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Methods to analyze RNA
expression
Is a specific RNA transcribed?
In which cells, under which condition is it
expressed?
How much is there?
Is the amount different from other cells/times?
RNA analysis techniques
1.  Low-to-mid-plex techniques:
–  Northern blot
–  Fluorescent in situ hybridization
–  Reverse transcription PCR (RT-PCR)
2.  Higher-plex techniques:
–  DNA microarray
–  Tiling array
–  RNA-Seq
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RNA sequencing analysis
The newest technology for RNA expression analysis
•  Provides data for all the genes expressed in a
particular sample (tissues, conditions, stages, etc.)
•  Coupled with high throughput sequencing
•  Quantitative
•  Highly technical and expensive
•  Rely heavily on computational statistical analysis
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Applications of RNA-Seq
From raw RNA seq data to transcriptome and
differential expression analysis.
1.  Abundance estimation
2.  Alternative splicing
3.  RNA editing
4.  Finding novel transcripts
And many more…..
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Experimental design
•  Control and experimental conditions
•  Every sample in at least duplicate (triplicate better)
•  Uniformity of tissue used can be critical to detect
small significant differences in transcription levels.
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Steps in RNA-seq
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Overall flow chart for RNA-seq
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Library construction
First RNA needs to be converted to cDNA as
sequencers are designed for DNA not RNA sequencing.
This is done using a special RNA-dependent DNA
polymerase known as Reverse transcriptase (RT). The
product is known as cDNA.
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Library construction
Each sample (tissue, time
point, etc..) is used to
prepare RNA. Each RNA
then is converted into a
library of cDNA fragments.
The libraries from several
different samples will be
sequenced together, so each
library has to receive an
individual tag (AKA index).
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Library construction
The double-stranded cDNA fragments are attached
(ligated) to small ds nucleotide sequences.
The SP will be used for sequencing later.
The index is a short DNA sequence that is specific to
each library.
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Library construction
After size selection and limited
amplification by PCR, the library
representing short fragments of
all the RNAs present in your
initial tissues/ embryos/cells is
ready for sequencing.
When done correctly the
number of DNA fragments
corresponding to one mRNA is
proportional to the initial amount
of that specific mRNA.
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Sequencing
•  The sequencer
• 
And the “flow cell” where the sequencing takes
place. Many libraries can be loaded together
on one flow cell. The data from each library
will be distinguished later because they each
have a different “index”.
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Data quality
Several quality checks are done by the sequencer software
to ensure that the sequencing reactions worked correctly.
Do not worry about these
You will receive individual files where the reads from each
of your libraries have been sorted out by their index.
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Initial data analysis
Initially each library is analyzed separately except for the two
files of each library if paired ends sequencing was done. 1)
The short reads are aligned on the reference genome if
available 2) The transcript(s) from each gene are
reconstructed. At that point the analysis is done with all the
libraries together looking at 3) differential expression and
statistical significance.
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Work flow for data analysis
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Work flow for data analysis
This flow chart also
shows when each
sample is done
separately or
compare
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Work flow for data analysis
From RNA-seq reads
to differential
expression results:
Oshlack et al. Genome
Biology 2010, 11:220
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The Tuxedo suite
A series of software can be used in
succession to perform the series of
steps needed for alignment, assembly
and expression analysis.
It is known as the Tuxedo protocol.
Information about software and
Tuxedo workflow was first described
in: Differential gene and transcript expression
analysis of RNA-seq experiments with TopHat and
Cufflinks.
Trapnell C, Roberts A, Goff L, Pertea G, Kim D,
Kelley DR, Pimentel H, Salzberg SL, Rinn JL, &
Pachter L (2012) Nature Protocols 7, 562–578
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The Tuxedo suite
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BOWTIE
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BOWTIE
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Bowtie and Tophat
Bowtie and TopHat align
each short RNA
sequence read to the
reference genome.
Bowtie does the initial
fast alignment. TopHat
handles reads spanning
two exons, including
alternative splicing.
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Tophat
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Tophat step 1
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Tophat step 2
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Tophat step 3
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Tophat step 4
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Tophat overall
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Bowtie and Tophat
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Visualizing TopHat data with IGV
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The Tuxedo suite
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Cufflinks
Cufflinks identifies splice
sites based on TopHat
placement of split reads
and will merge the short
reads to create a gene structure
based on RNAseq data.
It will also evaluate the proportion of each
alternative splice products.
The expression of each isoform will be
expressed in RPKM: Reads (for that
transcript) Per Kilobase (of gene) per
Million reads .
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Cufflinks
For figure on previous slide
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The Tuxedo suite
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The Tuxedo suite
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Differential expression
DEG = Differentially
Expressed Genes
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Cuffdiff
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Cuffdiff
•  Cuffdiff takes all the reads that
map to overlapping transcripts
(like isoforms) and infers which
reads belong to each variant
transcript from one gene.
•  It starts with the reads that map
uniquely to each isoform, then
iterate to find the most likely
distribution of the remaining
reads.
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Cummerbund
•  Visualization of data generated by Cuffdiff
•  Graphical representation of over or under
expression
•  Classification of genes by molecular pathways
•  Rather complicated, but some easy tools are available
in DNA subway
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Cummerbund
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Differential gene expression
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Volcano plot
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Expression Profile of Specific
Pathways: HeatMap
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