GeneExpression II:
1. Transcription Factor Binding Sites
2. Microarrays
26th May, 2010
Karsten Hokamp
Genetics Department
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TFBS prediction - Overview
• Introduction
• Methods
• Implementations
• Analyse 2kb upstream of eve
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TFBS prediction - Introduction
• TFBS = DNA motifs
= 5 – 20 bp long
= variable
= multiple occurrences/sites per gene
= combination of activators and repressors
• cis-regulatory regions
= clusters of TFBS -20kb – first intron
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TFBS prediction - Introduction
Example: MSE2 strip for eve (D. melanogaster):
(Janssens et al., 2006)
 understand transcriptional regulation
 infer regulatory networks
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TFBS prediction - Methods
• De novo motif prediction (overrepresentation)
• Searching for known motifs
• Phylogenetic Footprinting/Shadowing
• Clustering of TFBSs
• Integration of external data sources
(co-expression, structure)
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TFBS prediction - Overview
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Hannenhalli (2008, Bioinformatics)
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De novo motif prediction
• Search for over-represented motifs
• Frequency count
• Works well for yeast and prokaryotes
• Not so successful in higher organisms
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Using motif databases
• Search for known motifs
• Position specific scoring matrix (PSSM) or
Position weight matrix (PWM)
• Databases:
– Transfac
– Jasper
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Phylogenetic-based methods
• Search for islands of highly conserved regions
• Footprinting: elements conserved across
distant species
• Shadowing: elements conserved between
closely related species
• Pros: increases specificity
• Cons: conservation is not sufficient nor
necessary
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Practical:
• Try some tools on 2kp upstream sequence of
D. melanogaster eve and compare with
published results.
– Alibaba (de novo)
– Match (Tranfac)
– Meme (de novo)
– Promo (Tranfac)
– WeederH (phylogenetic footprinting)
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Other tools:
• Many more tools available for download:
– Sombrero
– FootPrinter
– PhyloGibbs
• Other Web-tools for groups of co-regulated
genes:
– RSAT
– NestedMICA
– WebMOTIFS
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TFBS prediction - Conclusion:
• No single tool gives accurate results
• Combination of predictions from multiple
tools might increase specificity
• Incorporate additional information for greater
precision
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Microarrays - Overview
•
•
•
•
•
•
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Introduction
Data Generation
Data Characteristics
Diagnostic Plots
Preprocessing
Statistical Analysis
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What is a microarray?
• A solid support onto which the sequences
from thousands of different genes are
immobilized
• Different array supports
- glass slide
- nylon membrane
- silicon chip
• Different probe types
- short oligonucleotides
- long oligonucleotides
- cDNA
• Each probe measures the expression of a single transcript
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Microarrays – How do they work?
Affymetrix Arrays : single colour
+
uninfected cells
infected cells
RNA
Reverse transcription
Label with dye
cDNA
Hybridize
Slide A
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Slide B
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Microarrays – How do they work?
Spotted Arrays : two colour
Prepare Sample
+
uninfected cells
Prepare Microarray
infected cells
Hybridize
target to
microarray
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Microarray: Subgrids
•
One pin per subgrid (printTip group, stratus)
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Microarrays – Data Extraction
• How to get data from the slides into the
computer?
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Data Extraction – Scanning
Slide
Scanner
Images (TIFF)
PRMS02-001-S100
CF010
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settings:
- laser power
- sensitivity
- focus
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channel 1 (green)
channel 2 (red)
composite
(green, yellow, red)
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Data Extraction – Quantification
align grid,
tag unreliable spots
Software:
-ImaGene
-GenePix
-ScanAlyze
...
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foreground (FG)
background (BG)
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Data File
Spot ID
FG
CH1
BG
CH1
FG
CH2
BG
CH2
FL
GFP
1241
671
6707
713
1
PA0080
570
495
599
384
0
PA0080
691
632
667
651
0
PA0122
703
610
653
619
0
PA0122
708
598
695
602
0
..
…
…
…
…
…
program assigns
numbers
representing
intensity of spot
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Quantification: Intensity Range
- area composed of pixel
- value range: 0 – 216 - 1
- value range: 0 – 65535
- saturation possible
- low intensities = noise
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Data Generation – Summary
•
•
•
•
•
•
•
RNA labelling and hybridization
Array Scanning
One image per channel
Load into quantification software
Flag flawed spots
Extract values
Text file with FG and BG intensities (per probe)
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Microarrays – Sources of Variation
.tiff Image
Files
Raw Data
File
Sample1 mRNA
Cy3 intensity
Cy3
RT
Cy3-cDNA
Cy5
RT
Sample2 mRNA
systematic
experimental
error
cDNA
array
Cy5-cDNA
uneven
hybridization
gel
print-tip
variations
Cy5 intensity
wavelength
dependent
intensity
dependent
background
variations
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image
processing
algorithmdependent
source: www.tigr.org
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Microarrays – Sources of Variation
• Technical:
– labelling
– hybridization
– slide quality
– scanning
– print-tip effect
– quantification
– experimenter
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• Biological:
– individual/strain/sample
– environment
– time point
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Microarrays – Data Characteristics
• Intensities vs. ratios
• Natural scale vs. log scale
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Intensities vs. Ratios
• Intensities:
ratio = ch2 / ch1
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ch1
ch2
gene1
517
2100
gene2
3200
13000
gene3
3200
800
gene4
12000
3000
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Intensities vs. Ratios
• Ratios:
ratio = ch2 / ch1
>0
ratio = 1 if ch1 = ch2
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ch1
ch2
ratio
gene1
517
2100
4.06
gene2
3200
13000
4.06
gene3
3200
800
0.25
gene4
12000
3000
0.25
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Intensities vs. Ratios
• Ratios
– convey expression changes
– hide base level differences
• But: absolute changes can be important,
too!
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Graphical Representation: Signal
Scatter Plot
ratio = 1
Y CH2: Cy5
18000
3000
3000
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X CH1: Cy3
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ch1
ch2
spot1
517
2100
spot2
3200
13000
spot3
3200
800
spot4
12000
3000
18000
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CH2: Cy5
Graphical Representation:
Signal Scatter Plot
ratio = 1
~ 10x
CH1: Cy3
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Frequency
Graphical Representation: Histogram
ratios
1
Ratios
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Raw vs. Log ratios
x = 2y
• Log transformation
ratios
x = basey
raw
log
8 = 23
0.1
-3.3
0.125 = 2-3
0.5
-1
1
0
2
1
10
3.3
y undefined for x <= 0
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Log ratios: scatter plot
log-ratio = 0
CH2: Cy5
CH2: log2(Cy5)
ratio = 1
CH1: log2(Cy3)
CH1: Cy3
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Frequency
Log ratios: histogram
ratios
1
Log-ratios
Ratios
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Microarrays – Data Characteristics
• ratios vs. intensities
– convey expression changes
– hide base level differences
• log ratios vs. raw ratios
– reduce spread
– provide symmetry
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Diagnostic plots
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•
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•
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histogram
scatter plot
box plot
MA plot
chip visualization
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Diagnostic plots – Histogram
bad
frequency
good
log(CH1)
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log(CH2)
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Diagnostic plots – Scatter plot
o.k.
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bad
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Diagnostic plots – MA plot
• Rotate scatter plot
by ~ 45 degree:
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Diagnostic plots – MA plot
• Rotate scatter plot
by ~ 45 degree:
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Diagnostic plots – MA plot
• Mathematically:
Minus
= log2(R) – log2(G)
= 0.5 * ( log2(R) + log2(G) )
Addition
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M
Diagnostic plots – MA plot
A
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2-fold cut-off
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2-fold cut-off
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2-fold cut-off
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Dye Swap
M = log(R/G)
Unequal labeling efficiency
Cy5
Cy3
Cy3-cDNA
Cy3
Cy5
A = ½ log(RG)
Cy5-cDNA
Strong bias towards Cy3!
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Dye Swap
Cy5
vs
Cy3
Cy3
vs Cy5
+
uninfected cells
+
infected cells
uninfected cells
cDNA
infected cells
cDNA
Merged Data set
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Dye Swap
M = log(R/G)
Unequal labeling efficiency
Cy3
Cy3-cDNA
A = ½ log(RG)
Cy5
Cy5-cDNA
A = ½ log(RG)
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Diagnostic plots – Box plot
outliers
whiskers
1.5 times interquartile range
Inter-quartile range
[
upper quartile
[
median
lower quartile
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Diagnostic plots – Box plot
o.k.
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Diagnostic plots – Box plot (printtip)
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Diagnostic plots – Chip visualization
good:
bad:
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Diagnostic plots: Summary
• histogram
– data distribution (intensities, ratios)
• scatter plot
– dye effect, print-tip effect
• box plot
– equal average ratio and distribution, print-tip effect
• MA plot
– dye effect and intensity-dependant ratio
• chip visualization
– spatial bias, scratches, bubbles, smears
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Microarrays – Preprocessing
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•
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Flagging
Background correction
Normalization
Flawed slides: Discard and repeat 
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Microarrays – Flagging
• Skip or keep (but warn)
• e.g. skip low intensities
and saturated spots
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Microarrays – Background correction
• Subtract background measurements from
foreground intensities
• Brings intensities lower to zero, increases
ratios:
example spot with five fold upregulation:
500 / 100 = 5
subtract background (50) from both channels
450 / 50 = 9
• Additional source of variance!
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Microarrays – Normalization
• Remove effect from intensities, dye bias,
spatial bias or print-tip variations:
– Global mean, median
– Loess, lowess
– Print-tip loess
– 2D loess
– Variance stabilazation (VSN)
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Microarrays – Normalization
M
Global
rawmean
LOESS
printTip
LOESS
A
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Microarrays – Normalization
printTip
global
LOESS
raw
LOESS
mean
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Microarrays – Discard and repeat
• Some slides turn out to be uncorrectable
and need to be repeated (unless a sufficient
number of replicates remains).
• Remember: bad data in = bad data out!
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Microarrays – Statistical Analysis
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•
•
•
Replicates
Variation
t-tests
multiple-testing
correction
• gene lists
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Statistical Analysis – Replicates
• Two types of repeats
• Technical:
– multiple copies of probes on array
– multiple repeats of hybridiztion (same RNA)
• Biological:
– multiple hybridizations with RNA from multiple
extractions
 Need replicates to measure variation!
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Statistical Analysis – Variation
• Biological variation different from technical
• Statistically incorrect to mix
• Important consideration for repeats:
High confidence in results for
a) one sample/patient/colony
b) group of samples/patients/colonies
 Prioritise biological repeats!
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Statistical Analysis – t-tests
Different classes of samples:
- find genes that are affected by a treatment
- p-value = degree of evidence
- H0: expression does not change
- t-test requires at least 2 replicates
provides p-value for each gene
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Statistical Analysis –
multiple-testing correction
Carrying out t-tests on 10,000 genes
 average of 500 will have p-value <= 0.05
Methods for multiple testing:
Bonferroni (very strict)
Benjamini-Hochberg  false-discovery rate (FDR)
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Statistical Analysis – Gene lists
• List of good candidate genes to follow up
• FP vs FN
• Fold-change vs p-value
Choice depends on downstream analysis
 Input for downstream analysis:
Clustering, pathway analysis, enrichment, etc.
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Analysis tools
• Stand-alone tools:
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R
BioConductor
ArrayNorm
TM4
GeneSpring (commercial)
• Web-based tools
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ArrayPipe
ExpressYourself
GenePublisher
GEPAS
GeneTraffic (commercial)
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Public Repositories
• ArrayExpress
– EBI, MIAME-compliant
• Gene Expression Omnibus (GEO)
– NCBI
– „world‘s first write-only database“
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Summary
• Many sources of variance
• Large numbers of replicates required for reliable
results
• Data: be aware of flaws/bias
• Flagging/discarding results in data loss
• Correction often possible but can insert artifacts
• However:
Microarrays can still help making great discoveries!
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