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Seattle Trip Report
Data Integration – Company Engagement – BigData
Denis C. Bauer | Research Scientist
19 November 2012

CMIS
About me
•   BSc (Germany) Bioinformatics + Hons (ITEE, UQ) “In Silico Protein Design” Machine Learning
•   PhD (IMB, UQ) “Quantitative models of Transcriptional regulation” Optimization
•   PostDoc (IMB, UQ) “Sorting the intranuclear proteom” Bayesian Networks
•   PostDoc (QBI, UQ) Bioinformatics for the Sequencing Facility Operation



                                          • Research Scientist (CSIRO)
                                                 “Data integration of ‘Omics data in CRC”
                                             •     Develop protocols for data generation
                                             •     Develop pipelines for analysis
                                             •     Research ways for data integration

                                                           pHealth (Garry Hannan)
Seattle: Future hub for life sciences?




Seattle Trip Report | Denis C. Bauer | Page 3
Primary Goal: Collaboration with
William Noble

    Bayesian Network                                 for   automatic
    grouping                         of genomic functional elements

    (TSS, gene) by learning                     simultaneously from
    measured                               genomic   features   (histone         Bill Noble

    modifications)




                                                                           Michael Hoffman


Seattle Trip Report | Denis C. Bauer | Page 4
Segway: predictions
 Histone Modifications
             H2M3     x0x00x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x0

             H3M4     x0x00x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x0

             H3M4     0x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x00000


 Bayesian Network



                                                                                                                                                         Train



 Segmentation & Classification




 Annotation




Presentation title | Presenter name | Page 5
Institute for Systems Biology: case study
for BigData
                                                TCGA has 20 different cancer
                                                types with up to 900 samples
                                                each.
                                                • Faster computers
                                                • Better approaches

Amazon: machine learning method for uncovering                                 Ilya Shmulevich
multivariate associations from large and diverse data sets.


Google: Use 10.000 – 600.000 cores and benefit from
Google expertise in compute and storage.




Seattle Trip Report | Denis C. Bauer | Page 6
ISB App Engine Presentation at Google IO 2012




http://popcorn.webmadecontent.org/4d3
  Seattle Trip Report | Denis C. Bauer | Page 7
Focusing on large scale and tactile interactive experiences that engross and
 envelope the visitor, Philip Worthington (1977-) created Shadow Monsters, a
 digital version of the traditional shadow puppet.



Seattle Trip Report | Denis C. Bauer | Page 8
Can CSIRO use outline-detection to do cool stuff ?




Seattle Trip Report | Denis C. Bauer | Page 9
Road Trip to Pacific Northwestern National Laboratory




Presentation title | Presenter name | Page 10
Road Trip to PNNL




Presentation title | Presenter name | Page 11
Road Trip to PNNL




Presentation title | Presenter name | Page 12
Road Trip to PNNL




Presentation title | Presenter name | Page 13
Road Trip to PNNL




Presentation title | Presenter name | Page 14
Road Trip to PNNL




Presentation title | Presenter name | Page 15
Enterprise-wide multidisciplinary
collaborations
PNNL predicts from sensor data if and when
radioactive material hits ground water.
Mathematical and visual prediction methods of
compute-intensive expert systems
Ian’s team develops a framework that allows
enterprise wide collaboration
     • Data sharing/annotation/provenance
     • Computational expert pipelines -> graphical
       programming -> domain experts
     • Developed for computer-grid infrastructure
                                                     Ian Gorton




Seattle Trip Report | Denis C. Bauer | Page 16
Commoditize parallelization
                                                                Computer Science & Engineering
                                                                University of Washington
Currently: Expert-system if !(embarrassingly parallel)
     • Deciding how to most efficiently bundle for parallel
       execution and how to resolve
     • The appropriate method can change with the actual load
       at runtime
Parallelization needs to become something the
compiler at run time works out for us
(just like we don’t write assembly code anymore)
     • SciDB
     • SKEWTUNE (better load for Hadoop)
     • HaLoop (Iterative parallele Data Processing)
                                                                 Magdalena Balazinska




Presentation title | Presenter name | Page 17
Commoditize parallelization (and
visualization)

HDInsight
             Hadoop on windows Server
             and Azure
             Integration with excel




PowerView
             Interactive graphics




Seattle Trip Report | Denis C. Bauer | Page 18
Collaboration options
                            • GS (Bill): Bayesian Network
                           • ISB (Ilya): Variant association
                         • CS (Magda): Iterative parallelization
                         • PNNL (Ian): Graphical programming
                                       Framework




Thank you
CMIS
Denis C. Bauer
Research Scientist
t +61 2 9325 3174
E Denis.Bauer@csiro.au
w www.csiro.au/cmis


CMIS

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Trip Report Seattle

  • 1. Seattle Trip Report Data Integration – Company Engagement – BigData Denis C. Bauer | Research Scientist 19 November 2012 CMIS
  • 2. About me • BSc (Germany) Bioinformatics + Hons (ITEE, UQ) “In Silico Protein Design” Machine Learning • PhD (IMB, UQ) “Quantitative models of Transcriptional regulation” Optimization • PostDoc (IMB, UQ) “Sorting the intranuclear proteom” Bayesian Networks • PostDoc (QBI, UQ) Bioinformatics for the Sequencing Facility Operation • Research Scientist (CSIRO) “Data integration of ‘Omics data in CRC” • Develop protocols for data generation • Develop pipelines for analysis • Research ways for data integration pHealth (Garry Hannan)
  • 3. Seattle: Future hub for life sciences? Seattle Trip Report | Denis C. Bauer | Page 3
  • 4. Primary Goal: Collaboration with William Noble Bayesian Network for automatic grouping of genomic functional elements (TSS, gene) by learning simultaneously from measured genomic features (histone Bill Noble modifications) Michael Hoffman Seattle Trip Report | Denis C. Bauer | Page 4
  • 5. Segway: predictions Histone Modifications H2M3 x0x00x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x0 H3M4 x0x00x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x0 H3M4 0x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x000000x00x00xxxxx00xx0x00xxxx0x00x0xx00x00x000x000x00x00000 Bayesian Network Train Segmentation & Classification Annotation Presentation title | Presenter name | Page 5
  • 6. Institute for Systems Biology: case study for BigData TCGA has 20 different cancer types with up to 900 samples each. • Faster computers • Better approaches Amazon: machine learning method for uncovering Ilya Shmulevich multivariate associations from large and diverse data sets. Google: Use 10.000 – 600.000 cores and benefit from Google expertise in compute and storage. Seattle Trip Report | Denis C. Bauer | Page 6
  • 7. ISB App Engine Presentation at Google IO 2012 http://popcorn.webmadecontent.org/4d3 Seattle Trip Report | Denis C. Bauer | Page 7
  • 8. Focusing on large scale and tactile interactive experiences that engross and envelope the visitor, Philip Worthington (1977-) created Shadow Monsters, a digital version of the traditional shadow puppet. Seattle Trip Report | Denis C. Bauer | Page 8
  • 9. Can CSIRO use outline-detection to do cool stuff ? Seattle Trip Report | Denis C. Bauer | Page 9
  • 10. Road Trip to Pacific Northwestern National Laboratory Presentation title | Presenter name | Page 10
  • 11. Road Trip to PNNL Presentation title | Presenter name | Page 11
  • 12. Road Trip to PNNL Presentation title | Presenter name | Page 12
  • 13. Road Trip to PNNL Presentation title | Presenter name | Page 13
  • 14. Road Trip to PNNL Presentation title | Presenter name | Page 14
  • 15. Road Trip to PNNL Presentation title | Presenter name | Page 15
  • 16. Enterprise-wide multidisciplinary collaborations PNNL predicts from sensor data if and when radioactive material hits ground water. Mathematical and visual prediction methods of compute-intensive expert systems Ian’s team develops a framework that allows enterprise wide collaboration • Data sharing/annotation/provenance • Computational expert pipelines -> graphical programming -> domain experts • Developed for computer-grid infrastructure Ian Gorton Seattle Trip Report | Denis C. Bauer | Page 16
  • 17. Commoditize parallelization Computer Science & Engineering University of Washington Currently: Expert-system if !(embarrassingly parallel) • Deciding how to most efficiently bundle for parallel execution and how to resolve • The appropriate method can change with the actual load at runtime Parallelization needs to become something the compiler at run time works out for us (just like we don’t write assembly code anymore) • SciDB • SKEWTUNE (better load for Hadoop) • HaLoop (Iterative parallele Data Processing) Magdalena Balazinska Presentation title | Presenter name | Page 17
  • 18. Commoditize parallelization (and visualization) HDInsight Hadoop on windows Server and Azure Integration with excel PowerView Interactive graphics Seattle Trip Report | Denis C. Bauer | Page 18
  • 19. Collaboration options • GS (Bill): Bayesian Network • ISB (Ilya): Variant association • CS (Magda): Iterative parallelization • PNNL (Ian): Graphical programming Framework Thank you CMIS Denis C. Bauer Research Scientist t +61 2 9325 3174 E Denis.Bauer@csiro.au w www.csiro.au/cmis CMIS

Notes de l'éditeur

  1. http://www.snap2objects.com/2009/05/70-designers-that-shaped-the-world/http://www.snap2objects.com/2009/05/70-designers-that-shaped-the-world/