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.

Agenda

In this tutorial, we will:

1. Setting the stage: Exons and SNPs
2. Get your workspace ready
3. Analysis
   1. Locate Exons
   2. Find exons with the most SNPs
   3. Count the number of SNPs per exon
   4. Sort the exons by SNPs count
   5. Select top five exons
   6. Recover exon info
   7. Display data in UCSC genome browser
4. Histories and workflows: A brief introduction
   1. List your histories
   2. Convert your analysis history into a workflow
   3. The workflow editor
   4. Run workflow on different data
5. Share your work
6. 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.

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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…

Get your workspace ready

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

Tip: How do I create an account on a public Galaxy instance?

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:

   • UseGalaxy.eu (EU)

   • UseGalaxy.fr (FR)

   • UseGalaxy.org (US)

   • UseGalaxy.org.au (AU)

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

   

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.

   

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

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Tip: How to enable the Activity Bar

If you do not see the Activity Bar it can be enabled as follows:

1. Click on the “User” link at the top of the Galaxy interface
2. Select “Preferences”
3. Scroll down and click on “Manage Activity Bar”

4. Toggle the “Enable Activity Bar” switch and voila!

   

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.

   Tip: Creating a new history

   To create a new history simply click the new-history icon at the top of the history panel:

   

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.

   

Analysis

Locate 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

   

   This brings up the upload interface:

   

2. Click Paste/Fetch data and paste in the following URLs in the box that appears.

   https://zenodo.org/record/4104428/files/UCSC-hg38-chr22-Coding-Exons.bed
   https://zenodo.org/record/4104428/files/UCSC-hg38-chr22-dbSNP153-Whole-Gene-SNPs.bed
   

3. Click Start, and then Close

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

   Tip: Renaming a dataset

   • 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.

Comment

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

Tip: Optional: Obtaining the Data directly from UCSC

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

   

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

   

   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

   Comment

   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:

   

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

   Comment

   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):

   

   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:

   

   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:

   

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

   

   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:

   

   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).

Comment: Search bar

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+galaxy1) 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:

2. Click Run Tool
3. Wait for the job to finish

4. View the resulting file (with the galaxy-eye (eye) icon). If everything went okay, you should see a file that looks similar to this:

   

   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!

   Tip: Where do I find tool documentation?

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

   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.

Question

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

Solution

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 1.8+galaxy0) (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)

   

   • 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)

   

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

   

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.

Question

How many exons are there in total in your file?

Solution

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 4,241 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 ( Galaxy version 1.1.1sort1) data in ascending or descending order:

   • “Sort Query”: Output from Datamash tool
   • In “Column selections” set the following:
     • “on column”: Column: 2
     • “in”: Descending order
     • “Flavor”: Fast numeric sort

2. Click Run Tool

3. Examine the output file.

   

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

Question

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

Solution

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 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 (previous step of the analysis)
   • “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.

Recover 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 (previous step of the analysis)
   • “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:

   

Display 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).

   Tip: Changing database/build (dbkey)

   • Click the desired dataset’s name to expand it.

   • Click on the “?” next to database indicator:

     

   • In the central panel, change the Database/Build field
   • Select your desired database key from the dropdown list: Human Dec. 2013 (GRCh38/hg38) (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.

   Tip: Changing the datatype

   • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
   • In the central panel, click galaxy-chart-select-data Datatypes tab on the top
   • In the galaxy-chart-select-data Assign Datatype, select bed from “New type” dropdown
     • 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

   

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.

   

   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.

   

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).

Histories and workflows: A brief introduction

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-history-options History Options menu on the top of the history panel:

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List your histories

You can create as many histories as you want. If you create a new history, your current history does not disappear. You can view your histories in two ways: (1) as a list or (2) side by side (also known as “Multiview”). Two Tip boxes below explain both of these approaches:

Tip: View a list of all histories

There are multiple ways in which you can view your histories:

1. Viewing histories using switch-histories “Switch to history” button. This is best for quickly switching between multiple histories.

   Click the “Switch history” icon at the top of the history panel to bring up a list of all your histories:

2. Using the “Activity Bar”:

   Click the “Show all histories” button within the Activity Bar on the left:

3. Using “Data” drop-down:

   Click the “Data” link on the top bar of Galaxy interface and select “Histories”:

4. Using the Multi-view, which is best for moving datasets between histories:

   Click the galaxy-history-options menu, and select galaxy-multihistory Show histories side-by-side

Tip: View histories side-by-side

You can view multiple Galaxy histories at once. This allows to better understand your analyses and also makes it possible to drag datasets between histories. This is called “History multiview”. The multiview can be enabled either view History menu or via the Activity Bar:

1. Enabling Multiview via History menu is done by first clicking on the galaxy-history-options “History options” drop-down and selecting galaxy-multihistory “Show Histories Side-by-Side option”:

   

2. Clicking the galaxy-multihistory “History Multiview” button within the Activity Bar:

   

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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 “History options” dropdown at the top of your history panel and select Extract workflow.

   

   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.

   

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:

   Open image in new tab
   
   

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.

   Open image in new tab
   
   

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

   Open image in new tab
   
   

   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.

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

   Open image in new tab
   
   

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.

Comment

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.

   Tip: Creating a new history

   To create a new history simply click the new-history icon at the top of the history panel:

   

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. To do this:

   • Bring up a list of all you histories:

   Tip: View a list of all histories

   There are multiple ways in which you can view your histories:

   1. Viewing histories using switch-histories “Switch to history” button. This is best for quickly switching between multiple histories.

      Click the “Switch history” icon at the top of the history panel to bring up a list of all your histories:

   2. Using the “Activity Bar”:

      Click the “Show all histories” button within the Activity Bar on the left:

   3. Using “Data” drop-down:

      Click the “Data” link on the top bar of Galaxy interface and select “Histories”:

   4. Using the Multi-view, which is best for moving datasets between histories:

      Click the galaxy-history-options menu, and select galaxy-multihistory Show histories side-by-side

   • Display the “Galaxy 101” history in the Center pane by clicking “Open in Center pane” button:

   • Drag an drop Exons dataset into the empty history created at step 1

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

   https://zenodo.org/record/4104428/files/UCSC-hg38-chr22-Repeats.bed
   

   Tip: Importing via links

   • 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

   Tip: Obtaining the Data from UCSC

   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.

   

   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 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

   

   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:

   

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.

Question

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.

Tip: Sharing your History

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-history-options menu at the top of your history panel and select “history-share 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 Published Histories tab opened via the galaxy-histories-activity Histories activity
3. Share only with another user.
   • Enter an email address for the user you want to share with in the Please specify user email input below Share History with Individual Users
   • Your history will be shared only with this user.
4. Finding histories others have shared with me
   • Click on the galaxy-histories-activity Histories activity in the activity bar on the left
   • Click the Shared with me tab
   • 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 galaxy-history-options icon in your history and clicking Copy this History

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.

Conclusion

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.

You've Finished the Tutorial

Please also consider filling out the Feedback Form as well!

Key points

• Galaxy provides an easy-to-use graphical user interface for often complex command-line tools

• Galaxy keeps a full record of your analysis in a history

• Workflows enable you to repeat your analysis on different data

• Galaxy can connect to external sources for data import and visualization purposes

• Galaxy provides ways to share your results and methods with others

Frequently Asked Questions

Feedback

Did you use this material as an instructor? Feel free to give us feedback on how it went.
Did you use this material as a learner or student? Click the form below to leave feedback.

Citing this Tutorial

1. Saskia Hiltemann, Nicola Soranzo, Clemens Blank, Anton Nekrutenko, Björn Grüning, Anne Pajon, Helena Rasche, Galaxy Basics for genomics (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/introduction/tutorials/galaxy-intro-101/tutorial.html Online; accessed TODAY
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{introduction-galaxy-intro-101,
author = "Saskia Hiltemann and Nicola Soranzo and Clemens Blank and Anton Nekrutenko and Björn Grüning and Anne Pajon and Helena Rasche",
	title = "Galaxy Basics for genomics (Galaxy Training Materials)",
	year = "",
	month = "",
	day = ""
	url = "\url{https://training.galaxyproject.org/training-material/topics/introduction/tutorials/galaxy-intro-101/tutorial.html}",
	note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol} Computational Biology}
}

                   

Funding

These individuals or organisations provided funding support for the development of this resource

PSU

ErasmusMC

ELIXIR Europe

UFR

de.NBI

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl https://training.galaxyproject.org/training-material/api/topics/introduction/tutorials/galaxy-intro-101/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools:
- name: text_processing
  owner: bgruening
  revisions: d698c222f354
  tool_panel_section_label: Text Manipulation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: text_processing
  owner: bgruening
  revisions: d698c222f354
  tool_panel_section_label: Text Manipulation
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: bedtools
  owner: iuc
  revisions: a1a923cd89e8
  tool_panel_section_label: BED
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: datamash_ops
  owner: iuc
  revisions: 562f3c677828
  tool_panel_section_label: Join, Subtract and Group
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: datamash_ops
  owner: iuc
  revisions: 4c07ddedc198
  tool_panel_section_label: Join, Subtract and Group
  tool_shed_url: https://toolshed.g2.bx.psu.edu/

Feedback

5 stars	179
4 stars	42
3 stars	10
2 stars	1
1 stars	4

October 2024

• 5 stars: Liked: the form that information was organized and the hands on allowe us practice Disliked: this is my first experience, it was good i dont have any constructive criticism
• 4 stars: Liked: It was very easy to follow
• 5 stars: Liked: Detail is good Disliked: I think the content is quite good and detailed enough for the basics
• 5 stars: Liked: I liked that there was a video tutorial as well because some parts were confusing. Disliked: I think that Sort the exons by SNPs count was a bit confusing without the video.
• 5 stars: Liked: it was clear, to the point, very informative, it explains why and how. comprehensive. Disliked: the pictures and videos provided here don't correspond with the outcome, could be distracting to some students.
• 5 stars: Liked: Simple workflow with tons of examples! Disliked: Have the solution for the last question like it is done for the other questions.
• 4 stars: Liked: Fundamentals were covered. Disliked: Animation or gifs (?)
• 5 stars: Liked: Simple, on point, not complicated, highlight possible error Disliked: Please add the picture for every step
• 5 stars: Liked: I liked the workflow creator option which saves the previous steps so you don't have to repeat the steps.
• 5 stars: Liked: the detailed step by step for beginners Disliked: explication of all possible application of a given workflow
• 5 stars: Liked: Organized well
• 5 stars: Liked: The explanation was detailed and shows all the tools. Disliked: They could improve by being able to carry out more examples as tests.
• 3 stars: Liked: The step-by-step process Disliked: The visuals as it seems to boring and not catchy
• 5 stars: Liked: very detailed steps and instruction
• 5 stars: Liked: How to create a workflow it's amazing see how runs Disliked: All the process was clearly explained I love it, thank you so much
• 5 stars: Liked: Tee way to run a workflow with new dataset Disliked: I will appreciate more explanation gifs
• 3 stars: Liked: Use the correct version and updated one
• 4 stars: Disliked: Personally I think the part Run workflow on different data is a little bit repeated. The main idea is to copy the first step into a new history. But you changed so many histories made me confused.
• 5 stars: Liked: How thorough it was Disliked: Honestly, it's perfect the way it is.
• 4 stars: Liked: I liked the way natalie takes through the tutorial. Disliked: I think more human microbiome workflows could be present as it is gaining traction.
• 4 stars: Liked: It was informative and easy to follow Disliked: It was a bit long. Please consider splitting it
• 5 stars: Liked: the tutorial is simple and easy to follow
• 5 stars: Liked: it was easy to follow
• 5 stars: Liked: easy to follow tutorial Disliked: I think to add the scientific background of the steps done
• 5 stars: Liked: The step by step explanations provided to perform analysis.
• 5 stars: Liked: step detail Disliked: nil
• 5 stars: Liked: The explanations were very clear
• 4 stars: Liked: It is well-structured in general, the topic was well enough described and it dissected the bioinformatics challenge and how to solve it with Galaxy. Disliked: It's confusing sometimes with the tips below the steps. I'll rather more tips and steps unified, otherwise the tutorial gets, at least for me, confusing. Along with that, I found a bit confusing to communicate by Slack because of the amount of "#", however, I just have to say that the people answering questions were very helpful and knowledgeable.
• 5 stars: Liked: all the informations are easy and perfectly explained Disliked: nothing
• 3 stars: Liked: Iw was succesfull and very useful Disliked: Only the images could be more expressive
• 5 stars: Liked: It was easy to follow the steps
• 5 stars: Liked: I liked the method of guidance employed very much, the tutorial was easy to follow with the guidance being just enough to make it feel like i am discovering the platform features myself yet supportive enough to keep me going when i stumble. Disliked: It seems that few of the tools you mentioned have updated their interfaces and look slightly different from the screenshots in the tutorials, this can be daunting to a beginner like myself so please include a note that mentions the difference of version can change the tool interface.
• 5 stars: Liked: The step-by-step explanations.
• 5 stars: Liked: Clearly demonstrate Disliked: NONE
• 5 stars: Liked: Clear steps to follow greatly aided by the screenshots. I liked that the tips were hidden, so I could lightly test my knowledge. Disliked: I do not think the tutorial could be clearer, thank you very much!
• 5 stars: Liked: Clearness
• 5 stars: Liked: The explanation of workflows
• 5 stars: Liked: useful
• 5 stars: Liked: I'm a biotech honors student , I already knew what chromosomes, SNPs ,exons etc however I liked that these terms were still well explained for those that are not well conversant with science terminologies. I also liked that the instructions were accompanied with screenshots,this made it easier to follow along. Disliked: Providing more screenshots when it comes to editing the workflow would go a long way. I kinda got stuck ,almost gave up but then i figure it out
• 5 stars: Liked: Clear and slow language from the video. Easy to follow
• 5 stars: Liked: Well explained, like the boxes and the organization.
• 5 stars: Liked: It was simple and clear to follow through
• 5 stars: Liked: Clearly demonstrate
• 5 stars: Liked: The Galaxy tools
• 4 stars: Liked: visual step-by-step instructions Disliked: none
• 5 stars: Liked: The step-by-step process of it all

September 2024

• 5 stars: Liked: Everything once I got my head around it, especially as only had 2 hours sleep due to poorly doggy. Disliked: I have no feedback at this stage. I thought it was very well set out and easy to follow, I particularly like the attached video, it is more helpful for me to have someone guide me through it.
• 5 stars: Liked: It was easy to follow step by step Disliked: I got stuck on omitting intermediates in the outputs, I felt this was not clear enough. I just had to click the eye logo to hide it in workflow editor. Sounds like a simple thing but at the time I was confused.
• 5 stars: Liked: Step by step explanations and examples. As much detail as I needed
• 5 stars: Liked: Easy to follow, clear instructions and visuals Disliked: As a second time around following this , I was able to successfully complete and really enjoyed. Felt much more confident
• 3 stars: Liked: Hands on Disliked: Some of the instructions are a bit muddled
• 5 stars: Liked: I liked that the tutorial guided me through everything step by step
• 5 stars: Liked: The user interface took a little getting used to, but I like the analysis it provides Disliked: There are a lot of menus and options. Perhaps a search option that includes the myriad of choices available
• 5 stars: Liked: really clear, liked having the extra info available in tip sections to hide or unhide when I wanted

August 2024

• 5 stars: Liked: The pace of tutorial Disliked: Always have room for improvements

July 2024

• 5 stars: Liked: extra help as expandable boxes
• 5 stars: Liked: clear and concise
• 5 stars: Liked: Everything

June 2024

• 5 stars: Liked: Great instructions with graphics to help find the menu option and to visualize how the output should be. Disliked: For the "Upload" step, another sub-step to first select the "Paste/Fetch Data" is needed to paste the links provided in the tutorial.
• 3 stars: Liked: The simple processing of the tutorial Disliked: For each instruction in the tutorial, you really need to explain the reason behind each instruction, otherwise we just become like automated machines.
• 5 stars: Liked: the instructions are very well written
• 5 stars: Liked: The complex become so easy
• 5 stars: Liked: The tools used in the Bioinformatics pipeline are so amazing. No coding, its just easy

May 2024

• 4 stars: Disliked: There is no answer to check my work for the last question? Which exon had the highest number of repeats? How many repeats were there?
• 4 stars: Liked: it helps me to find out how to use easy galaxy before i had always struggled with galaxy and i can't do anything. :-)

February 2024

• 1 stars: Disliked: It`s not working. I have tried the bedtools intersect intervals five times, including with the files provided via Zenodo. Each time a table is returned that lists the SNP twice instead of the intersection with the exon.