GIGA2: Galaxy – a collaborative platform for accessible, transparent, and reproducible Genomics

Presented at the second workshop of the Global Invertebrate Genomics Alliance.

A remix of my early 2015 talks with a focus on how Galaxy supports collaboration (within and between Galaxy instances), and how it could be used to support a community of researchers collaborating on a set of genome projects – while sharing tools and workflows reproducibly.

Transcript

1. Galaxy – a collaborative platform for accessible, transparent, and reproducible Genomics @jxtx / #usegalaxy https://speakerdeck.com/jxtx
2. A bit of what our lab is interested in...
   Genomics and Gene Regulation:
   ▪ How is control of gene expression encoded in the genome?
   ▪ How can we detect the elements involved?
   ▪ How do they act in a coordinated way in the cell? ▪ How do they evolve?
   Data intensive science:
   ▪ How can we support increasingly data intensive and quantitatively complex science?
   ▪ How can we improve the efficiency of scientific discovery
   ▪ How can we improve the quality the resulting science?
3. A continuing crisis in genomics research: reproducibility
4. What is reproducibility? (for computational analyses) Reproducibility is not provenance, reusability/ generalizability, or correctness Reproducibility means that an analysis is described/captured in sufficient detail that it can be precisely reproduced Yet most published analyses are not reproducible 
 (see e.g. Ioannadis et al. 2009 — 6/18 microarray experiments reproducible; Nekrutenko and Taylor 2012, 7/50 resequencing experiments reproducible) Missing software, versions, parameters, data…
5. Reproducibility Project: Cancer Biology Independently replicating 50 “high-impact” cancer studies from 2010-2012 (https://osf.io/e81xl/wiki/home/)
6. Vasilevsky, Nicole; Kavanagh, David J; Deusen, Amy Van; Haendel, Melissa; Iorns, Elizabeth (2014): Unique Identification of research resources in studies in Reproducibility Project: Cancer Biology. figshare. http://dx.doi.org/10.6084/m9.figshare.987130 32/127 tools 6/41 papers
7. #METHODSMATTER Figure 1 0.480 0.483 0.486 0.489 0.492 0.495 0.498 0.501 0.504 0.507 0.510 5.2 5.3 5.4 5.5 5.6 5.7 5.8a 5.8c 5.9 5.9rc 5.1 6 6.1 Frequency Fluctuation for site 8992 Default -n 3 -q 15 -n 3 -q 15 (Nekrutenko and Taylor, Nature Reviews Genetics, 2012)
8. Galaxy’s motivating questions How best can data intensive methods be accessible to scientists?
   How best to facilitate transparent communication of computational analyses? How best to ensure that analyses are reproducible?
9. Galaxy: accessible analysis system
10. 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 An open extensible platform for sharing tools, datatypes, workflows, ...
11. Integrating existing tools into a uniform framework • Defined in terms of an abstract interface (inputs and outputs) • In practice, mostly command line tools, a declarative XML description of the interface, how to generate a command line • Designed to be as easy as possible for tool authors, while still allowing rigorous reasoning
12. Galaxy analysis interface • Consistent tool user interfaces automatically generated • History system facilitates and tracks multistep analyses
13. Automatically and transparently tracks
    every step of every analysis
14. As well as user-generated
    metadata and annotation...
15. Galaxy workflow system • Workflows can be constructed from scratch or extracted from existing analysis histories • Facilitate reuse, as well as providing precise reproducibility of a complex analysis
16. Example: Workflow for differential expression analysis of RNA-seq using Tophat/ Cufflinks tools
17. Collaboration within a Galaxy instance
18. Everything in Galaxy can be shared or published
19. Everything in Galaxy can be shared or published
20. Data Libraries for organizing shared data
21. …with role-based access controls
22. Galaxy Pages for publishing analysis
23. Actual histories and datasets directly accessible from the text
24. Histories can be imported and the exact parameters inspected
25. Workflows and other entities can also be embedded
26. And imported for inspection, verification, and reuse
27. The Galaxy ecosystem
28. More than 70 known public Galaxy servers
    15+ general servers Domain specific servers including: Ballaxy for structure based computational biology,
    Cistrome for regulatory sequence analysis,
    Genomic Hyperbrowser: statistical integration of genomic data,
    GigaGalaxy: integrating workflows published in GigaScience,
    Pathogen Portal:comparative analysis of host response to pathogens,
    ...
    Dozens of large scale private Galaxy instances
38. Ways to use Galaxy The public web service at http://usegalaxy.org Install locally with many compute environments Deploy on a cloud using Cloudman Atmosphere
39. PSC, Pittsburgh SDSC, San Diego Galaxy Cluster • 256 cores • 2 TB memory Rodeo • 128 cores • 1 TB memory Corral/Stockyard • 20 PB disk Stampede • 462,462 cores • 205 TB memory Blacklight • 4,096 cores • 32 TB memory • Dedicated resources Trestles • 10,368 cores • 20.7 TB memory • Shared resources TACC Austin Nate Coraor Galaxy can scale: for example Galaxy main
40. Blacklight (PSC) Messaging Server Pulsar Galaxy Server Processes Stampede (TACC) Galaxy Server VMs (TACC) Pulsar Job control (AMQP) Data transfer (HTTPS) Data transfer (HTTPS) John Chilton Pulsar: Galaxy job runner that can run almost anywhere. No shared filesystem, stages all necessary Galaxy components
41. Bringing it all together: automate all the things! Unified ansible playbook for Galaxy main, cloud, and local deployments
42. Collaboration among Galaxy instances
43. 1 2 3 ∞ http://usegalaxy.org http://usegalaxy.org/community ... Galaxies on private clouds Galaxies on public clouds ... private Galaxy installations Private Tool Sheds Galaxy Tool Shed Greg von Kuster, Dave Bouvier
45. Repositories are owned by the contributor, can contain tools, workflows, etc. Backed by version control, a complete version history is retained for everything that passes through the toolshed Galaxy instance admins can install tools directly from the toolshed using only a web UI Support for recipes for installing the underlying software that tools depend on (also versioned)
53. Ensuring tool quality in the toolshed
54. Correctness of all configurations, dependencies automatically verified
    Contributed tools include functional tests which are run in a controlled environment Tool functional tests that passed An example of the information uploaded to a repository by the install_and_test_tool_shed_repositories test framework for tool functional tests that passed successfully.
55. “Intergalactic Utilities Commission”
    rotating committee that reviews tools for qualitiy and provides sign-offs and ratings Reviewed repositories
56. “devteam” tools
57. “iuc” tools
58. Galaxy toolshed summary • Allow users to share tools, datatypes, workflows, sample data, and automated installation scripts for tool dependencies • Version controlled • Community annotation, rating, comments, review • Dependency resolution • Integration with Galaxy instances to automate tool installation and updates
59. Visualization and analytics Jeremy Goecks
60. Why integrate visualization and analysis?
    Individual researchers are now producing their own reference genomes and functional genomic datasets: need to be easily able to create custom browsers
    Working with these datasets involves complex, parameter dependent analyses, interactive visualizations can aid in the analysis process
    Galaxy already provides a very sound model for abstracting interfaces to analysis tools
    Existing tool framework can be leveraged for
    visual analytics Jeremy Goecks
61. Trackster
    Entirely web standards based to support sharing, communicating, and collaborating around visualizations
    Dynamic and responsive
    Open source and extremely extensible
    Jeremy Goecks
62. Dynamic filtering on element properties (here, FPKM for putative transcripts)
63. Modifying Cufflinks parameters and locally reassembling
64. Exploring parameter spaces efficiently Jeremy Goecks
65. a b c d e f g ii iii i
66. e f g ii iii i
67. Custom build Visualize in trackster Easily build browsers and perform visual analysis for novel genomes
68. Toward a pluggable framework
    interactive web-based visualizations...
69. PhyloViz from Google Summer of Code student Tomithy Too
70. Circster,interactive circos-style plots
71. Visualization framework: Charts plugin
72. Visualization framework: Charts plugin
73. Visualization framework: Charts plugin
74. Some current directions
75. Supporting tool developers Planemo: command line tools to support Galaxy tool development tasks Support for github centric workflows in the toolshed to support collaborative tool development New approaches for dependency management and installation Citation, credit, and incentivation
76. User interfaces for large-scale data analysis
77. Galaxy’s user interface is designed to be simple and intuitive for users without informatics expertise Can we scale this user interface to the analysis of hundreds of samples while maintaining interface idioms and usability?
78. Users typically use many histories when working with many samples; New multiple history view makes working with 100s of histories easy Carl Eberhard
79. A not-so-new feature: mapping over multiple datasets However, this breaks down for complex combinations of datasets (e.g. many sets of paired end reads, in replicates)
80. Dataset Collections Organize user data Individual Datasets Collection Collection Contents John Chilton and Carl Eberhard
81. Operations over collections For “list” collections, existing tools can automatically be mapped across the entire collection Existing tools that support multiple inputs and one output act as reducers Many existing tools just work; but “structured” collections like “paired” need explicit support in tools
82. Map/reduce in workflows More Powerful Workflows Arbitrary # of Inputs (... paired). Run applications in parallel (one per input). Merged output for subsequent processing. John Chilton
83. Enhanced Tuxedo Suite Workflow RNA-Seq workflow based using the Tuxedo suite. John Chilton
84. Dataset Collections Extremely flexible for grouping collections of complex datasets, can be nested to arbitrary depth, structure is preserved through mapping More complex reductions, other collection operations in progress Towards 10,000 samples: workflow scheduling improvements (backgrounding, decision points, streaming)
85. Interactive programming environments Björn Grüning, Eric Rasche, John Chilton
86. For researchers without informatics expertise, the web UI and existing tools are often sufficient For informaticians, Galaxy provides an extensive API and wrappers (e.g. Bioblend) But, many users can do some programming, would like the benefits of Galaxy with the flexibility to do some scripting
87. Docker enables interactive———— environments Framework allows spinning up secure* isolated environments, that can interact with the Galaxy history through Galaxy’s API Initial implementation supporting iPython Notebook
88. Example from John Chilton
92. Dan Blankenberg Nate Coraor Dannon Baker Jeremy Goecks Anton Nekrutenko James Taylor Dave Clements Jennifer Jackson Engineering Support and outreach Leadership Carl Eberhard Dave Bouvier John Chilton Sam Guerler Martin Čech Enis Afgan Supported by the NHGRI (HG005542, HG004909, HG005133, HG006620), NSF (DBI-0850103), Penn State University, Emory University, and the Pennsylvania Department of Public Health Nitesh Turaga The “Core” Galaxy Team
93. Björn Grüning Uni Freiburg Peter Cock TJHI Kyle Ellrott UCSC Eric Rasche CPT Nicola Soranzo TGAC Brad Chapman HSPH Nuwan Goonasekera VeRSI Yousef Kowsar VLSCI Extended team and other contributors… And many others who have contributed to the main Galaxy code, tools to the ToolShed, participated in discussions, attended the Galaxy conferences, …
94. Galaxy is a community! Join us on irc, mailing lists, Galaxy Biostar Contribute code on bitbucket, github, or the ToolShed Join us for a Hackathon or our annual conference Fifth annual Galaxy Community Conference Hackathon, training day, and two days of talks