Bioinformatics Dr. Víctor Treviño [email protected] A7-421

BIOINFORMATICSDR. VÍCTOR TREVIÑ[email protected]

Functional Genomics I - Microarrays

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FUNCTIONAL GENOMICS TECHNOLOGIES

Transcriptomics Proteomics Metabolomics Genomics

SNP (Single Nucleotide Polymorphisms) CNV (Copy Number Variation, CGH)

Epigenomics

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MICROARRAYS Technology that provides measurments

of thousands of molecules in the same experiment and reasonable prices and precision

Generally in the size of a typical microscope slide (75 x 25 mm (3" X 1") and about 1.0 mm thick)

Biological Question

ExperimentalDesign

MicroarrayExperiment

Pre-processing

Differential Expression Clustering Prediction

Biology: Verification and Interpretation

…

Image Analysis

Background

Normalization

Sumarization

Transformation

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MICROARRAYS

Google Images

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GENE EXPRESSION

Molecular Cell Biology [Lodish,Berk,Matsudaira,Kayser,Kreiger,Scott,Zipursky,Danell] (5th Ed)

Gene Expression

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MEASURING GENE EXPRESSION

100bp200bp

- + - + - +

RWPE-1 DU-145 PC-3

100

bp la

dder

mRNA, Gene X

http://www.bio168.com/mag/1B8B368B092A/20-3.jpg

107 c

opie

s

106 c

opie

s

105 c

opie

s

104 c

opie

s

103 c

opie

s

102 c

opie

s

10 c

opie

s

PCR

QPCR

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MICROARRAY - HIBRIDISATION

Microarrays Bioinformatics, Dov Stekel, Cambridge, 2003

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http://www.well.ox.ac.uk/genomics/facilitites/Microarray/Welcome.shtml

DNA MICROARRAY TECHNOLOGY

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MICROARRAYS


www.niaid.nih.gov/dir/services/rtb/microarray/overview.asp

http://metherall.genetics.utah.edu/Protocols/Microarray-Spotting.html

http://www.lbl.gov/Science-Articles/Archive/cardiac-hyper-genes.html

http://www.nrc-cnrc.gc.ca/multimedia/picture/life/nrc-bri_micro-array_e.html

http://learn.genetics.utah.edu/units/biotech/microarray/genechip.jpg


http://www.lbl.gov/Science-Articles/Archive/

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MICROARRAYS – PROBE PRODUCTION

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Affymetrix Images – 1 dyetwo-dyesMICROARRAY TECHNOLOGIES

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MICROARRAY QUALITY

Affymetrix Spotted Arrays Inkjet arrays


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MICROARRAYS

Dr. Hugo BarreraMicroarrays Course EMBO-INER 2005, Mexico City

mRNAExtraction

(and amplification)

Labelling

Hybridization

Scanning

StatisticalAnalysis

Image Analysis &Data Processing

PROCESS

Healty/Control Disease/TreatementREFERENCE TEST

Gene: A 1-1 B 1-0 C 3-3 D 0-3Gene: E 3-0 F 0-1 G 1-1 H 2-0Gene: I 2-2 J 0-0 K 3-0 L 2-1

Gene D 0.001Gene E 0.005Gene K 0.001

TWO-DYES

mRNA/cDNA

LabeledmRNA

DigitalImage

Microarray

Data

SelectedGenes

PRODUCTTEST

Gene: A 1 B 1 C 1 D 0Gene: E 4 F 1 G 1 H 2Gene: I 2 J 0 K 5 L 2

Sample

Gene D 0.001Gene E 0.005Gene K 0.001Gene J 0.003

ONE-DYE

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MICROARRAY SCANNING


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MICROARRAY – LASER AND THE SCANNED IMAGE

Dr. Hugo Barrera, Microarrays Course EMBO-INER 2005, Mexico City Microarrays Bioinformatics, Dov Stekel, Cambridge, 2003

5m Laser 10m Laser

Pre-processing

Image Analysis

Background

Normalization

Sumarization

Transformation

Microarray - Pre-Processing Purpose

Output: Data File(unique "global relative" measure of expression for every gene with

minimal experimental error)

Input: Scanned Image File

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MICROARRAY IMAGE ANALYSISTECHNOLOGIES

DNA Probes Oligos~2040nt

Target (cDNA, PCR products, etc.)

Copies per gene Usually 1Usually 3

OrganizationSectors (print-tip) n x m probsets

Probeset

mprobsets(~100)

ysectors(~=3)

x sectors (~=3) n probsets (~100)

Sectorsi x j spots (18x20)

Empty spotslanding lights

perfect match probes (pm)mismatch probes (mm)

Controls

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MICROARRAY - IMAGE ANALYSISTECHNOLOGIES

10,000 genes* 2 dyes

* 3 copies/gene* ~40 pixels/gene

= 2,400,00 values

only 10,000 values

10,000 genes* 20 oligos

* 2 (pm,mm)* ~ 36 pixels/gene

= 14,400,00 values

only 10,000 values

RAW DATA

Image AnalysisPre-processing

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IMAGE ANALYSISAddressing: Estimate location of spot centers.Segmentation: Classify pixels as foreground or background.Extraction: For each spot on the array and each dye

• foreground intensities• background intensities• quality measures.

Addressing Done by GeneChip Affymetrix software

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Addressing (by grid, GenePix)

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Segmentation

Circular feature Irregular feature shape

Finally compute Average

Background Reduction

Extraction:

DeterminingBackground

2-Color

Results (GenePix).gpr file "results" for one array

10,000 genes~ 30,000 values

(.gal files 1 file for a "list" of array)

Affymetrix

Results.cel file "results" for one array

(raw - no background reduced)

10,000 genes~ 400,000 values

Image Analysis

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IMAGE ANALYSIS

Segmentation(Spot detection)

BackgroundEstimation

ValueValue = Spot Intensity – Spot Background

Gene 1Gene 2Gene 3

.

.Gene k

.

.Gene N

Sample 1100209

-7..

9882..

2298

Sample 198

42092..

9711..

28

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Gene 1Gene 2Gene 3

.

.Gene k

.

.Gene N

Sample 1100209

-7..

9882..

2298

Sample 198

42092..

9711..

28 G=Sample 1

R=

Sam

ple

1

G=Sample 1

R=

Sam

ple

1

Log2

Log2

[email protected] TRANSFORMATION – TWO DYES

Gene 1Gene 2Gene 3

.

.Gene k

.

.Gene N

Sample 1100209

-7..

9882..

2298

Sample 198

42092..

9711..

28

(log2 scale)

RG

1 value?

22

2

GRLogA

GRLogM

A

M

MA-PlotG=Sample 1

R=

Sam

ple

1

8 10 12 14 16

-4-3

-2-1

01

(log2(G)+log2(R)) / 2

log2

(R)-l

og2(

G)

A

M

"With-in"(2 color technologies)

Normalization – 2 dyes

(assumption: Majority No change)

Normalization – 2 dyes

(assumption: Majority No change)

Before

After

"With-in"(2 color technologies)

Normalization – 2 dyes"With-in" Spatial

(2 color technologies)

Before NormalizationAftter loess

Global Normalization

Aftter loessby Sector (print-tip)

Normalization

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Gene 1Gene 2Gene 3

.

.Gene k

.

.Gene N

Sample 1100209

-7..

9882..

2298

Log2

7 8 9 10 11 12

0.0

0.5

1.0

1.5 density(x = log2(t[, 15] + 200), adjust = 0.475)

N = 3840 Bandwidth = 0.1051

Den

sity

9 10 11 12 13 14 15 16

0.0

0.2

0.4

0.6

0.8

1.0

log intensity

dens

ity

10 11 12 13 14 15

0.0

0.2

0.4

0.6

0.8

x

dens

ity

Before normalization After normalization

Between-slides

Normalization – 1 or 2 dyes

quantileMAD (median absolute deviation)

scaleqspline

invariantset

loess

Sumarization = "Average"(Intensities)

Summarization – AffymetrixOligonucleotide dependent technologies

Usual Methods:• tukey-biweight• av-diff• median-polish

PMMM

The "summarization" equivalent in two-dyes technologies is the average of gene replicates within the slide.

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MICROARRAYS – FILTERING / TREATING UNDEFINED VALUES Some spots may be defective in the printing

process Some spots could not be detected Some spots may be damaged during the assay Artefacts may be presents (bubbles, etc)

Use replicated spots as averages Remove unrecoverable genes Remove problematic spots in all arrays Infer values using computational methods

(warning)

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MICROARRAY – DATA FILTERING More than 10,000 genes Too many data increases Computation Time and

analysis complexity Remove

Genes that do not change significantly Undefined Genes Low expression

Keeping Large signal to noise ratio Large statistical significance Large variability Large expression

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Image Analysis`

Background Subtraction

Normalization

Summarization

Transformation

Data Processing

BackgroundDetection & Subtraction

a)

Filtering

Microarray

ImageScanning

SpotDetection

IntensityValue

Affymetrix

Two-dyes

b) Image Analysis and Background Subtraction

c)

Transformation

BetweenWithin

d)

A=log2(R*G)/2

M=

log2

(R/G

) Normalization

MICROARRAY PRE-PROCESSING SUMMARY

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MICROARRAY REPOSITORIES

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MICROARRAY APPLICATIONS

Microarray Technology Through Applications, F. Falciani, Taylor & Francis 2007

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MICROARRAY DATA MATRIX

Gene 1Gene 2Gene 3

.

.

.

.Gene N

Class ASamples

Class BSamples

Normal Tissue,Cancer A,

Untreated,Reference,

…

Tumour Tissue,Cancer B,Treated,Strains,…

….

….

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MICROARRAYS – WHAT CAN BE DONE WITH DATA? Differential Expression Unsupervised Classification Biomarker detection Identifying genes related to survival times Regression Analysis Gene Copy Number and Comparative Genomic

Hibridization Epigenetics and Methylation Genetic Polymorphisms and SNP's Chromatin Immuno-Precipitation On-Chip Pathogen Detection …

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Differential Expression

Positive Negative

SamplesA

SamplesB

SamplesA

SamplesB

Gene Selection

µ=dµ=d

Exp

ress

ion

Leve

l

DIFFERENTIAL EXPRESSION

Gene 1Gene 2Gene 3

.

.

.

.Gene N

Class ASamples

Class BSamples



…


p-value FDR q-Value

Biomarker Detection

Positive Negative

SamplesClass A

SamplesClass B

SamplesClass A

SamplesClass B

µ=dµ=d

Gene Selection

Exp

ress

ion

Leve

l

Biomarker Discovery

Gene 1Gene 2Gene 3

.

.

.

.Gene N

Class ASamples

Class BSamples



…


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A C G B H E D I K M LSamples

Co-ExpressedGenes

Unsupervised Sample ClassificationH

J2.b

HJ0

He0

He2

.b

Hh6

.tw

Hh4

.b

Hh2

.b

Hh4

.tw

Hh2

.tw

Hh0

Hh6

.b

IL-8WNT-5b2BLKBIRC4I -TACAKT3CARD9INTEGRIN-alpha4SLIT-1PDGF -C ChainEphA 3NEURITINBCL11BCUGBP2EphB-4AXLBMP-6LIF RGM-CSF RalphaPTGS2CDKN2APTGESIL-18NGFRAP1PECAMLMNAINTEGRIN-beta2PDGF -B ChainTSSC3IGF-I IAGRINTACSTD2TNFRSF21HTATIP2GALECTIN 3CCND1LTBRC-METEMP2EphrinB 2GRO-betaIL-13 R alpha1RIPK2IGFBP6BOKLPIG7EphrinA 1JUNerbB3BMP-4DR6CLUEMAP I IWNT-5aBMP-2CASP1CDKN1ABNIP2APCTFDP2MYBRB1ATP2A3TOP2BIL-2 R gam maPKC al phaCXCR-4BNIP3

HJ2

.b HJ0

He0

He2

.b

Hh6

.tw

Hh4

.b

Hh2

.b

Hh4

.tw

Hh2

.tw Hh0

Hh6

.b

IL-8WNT-5b2BLKBIRC4I-TACAKT3CARD9INTEGRIN-alpha4SLIT-1PDGF-C ChainEphA 3NEURITINBCL11BCUGBP2EphB-4AXLBMP-6LIF RGM-CSF RalphaPTGS2CDKN2APTGESIL-18NGFRAP1PECAMLMNAINTEGRIN-beta2PDGF-B ChainTSSC3IGF-IIAGRINTACSTD2TNFRSF21HTATIP2GALECTIN 3CCND1LTBRC-METEMP2EphrinB 2GRO-betaIL-13 R alpha1RIPK2IGFBP6BOKLPIG7EphrinA 1JUNerbB3BMP-4DR6CLUEMAP IIWNT-5aBMP-2CASP1CDKN1ABNIP2APCTFDP2MYBRB1ATP2A3TOP2BIL-2 R gammaPKC alphaCXCR-4BNIP3

HJ2.b HJ0

He0

He2.b

Hh6.tw Hh4.b

Hh2.b

Hh4.tw

Hh2.tw

Hh0

Hh6.b

IL-8WNT-5b2BLKBIRC4I-TACAKT3CARD9INTEGRIN-alpha4SLIT-1PDGF-C ChainEphA 3NEURITINBCL11BCUGBP2EphB-4AXLBMP-6LIF RGM-CSF RalphaPTGS2CDKN2APTGESIL-18NGFRAP1PECAMLMNAINTEGRIN-beta2PDGF-B ChainTSSC3IGF-IIAGRINTACSTD2TNFRSF21HTATIP2GALECTIN 3CCND1LTBRC-METEMP2EphrinB 2GRO-betaIL-13 R alpha1RIPK2IGFBP6BOKLPIG7EphrinA 1JUNerbB3BMP-4DR6CLUEMAP I IWNT-5aBMP-2CASP1CDKN1ABNIP2APCTFDP2MYBRB1ATP2A3TOP2BIL-2 R gammaPKC alphaCXCR-4BNIP3

a

B

Low

High

Expression

HJ2

.b HJ0

He0

He2

.b

Hh6

.tw

Hh4

.b

Hh2

.b

Hh4

.tw

Hh2

.tw Hh0

Hh6

.b


HJ2

.b

HJ0

He0

He2

.b

Hh6

.tw

Hh4

.b

Hh2

.b

Hh4

.tw

Hh2

.tw Hh0

Hh6

.b


123456789b

UNSUPERVISED CLASSIFICATION

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Genes Associated to Survival Times and Risk

Positive NegativeGene Selection

+

+

++++++++

+++++

Kaplan-Meier Plot

Time

Haz

ard

1.0

0.0

+

+

++++++++

+++++

Kaplan-Meier Plot

Time

Haz

ard

1.0

0.0

0.0 0.0

SURVIVAL TIMES

Gene 1Gene 2Gene 3

.

.

.

.Gene N

Class ASamples

Class BSamples



…


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Regression: Gene Association to outcome

Positive NegativeGene Selection

Dep

ende

nt V

aria

ble

Gene Expression

Dep

ende

nt V

aria

ble

Gene Expression

Slope ≠ 0 Slope = 0

REGRESSION

Gene 1Gene 2Gene 3

.

.

.

.Gene N

Class ASamples

Class BSamples



…


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M M M M M

M M M M M M M M

M M M M M

M M M

M M M

M M M

X X

Unmethylated Fraction Hypermethylated FractionSample Control Sample Control

Cleavage withmethylation-sensitive

restriction enzymeCleavage with

TasI Csp6I

CpG specificAdaptor Ligation Adaptor Ligation

CpG specificcleavage with

McrBC

Cleavage withmethylation-sensitive

restriction enzyme

Adaptor-specificamplification

Adaptor-specificamplification

Unmethylated fraction Hypermetylation fraction

Cy5(red)

Cy3(green)

Cy5(red)

Cy3(green)

Microarray Microarray

CPG METHYLATION

Labelling DetectionHybridisation

AA CG CC……

SNP1SNP2SNP3

3'

T

3'

T

3'

G

3'

C

3'

G

3'

GT G

GC

5'

5'

5'

5'SNP1

SNP2

SNP3

Products of 1nt primerextension (in solution)

Capture

C TGA

5'

GC

5'

CG

AA CG CC…

…SNP1SNP2SNP3

5'5'5'5'

+

Transcribed RNA+ reverse transcriptase

5' 5'

GCGCA^C

5'5'

TA C^AExtension

ddNTPs(one labelled)

5'

TA

5'

TA

5'

GC

5'

CG

5'

GC

5'

GC

AA CG CC……

SNP1SNP2SNP3

Extension(1nt)

+

Labelled ddNTPsPCR products+ DNA polymerase

TC GA

SNP1 SNP2 SNP3a

b

c

Chromatin Immuno-Precipitation(ChIP-on-Chip)

Precipitation ofAntibody-TF-DNA

complex

Fusion ofTag sequenceinto TF gene

Labelling ofprecipitated

DNA

MicroarrayHybridisation

IncubationDNA-Tagged TF

Transcription Factor Tag

Antibodyagainst

tag peptide

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(1) ACGGCTAGTCACAAC...(2) GCTAGTCACAACCCA...(3) GCTAGTCCGGCACAG......

Sample

Spotted Hybridized

(1) (2) (3)

PATHOGEN/PARASITES DETECTION

EXAMPLE 1: DIFFERENTIAL EXPRESSIONPlacenta 1 Placenta 2

mRNA ExtractionReference Pool

Labelling

MicroarrayHybridization(by duplicates)

Scanning &Data Processing

Detection ofDifferentially

Expressed Genes

Validation andAnalysis

Green GreenRed Red

t-test H0: µ = 0p-values correction: False Discovery Rate

Comparison With Known Tissue Specific Genes

ImageAnalysis

WithinNormalization

(per array)

BetweenNormalization

(all arrays)

(controls)

(Dr. Hugo Barrera)

a b

c dPlacenta/Reference Control/Control

51 52 56 54

(a) Microarray Experiment

Ratio(log2)

10 -6

Plac

enta

(b) T1dbase

T1 score

1 0

Lung

Th

alam

us

Amyg

dala

Sp

inal

Cor

d Te

stis

K

idne

y Li

ver

Pitu

itary

Th

yroi

d C

ereb

ellu

m

Hyp

otha

lam

us

Cau

date

Nuc

leus

E

xocr

ine

Panc

reas

Ly

mph

Nod

e Fr

onta

l Cor

tex

Stom

ach

Bre

ast

Bon

e M

arro

w

Panc

reat

ic Is

lets

U

teru

s O

vary

Sk

in

Hea

rt

Skel

etal

Mus

cle

Pros

tate

Th

ymus

Sa

livar

y G

land

Tr

ache

a

Plac

enta

2R

eplc

ate

2

Plac

enta

2R

eplic

ate

1Array:

Plac

enta

1 R

eplic

ate

1

Plac

enta

1R

eplic

ate

2

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OTHER MICROARRAYS


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ANTIBODIES MICROARRAYS


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PROTEIN MICROARRAYS


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CARBOHYDRATE MICROARRAY

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SMALL-MOLECULE MICROARRAYS

Documents

Bioinformatics Dr. Víctor Treviño [email protected] A7-421