galaxy: a web-based platform for accessible, …...accessible, reproducible, and transparent...
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Galaxy: A Web-based Platform for Accessible, Reproducible, and Transparent
High-throughput (Genome) Biology
Jeremy GoecksDepts. of Biology and Math & Computer Science
Emory University
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GenomicsGenomics
Identify and annotate all functional genomic elements
Understand interactions among elements
Apply genome knowledge to address biomedical challenges
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4“Will Computers Crash Genomics?”, Pennisi, E., Science, Feb 11, 2011
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5Figure 1. Contribution of di!erent factors to the overall cost of a sequencing project across time. Left, the four-step process: (i) experimental design and sample collection, (ii) sequencing, (iii) data reduction and management, and (iv) downstream analysis. Right, the changes over time of relative impact of these four components of a sequencing experiment. BAM, Binary Sequence Alignment/Map; BED, Browser Extensible Data; CRAM, compression algorithm; MRF, Mapped Read Format; NGS, next-generation sequencing; TAR, transcriptionally active region; VCF, Variant Call Format.
Pre-NGS(Approximately 2000)
Now(Approximately 2010)
Future(Approximately 2020)
0%
2040
6080
100%
Data reductionData management
Sample collection and experimental design
Sequencing Downstreamanalyses
Dat
a m
anag
emen
t
Figure 2. Cost of 1 MB of DNA sequencing. Decreasing cost of sequencing in the past 10 years compared with the expectation if it had followed Moore’s law. Adapted from [11]. Cost was calculated in January of each year. MB, megabyte.
$0.10
$1.00
$10.00
$100.00
$1,000.00
$10,000.00
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Cost per Mb of DNA Sequence Moore's law
Sboner et al. Genome Biology 2011, 12:125 http://genomebiology.com/2011/12/8/125
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A Key Challenge in Genomics
Generating data is easy✦ high-throughput/next-generation sequencing
(HTS/NGS) technologies improving rapidly✦ datasets are many MBs or GBs
Analyzing data is THE bottleneck✦ computation is essential due to dataset size
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Computation in Science?
Scientists unfamiliar with computation
Reproducibility hindered by complexity: systems, scripts, tools, parameters
Collaboration and publishing difficult because current media do not support computational artifacts well
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Galaxy Project: Fundamental Questions
When Biology (or any science) becomes dependent on computational methods:
✦ how best to make methods accessible to scientists?✦ how best to ensure that analyses are reproducible?✦ how best to enable transparent communication and
reuse of analyses?
✦
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Vision
Galaxy is an open, Web-based platform for accessible, reproducible, and transparent computational biomedical research
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What is Galaxy?
GUI for genomics✦ for complete analyses: analyze, visualize, share, publish
A free (for everyone) web service integrating a wealth of tools, compute resources, terabytes of reference data and permanent storage
Open source software that makes integrating your own tools and data and customizing for your own site simple
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Accessibility
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Accessibility
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Accessibility
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Accessibility
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Connecting Users with Accessibility
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Connecting Users with Accessibility
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Galaxy Accessibility
Only a Web browser is required
Standardization✦ tools, parameters, outputs all look the same
Easy to use output of one tool as input for another tool
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Accessibility for Tool Developers
Defined via abstract interface:
✦ inputs & outputs✦ parameters✦ how to generate
command line
As simple as possible but allows for rigorous
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Dozens of tools for different HTS applications packaged with Galaxy19
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Amplification
Many tools available in a single place means that tools can be combined in novel ways
Users, developers, community benefit
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Reproducibility
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Reproducibility in Genomics
18 Nat. Genetics experiments in microarray gene expression
<50% of reproducible
Problems✦ missing data (38%)✦ missing software, hardware
details (50%) ✦ missing method, processing
details (66%)
Ioannidis, J.P.A. et al. “Repeatability of published microarray gene expression analyses.” Nat Genet 41, 149-155 (2009)
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Reproducibility in Genomics
18 Nat. Genetics experiments in microarray gene expression
<50% of reproducible
Problems✦ missing data (38%)✦ missing software, hardware
details (50%) ✦ missing method, processing
details (66%)
Ioannidis, J.P.A. et al. “Repeatability of published microarray gene expression analyses.” Nat Genet 41, 149-155 (2009)
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14 re-sequencing experiments in Nat. Genetics, Nature, Science
0% reproducible?
Problems✦ missing primary data (50%)✦ tools unavailable (50%)✦ missing parameter setting,
tool versions (100%)
"Devil in the details," Nature, vol. 470, 305-306 (2011).
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Metadata = Reproducibility
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Automatic Metadata
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User Metadata
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Workflows can be constructed from scratch or extracted from existing analysis histories
Facilitate reuse and provide precise reproducibility of a complex analysis
Galaxy Workflow System
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Galaxy Workflows
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Example: Workflow for differential expression analysis of RNAseq using Tophat/Cufflinks tools
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Example: Diagnosing lowfrequency heterosplasmic sites in two tissues from the same individual
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Transparency ~ Sharing, Publishing, Reusing
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Everything can be shared
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Text
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Galaxy Pages
A web-based, interactive medium for presenting all aspects of an analysis: data, methods, visualization, and results
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Galaxy Page for a recent study on mitochondrial heteroplasmy
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Actual histories and datasets directly accessible from the text
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Histories can be imported and the exact parameters inspected
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Workflows and other entities can also be embedded
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And imported for inspection, verification, and reuse
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Galaxy's publishing features facilitate access and reproducibility without any extra leg work
One click grants access to the actual analysis you performed to generate your original results
✦ not just data access, the full pipeline + annotations✦ anyone can import your work and immediately
reproduce or build on it
The Power of Galaxy Publishing
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Three Ways to Use Galaxy
1. Public Website (http://usegalaxy.org)
2. Download and run locally
3. Run on the cloud
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Galaxy main site(http://usegalaxy.org)
Public Website, anybody can use
~500 new users per month, ~100 TB of user data, ~130,000 analysis jobs per month, every month is our busiest month ever...
Will continue to be maintained and enhanced, but with limits and quotas
Centralized solution cannot scale to meet data analysis demands
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Download and Run Locally
No configuration needed but everything can be configured✦ tools✦ computing cluster integration✦ proxy server and authentication
Prominent local installations at:✦ Cold Spring Harbor Lab✦ Dept. of Energy’s Joint Genome Institute✦ Harvard School of Public Health✦ U of Texas System✦ Netherlands Bioinformatics Center✦ Oxford College
Requires computing resources, technical expertise, and maintenance
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Galaxy on the Cloud
For extended or particular resource needs✦ customization necessary✦ oscillating data volume
For when informatics expertise or infrastructure is limited
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Work done by Enis Afgan
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Using Amazon EC2: Startup in 3 steps
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Like any other Galaxy instance plus: ✦ additional compute nodes acquired and released
automatically as needed✦ can share or publish an instance
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Challenges Going Forward
Promoting community involvement✦ tools, assays, analyses growing too fast for us alone✦ facilitate community contributions and usage of contributions
Scaling to many, many Galaxies✦ moving objects between Galaxies while ensuring reproducibility✦ enabling users to find useful “stuff”
Novel application areas✦ genomics ideal application area -- what next?
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Supported by the NHGRI (HG005542, HG004909, HG005133), NSF (DBI-0850103), Penn State University, Emory University, and the Pennsylvania Department of Public Health
Dan Blankenberg Nate Coraor
Greg von Kuster
Enis Afgan Dannon Baker
Jeremy Goecks
Anton NekrutenkoJames Taylor
Dave Clements Jennifer Jackson
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http://galaxyproject.org
Galaxy publications: http://galaxyproject.org/wiki/Citing
Galaxy is hiring! http://galaxyproject.org/wiki/GalaxyisHiring
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Thanks! Questions?
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Visualization
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Integration with many existing browsers extensible
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Genome Browser✦ physical depiction of data✦ visually identify correlations✦ find interesting regions, features
Galaxy✦ tool integration framework✦ heavy focus on usability✦ sharing, publication framework
Trackster
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Dynamic Filtering
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Integrating Tools and Visualization
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Integrating Tools and Visualization
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Integrating Tools and Visualization
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Scaling Galaxy: two distinct problems
So much data, not enough infrastructure ✦ solution: encourage local Galaxy instances, cloud
Galaxy, support increasingly decentralized model
So many tools and workflows, not enough manpower
✦ focus on building infrastructure to allow community to integrate and share tools, workflows, and best practices
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http://usegalaxy.org
http://usegalaxy.org/community
Galaxy Tool Shed
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Galaxies on private clouds
Galaxies on public clouds
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private Galaxy installations
private Tool Sheds
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“The expanding transcriptome: the genome as the 'Book of Sand'”, Soares and Valcarcel, The EMBO Journal (2006) 25
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Figure 1. Contribution of di!erent factors to the overall cost of a sequencing project across time. Left, the four-step process: (i) experimental design and sample collection, (ii) sequencing, (iii) data reduction and management, and (iv) downstream analysis. Right, the changes over time of relative impact of these four components of a sequencing experiment. BAM, Binary Sequence Alignment/Map; BED, Browser Extensible Data; CRAM, compression algorithm; MRF, Mapped Read Format; NGS, next-generation sequencing; TAR, transcriptionally active region; VCF, Variant Call Format.
Pre-NGS(Approximately 2000)
Now(Approximately 2010)
Future(Approximately 2020)
0%
2040
6080
100%
Data reductionData management
Sample collection and experimental design
Sequencing Downstreamanalyses
Dat
a m
anag
emen
t
Figure 2. Cost of 1 MB of DNA sequencing. Decreasing cost of sequencing in the past 10 years compared with the expectation if it had followed Moore’s law. Adapted from [11]. Cost was calculated in January of each year. MB, megabyte.
$0.10
$1.00
$10.00
$100.00
$1,000.00
$10,000.00
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Cost per Mb of DNA Sequence Moore's law
Sboner et al. Genome Biology 2011, 12:125 http://genomebiology.com/2011/12/8/125
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Sboner et al. Genome Biology 2011, 12:125
Trends in Genomics