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C A M P A I G N
                                                        Chief Development Officer
                                                        Emory University
                       C A M P A I G N
                                                        School of Nursing Development
                                                        Emory University
                                                        1520 Clifton Road, NE
                                                        School of Nursing Development
                                                        Atlanta, Georgia 30322-4207
                                                        1520 Clifton Road, NE
                                                        P 404.727.1234
                                                        Atlanta, Georgia 30322-4207
                                                        E adorrill@emory.edu
                Discovery of human heteroplasmic sites  P 404.727.1234

              Enabling of human heteroplasmic sites
                 enabled NGS analysis with(out)
                                 E adorrill@emory.edu
                Discovery by an accessible interface to
                    cloud computing infrastructure to
                 enabled by infrastructure
                       the an accessible interface
                    cloud computing infrastructure
                   Enis Afgan, the Galaxy Team,Francesca Chiaromonte
                    Enis Afgan, Hiroki Goto, Ian Paul,
                                                       Anton Nekrutenko,
                                        James Taylor
                       Kateryna Makova, Anton Nekrutenko, James Taylor
                    Enis Afgan, Hiroki Goto, Ian Paul, Francesca Chiaromonte
                       Kateryna Makova, Anton Nekrutenko, James Taylor
                            Bioinformatics Open Source Conference, July 16, 2011, Vienna




missions: you are free to      blog or    live-blog about this presentation as long as you attribute the work to its au
CloudMan-as-a-Bridge
A. Users in different labs   B. Isolated Galaxy instance(s)   C. Dense data center
CloudMan-as-a-Bridge
A. Users in different labs   B. Isolated Galaxy instance(s)   C. Dense data center




SaaS
CloudMan-as-a-Bridge
A. Users in different labs   B. Isolated Galaxy instance(s)   C. Dense data center




SaaS                                                                         IaaS
CloudMan-as-a-Bridge
A. Users in different labs   B. Isolated Galaxy instance(s)   C. Dense data center




SaaS                         CloudMan + Galaxy                               IaaS
CloudMan Platform

A complete solution for instantiating and
managing cloud resources
  With automatically configured Galaxy (if desired)
  Scope of tools and reference datasets exceed Galaxy Main



Deploy a (Galaxy) cluster in minutes!
CloudMan features
•   Deployment on Amazon Web Services Cloud
    •   Wizard-guided setup: requires no computational expertise, no
        infrastructure, no software
•   Automated (thus reproducible) configuration for machine image, tools,
    and data
•   Four modes of cluster type setup
•   Dynamic persistent storage
•   Elastic resource scaling: manual or automatic based on workload
•   Standalone deployment, requiring no external dependencies or services
•   Customizable by individual users
•   Sharing of derived cluster instances -> even the customized ones!
CloudMan features
•   Deployment on Amazon Web Services Cloud
    •   Wizard-guided setup: requires no computational expertise, no
        infrastructure, no software
•   Automated (thus reproducible) configuration for machine image, tools,
    and data
•   Four modes of cluster type setup
•   Dynamic persistent storage
•   Elastic resource scaling: manual or automatic based on workload
•   Standalone deployment, requiring no external dependencies or services
•   Customizable by individual users
•   Sharing of derived cluster instances -> even the customized ones!
Deploying a cluster on AWS




1.
Deploying a cluster on AWS




1.
     2.
Deploying a cluster on AWS




1.
     2.
          3.
Deploying a cluster on AWS




1.
     2.
          3.
                 4.
CloudBioLinux + Galaxy
        + CloudMan =
     •   A lot of (NGS) tools immediately available
         and easily accessible
     •   700GB of reference genome data

Bowtie, BWA, Samtools, MAQ, BFAST, ABySS, Velvet,
MACS, Tophat, Cufflinks, MegaBLAST, BLAST, Sputnik,
Taxonomy, HyPhy, Lastz, Perm, GATK, Srma, Beam, Pass,
LPS, Plink, Haploview, Freebayes, Mosaik, Picard, ...
But what if your tool
  (or data) is missing?
1. Add it! (via automation)
   - CloudMan instances
   are self-contained



2. Save & share               Add tools
   - With individual users
   or make it public
                              Add data
Deployment sharing
Deployment sharing
Deployment sharing
Use CloudMan as SaaS

     Use CloudMan as PaaS

It’s automated, reproducible, extensible, and
                 transparent.
Enis Afgan          Dannon Baker               Dan Blankenberg         Nate Coraor




        Dave Clements        Jeremy Goecks               Jennifer Jackson      Greg von Kuster




          Kanwei Li           James Taylor                Kelly Vincent       Anton Nekrutenko




Supported by the NHGRI (HG005542, HG004909, HG005133), NSF (DBI-0850103), Penn State University,
                Emory University, and the Pennsylvania Department of Public Health

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F06-Cloud-Enabling NGS

  • 1. C A M P A I G N Chief Development Officer Emory University C A M P A I G N School of Nursing Development Emory University 1520 Clifton Road, NE School of Nursing Development Atlanta, Georgia 30322-4207 1520 Clifton Road, NE P 404.727.1234 Atlanta, Georgia 30322-4207 E adorrill@emory.edu Discovery of human heteroplasmic sites P 404.727.1234 Enabling of human heteroplasmic sites enabled NGS analysis with(out) E adorrill@emory.edu Discovery by an accessible interface to cloud computing infrastructure to enabled by infrastructure the an accessible interface cloud computing infrastructure Enis Afgan, the Galaxy Team,Francesca Chiaromonte Enis Afgan, Hiroki Goto, Ian Paul, Anton Nekrutenko, James Taylor Kateryna Makova, Anton Nekrutenko, James Taylor Enis Afgan, Hiroki Goto, Ian Paul, Francesca Chiaromonte Kateryna Makova, Anton Nekrutenko, James Taylor Bioinformatics Open Source Conference, July 16, 2011, Vienna missions: you are free to blog or live-blog about this presentation as long as you attribute the work to its au
  • 2. CloudMan-as-a-Bridge A. Users in different labs B. Isolated Galaxy instance(s) C. Dense data center
  • 3. CloudMan-as-a-Bridge A. Users in different labs B. Isolated Galaxy instance(s) C. Dense data center SaaS
  • 4. CloudMan-as-a-Bridge A. Users in different labs B. Isolated Galaxy instance(s) C. Dense data center SaaS IaaS
  • 5. CloudMan-as-a-Bridge A. Users in different labs B. Isolated Galaxy instance(s) C. Dense data center SaaS CloudMan + Galaxy IaaS
  • 6. CloudMan Platform A complete solution for instantiating and managing cloud resources With automatically configured Galaxy (if desired) Scope of tools and reference datasets exceed Galaxy Main Deploy a (Galaxy) cluster in minutes!
  • 7. CloudMan features • Deployment on Amazon Web Services Cloud • Wizard-guided setup: requires no computational expertise, no infrastructure, no software • Automated (thus reproducible) configuration for machine image, tools, and data • Four modes of cluster type setup • Dynamic persistent storage • Elastic resource scaling: manual or automatic based on workload • Standalone deployment, requiring no external dependencies or services • Customizable by individual users • Sharing of derived cluster instances -> even the customized ones!
  • 8. CloudMan features • Deployment on Amazon Web Services Cloud • Wizard-guided setup: requires no computational expertise, no infrastructure, no software • Automated (thus reproducible) configuration for machine image, tools, and data • Four modes of cluster type setup • Dynamic persistent storage • Elastic resource scaling: manual or automatic based on workload • Standalone deployment, requiring no external dependencies or services • Customizable by individual users • Sharing of derived cluster instances -> even the customized ones!
  • 10. Deploying a cluster on AWS 1. 2.
  • 11. Deploying a cluster on AWS 1. 2. 3.
  • 12. Deploying a cluster on AWS 1. 2. 3. 4.
  • 13. CloudBioLinux + Galaxy + CloudMan = • A lot of (NGS) tools immediately available and easily accessible • 700GB of reference genome data Bowtie, BWA, Samtools, MAQ, BFAST, ABySS, Velvet, MACS, Tophat, Cufflinks, MegaBLAST, BLAST, Sputnik, Taxonomy, HyPhy, Lastz, Perm, GATK, Srma, Beam, Pass, LPS, Plink, Haploview, Freebayes, Mosaik, Picard, ...
  • 14. But what if your tool (or data) is missing? 1. Add it! (via automation) - CloudMan instances are self-contained 2. Save & share Add tools - With individual users or make it public Add data
  • 18. Use CloudMan as SaaS Use CloudMan as PaaS It’s automated, reproducible, extensible, and transparent.
  • 19. Enis Afgan Dannon Baker Dan Blankenberg Nate Coraor Dave Clements Jeremy Goecks Jennifer Jackson Greg von Kuster Kanwei Li James Taylor Kelly Vincent Anton Nekrutenko Supported by the NHGRI (HG005542, HG004909, HG005133), NSF (DBI-0850103), Penn State University, Emory University, and the Pennsylvania Department of Public Health