file to exit the Scratchbook
  4. View a second dataset from your history
    • You should now see a second window with the new dataset
    • This makes it easier to compare the two outputs
  5. Repeat this for as many files as you would like to compare
  6. You can turn off the Scratchbook by clicking on the icon again


Histories

Copy a dataset to a new history

Sometimes you may want to use a dataset in multiple histories. You do not need to re-upload the data, but you can copy datasets from one history to another.

Tip: Copy a dataset to a new history

  1. Click on the icon (History options) on the top of the history panel
  2. Click on Copy Dataset
  3. Select the desired files
  4. Give a relevant name to the “New history”
  5. Click on the new history name in the green box that have just appear to switch to this history

Creating a new history

Histories are an important part of Galaxy, most people use a new history for every new analysis. Always make sure to give your histories good names, so you can easily find your results back later.

Tip: Creating a new history

Click the icon at the top of the history panelIf the is missing:

  1. Click on the icon (History options) on the top of the history panel
  2. Select the option Create New from the menu

Renaming a history

Tip: Renaming a history

  1. Click on Unnamed history (or the current name of the history) (Click to rename history) at the top of your history panel
  2. Type the new name
  3. Press Enter

Searching your history

Tip: Searching your history

To make it easier to find datasets in large histories, you can filter your history by keywords as follows:

  1. Click on the search datasets box at the top of the history panel.history search box
  2. Type a search term in this box
    • For example a tool name, or sample name
  3. To undo the filtering and show your full history again, press on the clear search button next to the search box


Interactive tools

Launch JupyterLab

Hands-on: Launch JupyterLab

Tip: Launch JupyterLab in Galaxy

Currently JupyterLab in Galaxy is available on Live.useGalaxy.eu, usegalaxy.org and usegalaxy.eu.

Hands-on: Run JupyterLab

  1. Interactive Jupyter Notebook :
  2. Click Execute
  3. The tool will start running and will stay running permanently
  4. Click on the User menu at the top and go to Active Interactive Tools and locate the JupyterLab instance you started.
  5. Click on your JupyterLab instance

Tip: Launch Try JupyterLab if not available on Galaxy

If JupyterLab is not available on the Galaxy instance:

  1. Start Try JupyterLab

Open interactive tool

Tip: Open interactive tool

  1. Go to User > Active InteractiveTools
  2. Wait for the to be running (Job Info)
  3. Click on

Launch RStudio

Hands-on: Launch RStudio

Depending on which server you are using, you may be able to run RStudio directly in Galaxy. If that is not available, RStudio Cloud can be an alternative.

Tip: Launch RStudio in Galaxy

Currently RStudio in Galaxy is only available on UseGalaxy.eu and UseGalaxy.org

  1. Open the Rstudio tool by clicking here
  2. Click Execute
  3. The tool will start running and will stay running permanently
  4. Click on the “User” menu at the top and go to “Active InteractiveTools” and locate the RStudio instance you started.

Tip: Launch RStudio Cloud if not available on Galaxy

If RStudio is not available on the Galaxy instance:

  1. Register for RStudio Cloud, or login if you already have an account
  2. Create a new project

Stop RStudio

Hands-on: Stop RStudio

When you have finished your R analysis, it’s time to stop RStudio.

  1. First, save your work into Galaxy, to ensure reproducibility:
    1. You can use gx_put(filename) to save individual files by supplying the filename
    2. You can use gx_save() to save the entire analysis transcript and any data objects loaded into your environment.
  2. Once you have saved your data, you can proceed in 2 different ways:
    • Deleting the corresponding history dataset named RStudio and showing a “in progress state”, so yellow, OR
    • Clicking on the “User” menu at the top and go to “Active InteractiveTools” and locate the RStudio instance you started, selecting the corresponding box, and finally clicking on the “Stop” button at the bottom.


Sequencing

Illumina MiSeq sequencing

Illumina MiSeq sequencing

Illumina MiSeq sequencing is based on sequencing by synthesis. As the namesuggests, fluorescent labels are measured for every base that bind at aspecific moment at a specific place on a flow cell. These flow cells arecovered with oligos (small single strand DNA strands). In the librarypreparation the DNA strands are cut into small DNA fragments (differs perkit/device) and specific pieces of DNA (adapters) are added, which arecomplementary to the oligos. Using bridge amplification large amounts ofclusters of these DNA fragments are made. The reverse string is washed away,making the clusters single stranded. Fluorescent bases are added one by one,which emit a specific light for different bases when added. This is happeningfor whole clusters, so this light can be detected and this data is basecalled(translation from light to a nucleotide) to a nucleotide sequence (Read). Forevery base a quality score is determined and also saved per read. Thisprocess is repeated for the reverse strand on the same place on the flowcell, so the forward and reverse reads are from the same DNA strand. Theforward and reversed reads are linked together and should always be processedtogether!For more information watch this video from Illumina

Nanopore sequencing

Nanopore sequencing

Nanopore sequencing has several properties that make it well-suited for our purposes

  1. Long-read sequencing technology offers simplified and less ambiguous genome assembly
  2. Long-read sequencing gives the ability to span repetitive genomic regions
  3. Long-read sequencing makes it possible to identify large structural variationsHow nanopore sequencing works

    When using Oxford Nanopore Technologies (ONT) sequencing, the change inelectrical current is measured over the membrane of a flow cell. Whennucleotides pass the pores in the flow cell the current change is translated(basecalled) to nucleotides by a basecaller. A schematic overview is given inthe picture above.When sequencing using a MinIT or MinION Mk1C, the basecalling software ispresent on the devices. With basecalling the electrical signals are translatedto bases (A,T,G,C) with a quality score per base. The sequenced DNA strand willbe basecalled and this will form one read. Multiple reads will be stored in afastq file.


Tools

Re-running a tool

Tip: Re-running a tool

  1. Expand one of the output datasets of the tool (by clicking on it)
  2. Click re-run the toolThis is useful if you want to run the tool again but with slightly different paramters, or if you just want to check which parameter setting you used.

Selecting a datast collection as input

Tip: Selecting a datast collection as input

  1. Click on Dataset collection in front of the input parameter you want to supply the collection to.
  2. Select the collection you want to use from the list

Select multiple datasets

Tip: Select multiple datasets

  1. Click on Multiple datasets
  2. Select several files by keeping the Ctrl (orCOMMAND) key pressed and clicking on the files of interest


User preferences

Getting your admin API key

Tip: Getting your admin API key

Galaxy admin accounts are specified as a comma-separated email list in the admin_users directive of galaxy.yml . If you have set up your Galaxy server using the Galaxy Installation with Ansible tutorial, this is set to admin@example.org.

  1. In your browser, open your Galaxy homepage
  2. Log in using the admin email, or register a new account with it if it is the first time you use it
  3. Go to User -> Preferences in the top menu bar, then click on Manage API key
  4. If there is no current API key available, click on Create a new key to generate it
  5. Copy your API key to somewhere convenient, you will need it throughout this tutorial


Workflows

Creating a new workflow

You can create a Galaxy workflow from scratch in the Galaxy workflow editor.

Tip: Creating a new workflow

  1. Click Workflow on the top bar
  2. Click the new workflow button
  3. Give it a clear and memorable name
  4. Clicking Save will take you directly into the workflow editor for that workflow
  5. Need more help? Please see the How to make a workflow subsection here

Opening the workflow editor

Tip: Opening the workflow editor

  1. Click on the name of the imported workflowWorkflow drop down menu showing Edit option
  2. Select the Edit workflow to open the workflow in the workflow editor

Extracting a workflow from your history

Galaxy can automatically create a workflow based on the analysis you have performed in a history. This means that once you have done an analysis manually once, you can easily extract a workflow to repeat it on different data.

Tip: Extracting a workflow from your history

  1. Remove any failed or unwanted jobs from your history.
  2. Click on History options (gear icon ) at the top of your history panel.
  3. Select Extract workflow
  4. Check the steps, enter a name for your workflow, and press the Create Workflow button.

Hiding intermediate steps

Tip: Hiding intermediate steps

When a workflow is executed, the user is usually primarily interested in the final product and not in all intermediate steps.By default all the outputs of a workflow will be shown, but we can explicitly tell Galaxy which outputs to show and which to hide for a given workflow.This behaviour is controlled by the little checkbox in front of every output dataset:Asterisk for `out_file1` in the `Select First` tool

Importing a workflow

Tip: Importing a workflow

  • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
  • Click on the upload icon at the top-right of the screen
  • Provide your workflow
    • Option 1: Paste the URL of the workflow into the box labelled “Archived Workflow URL”
    • Option 2: Upload the workflow file in the box labelled “Archived Workflow File”
  • Click the Import workflow button

Setting parameters at run-time

Tip: Setting parameters at run-time

  1. Open the workflow editor
  2. Click on the tool in the workflow to get the details of the tool on the right-hand side of the screen.
  3. Scroll down to the parameter you want users to provide every time they run the workflow
  4. Click on the arrow in front of the name to toggle to set at runtime

Renaming workflow outputs

Tip: Renaming workflow outputs

  1. Open the workflow editor
  2. Click on the tool in the workflow to get the details of the tool on the right-hand side of the screen.
  3. Scroll down to the Configure Output section of your desired parameter, and click it to expand it.
    • Under Rename dataset, give it a meaningful nameRename output datasets

Running a workflow

Tip: Running a workflow

  • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
  • Click on the (Run workflow) button next to your workflow
  • Configure the workflow as needed
  • Click the Run Workflow button at the top-right of the screen
  • You may have to refresh your history to see the queued jobs