The document provides information about RNA-seq analysis using R and Bioconductor. It begins with an introduction to the BCBB branch and its services assisting researchers with bioinformatics and computational projects. The document then discusses RNA-seq, R, and Bioconductor individually before explaining how they can be used together for RNA-seq analysis. Step-by-step tutorials and resources are provided for differential expression analysis and other tasks using R packages like DESeq2.

1. Date
Maarten Leerkes PhD
Genome Analysis Specialist
Bioinformatics and Computational Biosciences Branch
Office of Cyber Infrastructure and Computational Biology
RNA-seq with R-bioconductor
Part 1.

2. BCBB: A Branch Devoted to Bioinformatics and
Computational Biosciences
§ Researchers’ time is increasingly important
§ BCBB saves our collaborators time and effort
§ Researchers speed projects to completion using
BCBB consultation and development services
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2

3. BCBB Staff
3
Bioinformatics
Software
Developers
Computational
Biologists
Project Managers
and Analysts

4. Contact BCBB…
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– or – use this direct link
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5. Seminar Follow-Up Site
§ For access to past recordings, handouts, slides visit this site from the
NIH network: http://collab.niaid.nih.gov/sites/research/SIG/
Bioinformatics/
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Structural Biology
Phylogenetics
Statistics
Sequence Analysis
Molecular Dynamics
Microarray Analysis
BCBB: A Branch Devoted to Bioinformatics and
Computational Biosciences

7. Topics
§ What is R
§ What is Bioconductor
§ What is RNAseq
7

8. What is R
§ R is a programming language and software
environment for statistical computing and graphics.
The R language is widely used among statisticians
and data miners for developing statistical software[2]
[3] and data analysis.
8

9. What is R
§ R is an implementation of the S programming
language combined with lexical scoping semantics
inspired by Scheme. S was created by John
Chambers while at Bell Labs. There are some
important differences, but much of the code written for
S runs unaltered.
9

10. What is R
§ R is a GNU project. The source code for the R
software environment is written primarily in C, Fortran,
and R. R is freely available under the GNU General
Public License, and pre-compiled binary versions are
provided for various operating systems. R uses a
command line interface; there are also several
graphical front-ends for it.
10

11. DOWNLOAD R FROM CRAN:
http://cran.r-project.org/
11

12. 12

13. Topics
§ What is R
§ What is Bioconductor
§ What is RNAseq
13

14. What is bioconductor
14

15. Topics
§ What is R
§ What is Bioconductor
§ What is RNAseq
15

16. What is RNAseq
§ RNA-seq (RNA Sequencing), also called Whole
Transcriptome Shotgun Sequencing (WTSS), is a
technology that uses the capabilities of next-
generation sequencing to reveal a snapshot of
RNA presence and quantity from a genome at a
given moment in time.
16

17. Topics
§ What is R
§ What is Bioconductor
§ What is RNAseq
§ Comes together in: RNA-seq with R-bioconductor
17

18. Different kinds of objects in R
§ Objects.
§ The following data objects exist in R:
§ vectors
§ lists
§ arrays
§ matrices
§ tables
§ data frames
§ Some of these are more important than others. And
there are more.
18

19. 19

20. 20

21. A data frame is used for storing data
tables. It is a list of vectors of equal length.
§ A data frame is a table, or two-dimensional array-like
structure, in which each column contains
measurements on one variable, and each row
contains one case. As we shall see, a "case" is not
necessarily the same as an experimental subject or
unit, although they are often the same.
21

22. Combine list of data frames into single data frame, add
column with list index: list of vectors of equal length.
22

23. Methods: software carpentry:
http://swcarpentry.github.io/r-novice-inflammation/01-starting-with-data.html
23

24. Rna-seq with R
Demo: easyRNAseq
Source(“c:windowsmynamerna_seq_tutorial.R”)
source("/vol/maarten/rna_seq_tutorial2.R")
http://bioscholar.com/genomics/bioconductor-packages-analysis-rna-seq-data/

25. Current working directory cwd
25

26. Topics: start R
26

27. Topics: use R console and R command
line
27

28. Topics: use R console and R command
line
28

29. Topics: use R console and R command
line
29

30. Topics: use R console and R command
line
30

31. Topics: use R console and R command
line
31

32. Topics
§ What is R
§ What is Bioconductor
§ What is RNAseq
32

34. Sequencing by synthesis
§ Intro to Sequencing by Synthesis:
§ https://www.youtube.com/watch?v=HMyCqWhwB8E
34

35. FASTQ read with 50nt in Illumina format (ASCII_BASE=33).
There are always four lines per read.
35

36. 36

37. Paired end: read 1 in one fastq file
37

38. Paired end: read 2 in another fastq file
38

39. Numerous
possible
analysis
strategies
§ There
is
no
one
‘correct’
way
to
analyze
RNA-­‐seq
data
§ Two
major
branches
• Direct
alignment
of
reads
(spliced
or
unspliced)
to
genome
or
transcriptome
• Assembly
of
reads
followed
by
alignment*
*Assembly is the only option when working with a creature with no genome sequence,
alignment of contigs may be to ESTs, cDNAs etc
or transcriptome
Image from Haas & Zody, 2010

40. 40

41. Illumina clonal
expansion
followed by image
processing

42. Pile up sequences to reference genome
42

43. SAM format: what are sam/bam files
http://biobits.org/samtools_primer.html
43

44. 44

45. RNA
sequencing:
abundance
comparisons
between
two
or
more
condi9ons
/
phenotypes
CondiCon
1
(normal
Cssue)
CondiCon
2
(diseased
Cssue)
Isolate
RNAs
Sequence
ends
100s
of
millions
of
paired
reads
10s
of
billions
bases
of
sequence
Generate
cDNA,
fragment,
size
select,
add
linkers
Samples
of
interest
Map
to
genome,
transcriptome,
and
predicted
exon
junc9ons
Downstream
analysis

46. Compare two samples for abundance
differences
46

47. Transcript abundances differ in pile-up
47

48. Genes have ‘structure’, solve by mapping
§ This leads to for example analysis of intron-exon
structure

49. Genes and transcripts

50. Currrent
paradigm:
“cuff-suit”
50

51. Common
analysis
goals
of
RNA-­‐Seq
analysis
(what
can
you
ask
of
the
data?)
§ Gene
expression
and
differenCal
expression
§ AlternaCve
expression
analysis
§ Transcript
discovery
and
annotaCon
§ Allele
specific
expression
• RelaCng
to
SNPs
or
mutaCons
§ MutaCon
discovery
§ Fusion
detecCon
§ RNA
ediCng

52. Back
to
the
demo
§ IntroducCon
to
RNA
sequencing
§ RaConale
for
RNA
sequencing
(versus
DNA
sequencing)
§ Hands
on
tutorial

53. Rna-seq with R
Demo: easyRNAseq
Source(“c:windowsmynamerna_seq_tutorial.R”)
source("/vol/maarten/rna_seq_tutorial2.R")
http://bioscholar.com/genomics/bioconductor-packages-analysis-rna-seq-data/

54. 54

55. Deseq and DEseq2
§ method based on the negative binomial distribution,
with variance and mean linked by local regression
§ DEseq2:
§ No demo scripts available yet:
§ http://www.bioconductor.org/packages/release/bioc/
vignettes/DESeq2/inst/doc/DESeq2.pdf
55

56. The empirical frequency distribution of the hybridization signal intensity values for
Affymetrix microarray hybridization data for normal yeast cell genes/ORFs (Jelinsky
and Samson 1999).
Kuznetsov V A et al. Genetics 2002;161:1321-1332
Copyright © 2002 by the Genetics Society of America

57. Empirical relative frequency distributions of the gene expression levels.
Kuznetsov V A et al. Genetics 2002;161:1321-1332
Copyright © 2002 by the Genetics Society of America

58. 58

59. 59

60. Empirical (black dots) and fitted (red lines)
dispersion values plotted against the mean of the
normalised counts.
60

61. Plot of normalised mean versus log2 fold change
for the contrast untreated versus treated.
61

62. Histogram of p-values from the call to
nbinomTest.
62

63. MvA plot for the
contrast“treated”vs.“untreated”, using two
treated and only one untreated sample.
63

64. Heatmaps showing the expression data of
the 30 most highly expressed genes
64

65. Heatmap showing the Euclidean distances between the
samples as calculated from the variance stabilising
transformation of the count data.
65

66. Biological effects of condition and libType
66

67. Mean expression versus log2 fold change
plot. Significant hits (at padj<0.1) are
coloured in red.
67

68. Per-gene dispersion estimates (shown by
points) and the fitted mean- dispersion
function (red line).
68

69. Differential exon usage
§ Detecting spliced isoform usage by exon-level
expression analysis
69

70. Types of splicing
70

71. expression estimates from a call to testForDEU.
Shown in red is the exon that showed significant
differential exon usage.
71

72. Normalized counts. As in previous Figure,
with normalized count values of each exon
in each of the samples.
72

73. estimated effects, but after subtraction of
overall changes in gene expression.
73

74. Dependence of dispersion on the mean
74

75. 75

76. Distributions of Fold changes of exon
usage
76

77. 77

78. Resources: RNA-Seq workflow, gene-level
exploratory analysis and differential expression
78

79. 79

80. Outline
§ IntroducCon
to
RNA
sequencing
§ RaConale
for
RNA
sequencing
(versus
DNA
sequencing)
§ Hands
on
tutorial
§ hQp://swcarpentry.github.io/r-­‐novice-­‐inflammaCon/
§ hQp://swcarpentry.github.io/r-­‐novice-­‐inflammaCon/02-­‐func-­‐R.html
§ hQp://www.bioconductor.org/help/workflows/
§ hQp://www.bioconductor.org/packages/release/data/experiment/
html/parathyroidSE.html
§ hQp://www.bioconductor.org/help/workflows/rnaseqGene/

81. About bioconductor
High-throughput sequence analysis with R and Bioconductor:
http://www.bioconductor.org/help/course-materials/2013/useR2013/
Bioconductor-tutorial.pdf
http://bioconductor.org/packages/2.13/data/experiment/vignettes/
RnaSeqTutorial/inst/doc/RnaSeqTutorial.pdf
Also helpful: http://www.bioconductor.org/help/course-materials/2002/
Summer02Course/Labs/basics.pdf

82. http://www.nature.com/nprot/journal/v8/n9/
pdf/nprot.2013.099.pdf
82

84. The End
84