RNA Sequencing
Peter Tsai
Bioinformatics Institute, University of Auckland
What is RNA-seq?
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Study of transcriptomes
Identify known genes, exons, splicing events, ncRNA,
miRNA
Novel genes or transcripts
Abundances of transcripts (quantitive expression)
Differential expressed transcripts between different
conditions
Reconstructing transcriptome.
General workflow
Raw data
QC
Map to reference
genome
De novo
transcriptome
assembly
Estimate
abundance
Normalisation
Differential
expression
analysis
Require downstream annotation
Quality checks and mapping
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Use FastQC, SolexQA
Trim off low quality region, keep only proper-paired reads
Most QC software assume normality, but in RNA-seq data
you will probably see none-normality
You might see some duplicated reads, its probably due to
highly expressed gene.
Specific reference mapping tool that can map across splice
junctions between exons, i.e. Tophat
Specific de novo transcriptome assembly software for
reconstruction of transcriptomes from RNA-seq data, i.e.
Trinity
Expression value in RNA-seq
The total number of reads mapped to a gene/transcript
(Count data or raw counts or digital gene expression)
Complexity of using simple counts
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Sequencing depth: the higher the sequencing depth, the
higher the counts
Gene length: Counts are proportional to the length of the
gene times mRNA expression level
Counts distribution: difference on how counts are distributed
among samples.
Normalisation
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RPKM (Mortazavi et al, 2008)
◦ Reads Per Kilobase of exon model per Million mapped reads
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FPKM (Mortazavi et al, 2010)
◦ Fragments Per Kilobase of exon model per Million mapped
reads
◦ Paired-end RNA-Seq experiments produce two reads per
fragment, but that doesn't necessarily mean that both reads
will be mappable.
Replicate 2
Data exploration
Replicate 1
Gene.ID/Description
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
logFC
logCPM
LR
2.563086301 5.07961611
4.003686266 2.330395704
2.71372512 9.704651395
-2.052703196 3.402621025
1.95117636 4.438847349
2.465833373 12.20593577
1.817858683 5.308092036
1.577603322 6.556675456
1.20515812 4.542565518
1.233090336 10.08249873
1.120581944 12.14988136
1.045292369 4.913492018
1.089867189 3.885246135
1.353955354 2.21406615
1.049933686 3.281031472
-1.032999983 1.480514873
-1.313778857 4.325330722
0.864451602 4.338668381
-0.766266641
5.2972332
PValue
FDR
28.4599795
9.57E-08
2.72E-05
28.3288251
1.02E-07
2.72E-05
25.01930526
5.68E-07
0.000100653
21.11492168
4.33E-06
0.000575287
19.21195535
1.17E-05
0.001244651
10.91756889 0.000952565
0.084460792
10.3738524 0.001278126
0.097137553
9.690419768 0.001852312
0.110687766
9.670466698 0.001872537
0.110687766
9.289827985 0.002304298
0.122588652
7.710102379 0.005491264
0.265577482
7.039209923 0.00797442
0.350270537
6.912558621 0.008559242
0.350270537
5.976193603 0.014500264
0.551010036
5.737563572 0.016605812
0.588952795
4.712476717 0.029944481
0.995653998
4.169234925 0.041164384
0.998742102
3.479808135 0.062121942
0.998742102
3.443865378 0.063486998
0.998742102
Up-regulated
Down-regulated
ERCC spike-in control
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Set of external RNA transcripts with known concentration.
Dynamic range and lower limit of detection
Fold-change response
Internal control, in order to measure against defined
performance criteria
Dynamic range and lower limit of detection
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The dynamic range can be
measured as the difference
between the highest and
lowest concentration.
Measure of sensitivity, and it is
defined as the lowest molar
amount of ERCC transcript
detected in each sample
Fold-change response
How much library depth is needed for
RNA-seq?
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Depends on a number of factors
◦ Biological questions
 Complexity of the organism
 Types of analysis
 Types of RNA, miRNA, lncRNA.
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Literature search for similar work
Pilot experiment
Summary
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Have 3 or more biological replicates
Analysis your data with different normalisation
methods
Perform data exploration
Use a standard spike-in as internal control
Validation with qPCR