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Galaxy: an Open Web-Based Analysis Platform

Galaxy: an Open Web-Based Analysis Platform. Core4Life Retreat Bioinformatics Unit CRG (Barcelona, Spain) Ernesto Lowy 22-23 November 2012. Introduction. Developed by Nekrutenko and others at Penn State, along with James Taylor at Emory University

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Galaxy: an Open Web-Based Analysis Platform

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  1. Galaxy: an Open Web-Based Analysis Platform Core4Life Retreat Bioinformatics Unit CRG (Barcelona, Spain) Ernesto Lowy 22-23 November 2012

  2. Introduction • Developed by Nekrutenko and others at Penn State, • along with James Taylor at Emory University • Galaxy is an open web-based tool for biomedical research • Accessible: users without programming experience • can easily specify parameters and run tools and workflows • Reproducible: Galaxy captures information so that any • user can repeat and understand a complete computational • analysis

  3. Introduction • Allow experimental biologists to do complex bioinformatics analyses (including BigData) without computational skills • Galaxy is completely open source and is unique • There are commercial solutions but are a waste of money • because technology changes every 2 weeks

  4. Galaxy Interface Analysis Modules History Data browser

  5. Running Bowtie trough Galaxy Choosing Bowtie Parameters Input Fastq file

  6. Checking Results of Bowtie Run finished Alignment in SAM format

  7. Workflows • Workflows can be easily created from ‘History’

  8. Type of Installation • Free Public Server • At: • UseGalaxy.org • NBIC • Other listed on Galaxy Wiki • Pro: • Not need installation • Con: • Slow uploads • Long queues before launching • the analysis • Old versions of some tools • Lack of control on your data • Local Installation • Pro: • Add your own tools • Adapt resources used by Galaxy depending on the number of users • and the type of analyses • Data is secured in your own institution • Con: • Dedicated staff for setting-up the platform

  9. Galaxy@CRG • Currently being developed by the Bioinformatics Unit (mainly Jean François Taly) • Will: • Include our (Bioinformatics Core) pipelines • Replace commercial solutions we have in our Institute • Increase the visibility of the tools developed in-house • Be an interface to the High Performance Computing infrastructure at the CRG • Finally: • CRG will be trained on Bioinformatics data analysis using Galaxy as an interface

  10. Implementation details

  11. THANKS FOR YOUR ATTENTION!!

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