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Welcome to an OpenHelix tutorial.

To maintain the color schemes/cues and the animations, if you import these slides into other slide sets please click the checkbox in the PowerPoint Insert window that maintains slide format. Otherwise important information may be lost.

Welcome to the OpenHelix introductory tutorial on the Galaxy genomics analysis resource. Galaxy is not a database, but rather an analysis resource and tool.

Galaxy is a genomics analysis platform that allows researchers to obtain data from various databases such as the UCSC Genome Browser, ENCODE data and many other sources, prepare and manipulate the data, and perform various analyses on the data in ways that might not be possible at the original sites. Galaxy also includes a workflow tool that allows the user to save and share various customized and useful analysis steps.

Galaxy is undergoing rapid growth and change at this time. Our goal in this tutorial is to help the new user understand what the Galaxy tool has to offer, and the basics of using the site. As Galaxy develops, so will our tutorial on it, so please check for updated versions regularly.

The materials in this presentation were prepared by Dr. Warren Lathe of OpenHelix, with guidance from the Galaxy team.

The agenda for the tutorial is shown here.

We will begin with a short introduction to Galaxy, credits to the developer team, and point out a few publications.

We will then explore how the interface works and how to get data, prepare data, and analyze data using the Galaxy tool. We’ll describe how to save your analyses for sharing and future analysis.

Lastly, we will summarize what we learned and walk through one exercise to get better acquainted with the tool.

Galaxy is a research tool that is of great use to the researcher and the developer. Through the Galaxy tool, the researcher can obtain data from many different sources and databases, prepare the data for further analysis and analyze the data using many different included tools or analysis tools that can be added by developers.

The data types you start with will vary based on your research interests. The possible preparations and manipulations you can choose will be customized for your needs. The analyses you can perform will be nearly endless. But the basic conceptual framework is: obtain data, prepare data, and analyze the data.

In this tutorial we will examine these basic concepts. But you should not be limited to these examples. You should be able to ask amazing and complex questions of the data using the Galaxy framework.

If you go to the URL shown here, you will find a short introduction to Galaxy.

We’ll start at the bottom of the page, since this is where we will learn what Galaxy is. In the red section at the bottom, you will learn the rationale for Galaxy. Galaxy is not a database, but rather it is an analysis tool. The amount of data and number of databases is increasing exponentially, but the tools available to analyze this data are small in number and have a difficult time keeping up with new data types. Galaxy aims to provide an analysis resource that allows the user to pull data from many different databases and analyze that data. It also aims to give the user a way to reproduce and share that analysis. Galaxy integrates many databases and analysis tools and is continually growing and evolving.

The Galaxy tool was created for both bench scientists to have a relatively simple tool to use to do data analysis, and for the bioinformatics developer to integrate tools and analyses for the user. This tutorial is aimed at that first group of users, the biologists who wish to perform data analysis, but developers might find it useful. We also suggest that developers view the FAQs and screencasts for more information on how to integrate your tools into the Galaxy framework.

This integration of databases, tools and workflows allows for the ability to collaborate between scientists and between scientists and developers. This also allows researchers to share their analysis and for other researcher to reproduce their analysis where otherwise they might not have.

The website is available for anyone to use right now if you click the link to go to the public site.

Clicking that link will take you to the main Galaxy interface. Before we delve further into the resources, let’s give credit where credit is due. The Galaxy tool was developed by the team shown, and is hosted at the Center for Comparative Genomics and Bioinformatics at Pennsylvania State University. By clicking this link at the bottom of the center panel of the site, you can view the members of the development team and learn more about who is putting these tools together.

The Galaxy team is continually collecting new data inputs and analysis tools to make Galaxy ever more useful to the researcher. You might find the menu items or data sources change from what you see here in the tutorial, but the basic functionality and the analysis tools will remain.

OpenHelix is a separate company that provides training on bioinformatics resources. Galaxy sponsors us to provide the freely available training materials offered here.

One final note: this software is free to use for academic ad non-profit institutions and personal use situations. If you are in a company setting, you can use the website freely.

To understand more about Galaxy, or to cite it, here are two publications that might help. The first is a paper in Genome Research in 2005 that introduced Galaxy and the second is a more recent chapter in Current Protocols in Bioinformatics that explains in more detail how a researcher might perform data analyses using Galaxy. New papers will come along with additional perspectives and updates as well.

Let’s focus back on the main interface. On the homepage top menu, you will also see links to various resources including a wiki, blog and email link to report bugs. Additionally, the link to screencasts will take you to this page of short screencasts that explain many of the tools and functions available on Galaxy. These are regularly added and updated, and can help you to go deeper on specific features and functions of Galaxy that may be helpful for your research.

This tutorial is meant as an introduction to the basic concepts of using the resource. If you need to learn in more detail about a specific tool on the site, or familiarize yourself with a new tool, the screencasts are an excellent place to come and do so.

[end of Introduction and Credits] That completes our introduction and credits.

[beginning Interface] In this section we explore the Galaxy interface.

Galaxy is free and publicly available. To use the site and analysis features of Galaxy, registration is not required, but before you start using Galaxy, you might want to register on the site. Registration is free and allows the user to create workflows and save analyses, very useful features that increase the effectiveness of the tool. For the purposes of this tutorial, we’ll be registered users to show you the full feature set of the tool. You might want to register to better follow along if you wish to do so.

To register, all one need to do its click the “create” link after “Account.” That will open the “Create Account” menu where you fill out your email address and a password. It’s that easy. You can also subscribe to the mailing list where users and developers discuss features and functionality of Galaxy.

The interface to the Galaxy straightforward. There are three sections. The left column contains links to the downloading, preparation and analysis tools. The center column is where the menus and data will appear. The right hand column will show you the history of your analysis steps, allow you view data and results, and more.

Let’s do a really quick sample step of obtaining data to give you an idea of how the interface works. In the next section we’ll be explaining more about getting data and the types of data you can obtain, but for now we’ll be doing this just to give you an example.

Click the link that says “Get Data.”

Once you click that menu heading, links to various actions will appear. These actions will allow you to obtain, prepare, manipulate and analyze data depending on which menu heading you’ve chosen. Here we have links to various databases from which to obtain data.

We are going to click the link that says “UCSC Main table browser.” This will take us to the UCSC Table Browser, which would show up in the center column here. We would obtain data and export it here back to Galaxy. We’ll introduce you to the mechanics of that in the next section. Right now, we’ll show you some sample imported data in Galaxy for a region of the human genome.

You’ll see that once the data has been imported, you’ll receive a message in the middle column to that effect. On the right hand history column, you’ll see your imported data summary.

You can view a summary and sample of the data by clicking the heading link. This will tell you some basic statistics, show an example of the data and the data columns, and have links to give you the ability to view the data or edit the data summary.

By clicking the “eye” you see here, you can view the data file.

Once you click this link, the data file will appear in the center column.

You can also edit the attributes of the data file summary by clicking the icon of the pencil you see here.

The edit window will now appear in the center column. Here you can edit attributes like the name of the data file, information on how it was obtained, column information and more.

You can also convert this to a new data format, or the designated data type, if the Galaxy software had incorrectly assigned it.

As you upload other data files, modify files you have and perform analyses, each step will show up here in the right column, the “History” column, where our first step is currently showing.

You will have options of actions you can perform on this history. Clicking on the “Options” link here will take you to that menu, which we will look at in more detail later in this tutorial.

Your history options will appear here in the middle column.

One word of warning, most of these options will not be available to you unless you have registered on the site as we briefly explained earlier.

Here you can rename the history of your analysis or list previously stored histories. Any analysis sessions you perform while logged in will be saved and you can access, rename or delete those histories here. You can also switch to a previous history if needed, or share a history of your analysis to another user. This allows the user to perform many analyses without fear of losing steps and be able to reproduce the analysis at a later date. This is a very useful tool to save, build on or modify, and rerun complicated analyses.

Galaxy has also instituted a “Workflow” function. This allows the user to create a workflow or series of steps, from a history or from scratch, of frequently repeated analysis steps. This workflow can then be revisited and run again on a different set of data and shared with other users.

We’ll be looking at both histories and workflows in more detail in a later section.

[end of Interface] That completes the section on the Galaxy Interface.

[beginning Getting Data] In this section we will look at how to get data into the Galaxy tool.

On the Galaxy interface, the section in the left column includes all the links for getting data into Galaxy, preparing the data and analyzing it.

This first section, “Get Data” has many venues for you to add all types of data to Galaxy in order to start your analysis.

As Galaxy expands, you’ll find that there will be many more sources added to this already impressive list. Here you will find access to large amounts of data from the UCSC genome database, the BioMart database, your own data and several other sources. In this section, we will show you the UCSC Table Browser, and introduce you briefly to others such as how to upload you own data and how to access data from BioMart.

Right now, let’s upload some data from the UCSC Table Browser database. The UCSC Table Browser includes genomic data from dozens of species. It allows the user to customize a search with powerful filters and intersections of data to obtain exactly the data the user needs. We’ll just go through some basics of getting data, but if you haven’t used the Table Browser before, we suggest you view our tutorial on the UCSC Table Browser to get acquainted with this database search tool.

Click the link “UCSC Main Table Browser” link under the “Get data” heading.

The Table Browser Interface will now appear.

We are going to get known gene data from the human X chromosome. To do so, we’ll just choose the right data set: Human genome assembly “Mar. 2006” and the “UCSC Genes” dataset from the “Genes and Gene Prediction Tracks” group.

We’ll put the position “chrX.”

Leaving all the rest as default, including the output format as BED and the “Send output to Galaxy” checkbox checked, we’ll click the link “Get Output.”

We can leave all the next steps with their default settings for our example, but you could create a custom track for the UCSC Genome Browser, and create records for individual gene features here.

We’ll just send this to the Galaxy tool by clicking the “Send query to Galaxy” link.

Once you do that, you’ll see the dataset now appears in your history. Here you’ll see a summary of the data and the links to view the data, edit the attributes or delete it from the history column, as we discussed in the previous section.

There are many other data sources from which to obtain needed data, such as microbial genome data, data from Ensembl’s BioMart and the ENCODE data. New data sources are being continually added and if you know of any that you wish to see added, don’t hesitate to contact the Galaxy team, they will be more than willing to entertain the request.

You can also upload your own data.

I’ve prepared a data set of chromosomal locations on the “X” chromosome in the “interval” format as an example. This format is basically a listing of chromosomal locations or intervals in a tab delineated text format, each row having specific purposes (the first being the chromosome, second and third the start and end position, etc). We’ll look at that a bit closer later in this tutorial. We’ve included a copy of this file on our download site at the URL shown if you wish to try this also. There are many other formats you can upload in. To see an explanation of the formats and how they are built, just scroll down on this page.

To upload your file, you’ll either paste the contents of your file into the window here, or click the browse button here and find the file on your computer. Here I’ve found and uploaded a file from my desktop.

You’ll then choose which file format the file is in. In this case it is in the interval format, but as you see there are many other possible formats. You can also choose “Auto-detect”, which works pretty well to determine the format of a data file if you are not quite sure of the format, or even if you just don’t want to look for the format type on the list.

Once you’ve chosen the file format, you choose which species the data has been obtained from. There are many to choose from. Ours is from Homo sapiens, so that is what we will choose.

Then click “Execute.”

[end of Getting Data] That completes the section on Getting Data.

[beginning of Preparing Data] In this section we will look at a few of the many tools that allow you to prepare and manipulate your datasets.

Once you obtain the data you need, you are then able to prepare, sort and filter that data in Galaxy.

There are many tools available to you in Galaxy to do this. We are only going to show you few to get you started, but do check out the many tools available to you.

Here we are going to start with the data we uploaded from the UCSC Table Browser in the earlier section.

Often, data has more columns than we need or we would like to rearrange that data. This is where “Text Manipulation” can help. Here you’ll find many tools to allow you to manipulate your text.

We are going to cut some columns out of this data. We only need the chromosomal location, gene names, strand and exon count, so we are going to limit our data to those by clicking the “Cut columns from a table” link.

Once you click that link, you’ll find some choices. “Cut” here doesn’t mean “cut out and delete,” but rather to cut out and save as a separate file.

So, in other words, if you look at our columns from our uploaded data, you’ll see we want columns 1 through 4, 6 and 10. So, we’ll cut those out and save them as a new dataset.

So, we’ll put in the “Cut columns” text box as “c1,c2,c3,c6,c4,c10”

Notice we’ve rearranged the columns too, by putting column 6 (the strand), before column 4 (gene ID), we’ll rearrange those columns in our new dataset.

So, we click “Execute”, and we’ll now have a dataset with 6 columns in the order we want them to be.

As you see, we’ve now eliminated unnecessary data columns and have a new dataset. The name isn’t particularly descriptive, so we are going to change the name by clicking on the pencil icon to edit attributes.

In the “Name” text box, we’ll type “ChrXGenes” and click “Save.”

Once you do that, you’ll notice that the name has changed in our history. Also notice we have the 6 columns we wanted in the order we wanted them.

We are going to do that throughout this tutorial without actually showing that step, so if you are following along, you’ll notice that some of our history names are a bit different than the original default name. If you do, just do this step and change the name.

Now, many times the data is not the type that you need. If you remember, when we downloaded our UCSC dataset we saved it in browser extensible data, or BED format. However, for the next part of our example we are going to want it as “Interval” data. This is a simple format of chromosome intervals that has chromosome location, gene name and strand.

We can do that in the attributes editing menu. Just click the pencil icon again.

You’ll notice below the “Edit Attributes” section, there is a “Change data type” section.

There are dozens of types of data formats to change to. We are going to change ours to “Interval” data by selecting “interval” in the pulldown menu.

You’ll now see a series of pull down menus that ask you to define your columns correctly. There are three required columns, chromosome number and the start and end locations. These are usually detected and selected for you. There are also two other columns you can select. Our strand column is “4” and the “Name/Identifier” column is “5.” We will check these boxes, change the numbers to that in the pull down menus and click “Save”.

This will now change our data type to “Interval”. Now our columns are defined and we are ready to further process and analyze our data.

There is also many other things you can do with the data. Here is a menu that allows you to filter and sort the data on columns.

We will click that menu heading.

You’ll see several choices.

Let’s say we want only those genes that have a certain number of exons.

We can filter our data by clicking this link to filter. You’ll see we can choose which dataset we want to filter and the conditions.

We are going to choose our reformatted dataset and then filter column 6, or our exon count, for only those genes that have one or two exons. We’ll do this by entering “c6<=2”.

The syntax you use to filter is quite simple, and you can learn more about the syntax and examples below.

We then click “Execute” to filter our data based on our conditions.

We now have a filtered dataset. As you see in the summary of our last history item, we’ve retained less than 20% of the genes on the X chromosome from our original dataset.

Our filtered dataset was given a default name, but we might want to rename it to something more descriptive. We will edit the name, as we did before, by clicking the pencil icon and typing in a more descriptive name.

Before we go to the next section, we want to perform one more action.

In the next section of the tutorial we are going to join two dataset in an analysis, so to prepare for that, we are going to want to upload some more data from the UCSC Table Browser.

Namely, we will obtain predicted Dnase hypersensitive sites as you see here. You can accomplish this by doing the same steps as we showed in the earlier section and uploading, from the July 2003 assembly, the “dnasehs” table of “dnasehs” track in the “regulation group.”

We’ll now send that to Galaxy.

You might also notice that this dataset is from an earlier assembly, or version, of the human genome, thus our coordinates might not match with our earlier dataset that is from a later assembly.

We can change that with the “Lift-Over” tool you see here. We’ll click that.

Here we’ll choose the dataset nhgriDnase and change the coordinates to the later human genome assembly “hg18” and click “Execute.”

This will now give us two new datasets. This first one has no unmapped locations, so we’ll delete it by clicking the ‘x’ and the second one we’ll rename as we did before.

We now have a new dataset, Dnase hypersensitive sites on the human X chromosome.

Along with the dataset of genes with 2 or fewer exons on the X chromosome, we are now prepared to do some analysis.

As you see, there are a lot more manipulations and actions you can perform to prepare your data. We’ve only shown you a few to give you an idea of what you might be able to do, and to introduce the Galaxy tools.

Let’s do some analysis on this data, let’s click “Operate on Genomic Intervals” to start on the next section.

[end of Preparing Data] That completes the section on preparing data.

[beginning of Analyzing Data] In this section we will look a few of the many tools that allow you to analyze your data.

Once you have prepared your data, you will have many options to perform further analysis. The number of analysis tools is large and growing, We will only show you a few here. Please note: to retain reproducibility, the analysis tools you’ll find here will always be available at Galaxy, even as new and updated tools are added. Their exact location may shift, however.

“Operate on Genomic Intervals” has many very useful data analysis and preparation tools and we’ll take a look at these in this section using a couple tools as examples.

We will be getting upstream flanking regions from our gene locations and then determining if there are any putative Dnase hypersensitive sites in those flanking regions.

To do this, we will use two tools in this section, “Get flanks…” and “Join….” “Get flanks…” returns upstream and downstream regions of chromosomal locations. Then “Join…” will find locations that overlap between two datasets and return a new dataset of those joined locations with data columns from both original datasets.

Let’s start by clicking the “Get flanks…” link here.

Once you click that link you will be presented with the form here. Choose the dataset from which you want to obtain flanking regions. Ours is the one we earlier obtained from the UCSC Table Browser, filtered and renamed.

We leave the region from which we want flanking regions at the default “Whole feature,” (we could have alternatively asked for flanking regions of exons or introns, etc.) and we’ll leave the location of the flanking region as “Upstream” (alternatively we could have chosen “Downstream”). We’ll also leave the “Offset” as default.

We will choose to return a flanking region of 500 base pairs here.

Click “Execute”.

This will return a new dataset of the 500 base pair upstream regions from the gene locations on chromosome X.

As you can see, we’ve skipped showing the steps and already opened the summary and changed the name of this dataset as we had done with earlier datasets.

Now we can join this dataset with our Dnase hypersensitive sites by clicking the “Join…” link here.

You’ll see here we’ve changed the name of the flanking regions data to “ChrXGenes <= 2exons, upstream flanking.” It’s not necessary, but go ahead if you want.

Once you click that link, you will find this menu. Here you will choose the two datasets in your history that you wish to join. We’ll choose the flanking regions and the dataset of the Dnase hypersensitive sites.

We’ll leave the minimum overlap as the default of at least 1, but we could have chosen a higher minimum.

We’ll leave the return menu as default to return only those data records that have overlap and have been joined.

We’ll then click “Execute.”

Let’s open the history summary and view the data by clicking the “eye” icon. As you see in the history summary when we opened it, our new dataset has 13 upstream regions of genes in the X chromosome. These are flanking regions that have overlap with Dnase hypersensitive sites. Here you’ll see the columns of data from the locations that had overlap. The first columns are those of the first dataset with chromosomal locations, strand, name and more. If you scroll over to the right, you’ll see the data columns from the second dataset, the hypersensitive sites, including location, name and score.

You could then analyze this data further by filtering for score or much more.

Many more analysis tools available. There are tools to perform analysis such as statistics capabilities. The available tools will be constantly growing, and new ones can first be found as beta offerings. For example, Galaxy has recently added Next Generation Sequencing analysis tools because of the explosion in that data. Let’s take a quick look at that.

The Next Generation Sequencing (or NGS) Toolbox, which is in beta at the time of development of this tutorial, offers lots of new tools. We’ve started from the beginning with a new history here. Let’s open the “QC and manipulation tools.” You’ll notice there are many tools here to choose from. For example, we could choose to draw a nucleotides distribution chart from a statistics file we might have uploaded. If you have NGS data to analyze, you might want to explore these a bit closer.

[end of Analyzing Data] That completes our look at a few of the many tools that allow you to analyze your data.

[beginning of Histories & Workflows] In this section we introduce Histories and Workflows.

One thing you can do with the histories of you analysis is give them a more descriptive name. The default is “Unnamed History,” but if you want to refer back to specific useful histories, you can give them a name by clicking this pencil icon.

Then in the resulting open text box you can type your desired name.

You can also add tags to your analysis, making it easier to search for later. Just click the tags link here. We’ll add three tags to this, dnase, regulation and Xchr.

Once registered and logged in, there are a lot of other options you have for your histories. Let’s click the “Options” link to see some of those.

There are a lot of options that make histories very helpful. List all the analysis histories you’ve done, create a new empty history to start a new analysis, make a history into a workflow, share your history with other users and change the permissions and more.

You can show deleted data within the current history or delete the current history.

Let’s list all saved histories by clicking the “Saved Histories” link.

This is a list of all saved histories created under this account. There is a lot you can do here. You can view all active histories, all the histories you have deleted from active (histories are never truly deleted) or view all histories. You can search your histories here.

You will see in the list the number of datasets, how old the history is and when it was created.

You can perform bulk actions by checking the boxes on several histories and then share, rename, delete or undelete all those selected histories.

You can also perform specific actions on individual histories such as switch to a past analysis as your current history, share it with another user, rename it or delete it.

Let’s share this most current history by clicking this share link.

Here you just need to type in the email of a Galaxy user (they must be a registered user of Galaxy of course) and your analysis history will be available to the user to view and work with.

Let’s go back to the options link to see some of the other choices available to you.

Click “Options” again and then click “Show Deleted Datasets.” The deleted dataset will reappear in the history. We can view it and if we decide to, we can undelete it by clicking the “here” link.

Let’s go back to the options to check out a few other things.

In the option menu, click “Extract Workflow to create a workflow.

When you have established and refined a series of analysis steps that might be handy to re-use in the future, you may want to create a workflow. A workflow stores your selected steps, and you can simply upload a new data set at any time and run the same series. This allows you and other users you share the workflow with to choose different datasets and then run the steps of the analysis automatically without having to perform each step individually.

If you’ve done an analysis that is particularly useful and you want to regularly, you can create a workflow from the history by clicking that link we just did. You can name the workflow, and as you see here, the analysis steps are added. You can select which ones you want to keep and create the workflow.

Just a word of note, the workflows do not work well in IE6.

Once you create the workflow, you can view all your workflows by clicking this link at the top of the Galaxy site. You’ll see any workflows you created and those that were shared with you by other users.

You can run a workflow from this menu. So, for example, we could click the “Dnase HS in X Flanking” and run that workflow.

But to give you a better understanding of creating and running workflows, let’s create a new simple one from the beginning by clicking this “Create new workflow” button.

Create a workflow by giving it a name, in this case I’ve named it “TestWorkFlow.” and click “Create.”

The new workflow will appear in your menu. You can now do several things to this workflow by clicking the arrow and bringing up this menu, but right now it has no steps, so let’s “Edit” the flow and add some analysis steps.

You’ll come to this blank canvas on which to add your steps and flow. Let’s do a simple workflow where the user inputs some gene location data and the the workflow analysis filters the data for a specific chromosome and obtains the upstream flanking regions.

First, click the “Filter and Sort” link in the left menu and then add a filter step by clicking “Filter.”

As you see, the Filter step is added to the canvas. The details and parameters of the filter are shown in the right column. We are going to leave this filter on column 1 for genes on chromosome 22.

We will need to prompt the user for a data input. We can do that here by clicking “Inputs.”

Once that menu opens, click “Input dataset” and the step will appear in your canvas and details on the right. You can put the name and short description here.

You can then connect the steps.

If we want the flow to start off with the user adding a dataset and then in the next step the data will be filtered, we’ll click the circled greater than sign on the right of the box and drag the mouse to the circled greater than sign on the left of the next box.

This will connect the two steps in a flow.

Now let’s add a third step, to get flanking regions of the filtered data.

Click on “Operate on Genomic Intervals” link and then the “Get flanks…” link.

This will add our third step. You can choose the region from which to get the flanking sequence, and the location and length of the flanking region. We’ll leave all this as default and save.

Connect the filter step to the “Get flanks” step and then click the “Save” and “Close” buttons above.

Once you do that, you are able to run the workflow from the workflow menu. You can also add it to your Galaxy interface menu for easy access. Let’s do that here.

Click the “Configure your workflow menu” button. Then select which workflows you want to show up on the Galaxy menu, we’ll choose the one we just created, and click “Submit.”

Your menu will now be updated. We can see it if we go back to the Galaxy homepage by clicking the “Analyze Data” link at the top.

If you click the “Workflows” menu heading you’ll see that there are links to the workflows, including the one we just added for easy access, TestWorkFlow.

Click on that link.

You’ll now see your workflow with the input dataset prompt and the steps.

Let’s quickly clear our history to start a new analysis.

Before we run this workflow, we’ll need to add a dataset to analyze.

Let’s open “Get Data” and get UCSC Table Browser Data.

We’ll obtain data from the Human genome, March 2006 assembly, choosing the knownGene table in the UCSC Genes track found in the Genes group. We’ll select the entire genome, leave the rest as default and click “Get Output.”

Leave the next form at the defaults and select “Send query to Galaxy.”

If we now go back to our workflow by clicking the workflow menu and our TestWorkFlow, you’ll see that now our dataset is one of the choices in the “Input dataset” step section. Choose that one if there are others, but it will be the default choice if it is the only or latest dataset in the history.

Now click “Run workflow.”

Galaxy will then perform all the steps of the workflow on the chosen dataset. They will be grey while waiting in the queue, yellow as they are running and then green when complete.

If we open the last step, we’ll see that now we have all the flanking regions of Chromosome 22 genes of the Human Genome.

Workflows are excellent methods to save analysis processes that you might need to perform on a regular basis. Also, you can share your workflows. If you have a colleague or student who needs to perform an analysis, you can create the steps of the analysis in a workflow and then share it. They can then run the analysis steps on their own data.

Histories and workflows allow the Galaxy user to reproduce complex analyses and collaborate simply and effectively.

[end of Histories & Workflows] That completes our look at histories and workflows.

[beginning of Summary] In this section we will briefly summarize the Galaxy tool.

Galaxy has a straightforward interface to allow you to obtain data from many different sources and to prepare the data for further analyses. You would then proceed to analyze your data with one of the many tools available, such as operations on genomic intervals.

The tools are in the left hand column, the tool interfaces and data views are shown in the middle column, and the history of your analysis steps is shown in the right history column.

Galaxy is a research and analysis tool that is of great use to biomedical researchers and software developers. With the Galaxy tools, the researcher can obtain data from many different sources and databases, prepare the data for further analysis, and analyze the data using many different available analysis tools. Additional analysis tools can be added by developers.

The ability to save and share histories and queries is a very useful and important feature of Galaxy, that allows the user to collaborate and, importantly, reproduce computational analyses on biological data.

In the options, you’ll see the ability view saved and shared histories and to share that history with another registered user. The user would then be able to view the history, perform or modify the analysis. There are many other options here as well including deleting, starting and changing permissions on histories.

Additionally, Galaxy allows you to create, run and share workflows of complex analyses that can be repeated on many different datasets as needed.

Effectively using the many features of Galaxy should enable you to take your analyses further than you may have before, and it will encourage you to imagine new ways to collect, combine, and make new discoveries with data of interest to your research.

[end of Analyzing Data] That completes our summary.

[beginning of Summary] In this section we will walk through one of the exercises to get you better acquainted with Galaxy.

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