Galaxy 101

Creative Commons License: CC-BY Questions:
  • Which coding exon has the highest number of single nucleotide polymorphisms (SNPs) on human chromosome 22?

  • Familiarize yourself with the basics of Galaxy

  • Learn how to obtain data from external sources

  • Learn how to run tools

  • Learn how histories work

  • Learn how to create a workflow

  • Learn how to share your work

Time estimation: 1 hour
Level: Introductory Introductory
Supporting Materials:
Published: Dec 19, 2016
Last modification: Dec 4, 2023
License: Tutorial Content is licensed under Creative Commons Attribution 4.0 International License. The GTN Framework is licensed under MIT
purl PURL:
rating Rating: 4.3 (11 recent ratings, 138 all time)
version Revision: 123

This tutorial aims to familiarize you with the Galaxy user interface. It will teach you how to perform basic tasks such as importing data, running tools, working with histories, creating workflows, and sharing your work.

Comment: 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.


In this tutorial, we will:

  1. Setting the stage: Exons and SNPs
    1. Locating Exons
  2. Analysis
    1. Find exons with the most SNPs
    2. Count the number of SNPs per exon
    3. Sort the exons by SNPs count
    4. Select the top five exons
    5. Recovering exon info
    6. Displaying data in UCSC genome browser
  3. Galaxy management
    1. Convert your analysis history into a workflow
    2. The workflow editor
    3. Run workflow on different data
    4. Share your work
  4. Conclusion

Setting the stage: Exons and SNPs

We start with the question: In human chromosome 22, which exon has the highest number of single nucleotide polymorphisms (SNPs)?

Comment: Background

Not everyone has the same background and that’s ok! You may have studied different organisms with different gene models, or you may not be familiar with the biological aspect at all. The biological background is not necessary for following this tutorial, we just like to provide research context for any tutorial. Here are brief explanations of some of the concepts mentioned in this tutorial:

  • Nucleotides are the A, C, T, and Gs that make up DNA.
  • Chromosomes can be thought of as a very long piece of DNA (string of A, C, T, Gs) Some organisms have chromosomes, in this tutorial we will use Human chromosome number 22.
  • Features are regions of the chromosome that are interesting for one reason or another. Some examples of features include genes, terminators, transcription start sites, and repeat regions.
  • Genes are one kind of interesting feature, a region that will be transcribed into RNA before being translated into proteins.
  • Exons are fundamental components of eukaryotic genes. A typical eukaryotic gene contains numerous exons separated by introns. An entire gene containing both exons and introns is transcribed into a pre-messenger RNA or pre-mRNA. During maturation introns are excised from the pre-mRNA in a process called splicing. A mature messenger RNA, or simply mRNA, is then translated into protein during the process of translation.

    Eukaryotic gene model. Open image in new tab

    Figure 1: An original piece of DNA containing introns and exons has the introns cut out before the exons are joined together to form the mRNA. (Image from WikiMedia, under the Public Domain)

SNP is an abbreviation for single-nucleotide polymorphism. It is pronounced as “snip”. SNPs are single nucleotide differences between a sequenced individual compared to some reference sequence; where one individual might have an A, another could have a C in that position. Databases of SNPs have been created for many organisms and they include any single nucleotide deviation from the reference sequence which appears in some percentage of the population (e.g. >1%). These are often especially interesting to geneticists as the causes of certain inheritable diseases.

You may be familiar with the UCSC Genome Browser or another resource like it, and know that you can find the data there. But even with your data in hand, you still have the question: “how do I actually compute this?” There is really a straightforward answer: Galaxy. So let’s try it…

Browse to your favorite Galaxy instance and log in or register.

  1. To create an account at any public Galaxy instance, choose your server from the available list of Galaxy Platforms.

    There are several UseGalaxy servers:

  2. Click on “Login or Register” in the masthead on the server.

    Login or Register on the top panel

  3. On the login page, find the Register here link and click on it.

  4. Fill in the the registration form, then click on Create.

    Your account should now get created, but will remain inactive until you verify the email address you provided in the registration form.

    Banner warning about account with unverified email address

  5. Check for a Confirmation Email in the email you used for account creation.

    Missing? Check your Trash and Spam folders.

  6. Click on the Email confirmation link to fully activate your account.

    galaxy-info Delivery of the confimation email is blocked by your email provider or you mistyped the email address in the registration form?

    Please do not register again, but follow the instructions to change the email address registered with your account! The confirmation email will be resent to your new address once you have changed it.

    Trouble logging in later? Account email addresses and public names are caSe-sensiTive. Check your activation email for formats.

The Galaxy interface consists of three main parts:

  1. The available Tools are listed on the left
  2. The Central Panel will let you run analyses and view outputs
  3. Your analysis History is recorded on the right
Galaxy interface screenshot showing History panel on the right, tools panel on the left, and main panel in the center. Galaxy interface screenshot showing History panel on the right, tools panel on the left, and main panel in the center.
Open image in new tab

Figure 2: Galactic triptych: the three panels of Galaxy interface: Tools, Center Panel, and History

When you start Galaxy for very first time, your history will be empty. Let’s add some data to it.

Hands-on: Create history
  1. Make sure you start from an empty analysis history.

    Click the new-history icon at the top of the history panel.

    If the new-history is missing:

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

  2. Rename your history to be meaningful and easy to find. You can do this by clicking on the title of the history (which by default is Unnamed history) and typing Galaxy 101 as the name. Do not forget to hit the Enter key on your keyboard to save it.

    Rename the history.

Locating Exons

First we need to get some data into our history. You can upload files from your computer, or Galaxy can also fetch data directly from external sources. We know UCSC has exon locations for humans and we can use Galaxy to import the data for chromosome 22, directly from the UCSC table browser.

Hands-on: Upload SNPs and Exons
  1. At the top of the Tools panel (on the left), click galaxy-upload Upload Data

    upload data button.

    This brings up a box:


  2. Click Paste/Fetch data and paste in the following URLs in the box that appears.
  3. Click Start, and then Close

  4. When they are ready, rename galaxy-pencil the datasets to Exons and SNPs, respectively.

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

For this tutorial, we made the input datasets available on Zenodo for you. However, these datasets can also obtained directly from UCSC, without leaving Galaxy. Below we describe how you can do this, but it is not necessary for this tutorial. Note that since the data in UCSC is updated frequently, you might get slightly different results in the rest of this tutorial if you use these files.


In order to get the datasets from the UCSC server, you need to have an account in an instance.

This tool works a bit differently than most Galaxy tools, but if you wish to obtain the newest data from UCSC, you can do that as follows:

Hands-on: Obtaining Exons from UCSC
  1. UCSC Main tool table browser:

    In the tool menu, navigate to Get Data -> UCSC Main table browser

    `UCSC Main table browser` menu entry.

    You will be taken to the UCSC table browser, which looks something like this:

    `UCSC table browser` tool, first screen for exons.

    Now set the following parameters:

    • “clade”: Mammal
    • “genome”: Human
    • “assembly”: Dec. 2013 (GRCh38/hg38)
    • “group”: Genes and Gene Predictions
    • “track”: GENCODE v36 (or a more recent version)
    • “table”: knownGene
    • param-text “region” should be changed to position with value chr22
    • “output format” should be changed to BED - browser extensible data
    • param-check “Send output to” should have the option Galaxy checked

    If the “table” drop down menu does not show the knownGene option. Set “group” to All tables and scroll down.

  2. Click on the get output button and you will see the next screen:

    `UCSC table browser` tool, second screen for exons.

    Change Create one BED record per to Coding Exons and then click on the Send query to Galaxy button.


    After this you will see your first history item in Galaxy’s right panel. It will go through the gray (preparing/queued) and yellow (running) states to become green (success):

    `UCSC Main on Human: knownGene` dataset is green.

    You might need to login to Galaxy again.

  3. When the dataset is green, click on the galaxy-eye (eye) icon to view the contents of the file. It should look something like this:

    Contents of the `UCSC Main on Human: knownGene` dataset.

    Each line represents an exon, the first three columns are the genomic location, and the fourth column contains the name of the exon.

  4. Let’s rename our dataset to something more recognizable.

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

    Your history should now look something like this:

    Rename dataset to `Exons`.

We now have information about the exon locations, but our original question was which exon contains the largest number of SNPs, so let’s get some information about SNP locations from UCSC as well:

Hands-on: Obtaining SNPs from UCSC

Again open the UCSC Main table browser tool and set the following parameters

  1. UCSC Main tool table browser:
    • “group” should be changed to Variation
    • param-text “region” should be changed again to position with value chr22
    • “output format” should be changed again to BED - browser extensible data

    `UCSC table browser` tool, first screen for SNPs.

    The “track” setting shows the version of the SNP database to get. In this example it is version 151, but you may select the latest one. Your results may vary slightly from the ones in this tutorial when you select a different version, but in general it is a good idea to select the latest version, as this will contain the most up-to-date SNP information.

  2. Click on the get output button to find a form similar to this:

    `UCSC table browser` tool, second screen for SNPs.

    Make sure that “Create one BED record per” is set to Whole Gene (Whole Gene here really means Whole Feature), and click on Send query to Galaxy. A second item will appear in your analysis history.

  3. Now rename galaxy-pencil your new dataset to SNPs so we can easily remember what the file contains.


Find exons with the most SNPs

Our objective is to find which exon contains the most SNPs. Therefore we have to intersect the file with the exon locations with the file containing the SNP locations (here “intersect” is just a fancy word for printing SNPs and exons that overlap side-by-side).

Different Galaxy servers may have tools available under different sections, therefore it is often useful to use the search bar at the top of the tool panel to find your tool.

Additionally different servers may have multiple, similarly named tools which accomplish similar functions. For these tutorials, you should select precisely the one that is described. However, in your real analyses, you’ll need to search among the various options to find the one that works for you.

Hands-on: Finding Exons

To find intersection we will be using intersect intervals tool from BEDTools package.

  1. bedtools intersect intervals ( Galaxy version 2.30.0) the intervals of two datasets side-by-side:

    Enter the word intersect in the search bar of the tool panel, and select the tool named bedtools Intersect intervals

    • “File A to intersect with B”: Select Exons
    • “Combined or separate output files”: Select One output file per 'input B' file"
    • “File B to intersect with A”: SNPs
    • “What should be written to the output file?”: Write the original entry in B for each overlap..., which means that only matches are included in the output (i.e. only exons with SNPs in it and only SNPs that fall in exons)

The interface of the tool should look like this:

Bedtool intersect interval interface.

All Galaxy tools include documentation. If you scroll down on this page, you will find the help of the tool.

  1. Click Run Tool
  2. Wait for the job to finish
  3. View the resulting file (with the galaxy-eye (eye) icon). If everything went okay, you should see a file that looks similar to this:

    Contents of the `Join` output dataset.

    Here column 4 contains exon IDs (e.g., ENST00000252835.5_cds_0_0_chr22_15528159_f) and column 10 lists SNP IDs (e.g., rs9617249) Remember that variations are possible due to using different versions of UCSC databases: as long as you have similar looking columns you did everything right!

Comment: If things didn't work...

Did the Intersect tool error with a memory failure? Or is this step executing for a long time? Most likely a setting was missed when extracting the data from the UCSC Table Browser. Try again, double checking that:

  • For both SNP and EXON: “region” is actually changed to position with value chr22
  • For EXON: “Create one BED record per” Coding Exons is selected (not Whole Gene as for the SNP data)
  • Carefully inspect the remaining Table Browser settings if these two most common reasons for problems were correct in your tool executions

Let’s take a look at this dataset. The first six columns correspond to the exons, and the last six columns correspond to the SNPs. Column 4 contains the exon IDs, and column 10 contains the SNP IDs. In our screenshot you see that the first lines in the file all have the same exon ID but different SNP IDs, meaning these lines represent different SNPs that all overlap the same exon.


For the first 3 exons in your file, what is the number of SNPs that fall into that exon?

At the time of writing, for hg38/GENCODE v29, joined with “Common dbSNPs(153)”, using ctrl-f (cmd-f on Mac OS) to look for how many times each is used:

Gene Occurences
ENST00000252835.5_cds_0_0_chr22_15528159_f 2
ENST00000643195.1_cds_0_0_chr22_15528192_f 2
ENST00000343518.11_cds_0_0_chr22_15690078_f 4

Count the number of SNPs per exon

Since each line in our file represents a single overlap between SNP and exon, we can find the total number of SNPs in an exon, simply by counting the number of lines that have the same exon ID. However, to be more “proper” we will instead count the number of unique SNP IDs per exon. So let’s do this for all the exons in our file:

Hands-on: Counting SNPs
  1. Datamash ( Galaxy version 1.1.0) (operations on tabular data):

    • “Input tabular dataset”: select the output dataset from bedtools intersect intervals tool
    • “Group by fields”: Column: 4 (the column with the exon IDs)

    Datamash tool interface.

    • Scroll tool interface down to “Operation to perform on each group”
      • “Type”: Count Unique values
      • “On column”: Column: 10 (this column contains SNPs ids like rs2236639. This we will count occurrences of unique SNP ids for each exon)

    Operation to perform on each group.

  2. Click Run Tool. Your new output dataset will look something like this:

    Contents of the `Group` output dataset.

This file contains only two columns. The first contains the exon IDs, and the second the number of times that exon ID appeared in the file - in other words, how many SNPs were present in that exon.


How many exons are there in total in your file?

Each line now represents a different exon, so you can see the answer to this when you expand the history item, as in the image above. The exact number you see for your dataset may be slightly different due to the updates to the exon and SNPs information in UCSC. In our case the dataset contains 4,242 lines, which is equal to the number of exons overlapped by at least one SNP.

Sort the exons by SNPs count

Now that we have a list of all exons, and the number of SNPs they contain, we would like to know which exon has the highest number of SNPs. We can do this by sorting the file on the second column.

Hands-on: Sorting
  1. Sort data in ascending or descending order:

    • “Sort Dataset”: Output from Datamash tool
    • “on column”: Column: 2
    • “with flavor”: Numerical sort
    • “everything in”: Descending order
  2. Click Run Tool

  3. Examine the output file.

    Contents of the `Sort` output dataset.

    The file should look similar to before sorting, but now the exons with the highest number of SNPs are at the top.


Which exon has the highest number of SNPs in your file?

When this tutorial was last updated, ENST00000253255.7_cds_0_0_chr22_46256561_r had 27 SNPs. Keep in mind this may depend on your settings when getting the data from UCSC.

Select the top five exons

Let’s say we want a list with just the top-5 exons with highest number of SNPs.

Hands-on: Select first
  1. Select first ( Galaxy version 1.1.0) lines from a dataset (head):

    • “File to select”: The output from Sort tool
    • Operation”: Keep first lines
    • “Number of lines”: 5
  2. Click Run Tool

  3. Examine the output file, this should contain only the first 5 lines of the previous dataset.

Recovering exon info

Congratulations! You have now determined which exons on chromosome 22 have the highest number of SNPs, but what else can we learn about them? One way to learn more about a genetic location is to view it in a genome browser. However, in the process of getting our answer, we have lost information about the location of these exons on the chromosome. But fear not, Galaxy saves all of your data, so we can recover this information quite easily.

Hands-on: Compare two Datasets
  1. Compare two Datasets to find common or distinct rows:

    • “Compare”: Exons
    • “Using column”: Column: 4
    • “against”: the output from Select first tool
    • “and column”: Column: 1
    • “to find”: Matching rows of 1st dataset
  2. Click Run Tool
  3. Examine your output file. It should contain the locations of your top 5 exons:

    Contents of the `Compare two Datasets` output dataset.

Displaying data in UCSC genome browser

A good way to learn about these exons is to look at their genomic surrounding. This can be done by using genome browsers. Galaxy can launch a genome browser such as IGV on your local machine, and it can connect to online genome browsers as well. An example of such an online genome browser is the UCSC genome browser.

Hands-on: UCSC genome browser
  1. First, check that the database of your latest history dataset is hg38. If not, click on the galaxy-pencil pencil icon and modify the Database/Build: field to Human Dec. 2013 (GRCh38/hg38) (hg38).

    • Click on the galaxy-pencil 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: hg38
    • Click the Save button

  2. Second, check that the format of your latest history dataset is bed. If not, click on the galaxy-pencil pencil icon and modify the Datatype field to bed.

    • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
    • In the central panel, click on the galaxy-gear Convert tab on the top
    • In the lower part galaxy-chart-select-data Datatypes, select bed
      • tip: you can start typing the datatype into the field to filter the dropdown menu
    • Click the Save button

  3. Click on the dataset in your history to expand it, then click on the galaxy-barchart (Visualize) icon

    visualize button on the expanded history item.

  4. To visualize the data in UCSC genome browser, click on display at UCSC (main) now visible at the top (blue box) of the Visualize menu.

    `display at UCSC main` link.

    This will upload the data to UCSC as custom track. To see your data look at the User Track near the top. You can enter the coordinates of one of your exons at the top to jump to that location.

    `User Track` shown in the UCSC genome browser.

UCSC provides a large number of tracks that can help you get a sense of your genomic area, it contains common SNPs, repeats, genes, and much more (scroll down to find all possible tracks).

Galaxy management

In Galaxy your analyses live in histories such as your current one. Histories can be very large, and you can have as many histories as you want. You can control your histories (switching, copying, sharing, creating a fresh history, etc.) in the galaxy-gear History Options menu on the top of the history panel:

History options menu.

If you create a new history, your current history does not disappear. If you would like to list all of your histories just use the galaxy-columns Multi-history view:

Overview of histories with their datasets.

Here you can:

  • switch between different histories,
  • delete a history,
  • purge it (i.e. permanently delete it, this action cannot be reversed)
  • copy histories
  • copy datasets between histories (by dragging and dropping)

You can always return to your analysis view by clicking on Home icon galaxy-home (Analyze Data on older versions of Galaxy) in the top menu bar.

Convert your analysis history into a workflow

When you look carefully at your history, you can see that it contains all the steps of our analysis, from the beginning to the end. By building this history we have actually built a complete record of our analysis with Galaxy preserving all parameter settings applied at every step. But when you receive new data, or a new report is requested, it would be tedious to do each step over again. Wouldn’t it be nice to just convert this history into a workflow that we will be able to execute again and again?

Galaxy makes this very easy with the Extract workflow option. This means any time you want to build a workflow, you can just perform the steps once manually, and then convert it to a workflow, so that next time it will be a lot less work to do the same analysis.

Hands-on: Extract workflow
  1. Clean up your history: remove any failed (red) jobs from your history by clicking on the galaxy-delete button.

    This will make the creation of the workflow easier.

  2. Click on galaxy-gear (History options) at the top of your history panel and select Extract workflow.

    `Extract Workflow` entry in the history options menu.

    The central panel will show the content of the history in reverse order (oldest on top), and you will be able to choose which steps to include in the workflow.

    Selection of steps for `Extract Workflow` from history.

  3. Replace the Workflow name to something more descriptive, for example Find exons with the highest number of features.

    While we created this workflow initially to analyse SNPs, if we had similarly formatted datasets we could use this workflow to find different features.

  4. If there are any steps that shouldn’t be included in the workflow, you can uncheck them in the first column of boxes.

  5. Click on the Create Workflow button near the top.

    You will get a message that the workflow was created. But where did it go?

  6. Click on Workflow in the top menu of Galaxy. Here you have a list of all your workflows. Your newly created workflow should be listed at the top:

    `Your workflows` list.

The workflow editor

We can examine the workflow in Galaxy’s workflow editor. Here you can view/change the parameter settings of each step, add and remove tools, and connect an output from one tool to the input of another, all in an easy and graphical manner. You can also use this editor to build workflows from scratch.

Hands-on: Extract workflow
  1. Click on the triangle to the right of your workflow name.

    `Edit` option in the menu for the latest workflow.

  2. Select Edit to launch the workflow editor. You should see something like this:

    Workflow editor.

    When you click on a workflow step, you will get a view of all the parameter settings for that tool on the right-hand side of your screen.

    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

  3. Re-arrange the boxes so you can clearly see the data flow. The default automatic layout hides some of the connections due to overlapping and box placement.

  4. Make sure the check boxes for out_file1 in the Select First and Compare two Datasets tools are selected. Make sure that everything else is not selected.

    Now, when we run the workflow, we will only see the final two outputs, i.e. the table with the top-5 exons and their SNP counts, and the file with exons ready for viewing in a genome browser.

  5. The box named Exons is named ok, but we want to change SNPs since this workflow is not specific to SNPs

    • Click on the box corresponding to the SNPs input dataset
    • change the Label to Features on the right-hand side of your screen.
  6. Let’s also rename the outputs:

    • Click on the Select first tool in the workflow editor
    • In the menu on the right click on Configure Output: 'out_file1'
      • Under Rename dataset, and enter a descriptive name for the output dataset like Top 5 exon IDs

    Rename the output of the `Select first` tool step.

  7. Repeat this for the output of the Compare two Datasets tool, naming it Top 5 exons

  8. Save your workflow (important!) by clicking on the galaxy-save icon at the top right of the screen.

  9. Return to the analysis view by clicking on the Home icon galaxy-home (or Analyze Data on older versions of Galaxy) at the top menu bar.


We could validate our newly built workflow by running it on the same input datasets that we used at the start of this tutorial, in order to make sure we do obtain the same results.

Run workflow on different data

Now that we have built our workflow, let’s use it on some different data. For example, let’s find out which exons have the highest number of repeat elements.

Hands-on: Run workflow
  1. Create a new history and give it a name.

    Click the new-history icon at the top of the history panel.

    If the new-history is missing:

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

  2. We will need the list of exons again. We don’t have to get this from UCSC again, we can just copy it from our previous history. The easiest way to do this is to go to the galaxy-columns history overview. Here you can just drag and drop datasets from one history to another.

    Drag and drop of `Exons` dataset in the history overview.

  3. Click the Home icon galaxy-home (or Analyze Data on older Galaxy versions) at the top to return to the main analysis window

  4. Upload Data the Repeats file from Zenodo
    • Copy the link location
    • Click galaxy-upload Upload Data at the top of the tool panel

    • Select galaxy-wf-edit Paste/Fetch Data
    • Paste the link(s) into the text field

    • Press Start

    • Close the window

    Again, for reproducibility we obtain the data from Zenodo ensuring that the results will never change, allowing us to do good science! However if you wish to obtain UCSC data:

    Hands-on: Obtaining Exons from UCSC
    1. UCSC Main tool table browser:

      In the tool menu, navigate to Get Data -> UCSC Main - table browser

      Now set the following parameters:

      • “clade”: Mammal
      • “genome”: Human
      • “assembly”: Dec. 2013 (GRCh38/hg38)
      • “group”: Repeats
      • param-text “region” should be changed to position with value chr22
      • “output format” should be changed to BED - browser extensible data
      • param-check “Send output to” should have the option Galaxy checked

    Click on get output and then Send query to Galaxy on the next screen.

  5. Rename galaxy-pencil the dataset to Repeats

  6. Open the workflow menu (top menu bar). Find the workflow you made in the previous section, and select the option Run.

    `Run` option in the workflow menu.

    • Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
    • Click on the workflow-run (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

    The central panel will change to allow you to configure and launch the workflow.

  7. Select appropriate datasets for the inputs as shown below, then scroll down and click Run workflow.
    • Select Expand to full workflow form
    • param-file “Exons”: the Exons file you copied from our previous history
    • param-file “Features”: the Repeats file we downloaded from UCSC

    Settings for running the workflow.

    Comment: Potential workflow issues
    • Galaxy validates the workflow inputs to ensure they’re correct. It may show a validation error at the start, until you select Exons for the Exons input, and your repeats for the Features input.
    • If you see an “Invalid column choice” error, you need to specify which column you want to use. If you have to type the column number, you need to type just the number e.g. 4 (not Column 4 or anything else).
  8. Once the workflow has started, you will initially be able to see all its steps, but the unimportant intermediates will disappear after they complete successfully:

    Datasets appearing in the history.

Comment: Unhiding hidden datasets

Because most intermediate steps of the workflow were hidden, once it is finished you will only see the final two datasets. If we want to view the intermediate files after all, you can click the “## hidden” just below the history’s name.


Which exon had the highest number of repeats? How many repeats were there?

Share your work

One of the most important features of Galaxy comes at the end of an analysis. When you have published striking findings, it is important that other researchers are able to reproduce your in-silico experiment. Galaxy enables users to easily share their workflows and histories with others.

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

Access the history sharing menu via the History Options dropdown (galaxy-history-options), and clicking “history-share Share or Publish”

  1. Share via link
    • Open the History Options galaxy-gear menu (gear icon) at the top of your history panel and select Share or Publish
      • galaxy-toggle 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
    • galaxy-toggle 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 galaxy-gear History options icon in your history and clicking Copy

Hands-on: Share history and workflow
  1. Share one of your histories with your neighbour.
  2. See if you can do the same with your workflow!
  3. Find the history and/or workflow shared by your neighbour. Histories shared with specific users can be accessed by those users in their galaxy-gear history menu under Histories shared with me.


trophy Well done! You have just performed your first analysis in Galaxy. You also created a workflow from your analysis so you can easily repeat the exact same analysis on other datasets. Additionally you shared your results and methods with others.