CSP

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CSP

  1. 1. Brief  explana,on  of   “Integra,ng  dilu,on-­‐based  sequencing   and  popula,on  genotypes     for  single  individual  haplotyping” Hirotaka  Matsumoto
  2. 2. INTRODUCTION
  3. 3. Single  individual  haplotyping  (SIH) •  Infer  haplotypes  from  sequence  fragments. (SNP  fragments)
  4. 4. Single  individual  haplotyping  (SIH) •  Infer  haplotypes  from  sequence  fragments.
  5. 5. Single  individual  haplotyping  (SIH) •  Infer  haplotypes  from  sequence  fragments.
  6. 6. Dilu,on-­‐based  sequencing •  SIH  needs  long  DNA  sequencing  reads   •  Dilu,on-­‐based  sequencing  can  produce  long  reads   –  Fosmid  pool-­‐based  NGS       –  Long  fragment  technology   –  Dilu,on-­‐amplifica,on-­‐based  sequencing
  7. 7. Process  of  dilu,on-­‐based  seq DNA  fragments  are  separated  into  mul,ple  low-­‐concentra,on  dilu,ons.     ASer  sequencing  and  mapping  an  aliquot,  mapped  reads  form  clusters   which  correspond  to  DNA  fragments.     Clusters  are  merged  into  read  fragments  (SNP  fragments) (i)     (ii)       (iii)    
  8. 8. Chimeric  fragment  (CF) •  Problem  of  producing  chimeric  fragments  (CFs)   –  Reads  with  different  chromosomal  origins  are  regarded  as  one  cluster   and  merged  into  a  fragment  when  an  aliquot  happen  to  have  some   long  DNA  fragments  derived  from  the  same  region.   –  CFs  significantly  decrease  the  accuracy  of  SIH.
  9. 9. METHOD     target:  detec,on  of  CFs
  10. 10. Detec,on  of  CFs •  Basis  of  our  strategy   – CFs  correspond  to  an  ar,ficially  recombinant   haplotype  and  differ  from  biological  haplotypes  in   the  popula,on.  
  11. 11. PHASE •  Sta,s,cal  phasing  method   –  Infer  haplotypes  from  popula,on.   –  The  diversity  of  haplotypes  is  limited  and  there  are   conserved  haplotypes.   •  We  use  PHASE  to  obtain  the  haplotype  candidates.   –  Example  of  output   A  candidate  of  haplotypes   and  its  probability.
  12. 12. CF  detec,on  model •  We  model  the  probabili,es  that  a  SNP   fragment  is  normal  fragment  and  chimeric   fragment.   •  With  there  probabili,es  we  develop  a   indicator  “CSP”  which  evaluates  the  chimerity   of  a  SNP  fragment.
  13. 13. NF  probability •  NF  probability   –  The  probability  that  a  SNP  fragment  is  normal  fragment  (NF).   –  Calculate  the  consistency  between  sta,s,cally  phased  haplotypes  and   a  fragment.  
  14. 14. CF  probability •  CF  probability   –  The  probability  that  a  SNP  fragment  is  chimeric  fragment.   –  LeS  and  right  parts  are  derived  from  different  haplotypes.   ll
  15. 15. CSP •  Chimericy  based  on  sta,s,cal  phasing  (CSP)   •  Low  CSP  values  means   – the  fragment  correspond  to  recombinant  of   sta,s,cally  phased  haplotypes.   – the  fragment  is  suspected  of  CF.
  16. 16. Sliding-­‐window  approach •  Running  ,me  of  PHASE  increases  according  to  SNP   fragment  size.   –  Complexity  of  popula,on  haplotypes  increase   exponen,ally.   •  We  use  sliding-­‐window  approach  (W=5). sliding-­‐window
  17. 17. RESULT
  18. 18. dataset •  Dilu,on-­‐based  sequencing   – Kaper’s  data   – Duitama’s  data   •  True  haplotypes   – Trio-­‐based  haplotypes   •  True  NFs  and  CFs   – Defined  by  true  haplotypes
  19. 19. CSP  distribu,on •  CSP  of  CFs  is  lower  than  that  of  NFs Theore,cal  lowest  value  (W=5)    -­‐    Change  haplotype  origin  at  second  or  third  site. Fragment:        00011   Haplotypes:  00000  /  11111
  20. 20. CF  detec,on •  CSP  is  a  highly  efficient  measure  to  detect  CFs.
  21. 21. SIH  accuracy  aSer  removing  CFs •  The  accuracies  of  SIH  increased  significantly   aSer  removing  CSs  detected  by  CSP.
  22. 22. CONCLUSION •  CSP  is  a  highly  efficient  measure  to  detect   chimeric  fragments.   •  SIH  accuracy  increased  significantly  aSer   removing  CFs  candidates  detected  using  CSP.
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