Rna seq

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Rna seq

  1. 1. RNA-­‐seq:  A  high-­‐resolu1on   View  of  the  Transcriptome  
  2. 2. The  Central  Dogma  
  3. 3. +  
  4. 4. =  
  5. 5. Your  Nature  Paper  
  6. 6. RNA-­‐seq  protocol  schema<c  
  7. 7. Approaches  to  RNA-­‐seq   Nature  Biotech  (2010)  28,  421-­‐423  
  8. 8. Alignment  
  9. 9. RNA-­‐seq  Alignment  
  10. 10. Run  Time  
  11. 11. Alignment  Yield  
  12. 12. Splice  Read  Placement  Accuracy  
  13. 13. Impact  on  Transcript  Assembly  
  14. 14. Transcript  Quan<fica<on  
  15. 15. Models  for  RNA-­‐seq   •  Count-­‐based  models   •  Mul<-­‐reads  (isoform  resolu<on)   •  Paired-­‐end  reads  (include  length  resolu<on   step)   •  Posi<onal  bias  along  transcript  length   •  Sequence  bias  
  16. 16. Read  Coun<ng  
  17. 17. Mortazavi,  2008,  NMeth  
  18. 18. L.  Pachter  (2011)  arXiv:1104.3889v  
  19. 19. Sequence  Bias-­‐-­‐priming   Hansen  (2010),  NAR  
  20. 20. Sample-­‐specific  Sequence  Bias  
  21. 21. Models  for  RNA-­‐seq  
  22. 22. Result  of  Quan<fica<on  
  23. 23. Clustering  and  Visualiza<on  
  24. 24. Hierarchical  Clustering   Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8
  25. 25. Hierarchical  Clustering   Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8
  26. 26. Hierarchical  Clustering   Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8
  27. 27. Hierarchical  Clustering   Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8
  28. 28. Distance  Metrics   !  Euclidean distance !  Manhattan distance !  Minkowski distance (generalized distance)
  29. 29. Distance  Metrics   •  Correla<on   –  maximum  value  of  1  if  X  and  Y  are  perfectly  correlated   –  minimum  value  of  -­‐1  if  X  and  Y  are  exactly  opposite   –  d(X,Y)  =  1  –  rxy   •  Many,  many  others   •  Choice  of  distance  metric  can  be  driven  by   underlying  data  (eg.,  binary  data,  categorical   data,  outliers,  etc.)  
  30. 30. Example  of  Distance  Metric  Choice  
  31. 31. Example   •  •  •  •  •  dat  =  matrix(rnorm(10000),ncol=20)   dat[1:100,1:10]  =  dat[1:100,1:10]+1   hclust   dist   as.dist(1-­‐cor)  
  32. 32. Differen<al  Expression  
  33. 33. MA  Plot  
  34. 34. DE  False  Posi<ve  Rates  
  35. 35. DE  Evalua<on  
  36. 36. DE  Soaware  Run<me  
  37. 37. RNA-­‐seq  workflow  as   proposed  by  Anders  et  al.   in  Nature  Protocols  
  38. 38. MA  Plot  
  39. 39. Fusion  Gene  Detec<on  
  40. 40. Fusion  gene  schema<c  
  41. 41. Fusion  Detec<on  
  42. 42. False  Posi<ve  Fusion  Detec<on  
  43. 43. Experimental  Design   •  What  are  my  goals?   –  Differen<al  expression?   –  Transcriptome  assembly?   –  Iden<fy  rare,  novel  trancripts?   •  System  characteris<cs?   –  Large,  expanded  genome?   –  Intron/exon  structures  complex?   –  No  reference  genome  or  transcriptome  
  44. 44. Experimental  Design   •  Technical  replicates   –  Probably  not  needed  due  to  low  technical   varia<on   •  Biological  replicates   –  Not  explicitly  needed  for  transcript  assembly   –  Essen<al  for  differen<al  expression  analysis   –  Number  of  replicates  oaen  driven  by  sample   availability  for  human  studies   –  More  is  almost  always  beger  
  45. 45. Links  of  Interest   •  •  •  •  •  hgp://bioconductor.org   hgp://biostars.org   hgp://www.rna-­‐seqblog.com/   hgps://genome.ucsc.edu/ENCODE/   hgp://www.ncbi.nlm.nih.gov/gds/  
  46. 46. Visualizing  Splicing  

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