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RNA-­‐seq:	
  A	
  high-­‐resolu1on	
  
View	
  of	
  the	
  Transcriptome	
  
The	
  Central	
  Dogma	
  
+	
  
=	
  
Your	
  Nature	
  Paper	
  
RNA-­‐seq	
  protocol	
  schema<c	
  
Approaches	
  to	
  RNA-­‐seq	
  

Nature	
  Biotech	
  (2010)	
  28,	
  421-­‐423	
  
Alignment	
  
RNA-­‐seq	
  Alignment	
  
Run	
  Time	
  
Alignment	
  Yield	
  
Splice	
  Read	
  Placement	
  Accuracy	
  
Impact	
  on	
  Transcript	
  Assembly	
  
Transcript	
  Quan<fica<on	
  
Models	
  for	
  RNA-­‐seq	
  
•  Count-­‐based	
  models	
  
•  Mul<-­‐reads	
  (isoform	
  resolu<on)	
  
•  Paired-­‐en...
Read	
  Coun<ng	
  
Mortazavi,	
  2008,	
  NMeth	
  
L.	
  Pachter	
  (2011)	
  arXiv:1104.3889v	
  
Sequence	
  Bias-­‐-­‐priming	
  

Hansen	
  (2010),	
  NAR	
  
Sample-­‐specific	
  Sequence	
  Bias	
  
Models	
  for	
  RNA-­‐seq	
  
Result	
  of	
  Quan<fica<on	
  
Clustering	
  and	
  Visualiza<on	
  
Hierarchical	
  Clustering	
  
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8
Hierarchical	
  Clustering	
  
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8
Hierarchical	
  Clustering	
  
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8
Hierarchical	
  Clustering	
  
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8
Distance	
  Metrics	
  
! 

Euclidean distance

! 

Manhattan distance

! 

Minkowski distance (generalized distance)
Distance	
  Metrics	
  

•  Correla<on	
  

–  maximum	
  value	
  of	
  1	
  if	
  X	
  and	
  Y	
  are	
  perfectly	
  c...
Example	
  of	
  Distance	
  Metric	
  Choice	
  
Example	
  
• 
• 
• 
• 
• 

dat	
  =	
  matrix(rnorm(10000),ncol=20)	
  
dat[1:100,1:10]	
  =	
  dat[1:100,1:10]+1	
  
hcl...
Differen<al	
  Expression	
  
MA	
  Plot	
  
DE	
  False	
  Posi<ve	
  Rates	
  
DE	
  Evalua<on	
  
DE	
  Soaware	
  Run<me	
  
RNA-­‐seq	
  workflow	
  as	
  
proposed	
  by	
  Anders	
  et	
  al.	
  
in	
  Nature	
  Protocols	
  
MA	
  Plot	
  
Fusion	
  Gene	
  Detec<on	
  
Fusion	
  gene	
  schema<c	
  
Fusion	
  Detec<on	
  
False	
  Posi<ve	
  Fusion	
  Detec<on	
  
Experimental	
  Design	
  
•  What	
  are	
  my	
  goals?	
  
–  Differen<al	
  expression?	
  
–  Transcriptome	
  assembl...
Experimental	
  Design	
  
•  Technical	
  replicates	
  
–  Probably	
  not	
  needed	
  due	
  to	
  low	
  technical	
 ...
Links	
  of	
  Interest	
  
• 
• 
• 
• 
• 

hgp://bioconductor.org	
  
hgp://biostars.org	
  
hgp://www.rna-­‐seqblog.com/...
Visualizing	
  Splicing	
  
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
Rna seq
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  
  • MaheshBachu

    Jan. 27, 2015
  • jasoncummings2

    Jul. 19, 2014

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