English okay? Masters studies offer tracks: This is part of: VL Microarray data analyis

English okay? Masters studies offer tracks:This is part of:

VL Microarray data analyisTuesday, 8:30 – 10:00

Ü Thursday 10:15-11:45 (start: Oct. 23)

Next semester: Praktikum + SeminarThereafter possibility for Masters thesis.  Anwesenheitspflicht in VL und Ü (Liste!) Literature:See course web page. 

1 21. Okt Microarray-Technologien Martin Vingron

2 28. Okt Grundlagen der Datenanalyse Christine Steinhoff

3 4. Nov Varianzanalyse I Christine Steinhoff

4 11. Nov Varianzanalyse II Christine Steinhoff

5 18. Nov LOWESS, Varianzstabilisierung Anja von Heydebreck

6 25. Nov Statistisches Testen Anja von Heydebreck

7 2. Dez Clusterverfahren Anja von Heydebreck

8 9. Dez Klassifikation, Lin. Diskriminanzanalyse Rainer Spang

9 16. Dez Anwendungen in der Krebsforschung Rainer Spang

10 6. Jan Hauptkomponentenanalyse Martin Vingron

11 13. Jan Statistische Lerntheorie Rainer Spang

12 20. Jan Sequenzannotation Rainer Spang

13 27. Jan Bayessche Netzwerke Rainer Spang

14 3. Feb Regulation Martin Vingron15 10. Feb Zusammenfassung, Wiederholung, Ausblick

Functional Genomics:

Genome Sequencing:Determination of DNA sequenceDerivation of amino acid sequencesAnalysis, comparison, classification

Study of gene function gene expression studies proteomicsmetabolic networks

DNAgene

transcription

messenger RNA (mRNA)

proteinsequence

structure

translation

A cell and its population of genes:

What is the problem?

Determine the amount of mRNA for each

gene that is present in a cell/tissue.

DNA forms double strands by a process calledhybridization:

Labeling

Hybridization

Expression Arrays

cDNA Arrays Oligonucleotide Arrays

Glas Arrays Membrane based Arrays

Glass Slide Microarrays

… were first produced at Stanford University (Schena et al, 1995).

Whole cDNA:500-1500 bp

Filter “Macro”arrays

… were first published by Lennon and Lehrach, 1991

Ca 21 cm

7.5x2.5cm

Oligonucleotide Arrays

… were first published by Lockhardt et al, 1996

...

...PMMM

1 2 3 4 ... 17 18 19 20probe pair

probe set

probe cell

... TGTGATGGTGGGAATGGGTCAGAAGGACTCCTATGTGGGTGACGAGGCC TTACCCAGTCTTCCTGAGGATACAC TTACCCAGTCTTGCTGAGGATACACca 25bp

GC AC

GC AC

GC AC

GC AC G

C ACG

C AC

Probe - Reference

GC AC G

C AC

There are other technologies, too, to estimate expression levels:

• EST sequencing – „electronic northern“

• SAGE: tags of mRNAs are concatenated and sequenced

• Reliability of results depends on depth of probing (number of ESTs, number of tags)

Why do we want to know?

• „tissue profiling“: which genes are expressed in a tissue

• Comparing healthy and diseased (e.g., tumor) tissue

• Studying dynamic processes: E.g., cell cycle (time series)

Example: Renal clear cell carcinoma

Comparison of kidney cancer cells to normal tissue. Which genes are altered in their expression?

T98-8880

N98-8880

Molecular Genome Analysis Dr. Judith Boer

G1 S G2 M

Spellman et al took several samples per time-point and hybridized the RNA to a glass chips with all yeast genes

Example: Cell cycle time course

Data processing

• Image collection

• Image analysis, intensity determination

• Within slide normalization

Trends in BiotechHess et al, 19(11),2001

OUPUT: Scanner + Scanner-Software

...

... Trends in BiotechHess et al, 19(11),2001

Different technologies

• Support: membrane or glass slide

• Spotted material: PCR product or oligo (short/long)

• Labeling: – 1-channel: radioactive, Affy

– Absolute values

– 2-channel: 2 color fluorescent labeling– Relative values

Quality issues

-0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5

0.0

0.2

0.4

0.6

0.8

1.0

43 a73-u02400vene.txt

log(fg.green/fg.red)

F̂

Kidney1Kidney2Kidney3Kidney4Kidney5Kidney6Onco1Onco2Onco3Onco4Onco5

subpopulations: PCR subpopulations: PCR

Remedies: improve PCR protocols; model “random effect” through plate-wise calibration

Remedies: improve PCR protocols; model “random effect” through plate-wise calibration

subpopulations: pin subpopulations: pin

-0.8 -0.6 -0.4 -0.2 0.0 0.2

0.0

0.2

0.4

0.6

0.8

1.0

41 (a42-u07639vene.txt) by spotting pin

log(fg.green/fg.red)

F̂

1:11:21:31:42:12:22:32:43:13:23:33:44:14:24:34:4

Remedies: handling of pins; pin-wise calibrationRemedies: handling of pins; pin-wise calibration

Distribution of intensities: log-normal?

intensities log intensities

QQPlot

Histogramm

Chip design

• Type of chip: – Global „whole genome“ (yeast, drosophila,

mouse, man)– Domain specific, e.g. cancer, infection

• Spots:– PCR products: E.g., 3´ UTR (avoid crosshyb.)– Oligos: uniqueness, stability

Databases

• Stanford• TIGR• Gene expression atlas • GEO• Arrayexpress

• MIAME standard: Minimum Information About a Microarray Experiment

Software

• R + Bioconductor

• Jexpress

• Genesprings

• Rosetta Resolver

Affymetrix technology

• Per gene, spot 20 perfectly matching oligos and 20 oligos with 1 mismatch

• Intensity: weighted average of pixel intensities in perfect and mismatch oligos

(More on this next week)