GeneExpression II: 1. Transcription Factor Binding Sites 2. Microarrays 26 th May, 2010

GeneExpression II:1. Transcription Factor Binding Sites

2. Microarrays

26th May, 2010

Karsten HokampGenetics Department

GeneExpression II 1BI2010

TFBS prediction - Overview

• Introduction

• Methods

• Implementations

• Analyse 2kb upstream of eve


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


TFBS prediction - Introduction


Example: MSE2 strip for eve (D. melanogaster):

(Janssens et al., 2006)

understand transcriptional regulation infer regulatory networks

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)



TFBS prediction - Overview

Hannenhalli (2008, Bioinformatics)Hannenhalli (2008, Bioinformatics)

De novo motif prediction

• Search for over-represented motifs

• Frequency count

• Works well for yeast and prokaryotes

• Not so successful in higher organisms


Using motif databases

• Search for known motifs• Position specific scoring matrix (PSSM) or

Position weight matrix (PWM)• Databases:

– Transfac– Jasper


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


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)


Other tools:

• Many more tools available for download:– Sombrero– FootPrinter– PhyloGibbs

• Other Web-tools for groups of co-regulated genes:– RSAT– NestedMICA– WebMOTIFS


TFBS prediction - Conclusion:

• No single tool gives accurate results

• Combination of predictions from multiple tools might increase specificity

• Incorporate additional information for greater precision


Microarrays - Overview

• Introduction• Data Generation• Data Characteristics• Diagnostic Plots• Preprocessing• Statistical Analysis


GeneExpression II 14

What is a microarray?

• A solid support onto which the sequences from thousands of different genes are immobilized

• Different probe types- short oligonucleotides- long oligonucleotides- cDNA

• Different array supports- glass slide- nylon membrane- silicon chip

• Each probe measures the expression of a single transcript

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Microarrays – How do they work?

+

uninfected cells infected cells

Affymetrix Arrays : single colour

RNA

Reverse transcriptionLabel with dye

cDNA

Hybridize

Slide A Slide B

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Microarrays – How do they work?

Prepare Sample

+


Spotted Arrays : two colour

Prepare Microarray

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?


Data Extraction – Scanning


ScannerSlide

PRMS02-001-S100

CF010settings: - laser power - sensitivity - focus

Images (TIFF)

channel 1 (green) channel 2 (red) composite (green, yellow, red)

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Data Extraction – Quantification


align grid,align grid,tag unreliable spotstag unreliable spots

program assigns program assigns numbers numbers

representing representing intensity of spotintensity of spot

Software:

-ImaGene

-GenePix

-ScanAlyze

...

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

.. … … … … …

Data File

foreground (FG)background (BG)

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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)



Cy3

Cy5

Cy5-cDNA

Cy3-cDNA

RT

RT

cDNAarray

Cy5 intensity

Cy3 intensity

Sample2 mRNA

Sample1 mRNA

wavelength dependent

intensity dependent

uneven hybridization gel

print-tip variations

background variations

image processing algorithm-dependent

systematic experimental error

.tiff Image Files

Raw Data File

Microarrays – Sources of Variation

source: www.tigr.org

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Microarrays – Sources of Variation

• Technical:– labelling– hybridization– slide quality– scanning– print-tip effect– quantification– experimenter


• Biological:– individual/strain/sample– environment– time point

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Microarrays – Data Characteristics

• Intensities vs. ratios• Natural scale vs. log scale


Intensities vs. Ratios

• Intensities:


ch1 ch2

gene1 517 2100

gene2 3200 13000

gene3 3200 800

gene4 12000 3000

ratio = ch2 / ch1

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• Ratios:


ch1 ch2 ratio

gene1 517 2100 4.06

gene2 3200 13000 4.06

gene3 3200 800 0.25

gene4 12000 3000 0.25

ratio = ch2 / ch1

> 0

ratio = 1 if ch1 = ch2

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• Ratios– convey expression changes– hide base level differences

• But: absolute changes can be important, too!


Graphical Representation: Signal Scatter Plot


X CH1: Cy3

Y

CH

2: C

y5

3000

18000

3000 18000

ch1 ch2

spot1 517 2100

spot2 3200 13000

spot3 3200 800

spot4 12000 3000

ratio = 1

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Graphical Representation: Signal Scatter Plot


CH1: Cy3

CH

2: C

y5

ratio = 1

~ 10x

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Graphical Representation: Histogram


ratios1

Ratios

Fre

qu

ency

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Raw vs. Log ratios

• Log transformation


raw log

0.1 -3.3

0.5 -1

1 0

2 1

10 3.3

x = basey

8 = 23

0.125 = 2-3

y undefined for x <= 0

x = 2y

ratios

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Log ratios: scatter plot


CH1: Cy3

CH

2: C

y5

ratio = 1

CH1: log2(Cy3)

CH

2: l

og

2(C

y5)

log-ratio = 0

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Log ratios: histogram


ratios1

Ratios

Fre

qu

ency

Log-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


Diagnostic plots

• histogram• scatter plot• box plot• MA plot• chip visualization


Diagnostic plots – Histogram


good bad

log(CH1) log(CH2)

freq

uenc

y

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Diagnostic plots – Scatter plot


o.k. bad

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Diagnostic plots – MA plot

• Rotate scatter plot by ~ 45 degree:



• Rotate scatter plot by ~ 45 degree:



• Mathematically:


= log2(R) – log2(G)

= 0.5 * ( log2(R) + log2(G) )

Minus

Addition

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A

M

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2-fold cut-off


2-fold cut-off


2-fold cut-off



Cy3

Cy5

Cy5-cDNA

Cy3-cDNA

Unequal labeling efficiency

M =

lo

g(R

/G)

A = ½ log(RG)

Dye Swap

Strong bias towards Cy3!

Cy5

Cy3

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Dye Swap

+


cDNA

+


cDNA

Merged Data set

Cy5 vs Cy3 Cy3 vs Cy5

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A = ½ log(RG)

Cy3

Cy5

Cy5-cDNA

Cy3-cDNA

Unequal labeling efficiency

Dye SwapM

= l

og

(R/G

)

A = ½ log(RG)

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Diagnostic plots – Box plot


[median

lower quartile

upper quartile

Inter-quartile range

whiskers

1.5 times inter-quartile range

[

outliers

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Diagnostic plots – Box plot


o.k. bad

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Diagnostic plots – Box plot (printtip)


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


Microarrays – Preprocessing

• Flagging• Background correction• Normalization• Flawed slides: Discard and repeat


Microarrays – Flagging

• Skip or keep (but warn)• e.g. skip low intensities and saturated spots


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!


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)




rawGlobal meanLOESS

A

M

printTip LOESS

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rawglobal meanLOESSprintTip LOESS

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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!


Microarrays – Statistical Analysis

• Replicates• Variation• t-tests• multiple-testing correction• gene lists


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!

Statistical Analysis – Variation

• Biological variation different from technical• Statistically incorrect to mix• Important consideration for repeats:

High confidence in results fora) one sample/patient/colonyb) group of samples/patients/colonies


Prioritise biological repeats!

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


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)


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.


Analysis tools

• Stand-alone tools:– R– BioConductor– ArrayNorm– TM4– GeneSpring (commercial)

• Web-based tools– ArrayPipe– ExpressYourself– GenePublisher– GEPAS– GeneTraffic (commercial)


Public Repositories

• ArrayExpress– EBI, MIAME-compliant

• Gene Expression Omnibus (GEO)– NCBI– „world‘s first write-only database“


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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END


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GeneExpression II: 1. Transcription Factor Binding Sites 2. Microarrays 26 th May, 2010