This document provides an overview of RNA-seq and its applications. It discusses key aspects of RNA-seq including transcriptome profiling, alignment, quantification, differential expression analysis, clustering and visualization. It also covers experimental design considerations and highlights some commonly used tools and software. The document is a comprehensive guide that describes the RNA-seq workflow and analysis from start to finish.

1. RNA-­‐seq:
A
high-­‐resolu1on
View
of
the
Transcriptome

3. The
Central
Dogma

6. +

8. =

9. Your
Nature
Paper

12. RNA-­‐seq
protocol
schema<c

13. Approaches
to
RNA-­‐seq
Nature
Biotech
(2010)
28,
421-­‐423

14. Alignment

15. RNA-­‐seq
Alignment

18. Run
Time

19. Alignment
Yield

20. Splice
Read
Placement
Accuracy

21. Impact
on
Transcript
Assembly

22. Transcript
Quan<fica<on

24. 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

25. Read
Coun<ng

26. Mortazavi,
2008,
NMeth

27. L.
Pachter
(2011)
arXiv:1104.3889v

28. Sequence
Bias-­‐-­‐priming
Hansen
(2010),
NAR

29. Sample-­‐specific
Sequence
Bias

31. Models
for
RNA-­‐seq

32. Result
of
Quan<fica<on

33. Clustering
and
Visualiza<on

39. Hierarchical
Clustering
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8

40. Hierarchical
Clustering
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8

41. Hierarchical
Clustering
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8

42. Hierarchical
Clustering
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8

43. Distance
Metrics
!
Euclidean distance
!
Manhattan distance
!
Minkowski distance (generalized distance)

44. 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.)

45. Example
of
Distance
Metric
Choice

46. Example
•
•
•
•
•
dat
=
matrix(rnorm(10000),ncol=20)
dat[1:100,1:10]
=
dat[1:100,1:10]+1
hclust
dist
as.dist(1-­‐cor)

49. Differen<al
Expression

50. MA
Plot

54. DE
False
Posi<ve
Rates

55. DE
Evalua<on

56. DE
Soaware
Run<me

58. RNA-­‐seq
workflow
as
proposed
by
Anders
et
al.
in
Nature
Protocols

59. MA
Plot

62. Fusion
Gene
Detec<on

63. Fusion
gene
schema<c

65. Fusion
Detec<on

66. False
Posi<ve
Fusion
Detec<on

67. 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

68. 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

69. 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/

71. Visualizing
Splicing