Introduction to Galaxy


Galaxy is a platform for doing reproducible bioinformatics research. It provides a friendly interface to a vast number of complex command line tools, and it encourages consistent science by using identical software and interfaces across all Galaxy instances.

At the CPT we depend on Galaxy for all of our computer-based analysis; we can launch long running jobs and return to our lab work, while Galaxy keeps track of where and how far along our analysis is, during runs of our pipelines.


Fig. 1 Main Galaxy View

Galaxy consists of a three panel interface. On the left are your tools, in the center you’ll do your analysis and view the results, and on the right is your history.


Fig. 2 Tool Panel

These are all groups of tools you can run in Galaxy. A tool is something that generates or transforms data. Some examples of tools would be a Gene Caller, which reads your genome and returns a list of gene locations, or Blast, which would search your protein sequences against a database.

In Galaxy, tools are simple interfaces to the complex software behind them. Galaxy tools enhance productivity by ensuring that your input files are the correct format at every step.


Fig. 3 History Panel

The history panel keeps track of what you’ve done. Each entry is called a “dataset” in Galaxy terminology. Datasets are usually just a single file (like a fasta genome), but may be complex files (like html web pages). The colors indicate in which of the three states a job could be; grey for jobs that have been submitted to Galaxy, yellow for a jobs that are currently running, and red/green for a jobs that are completed.


Sometimes jobs fail and turn red! If they do, don’t worry–it likely isn’t your fault. Just be sure to click the bug icon so we can be made aware of what went wrong.


Fig. 4 The bug icon is in the bottom left. Please click it if you see it!


Tools are the central feature of Galaxy, they’re what Galaxy is all about: easy-to-use access to powerful command line tools. We use the language “tool” to describe some command line program that has been “wrapped” for use in Galaxy. Many of the command line options are available to be tweaked and fiddled with in the Galaxy tool interface.


Fig. 5 This Galaxy tool has a large number of options. Many tools are highly configurable to meet the needs of a wide variety of end users.

To run a tool, first read the tool’s help box at the bottom, it may give you very important information regarding running the tool, and the options available to you.

Next go over the options in the tool interface, many are set to default values and those options may not be what you intended to happen.

Nearly all Galaxy tools process some input files and produce some output files. When you have an input box that lets you select a file, Galaxy will ensure that you cannot pass datasets of an incorrect format to a tool.

When you’ve finished configuring a tool, and execute it, it will show up as a set of output files in the history on the right, which we’ll cover in the next section..


A job is a tool run with a set of parameters, and it sits somewhere in Galaxy’s queue. We have lots of Galaxy users and can only run so many jobs at once. Jobs produce one or more output files.

Looking at an individual output dataset, there will be several features that will be of interest to you, and a couple that won’t:

In this example we see dataset #29, which is a table of Start Codon Usages.


Fig. 6 An example Dataset

This is the collapsed view of a dataset. You’ll see three icons, an eye, a pencil, and an X.

  • The eye icon will display the dataset in the main window
  • The pencil icon is used to edit the information about the dataset (i.e. metadata, such as filename, file type, and other more obscure facts)
  • And the X icon will delete a dataset, for which you no longer have any use

When you delete a dataset accidentally, don’t worry! It isn’t gone permanently.


Fig. 7 History Header

See the text “6 deleted”? Clicking on the word “deleted” will show you the deleted items in your history.

Clicking anywhere on the title of the dataset, “Start Codon Usages” will expand to the full view:


Fig. 8 Example Dataset in the expanded view.

At the bottom of the dataset you can see a preview of the table. Near the top you’ll note the it is a “tabular” format. Galaxy keeps track of file formats to ensure you only use correctly-formatted data for tools.

The history dataset view is information dense, so we’ll go over the icons, their colloquial names, and their functions.

  1. Eyeball views the dataset
  2. Pencil modifies metadata
  3. X sends a dataset to the trash. Remember, you can recover deleted datasets.
  4. Save downloads the dataset to your hard-drive. You don’t need to do this, as Galaxy will always have a copy for you.
  5. Information views details about the tool that was run and how it was configured.
  6. Rerun is a very commonly used button. This lets you re-run the tool, with the same parameters configured.
    • Need to run the same tool with slightly different parameters? Don’t waste time filling out the tool form; re-run it and tweak those.
    • Job failed? Try modifying the tool inputs and re-running it.
  7. Visualize lets you visualize your dataset in a couple of different ways. We don’t use this very often in the course as it isn’t appropriate to our analysis. However, some of the useful visualizations include: a “charts” visualization that lets you build graphs from your tabular dataset, and an Multiple Sequence Alignment (MSA) visualization plugin in Galaxy which lets you interactively explore MSAs.
  8. Tags let you annotate datasets with tags. We don’t use this feature.
  9. Comments let you comment on a dataset to remind yourself why you did it, or maybe to annotate some interesting results you found in the output.


Histories are used to separate out your analyses and help you keep track of what you’ve done. You might make a new history for a task like assembling a genome, annotating a genome, or doing some comparative analysis between phages. It’s good practice to title your history something that will be useful to you when you come back later. Who knows what “Untitled History” last edited on 2012-02-01 was for? Good names are important!

At the top right of your history you’ll see a couple icons. A refresh symbol, a gear, and the new history view.


Fig. 9 History Menu

The refresh button can be used to refresh your history if you’re impatient, like I can be. The gear icon provides you with the old interface to switch histories and modify the view. We’ll be using the new “View all histories” view:


Fig. 10 The new (in 2015) Galaxy History Switcher. It is a huge improvement over the old one because you can easily move datasets between histories

At the top left you’ll see a done button, which will let you exit this view when you’re done. On the left is your current history. To the right of that are your other histories. The switch to button allows you to switch histories. Any new tools you run will be done in this history.

Uploading Data

Switch back to the main view of Galaxy (i.e. click done if you’re still in the switch history menu from above). If you ever find yourself lost in Galaxy, you can always click Analyze Data on the big blue bar at the top, and it’ll take you home.


Fig. 11 Upload

This button will bring up your upload menu and allow you to import data into Galaxy.


Fig. 12 Upload Window

You can drag and drop files to upload them, or use the Choose Local File button in the bottom menu.


Fig. 13 Upload Menu

There are a couple other options for advanced users:

  • Choose FTP file allows you to select a file you’ve uploaded via FTP. For files >2GB, this is required.
  • Paste/Fetch data allows you to paste in a bit of text or a URL. Galaxy will import that into your history.

Once the file has been detected by Galaxy, you’ll see it pop up in the upload window:


Fig. 14 Uploading File

You can set the file type if Galaxy doesn’t detect it properly, but that is a rare case, and before overriding Galaxy you should double check that your file is formatted properly.

When you’ve selected all the files you wish to upload, click Start in the bottom right of the upload menu.


Fig. 15 Starting Upload

The dataset will indicate to you that it is uploaded in the upload window,


Fig. 16 Uploading...

at which time you can close that window with Escape or the Close button in the bottom right.

The dataset will turn yellow


Fig. 17 Processing an upload

And then green when it is ready.


Fig. 18 Finished upload


Workflows are merely collections of jobs where some jobs depend on the outputs of other jobs. Say you’re faced with a task like the following:

  1. Load data from apollo
  2. Extract all of genes as DNA sequences
  3. Translate those to protein sequences
  4. Run those proteins through BlastP

If you did these tasks one by one, you would have to keep track of at least 4 different files, one as the output of each step. You have to wait for each program to finish, before you can execute the next one. What if step 2 took 10 minutes? And Step 3 took 15? You would have to check back every few minutes to see if your job was done before you could start the next.

Thus, enter workflows:


Fig. 19 An example workflow encapsulating the four steps from our example workflow

Workflows solve numerous problems for us:

  • Run tools immediately
    • The next step can start as soon as data is available, a human does not have to manually start it
  • Discard useless data
    • In the above example we probably only cared about the output BLAST results, we don’t care about storing the intermediate files forever.
  • Simplified interfaces
    • In the same way that Galaxy tools hide the complexity of hundreds of command line options and working in Linux, Workflows hide the complexity of many Galaxy tools.
    • You, as a user, are probably not interested in the fact that we have to extract features from a GFF3 file, and then translate those to protein sequences.



Fig. 20 Before you create a new workflow, check the published workflow page. Another user may have created a workflow you can use.

You will often be asked to import workflows. You can do this by going to the Published Workflow page, and finding a workflow you’re interested in.


Fig. 21 Importing workflows is easy, just click the little down arrow, and select “Import”


Workflows which you have created, or imported, are available under the Workflow menu at the top of Galaxy.


Fig. 22 Some of the author’s Galaxy workflows. The author has somewhere around 60 different workflows, as they are instrumental in running complex analyses on Galaxy


Fig. 23 To run a workflow, click the down arrow, and select the Run option

The run workflow interface can be somewhat overwhelming. For the large part, the tools are pre-configured for you. As the course progresses we’ll cover in detail what each portion of the workflow does.


Fig. 24 Running a workflow. Some boxes which are automatically expanded may require your attention, the ones which are closed may not require attention

Just like with tools, there is an Execute button at the bottom which will launch the workflow.


At this point you should be fairly comfortable:

  • Uploading data
  • Job Outputs
  • Running Tools
  • Switching between histories
  • Importing workflows
  • Running workflows