Galaxy Training Network

Introduction to Galaxy

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What is Galaxy?

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Data Intensive analysis for everyone

Versatile and reproducible workflows
Web platform
Open source under Academic Free License
Developed at Penn State, Johns Hopkins, OHSU and Cleveland Clinic with substantial outside contributions

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The Galaxy Team is composed by bioinformaticians and software engineers
OHSU = Oregon Health & Science University
Workflows are similar to cooking recipes. This metaphor helps comprehension of workflows for users researchers, citizens and hobbyists that are not familiar with data intensive research.

Core values

Accessibility
- Users without programming experience can easily upload/retrieve data, run complex tools and workflows, and visualize data
Reproducibility
- Galaxy captures information so that any user can understand and repeat a complete computational analysis
Transparency
- Users can share or publish their analyses (histories, workflows, visualizations)
- Pages: online Methods for your paper

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accessible reproducible transparent research means sharing everything.

The Galaxy framework aims to make as simple as possible for researchers to:

share their analyses
track all used tools and versions
check all parameters
justify each step in the analysis
publish the findings with all aforementioned information

Pages: interactive, web-based documents that describe a complete analysis.

Galaxy growth

More than 7,000 ready to use tools for users
More than 10,000 citations
More than 160 public Galaxy resources
- 120+ public servers, many more non-public
- Both general-purpose and domain-specific

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The number of public Galaxy resources should be regularly updated.

Galaxy timeline

Galaxy ecosystem

User Interface

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So now that we know what Galaxy and the Galaxy Project are all about, let's look at the Galaxy interface.

Main Galaxy interface

Galaxy user interface

Home page divided into 3 panels

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Top menu

Link	Usage
Analyze Data	go back to the homepage
Workflow	access existing workflows or create new one using the editable diagrammatic pipeline
Visualize	create new visualisations and launch Interactive Environments
Shared Data	access data libraries, histories, workflows, visualizations and pages shared with you
Help	links to Galaxy Help Forum (Q&A), Galaxy Community Hub (Wiki), and Interactive Tours
User	your preferences and saved histories, datasets, pages and visualizations

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Tools

Tool interface

The tool search helps in finding a tool in a crowded toolbox

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Tool interface

A tool form contains:
- input datasets and parameters
- help, citations, metadata
- an Execute button to start a job, which will add some output datasets to the history
New tool versions can be installed without removing old ones to ensure reproducibility

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The tool form is generated from a simple XML file describing:

the input datasets and their datatypes
the tool parameters (numerical, text, boolean, selections, colour)
the dependencies required to run the tool
how to generate a command to execute the tool with the specified inputs and parameters
the output datasets the tool should produce and their datatypes
tests
help, citations
various metadata (e.g. the tool version)

Tools can be viewed as tiny LEGO pieces: each one solves a specific problem, and they can be combined together to build complex analysis pipelines.

Tool Shed

Free "app" store: Galaxy Tool Shed
- Thousands of tools already available
- Most software can be integrated
  - If a tool is not available, ask the Galaxy community for help!
- Only a Galaxy admin can install tools

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History

Location of all analyses
- collects all datasets produced by tools
- collects all operations performed on the data
For each dataset (the heart of Galaxy’s reproducibility), the history tracks
- name, format, size, creation time, datatype-specific metadata
- tool id, version, inputs, parameters
- standard output (stdout) and error (stderr)
- state (waiting, running, success, failed)
- hidden, deleted, purged

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We say datasets to refer to files as well as databases
Purged means permanently deleted

Multiple histories

You can have as many histories as you want
- each history should correspond to a different analysis
- and should have a meaningful name

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Give it a good name so you can find it later, which can otherwise become difficult when you have a large number of old histories.
You can drag and drop datasets between histories

History behavior is controlled by the History options (gear icon) History menu

History options gear button

Create new history (+ icon) will not make your current history disappear
To see all of your histories, use the history switcher
Copy Datasets from one history to another and save disk space for your quota

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Copying datasets between histories does not affect your quota, only a single copy of the file is stored on disk because datasets are never modified after creation.

Loading data

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So now you know about the tools to manipulate data and the history where you can see your data, your inputs and outputs. Let's discuss how to get data into Galaxy

Importing data

Copy/paste some text
Upload files from your local computer
Upload data from an internet URL
Upload data from online databases: UCSC, BioMart, ENCODE, modENCODE, Flymine etc.
Import from Shared Data (libraries, histories, pages)
Upload data from FTP

See Getting data into Galaxy

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Datatypes

Tools only accept input datasets with the appropriate datatypes
When uploading a dataset, its datatype can be either:
- automatically detected
- assigned by the user
Datasets produced by a tool have their datatype assigned by the tool
To change the datatype of a dataset, either:
- galaxy-pencil Edit attributes and Datatypes (if original wrong), or
- galaxy-pencil Edit attributes and Convert

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When you upload data, Galaxy will try to autodetect the format of the data, but can sometimes get it wrong, so you may need to correct it later.
Edit Attributes → Datatype is used to fix a wrongly assigned datatype
Edit Attributes → Convert Formats creates a new dataset using a tool that converts the original dataset in the new format
New datatypes can be added to the Galaxy code base, if missing

Reference datasets

Example: reference Genome

Genome build specifies which genome assembly a dataset is associated with
- e.g. mm10, hg38...
Can be assigned by a tool or by the user
Users can create custom genome builds
New builds can be added by the admin

Genome Builds

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Just like datatypes, you can specify which genome assembly your dataset is about. Some tools need to know this, and Galaxy can tell the tool for you.

Workflows

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Now that you've got data into Galaxy, you know you can use tools to manipulate this data, and histories to keep track of what you've done. You're only missing one key part: workflows. These help you easily reproduce the exact analysis that you ran.

Workflow Editor

Workflow interface

Extracted from a history
Built manually by adding and configuring tools using the canvas
Imported using an existing shared workflow

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Biologist:

workflows are great
single button to run all of these 50 different tools
a lot of work once to figure out analysis, but easy in the future to just rerun, go get coffee and wait for thing to be done :)

Bioinf / dev:

Boxes are workflow steps
- 2 types: input and tool steps
Steps are connected by arrows representing the flow of datasets
Tool panel on the left with Inputs on top (to add input datasets and collections)
Small tool form on the right
Extracting a workflow from a history allows to easily convert an existing history into an analysis workflow

Why would you want to create workflows?

Re-run the same analysis on different input data sets
Change parameters before re-running a similar analysis
Make use of the workflow job scheduling
- jobs are submitted as soon as their inputs are ready
Create sub-workflows: a workflow inside another workflow
Share workflows for publication and with the community

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Potential information overload for newbies

Visualizations

Datatypes know what tools can be used to visualize datasets:
- Sequencing data has a button for visualizing in IGV
- Tabular data will prompt you to build charts
- Protein data can be seen in a 3D viewer
Interactive environments: Jupyter, RStudio, etc

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Share everything you do in Galaxy - histories, workflows, and visualizations
- Directly using a Galaxy account's email addresses on the same instance
- Using a web link, with anyone who knows the link
- Using a web link and publishing it to make it accessible to everyone from the Shared Data menu

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Community

Support forum: Galaxy Help
Community curated documentation: Galaxy Community Hub
Events all around the world
Galaxy Training for scientists, developers, admins, instructors: Galaxy Training Community
- Training questions? Chat with us on Gitter

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know was a lot
we'll come back
slides are always available online
first real analysis after the coffee?

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Thank you!

This material is the result of a collaborative work. Thanks to the Galaxy Training Network and all the contributors!

Galaxy Training Network

This material is licensed under the Creative Commons Attribution 4.0 International License.

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Introduction to Galaxy

What is Galaxy?

Core values

Galaxy growth

Galaxy ecosystem

User Interface

Main Galaxy interface

Top menu

Tools

Tool interface

Tool Shed

History

Multiple histories

History options menu

Loading data

Importing data

Datatypes

Reference datasets

Example: reference Genome

Workflows

Workflow Editor

Why would you want to create workflows?

Visualizations

Sharing data

Community

Related tutorials

Thank you!

What is Galaxy?

Help