SAP HANA: Re-Thinking Information Processing for Genomic and Medical Data

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SAP HANA: Re-Thinking Information Processing for Genomic and Medical Data

  1. 1. SAP  HANA:  Re-­‐Thinking  Informa7on  Processing  for   Genomic  and  Medical  Data   Prof.  Dr.  Hasso  Pla,ner   Chairman  of  the  Supervisory  Board,  SAP  AG   Professor,  Hasso  Pla,ner  Ins?tute  
  2. 2. Real-Time Personalized Medicine is Within Our Reach Informa?on  and  Feedback  within  the  Window  of  Opportunity   Pa?ents   Doctors   Insurers   Researchers   Real-­‐Time  Data  Capture   and  Analysis   SAP  HANA  Healthcare  PlaPorm   Electronic   Genomics   Medical   Records   Annota?ons   ...   All  Relevant  Medical  Informa?on  Our   Can  we  Analyze  and  Interpret  all  Pa?ent  Data  Challenge:   on  a  Mobile  Device  During  a  Pa?ent’s  Visit?   2  
  3. 3. Innovation in Personalized Medicine can beDriven Using a “Design Thinking” Approach Human Factors Business Technical Factors Factors 3  
  4. 4. What  Professionals  Desire  is  Simple   Use  Case  1:  Clinician     Iden?fy  Clinically  Ac?onable  Gene?c  Variants    (e.g.  Causing  Tumor  Forma?on)  in  Order  to  Deliver    Personalized  Medical  Treatment     Needs:   •  Real-­‐Time  Comparison  of  Variants   to  Assess  Causal  Ones   •  Access  to  all  Pa?ent-­‐Specific  Data   Any?me  and  Anywhere   Desirability Viability Feasibility 4  
  5. 5. What  Professionals  Desire  is  Simple   Use  Case  2:  Researcher   Iden?fy  Causal  Variants  or  Muta?ons  in  Cohorts  (>  10,000  Individuals)  Suffering  from   Diseases  of  Interest,  e.g.  Au?sm   Needs:   •  Comparison  of  Variants  in  Diseased   and  Healthy  Cohorts   •  Flexible  Queries  to  Verify   Hypotheses  in  Real-­‐Time   Desirability Viability Feasibility 5  
  6. 6. Only  a  Deeply  Collabora7ve  Effort  can  be   Viable  From  a  Business  Perspec7ve   Patients Customers SAP HANA Universities Partners"We  have  been  thrilled  to  work  with  SAP  and  HPI  on  a  collabora?on  to  accelerate  DNA  sequence  analysis.  In  our   pilot  projects,  we  are  seeing  drama?c  speedups  in  compu?ng  on  human  genome  varia?on  data  from  many  samples.  We  are  dreaming  of  what  will  soon  be  possible  as  we  integrate  phenotype,  genomics,  proteomics,  and   exposome  data  to  empower  complex  trait  mapping  using  millions  of  health  records.”     -­‐  Professor  Carlos  D.  Bustamante  at  the  Stanford  University  School  of  Medicine       Desirability Viability Feasibility 6  
  7. 7. SAP  HANA  is  the     Technology  Enabler  for  This  Vision  Advances  in  Hardware  •  Mul?-­‐core  Architectures,   •  64  bit  Address  Space  –   e.g.  4  CPUs  x  10  Cores  on   4TB  in  Current  Servers   A   Each  Node   •  25GB/s  Data  Throughput  •  Scaling  Across  Servers,   •  Costs  per  Enterprise  Class   e.g.  100  Nodes  x  40  Cores   Server  Node  (40  Cores)     approx.  29,000  USD    Advances  in  SoQware   T   +   +   +  +   Text Retrieval Insert Only Compression Partitioning Multi-Core Dynamic and Extraction Parallelization Multithreading 7  
  8. 8. SAP  HANA  is  the  Technology  Enabler  for  This  Vision   Due  to  the  Power  of  Mathema?cs  and  Distributed  Compu?ng,   SAP  HANA  can  Predictably  Complete  any  Informa?on  Processing   Task,  However  Complex,  Within  a  Given  Time-­‐Window.     It  is  Only  a  Ma,er  of  Scaling  the  Hardware  –  There  are  no  Other   Variables  or  Unknowns     Scanning  3MB/msec/core   Inser?ng  1.5M  Records/sec   Aggrega?ng  12.5M  Records/sec/core   Desirability Viability Feasibility 8  
  9. 9. More  Than  Just  a  Faster  Database,  SAP  HANA   is  a  Revolu7onary  Compu7ng  PlaTorm   + Desirability Viability Feasibility 9  
  10. 10. SAP  HANA  Customers  Have  Already   Demonstrated  Amazing  Results   Enterprise Applications YODOBASHI   NONGFU  SPRING   LEADING  AIRLINE  100,000x  Faster   128,000x  Faster   43,000x  Faster  Sales  Data   Op?miza?on  of   Real-­‐Time  Analysis  for   Transporta?on   Pricing  of  Campaign  Mailing   Routes     Tickets        Speed-­‐Up  From   Speed-­‐Up  From     Speed-­‐Up  3  Days  To  2.5  sec   25  Hours  To  0.7  sec   From  12     Hours  To  1  sec     Desirability Viability Feasibility 10  
  11. 11. SAP  HANA  Customers  Have  Already   Demonstrated  Amazing  Results   Healthcare Industry MEDTRONIC   MITSUI  KNOWLEDGE  INDUSTRY   CHARITÉ  60x  Faster   408,000x  Faster  Than   1,000x  Processing  Queries   Tradi?onal  Disk-­‐Based   Faster  Tumor     Systems  in  Technical  PoC   Data  10x  Data     Analyzed  in  Compression  From   216x  Faster  DNA  Analysis   Seconds  1.5  TB  To  150  GB   Results  -­‐  From  2-­‐3  Days  To  20   Instead  of     Minutes   Hours  250x  Be,er      Complaint  Analysis     2-­‐10  sec    (Long  Text  Data)     For  Report   Desirability Viability Feasibility   Execu?on   11      
  12. 12. We  Can  Drama7cally  Accelerate  Each  Step   of  the  DNA  Analysis  Pipeline   Mobile Real-time AnalysisSequencing Service/Lab Computational Pipeline e.g. Clinicians AND e.g. Biologist e.g. Bioinformatician Researchers Sequencing Alignment Variant Calling Annotation and Analysis Follow-up Patient Raw DNA Mapped Discovered andSamples ReadS Genome Variants Validation 12  
  13. 13. First  Results  in  Alignment  Are  Promising   Mobile Real-time AnalysisSequencing Service/Lab Computational Pipeline e.g. Clinicians AND e.g. Biologist e.g. Bioinformatician Researchers Sequencing Alignment Variant Calling Annotation and Analysis Follow-up Patient Raw DNA Mapped Discovered andSamples ReadS Genome Variants Validation SAP  HANA  Improves  Alignment  Performance  at  Higher  Accuracy!*   Faster   BWA-­‐SW  28.3h    |    SAP  HANA  3.6h     Higher  Accuracy   BWA-­‐SW  0.53%  Misaligned    |    SAP  HANA  0.35%  Misaligned     BWA-­‐SW  0.34%  Unaligned    |    SAP  HANA  0.14%  Unaligned         13   *  Comparisons  done  with  simulated  full  genome,  30x  coverage,  100  bases  per  read,  single  ended    
  14. 14. First  Results  in  Annota7on   and  Analysis  Are  Promising   Mobile Real-time AnalysisSequencing Service/Lab Computational Pipeline e.g. Clinicians AND e.g. Biologist e.g. Bioinformatician Researchers Sequencing Alignment Variant Calling Annotation and Analysis Follow-up Patient Raw DNA Mapped Discovered andSamples ReadS Genome Variants Validation Annota7on   •  Report  SNPs  (Single  Nucleo?de  Polymorphisms)  Failing  Quality  Control   82x  faster   UCSC  102.47  sec    |    SAP  HANA  1.25  sec   Analysis     •  Compute  the  Alterna?ve  Allele  Frequency  for  Each  Variant  in  a  Genomic  Region     600x  faster   (Chromosome  1,  Posi?ons  100,000-­‐200,000)   VCFtools  259  sec    |    SAP  HANA  0.43  sec   •  Compute  the  Total  Number  of  Missing  Genotypes  for  Each  Individual   270x  faster   VCFtools  548  sec    |    SAP  HANA  2  sec   Supported  By:  Carlos  Bustamante  lab   14  
  15. 15. Example  Solu7on:  Molecular  Health   Assessing  Therapy  Effec7veness  •  Proac?vely  Analyze  Therapy   Alterna?ves  and  Provide   Decision  Support  When   Clinician  Talking  to  Pa?ent  •  Combine  Genomic  Data     With  Electronic  Medical   Records  to  Iden?fy  Best  Therapy   for  Pa?ent   15  
  16. 16. Example  Solu7on:  HANA  Oncolyzer   Real-­‐7me  Access  to  Pa7ent  Data  •  Mobile  Access  to   Complete  History  of   Pa?ent-­‐Specific  Events  •  Combined  Search  in   Structured  and   Unstructured  Clinical   Data  Sources  •  Interac?ve  Analysis  and  Explora?on  of  Pa?ent  Records     on-­‐the-­‐fly  on  Doctor’s  Mobile  Devices   16  
  17. 17. We  Have  the  Building  Blocks  to  Take  the     Next  Big  Step  in  Personalized  Medicine  •  Mobile  and  Flexible   Informa?on  and  Feedback  within  the  Window  of  Opportunity   Access  to  any  Pa?ent-­‐ Related  Data   Pa?ents   Doctors   Insurers   Researchers  •  Real-­‐Time  Analysis   Using  In-­‐Memory   Real-­‐Time  Data  Capture   and  Analysis   Technology   SAP  HANA  Healthcare  PlaPorm  •  Data  Integra?on  from   Electronic   Heterogeneous   Genomics   Medical   Records   Annota?ons   ...   Sources   All  Relevant  Medical  Informa?on   17  
  18. 18. The  Future:  Redefining  the  Possible  with   Real-­‐Time  Informa7on  Enable  Clinicians  to:   Enable  Researchers  to:  •  Make  Evidence-­‐Based   •  Inves?gate  the  Genomes  of   Therapy  Decisions  at  the   Millions  of  High-­‐Risk  Pa?ents   Pa?ent’s  Bed   on  a  Cluster  <  10M  USD  •  Supervise  High-­‐Risk   •  Analyze  the  Results  in   Pa?ents  to  Prevent   Real-­‐Time   Emergencies   18  
  19. 19. The  Power  of  Mul7disciplinary  Teams  Only  Strong  Partners  Build  Strong  Co-­‐Opera?ve  Success  Stories  SAP:  Global  Sowware  Vendor  and  Expert  for  Enterprise  Technologies  World-­‐Wide   +   DesignHasso  Plabner  Ins7tute:  Academic  Research   Thinking TeamsIns?tute  for  IT  Systems  Engineering   +   You  Carlos  Bustamante  Lab:  Leading  Stanford  Lab  On  Human  Popula?on  Genomics  and  Global  Health   Join our partnership! 19  
  20. 20. New  Ways  of  Real-­‐Time  Collabora7ve   Personal  Medicine   20  
  21. 21.   Thank  you!        Join  us:  hana-­‐healthcare-­‐plaPorm@sap.com     You  are  Invited  to  Visit  our  Booth   and  A,end  our  Partner  Presenta?ons.   21  

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