Galaxy: Genome Informatics 2007

Galaxy talk presented at Genome Informatics in 2007. Note the workflow editor noodles are not yet colored correctly. I think I wrote the editor only a few months before while sitting in a lobby at ISMB in Vienna. One of the first Galaxy Team slides at the end! Dan and Greg both had posters at this meeting so they got cool slide-up animations on relevant slides and halos at the end. Fancy.

Transcript

1. Galaxy http://g2.bx.psu.edu James Taylor, Courant Institute, NYU
2. “Biology is an experimental science that is experiencing an explosion of new data. This requires biologists to increase the scale and sophistication in the information technology used for their research”
3. Making sense of this explosion of data requires developing sophisticated computational methods
4. Making sense of this explosion of data requires developing sophisticated computational methods ...and making these methods accessible
5. Success in some areas...
6. Data warehouses (storage and querying)
7. Data warehouses (storage and querying) Data visualization (browsers)
8. Data warehouses (storage and querying) Data visualization (browsers) Analysis? Accessible? Efficient? Reproducible?
9. Wasting Time • For developers, building user interfaces is both time consuming and highly repetitive, yet doing it well is hard • Without accessible interfaces, experimentalists end up using ill suited / inefficient tools, hiring inexperienced students, ... • Even with accessible interfaces, users waste time moving data between data sources and tools, converting between data formats, ...
10. Wasting Time • For developers, building user interfaces is both time consuming and highly repetitive, yet doing it well is hard • Without accessible interfaces, experimentalists end up using ill suited / inefficient tools, hiring inexperienced students, ... • Even with accessible interfaces, users waste time moving data between data sources and tools, converting between data formats, ...
11. Wasting Time • For developers, building user interfaces is both time consuming and highly repetitive, yet doing it well is hard • Without accessible interfaces, experimentalists end up using ill suited / inefficient tools, hiring inexperienced students, ... • Even with accessible interfaces, users waste time moving data between data sources and tools, converting between data formats, ...
12. Wasting Potential • New technologies allow individuals labs to generate massive amounts of experimental data • However, effectively analyzing this data still requires specific technical / computational skills • The easier it is for experimentalists to work with sophisticated computational tools, the greater the potential for biological discovery
13. Wasting Potential • New technologies allow individuals labs to generate massive amounts of experimental data • However, effectively analyzing this data still requires specific technical / computational skills • The easier it is for experimentalists to work with sophisticated computational tools, the greater the potential for biological discovery
14. Wasting Potential • New technologies allow individuals labs to generate massive amounts of experimental data • However, effectively analyzing this data still requires specific technical / computational skills • The easier it is for experimentalists to work with sophisticated computational tools, the greater the potential for biological discovery
16. What is Galaxy? • An open-source framework for integrating various computational tools and databases into a cohesive workspace • A web-based service we (Penn State) provide, integrating many popular tools and resources for comparative genomics • A completely self-contained application for building your own Galaxy style sites
17. Galaxy’s web user interface
22. Integrating tools into Galaxy
23. Why integrate tools with Galaxy? • Galaxy makes it substantially easier to give your tools user interfaces • The resulting user interfaces are of high quality, and continually improved • Your tools gain value by being integrated with data sources and other tools
24. Integrating web based tools
27. To learn more about integrating external web sites with Galaxy, see Greg von Kuster, Poster #109
28. Integrating command line tools
31. HTML inputs generated from abstract parameter description
32. HTML inputs generated from abstract parameter description
33. Automatic input validation based on type, or more...
34. Tool help generated from a simple text format
36. } Template for generating command line from parameter values
37. Functional tests to be run with the “full stack” in place
38. Dealing with more complex interface needs
41. Repeating sets of parameters
42. Template language for building complex command lines
46. Conditional groups, grouping constructs can be nested
47. Command line tool expects a configuration file
48. Configuration file is generated based on user input
49. • Out of the box • Operations on genomic intervals • Extracting regions from and manipulating genome-wide multiple sequence alignments • General text manipulation, filtering, sorting, grouping, graphing • EMBOSS • In development • Statistical genetics suite based on RGenetics • Phlogenetic analysis based on HyPhy Galaxy tool suites
50. • Out of the box • Operations on genomic intervals • Extracting regions from and manipulating genome-wide multiple sequence alignments • General text manipulation, filtering, sorting, grouping, graphing • EMBOSS • In development • Statistical genetics suite based on RGenetics • Phlogenetic analysis based on HyPhy Galaxy tool suites To learn more about Galaxy’s tools for working with alignments, see Dan Blankenberg, Poster #19
52. Dataflow in Galaxy • Tools in Galaxy have a well defined abstract interface • Tools generate datasets of specific types (history items), which are used as the inputs of other tools • Can almost always determine the inputs and outputs of a tool • (given the values of certain parameters for more complex tools)
53. Reusable analysis workflows
54. Workflow editor
61. Workflow construction by example
67. Workflow support future • Intuitive interfaces for running workflows and configuring runtime parameterization • Control flow constructs to allow dealing with ambiguities that are not resolved until runtime • Support for repetitive invocation of tools and workflows, and aggregation of results • Saving and sharing of workflows (reproducibility!) • Dealing with changes to tool interfaces • Running workflows without the web interface
69. Acknowledgements • UCSC Genome Browser team • Biomart team • GMOD team • National Science Foundation
70. The Galaxy Team Guru Ananda Dan Blankenberg Nate Coraor Jianbin He Greg von Kuster Ross Lazarus (Harvard) Anton Nekrutenko James Taylor (NYU)