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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	
  
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	
  
Innovation in Personalized Medicine can be
Driven Using a “Design Thinking” Approach


                       Human
                      Factors




           Business             Technical
           Factors               Factors




                                            3	
  
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	
  
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	
  
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	
  
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	
  
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	
  
More	
  Than	
  Just	
  a	
  Faster	
  Database,	
  SAP	
  HANA	
  
  is	
  a	
  Revolu7onary	
  Compu7ng	
  PlaTorm	
  




                                      +




                 Desirability   Viability   Feasibility
                                                                      9	
  
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	
  
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	
  
	
                                                                                            	
  
We	
  Can	
  Drama7cally	
  Accelerate	
  Each	
  Step	
  
                   of	
  the	
  DNA	
  Analysis	
  Pipeline	
  
                                                                     Mobile Real-time Analysis
Sequencing 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
                                                                                      and
Samples             ReadS                Genome                 Variants
                                                                                   Validation




                                                                                       12	
  
First	
  Results	
  in	
  Alignment	
  Are	
  Promising	
  
                                                                                                                                Mobile Real-time Analysis
Sequencing 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
                                                                                                                                                                and
Samples                      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	
  
                                                                                   	
  
First	
  Results	
  in	
  Annota7on	
  
                                                  and	
  Analysis	
  Are	
  Promising	
  
                                                                                                                                          Mobile Real-time Analysis
Sequencing 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
                                                                                                                                                                  and
Samples                                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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
                                  +	
  
                                                                        Design
Hasso	
  Plabner	
  Ins7tute:	
  Academic	
  Research	
                Thinking
                                                                        Teams
Ins?tute	
  for	
  IT	
  Systems	
  Engineering	
  
                                  +	
  
                                                                              You	
  
Carlos	
  Bustamante	
  Lab:	
  Leading	
  Stanford	
  Lab	
  On	
  
Human	
  Popula?on	
  Genomics	
  and	
  Global	
  Health	
  


                              Join our partnership!
                                                                                    19	
  
New	
  Ways	
  of	
  Real-­‐Time	
  Collabora7ve	
  
          Personal	
  Medicine	
  




                                                       20	
  
 
               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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SAP HANA: Re-Thinking Information Processing for Genomic and Medical Data

  • 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. 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. Innovation in Personalized Medicine can be Driven Using a “Design Thinking” Approach Human Factors Business Technical Factors Factors 3  
  • 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. 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. 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. 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. 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. More  Than  Just  a  Faster  Database,  SAP  HANA   is  a  Revolu7onary  Compu7ng  PlaTorm   + Desirability Viability Feasibility 9  
  • 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. 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. We  Can  Drama7cally  Accelerate  Each  Step   of  the  DNA  Analysis  Pipeline   Mobile Real-time Analysis Sequencing 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 and Samples ReadS Genome Variants Validation 12  
  • 13. First  Results  in  Alignment  Are  Promising   Mobile Real-time Analysis Sequencing 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 and Samples 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. First  Results  in  Annota7on   and  Analysis  Are  Promising   Mobile Real-time Analysis Sequencing 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 and Samples 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. 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. 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. 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. 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. 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   +   Design Hasso  Plabner  Ins7tute:  Academic  Research   Thinking Teams Ins?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. New  Ways  of  Real-­‐Time  Collabora7ve   Personal  Medicine   20  
  • 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