Frequently Asked Questions

Which icons are available to use in my tutorial?

Tip: Which icons are available to use in my tutorial?

To use icons in your tutorial, take the name of the icon, ‘details’ in this example, and write something like this in your tutorial:

{% icon details %}

The following icons are currently available:

announcement
code-in
code-out
cofest
comment
congratulations
curriculum
details
docker_image
email
exchange
event
feedback
galaxy-barchart
galaxy-bug
galaxy-chart-select-data
galaxy-clear
galaxy-columns
galaxy-cross
galaxy-dropdown
galaxy-eye
galaxy-gear
galaxy-history
galaxy-home
galaxy-info
galaxy-library
galaxy-pencil
galaxy-refresh
galaxy-rulebuilder-history
galaxy-save
galaxy-scratchbook
galaxy-selector
galaxy-star
galaxy-tags
galaxy-toggle
galaxy-upload
galaxy-wf-connection
galaxy-wf-new
galaxy-wf-report-download
galaxy_instance
github
gitter
gtn-theme
hall-of-fame
hands_on
help
history-annotate
history-share
instances
interactive_tour
keypoints
language
last_modification
level
license
linkedin
new-history
objectives
orcid
param-check
param-collection
param-file
param-files
param-repeat
param-select
param-text
question
references
requirements
rss-feed
search
slides
solution
sticky-note
time
text-document
tip
tool
topic
trophy
tutorial
twitter
warning
wf-input
workflow-runtime-toggle
workflow
workflow-run
video
zenodo_link


Analysis

Do I need to create collections to run MaxQuant analysis or can I use single sample inputs?

Collections are not necessary to run MaxQuant but they make the history more clean and easier to navigate. The multiple datasets options allows to select multiple files that are not part of a collection and will give the same result as with a collection as input.

How can I include custom modifications into MaxQuant in Galaxy?

Unfortunately the inclusion of custom modifications is not possible by the user because it requires profound changes in the underlying code. Please let us know the modification you need by creating a new issue: https://github.com/galaxyproteomics/tools-galaxyp/issues entitled MaxQuant new modification request.

Which isobaric labeled quantification methods does MaxQuant in Galaxy support?

The current MaxQuant version supports: iTRAQ 4 and 8 plex; TMT 2,6,8,10,11 plex; iodoTMT6plex. Includion of TMT16 plex is in preparation.

How can I adapt this tutorial to my own data?

If you would like to run this analysis on your own data, make sure to check which V-region was sequenced. In this tutorial, we sequenced the V4 region, and used a corresponding reference for just this region. If you sequenced another V-region, please use an appropriate reference (either the full SILVA reference, or the SILVA reference specific for your region). Similarly, the Screen.seqs step after the alignment filtered on start and end coordinates of the alignments. These will have to be adjusted to your V-region.

How can I adapt this tutorial to my own data?

If you would like to run this analysis on your own data, make sure to check which V-region was sequenced. In this tutorial, we sequenced the V4 region, and used a corresponding reference for just this region. If you sequenced another V-region, please use an appropriate reference (either the full SILVA reference, or the SILVA reference specific for your region). Similarly, the Screen.seqs step after the alignment filtered on start and end coordinates of the alignments. These will have to be adjusted to your V-region.

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.

Troubleshooting errors

When you get a red dataset in your history, it means something went wrong. But how can you find out what it was? And how can you report errors?

Tip: Troubleshooting errors

When someting goes wrong in Galaxy, there are a number of things you can do to find out what it was. Error messages can help you figure out whether it was a problem with one of the settings of the tool, or with the input data, or maybe there is a bug in the tool itself and the problem should be reported. Below are the steps you can follow to troubleshoot your Galaxy errors.

  1. Expand the red history dataset by clicking on it.
    • Sometime you can already see an error message here

  2. View the error message by clicking on the bug icon

  3. Check the logs. Output (stdout) and error logs (stderr) of the tool are available:
    • Expand the history item
    • Click on the icon
    • Scroll down to the Job Information section to view the 2 logs:
      • Tool Standard Output
      • Tool Standard Error
  4. Submit a bug report! If you are still unsure what the problem is.
    • Click on the bug icon
    • Write down any information you think might help solve the problem
      • See this FAQ on how to write good bug reports
    • Click Report button
    • In the meantime, you can ask for help in the Galaxy Gitter Channel or the GTN Gitter Channel, or you can browse the Galaxy Help Forum to see if others have encountered the same problem before.

Creating an account

To get access to all features of a Galaxy instance, you need to create an account.

Tip: Creating an account

To create an account:

  1. Click Login or Register
  2. At the bottom of the form click Register here
  3. Fill the form and click Create


Ansible

Operating system compatibility

Tip: Operating system compatibility

These Ansible roles and training materials were last tested on Centos 7 and Ubuntu 18.04, but will probably work on other RHEL and Debian variants.

The roles that are used in these training are currently used by usegalaxy.*, and other, servers in maintaining their infrastructure. (US, EU, both are running CentOS 7)

If you have an issue running these trainings on your OS flavour, please report the issue in the training material and we can see if it is possible to solve.

Running Ansible on your remote machine

Tip: Running Ansible on your remote machine

It is possible to have ansible installed on the remote machine and run it there, not just from your local machine connecting to the remote machine.

Your hosts file will need to use localhost, and whenever you run playbooks with ansible-playbook -i hosts playbook.yml, you will need to add -c local to your command.

Be certain that the playbook that you’re writing on the remote machine is stored somewhere safe, like your user home directory, or backed up on your local machine. The cloud can be unreliable and things can disappear at any time.



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 list

    build 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


Contributing

How to Contribute to Galaxy

Contributing to Galaxy is a multi-step proces, this will guide you through it.

Tip: How to Contribute to Galaxy

To contribute to galaxy, a GitHub account is required. Changes are proposed via a pull request. This allows the project maintainers to review the changes and suggest improvements.

The general steps are as follows:

  1. Fork the Galaxy repository
  2. Clone your fork
  3. Make changes in a new branch
  4. Commit your changes, push branch to your fork
  5. Open a pull request for this branch in the upstream Galaxy repository

Git, Github, and Galaxy Core

For a lot more information about Git branching and managing a repository on Githubsee the Contributing with GitHub via command-linetutorial.

The Galaxy Core Architecture slides have a lot of import Galaxy core related information related to branches,project management, and contributing to Galaxy - under the Project Management section of the slides.



Contributors

Thanks!

First off, thanks for your interest in contributing to the Galaxy training materials!

Individual learners and instructors can make these training more effective by contributing back to them. You can report mistakes and errors, create more content, etc. Whatever is your background, there is a way to contribute: via the GitHub website, via command-line or even without dealing with GitHub.

We will address your issues and/or assess your change proposal as promptly as we can, and help you become a member of our community. You can also check our tutorials for more details.

How can I get started with contributing?

If you would like to get involved in the project but are unsure where to start, there are some easy ways to contribute which will also help you familiarize yourself with the project.

1. Checking existing tutorials

A great way to help out the project is to test/edit existing tutorials. Pick a tutorial and check the contents. Does everything work as expected? Are there things that could be improved?

Below is a checklist of things to look out for to help you get started. If you feel confident in making changes yourself, please open a pull request, otherwise please file an issue with any problems you run into or suggestions for improvements.

Basic

  • Test the tutorial on a running Galaxy instance
  • Language editing
    • Fix spelling and grammar mistakes
    • Simplify the English (to make it more accessible)

Intermediate

  • Metadata
    • Are the objectives, keypoints and time estimate filled in?
    • Do they fit with the contents of the tutorial?
  • Content
    • Is there enough background information provided in the introduction section and throughout the tutorial?
    • Question boxes
      • Add questions or question boxes where you think they might be useful (make people think about results they got, test their understanding, etc)
      • Check that answers are still up-to-date
    • Screenshots and Videos
      • Make sure there is also a textual description of the image/video contents
      • Does the screenshot add value to the tutorial or can it be removed?

Advanced

  • Workflows
    • Add a workflow definition file .ga if none is present
    • Check that the existing workflow is up-to-date with the tutorial contents
    • Enable workflow testing
  • Tours
    • Add a tour if none exists
    • Run the existing tour and check that it is up-to-date with the tutorial contents
  • Datasets
    • Check that all datasets used in the tutorial are present in Zenodo
    • Add a data-library.yaml file if none exists

2. Reviewing pull requests

Another great way to help out the project is by reviewing open pull requests. You can use the above checklist as a guide for your review. Some documentation about how to add your review in the GitHub interface can be found here

How can I contribute in "advanced" mode?

Most of the content is written in GitHub Flavored Markdown with some metadata (or variables) found in YAML files. Everything is stored on our GitHub repository. Each training material is related to a topic. All training materials (slides, tutorials, etc) related to a topic are found in a dedicated directory (e.g. transcriptomics directory contains the material related to transcriptomic analysis). Each topic has the following structure:

Structure of the repository

  • a metadata file in YAML format
  • a directory with the topic introduction slide deck in Markdown with introductions to the topic

  • a directory with the tutorials:

    Inside the tutorials directory, each tutorial related to the topic has its own subdirectory with several files:

    • a tutorial file written in Markdown with hands-on
    • an optional slides file in Markdown with slides to support the tutorial
    • a directory with Galaxy Interactive Tours to reproduce the tutorial
    • a directory with workflows extracted from the tutoria
    • a YAML file with the links to the input data needed for the tutorial
    • a YAML file with the description of needed tools to run the tutorial
  • a directory with the Dockerfile describing the details to build a container for the topic (self-study environments).

To manage changes, we use GitHub flow based on Pull Requests (check our tutorial):

  1. Create a fork of this repository on GitHub
  2. Clone your fork of this repository to create a local copy on your computer and initialize the required submodules (git submodule init and git submodule update)
  3. Create a new branch in your local copy for each significant change
  4. Commit the changes in that branch
  5. Push that branch to your fork on GitHub
  6. Submit a pull request from that branch to the original repository
  7. If you receive feedback, make changes in your local clone and push them to your branch on GitHub: the pull request will update automatically
  8. Pull requests will be merged by the training team members after at least one other person has reviewed the Pull request and approved it.

Globally, the process of development of new content is open and transparent:

  1. Creation of a branch derived from the main branch of the GitHub repository
  2. Initialization of a new directory for the tutorial
  3. Filling of the metadata with title, questions, learning objectives, etc
  4. Generation of the input dataset for the tutorial
  5. Filling of the tutorial content
  6. Extraction of the workflows of the tutorial
  7. Automatic extraction of the required tools to populate the tool file
  8. Automatic annotation of the public Galaxy servers
  9. Generation of an interactive tour for the tutorial with the Tourbuilder web-browser extension
  10. Upload of the datasets to Zenodo and addition of the links in the data library file.
  11. Once ready, opening a Pull Request
  12. Automatic checks of the changes are automatically checked for the right format and working links using continuous integration testing on Travis CI
  13. Review of the content by several other instructors via discussions
  14. After the review process, merge of the content into the main branch, starting a series of automatic steps triggered by Travis CI
  15. Regeneration of the website and publication on https://training.galaxyproject.org/archive/2021-09-01/
  16. Generation of PDF artifacts of the tutorials and slides and upload on the FTP server
  17. Population of TeSS, the ELIXIR’s Training Portal, via the metadata

Development process

To learn how to add new content, check out our series of tutorials on creating new content:

  1. Overview of the Galaxy Training Material
  2. Adding auto-generated video to your slides
  3. Contributing with GitHub via command-line
  4. Contributing with GitHub via its interface
  5. Creating a new tutorial
  6. Creating content in Markdown
  7. Creating Interactive Galaxy Tours
  8. Creating Slides
  9. Generating PDF artefacts of the website
  10. Including a new topic
  11. Running the GTN website locally
  12. Running the GTN website online using GitPod
  13. Tools, Data, and Workflows for tutorials
  14. Updating diffs in admin training

We also strongly recommend you read and follow The Carpentries recommendations on lesson design and lesson writing if you plan to add or change some training materials, and also to check the structure of the training material.

How can I fix mistakes or expand an existing tutorial using the GitHub interface?

Check our tutorial to learn how to use the GitHub interface (soon…)

How can I give feedback?

At the end of each tutorial, there is a link to a feedback form. We use this information to improve our tutorials.

For global feedbacks, you can open an issue on GitHub, write us on Gitter or send us an email.

What can I do to help the project?

In issues, you will find lists of issues to fix and features to implement (with the “newcomer-friendly” label for example). Feel free to work on them!

How can I report mistakes or errors?

The easiest way to start contributing is to file an issue to tell us about a problem such as a typo, spelling mistake, or a factual error. You can then introduce yourself and meet some of our community members.

How can I test an Interactive Tour?

Perhaps you’ve been asked to review an interactive tour, or maybe you just want to try one out. The easiest way to run an interactive tour is to use the Tour builder browser extension.

  1. Install the Tour Builder extension to your browser (Chrome Web Store, Firefox add-on).
  2. Navigate to a Galaxy instance supporting the tutorial. To find which Galaxy instances support each tutorial, please see the dropdown menu next to the tutorial on the training website. Using one of the usegalaxy.* instances (Galaxy Main, Galaxy Europe, or Galaxy Australia) is usually a good bet.
  3. Start the Tour Builder plugin by clicking on the icon in your browser menu bar
  4. Copy the contents of the tour.yaml file into the Tour builder editor window
  5. Click Save and then Run

How can I create new content without dealing with git?

If you feel uncomfortable with using the git and the GitHub flow, you can write a new tutorial with any text editor and then contact us (via Gitter or email). We will work together to integrate the new content.

What information should I include when reporting a problem?

Writing bug reports is a good skill to have as bioinformaticians, and a key point is that you should include enough information from the first message to help the process of resolving your issue more efficient and a better experience for everyone.

What to include

  1. Which commands did you run, precisely, we want details. Which flags did you set?
  2. Which server(s) did you run those commands on?
  3. What account/username did you use?
  4. Where did it go wrong?
  5. What were the stdout/stderr of the tool that failed? Include the text.
  6. Did you try any workarounds? What results did those produce?
  7. (If relevant) screenshot(s) that show exactly the problem, if it cannot be described in text. Is there a details panel you could include too?
  8. If there are job IDs, please include them as text so administrators don’t have to manually transcribe the job ID in your picture.

It makes the process of answering ‘bug reports’ much smoother for us, as we will have to ask you these questions anyway. If you provide this information from the start, we can get straight to answering your question!

What does a GOOD bug report look like?

The people who provide support for Galaxy are largely volunteers in this community, so try and provide as much information up front to avoid wasting their time:

I encountered an issue: I was working on (this server> and trying to run (tool)+(version number) but all of the output files were empty. My username is jane-doe.

Here is everything that I know:

  • The dataset is green, the job did not fail
  • This is the standard output/error of the tool that I found in the information page (insert it here)
  • I have read it but I do not understand what X/Y means.
  • The job ID from the output information page is 123123abdef.
  • I tried re-running the job and changing parameter Z but it did not change the result.

Could you help me?



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 window

  • By 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

Upload fastqsanger datasets via links

Tip: Upload fastqsanger datasets via links

  1. Click on Upload Data on the top of the left panel:

    UploadDataButton

  2. Click on Paste/Fetch:

    PasteFetchButton

  3. Paste URL into text box that would appear:

    PasteFetchModal

  4. Set Type (set all) to fastqsanger or, if your data is compressed as in URLs above (they have .gz extensions), to fastqsanger.gz

    ChangeTypeDropDown:

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

Upload few files (1-10)

Tip: Upload few files (1-10)

  1. Click on Upload Data on the top of the left panel
  2. Click on Choose local file and select the files or drop the files in the Drop files here part
  3. Click on Start
  4. Click on Close

Upload many files (>10) via FTP

Tip: Upload many files (>10) via FTP

  1. Make sure to have an FTP client installed

    There are many options. We can recommend FileZilla, a free FTP client that is available on Windows, MacOS, and Linux.

  2. Establish FTP connection to the Galaxy server
    1. Provide the Galaxy server’s FTP server name (e.g. usegalaxy.org, ftp.usegalaxy.eu)
    2. Provide the username (usually the email address) and the password on the Galaxy server
    3. Connect

  3. Add the files to the FTP server by dragging/dropping them or right clicking on them and uploading them

    The FTP transfer will start. We need to wait until they are done.

  4. Open the Upload menu on the Galaxy server
  5. Click on Choose FTP file on the bottom
  6. Select files to import into the history
  7. Click on Start


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


Further reading

Where can I read more about this analysis?

This tutorial was adapted from the mothur MiSeq SOP created by the Schloss lab. Here you can find more information about the mothur tools and file formats. Their FAQ page and Help Forum are also quite useful!

Where can I read more about this analysis?

This tutorial was adapted from the mothur MiSeq SOP created by the Schloss lab. Here you can find more information about the mothur tools and file formats. Their FAQ page and Help Forum are also quite useful!



Galaxy admin interface

Install tools via the Admin UI

Tip: Install tools via the Admin UI

  1. Open Galaxy in your browser and type `` in the tool search box on the left. If “” is among the search results, you can skip the following steps.
  2. Access the Admin menu from the top bar (you need to be logged-in with an email specified in the admin_users setting)
  3. Click “Install and Uninstall”, which can be found on the left, under “Tool Management”
  4. Enter `` in the search interface
  5. Click on the first hit, having devteam as owner
  6. Click the “Install” button for the latest revision
  7. Enter “” as the target section and click “OK”.


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 panel.

If 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

Import an history

Tip: Import an history

  1. Open the link to the shared history
  2. Click on the Import history button on the top right
  3. Enter a title for the new history
  4. Click on Import

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

Sharing your History

You can share your work in Galaxy. There are various ways you can give access one of your histories to other users.

Tip: Sharing your History

Sharing your history allows others to import and access the datasets, parameters, and steps of your history.

  1. Share via link
    • Open the History Options menu (gear icon) at the top of your history panel
      • Make History accessible
      • A Share Link will appear that you give to others
    • Anybody who has this link can view and copy your history
  2. Publish your history
    • Make History publicly available in Published Histories
    • Anybody on this Galaxy server will see your history listed under the Shared Data menu
  3. Share only with another user.
    • Click the Share with a user button at the bottom
    • Enter an email address for the user you want to share with
    • Your history will be shared only with this user.
  4. Finding histories others have shared with me
    • Click on User menu on the top bar
    • Select Histories shared with me
    • Here you will see all the histories others have shared with you directly

Note: If you want to make changes to your history without affecting the shared version, make a copy by going to History options icon in your history and clicking Copy



Igv

Add genome and annotations to IGV from Galaxy

Tip: Add genome and annotations to IGV from Galaxy

  1. Upload a FASTA file with the reference genome and a GFF3 file with its annotation in the history (if not already there)
  2. Install IGV (if not already installed)
  3. Launch IGV on your computer
  4. Expand the FASTA dataset with the genome in the history
  5. Click on the local in display with IGV to load the genome into the IGV browser
  6. Wait until all Dataset status are ok

  7. Close the window

    An alert ERROR Parameter "file" is required may appear. Ignore it.

  8. Expand the GFF3 dataset with the annotations of the genome in the history
  9. Click on the local in display with IGV to load the annotation into the IGV browser

  10. Switch to the IGV instance

    The annotation track should appear. Be careful that all files have the same genome ID

Add genome and annotations to IGV from Galaxy

Tip: Add genome and annotations to IGV from Galaxy

  1. Upload a FASTA file with the reference genome and a GFF3 file with its annotation in the history (if not already there)
  2. Install IGV (if not already installed)
  3. Launch IGV on your computer
  4. Expand the FASTA dataset with the genome in the history
  5. Click on the local in display with IGV to load the genome into the IGV browser
  6. Wait until all Dataset status are ok

  7. Close the window

    An alert ERROR Parameter "file" is required may appear. Ignore it.

  8. Expand the GFF3 dataset with the annotations of the genome in the history
  9. Click on the local in display with IGV to load the annotation into the IGV browser

  10. Switch to the IGV instance

    The annotation track should appear. Be careful that all files have the same genome ID

Add Mapped reads track to IGV from Galaxy

Tip: Add Mapped reads track to IGV from Galaxy

  1. Install IGV (if not already installed)
  2. Launch IGV on your computer
  3. Check if the reference genome is available on the IGV instance
  4. Expand the BAM dataset with the mapped reads in the history
  5. Click on the local in display with IGV to load the reads into the IGV browser

  6. Switch to the IGV instance

    The mapped reads track should appear. Be sure that all files have the same genome ID

Add Mapped reads track to IGV from Galaxy

Tip: Add Mapped reads track to IGV from Galaxy

  1. Install IGV (if not already installed)
  2. Launch IGV on your computer
  3. Check if the reference genome is available on the IGV instance
  4. Expand the BAM dataset with the mapped reads in the history
  5. Click on the local in display with IGV to load the reads into the IGV browser

  6. Switch to the IGV instance

    The mapped reads track should appear. Be sure that all files have the same genome ID



Instructors

What are the best practices for teaching with Galaxy?

We started to collect some best practices for instructors inside our Good practices slides

What Galaxy instance should I use for my training?

To teach the hands-on tutorials you need a Galaxy server to run the examples on.

Each tutorial is annotated with the information on which public Galaxy servers it can be run. These servers are available to anyone on the world wide web and some may have all the tools that are needed by a specific tutorial. If you choose this option then you should work with that server’s admins to confirm that think the server can handle the workload for a workshop. For example, the usegalaxy.eu

If your organization/consortia/community has its own Galaxy server, then you may want to run tutorials on that. This can be ideal because then the instance you are teaching on is the same you your participants will be using after the training. They’ll also be able to revisit any analysis they did during the training. If you pursue this option you’ll need to work with your organization’s Galaxy Admins to confirm that

  • the server can support a room full of people all doing the same analysis at the same time.
  • all tools and reference datasets needed in the tutorial are locally installed. To learn how to setup a Galaxy instance for a tutorial, you can follow our dedicated tutorial.
  • all participants will be able to create/use accounts on the system.

Some training topics have a Docker image that can be installed and run on all participants’ laptops. These images contain Galaxy instances that include all tools and datasets used in a tutorial, as well as saved analyses and repeatable workflows that are relevant.

Finally, you can also run your tutorials on cloud-based infrastructures. Galaxy is available on many national research infrastructures such as Jetstream (United States), GenAP (Canada), GVL (Australia), CLIMB (United Kingdom), and more.

How do I get help?

The support channel for instructors is the same as for individual learners. We suggest you start by posting a question to the Galaxy Training Network Gitter chat. Anyone can view the discussion, but you’ll need to login (using your GitHub or Twitter account) to add to the discussion.

If you have questions about Galaxy in general (that are not training-centric) then there are several support options.

Where do I start?

Spend some time exploring the different tutorials and the different resources that are available. Become familiar with the structure of the tutorials and think about how you might use them in your teaching.



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.


Introduction

What is Galaxy?

Galaxy is an open data integration and analysis platform for the life sciences, and it is particularly well-suited for data analysis training in life science research.

What is this website?

This website is a collection of hands-on tutorials that are designed to be interactive and are built around Galaxy:

Interactive training

This material is developed and maintained by the worldwide Galaxy community. You can learn more about this effort by reading our article.

How can I advertise the training materials on my posters?

We provide some QR codes and logos in the images folder.

What audiences are the tutorials for?

There are two distinct audiences for these materials.

  1. Self-paced individual learners. These tutorials provide everything you need to learn a topic, from explanations of concepts to detailed hands-on exercises.
  2. Instructors. They are also designed to be used by instructors in teaching/training settings. Slides, and detailed tutorials are provided. Most tutorials also include computational support with the needed tools, data as well as Docker images that can be used to scale the lessons up to many participants.

How can I cite the GTN?

We wrote an article about our efforts.

To cite individual tutorials, please find citation information at the end of the tutorial.

How is the content licensed?

The content of this website is licensed under the Creative Commons Attribution 4.0 License.

What are the tutorials for?

These tutorials can be used for learning and teaching how to use Galaxy for general data analysis, and for learning/teaching specific domains such as assembly and differential gene expression analysis with RNA-Seq data.



Learners

How can I get help?

If you have questions about this training material, you can reach us using the Gitter chat. You’ll need a GitHub or Twitter account to post questions. If you have questions about Galaxy outside the context of training, see the Galaxy Support page.

Where do I start?

If you are new to Galaxy then start with one of the introductory topics. These introduce you to concepts that are useful in Galaxy, no matter what domain you are doing analysis in.

If you are already familiar with Galaxy basics and want to learn how to use it in a particular domain (for example, ChIP-Seq), then start with one of those topics.

If you are already well informed about bioinformatics data analysis and you just want to get a feel for how it works in Galaxy, then many tutorials include Instructions for the impatient sections.

Where can I run the hands-on tutorials?

To run the hands-on tutorials you need a Galaxy server to run them on.

Each tutorial is annotated with information about which public Galaxy servers it can be run on. These servers are available to anyone on the world wide web and some may have all the tools that are needed by a specific tutorial.

If your organization/consortia/community has its own Galaxy server, then you may want to run tutorials on that. You will need to confirm that all necessary tools and reference genomes are available on your server and possible install missing tools and data. To learn how to do that, you can follow our dedicated tutorial.

Some topics have a Docker image that can be installed and run on participants’ laptops. These Docker images contain Galaxy instances that include all tools and datasets used in a tutorial, as well as saved analyses and repeatable workflows that are relevant. You will need to install Docker.

Finally, you can also run your tutorials on cloud-based infrastructures. Galaxy is available on many national research infrastructures such as Jetstream (United States), GenAP (Canada), GVL (Australia), CLIMB (United Kingdom), and more. These instances are typically easy to launch, and easy to shut down when you are done.

If you are already familiar with, and have an account on Amazon Web Services then you can also launch a Galaxy server there using CloudLaunch.

How do I use this material?

Many topics include slide decks and if the topic you are interested in has slides then start there. These will introduce the topic and important concepts.

Most of your learning will happen in the next step - the hands-on tutorials. This is where you’ll become familiar with using the Galaxy interface and experiment with different ways to use Galaxy and the tools in Galaxy.



Markdown

How can I create a tutorial skeleton from a Galaxy workflow?

Tip: How can I create a tutorial skeleton from a Galaxy workflow?

There are two ways to do this:

  1. Use planemo on your local machine. Please see the tutorial named “Creating a new tutorial” for detailed instructions.
  2. Use our web service

How can I create a tutorial skeleton from a Galaxy workflow?

Tip: How can I create a tutorial skeleton from a Galaxy workflow?

There are two ways to do this:

  1. Use planemo on your local machine. Please see the tutorial named “Creating a new tutorial” for detailed instructions.
  2. Use our web service


Other

Are there any upcoming events focused on Galaxy Training?

Yes, always! Have a look at the Galaxy Community Events Calendar for what coming up right now.

Sustainability of the training-material and metadata

This repository is hosted on GitHub using git as a DVCS. Therefore the community is hosting backups of thisrepository in a decentralised way. The repository is self-contained and contains all needed content and all metadata.In addition we mirror snapshops of this repo on Zenodo.



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 variations

How 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 tool

This 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 API key

Tip: Getting your API key

  1. In your browser, open your Galaxy homepage
  2. Log in, or register a new account, if it’s the first time you’re logging in
  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


Utilities

Got lost along the way?

Got lost along the way?

If you missed any steps, you can compare against the reference files, or see what changed since the previous tutorial.



Workflows

Annotate a workflow

Tip: Annotate a workflow

  • Open the workflow editor for the workflow
  • Click on Edit Attributes on the top right
  • Write a description of the worklow in the Annotation box
  • Add a tag (which will help to search for the workflow) in the Tags section

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

Importing a workflow using the search

Tip: Importing a workflow using the search

  • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
  • Click on the Import icon at the top-right of the screen

  • Click on search form in Import a Workflow from Configured GA4GH Tool Registry Servers (e.g. Dockstore)

  • Select the relevant TRS Server

  • Type the query

  • Expand the correct workflow

  • Click on the wanted version

    The workflow will be imported in your workflows

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

Make a workflow public

Tip: Make a workflow public

  • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows
  • Click on the interesting workflow
  • Click on Share
  • Clik on *Make Workflow Accessible and Publish**

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 name

      Rename output datasets

Viewing a workflow report

When creating a workflow in Galaxy, you can also define an output report page that should be created. Here you can display certain outputs of the pipeline (e.g. output files, tables, images, etc.) and other information about the run.

Tip: Viewing a workflow report

  • Go to User on the top menu bar of Galaxy.
  • Click on Workflow invocations
    • Here you will find a list of all the workflows you have run
  • Click on the name of a workflow invocation to expand itworkflow invocations list
  • Click on View Report to go to the workflow report page
  • Note: The report can also be downloaded in PDF format by clicking on the icon.

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