1

FINAL PROJECT- Key dates

31.12 –last day to decided on a project *

11-10/1- Presenting a proposed project in small groupsA very short presentation (Max 5 minutes) Title- Background Main question Major tools you are planning to use to answer the questions

1.3 Final submission

Gene Expression Analysis

Studying Gene Expression 1987-2010

3

Spotted microarray

One channel microarray

RNA profiling- Next Generation Sequencing

4

Applications

• Identify gene function– Similar expression can infer similar function

• Find tissue/developmental specific genes– Different expression in different cells/tissues

• Find genes affected by different conditions– Different expression under different conditions

• Diagnostics– Different genes expression can indicate a disease state

5

Different types of microarray technologies1. Spotted Microarray

Two channel cDNA microarrays.

2. DNA Chips

One Channel microarrays

(Affymetrix, Agilent),

6

http://www.bio.davidson.edu/Courses/genomics/chip/chip.html

Microarray Experiment

7

Experimental Protocol Two channel cDNA arrays

1. Design an experiment

(probe design)

2. Extract RNA molecules from cell

3. Label molecules with fluorescent dye

4. Pour solution onto microarray

– Then wash off excess molecules

5. Shine laser light onto array

– Scan for presence of fluorescent dye

6. Analyze the microarray image

8

Analyzing Microarray Images

Original Image

One geneor mRNA

One tissue or condition

9Cy3 Cy5Cy5Cy3

Cy5log2 Cy3

The ratio of expression is indicated by the intensity of the colorRed= High mRNA abundance in the experiment sample Green= High mRNA abundance in the control sample

Transforming raw data to ratio of expression

10Cy3 Cy5Cy5Cy3

Cy5log2 Cy3

The ratio of expression is indicated by the intensity of the colorRed= High mRNA abundance in the experiment sample Green= High mRNA abundance in the control sample

Transforming raw data to ratio of expression

11

Expression Data Format

cold normal hotuch1 -2.0 0.0 0.924 gut2 0.398 0.402 -1.329 fip1 0.225 0.225 -2.151 msh1 0.676 0.685 -0.564 vma2 0.41 0.414 -1.285 meu26 0.353 0.286 -1.503 git8 0.47 0.47 -1.088 sec7b 0.39 0.395 -1.358 apn1 0.681 0.636 -0.555 wos2 0.902 0.904 -0.149

Conditions

Gen

es /

mR

NA

s

12

One channel DNA chips

• Each sequence is represented by a probe set • 1 probe set = N probes (Affymetrix 16 probes of length

25 mer).• Unknown sequence or mixture (target)

colored with on\e fluorescent dye.• Target hybridizes to complimentary probes only• The fluorescence intensity is indicative of the

expression of the target sequence

13

Affymetrix Chip

14

• Spotted arrays – o Longer probes (~70), more stable reactionso Easy to make in the lab (by reverse transcription)o Highly specific

• DNA chipso More sensitive (higher density)o More coverageo Enable more flexible designs (e.g differentially

measuring splice variants)

Pros and cons

15

Designing probes for microarray experiments

• Probe on DNA chip is shorter than target– Choice of which section to hybridize

• Select a region which is unstructured– RNA folding, DNA stem-and-loop

• Choose region which is target-specific– Avoid cross-hybridization with other DNA

• Avoid regions containing variation– Minimize presence of mutation sites

16

Probe DesignTwo main factors to optimize

• Sensitivity– Strength of interaction with target sequence– Requires knowledge of target only

• Specificity– Weakness of interaction with other sequences– Requires knowledge of ‘background’

17

Sources of Inaccuracy

• Some sequences bind better than others– A–T versus G–C

• Low complexity sequences - Cross-hybridization

• Effects of experimental conditions– temperature

18

Splicing Specific Microarrays

Pre-mRNA mRNA

Total transcript level

+

19

Microarray Analysis

• Unsupervised-Partion Methods

K-meansSOM (Self Organizing Maps)

-Hierarchical Clustering

• Supervised Methods-Analysis of variance-Discriminate analysis-Support Vector Machine (SVM)

20

Clustering• Grouping genes together according to

their expression profiles.• Hierarchical clustering

Michael Eisen, 1998 :

Generate a tree based on similarity

(similar to a phylogenetic tree)– Each gene is a leaf on the tree

– Distances reflect similarity of expression

– Internal nodes represent functional groups

Results of Clustering Gene Expression

Limitations:– Hierarchical

clustering in general is not robust

– Genes may belong to more than one cluster

Clustering

22

Genes are clustered according to similar expression patterns

Self Organizing Maps

23

What can we learn from clusters with similar gene expression ??

• Similar expression between genes– One gene controls the other in a pathway– Both genes are controlled by another– Both genes required at the same time in cell

cycle– Both genes have similar function

• Clusters can help identify regulatory motifs– Search for motifs in upstream promoter regions

of all the genes in a cluster

Normalizedexpression datafrom microarrays

Experiment 1

Exp

erim

ent 2

Expe

rimen

t 3

Finding Regulatory Motifs Within Expression Clusters

Search promoter regions for shared sequence motifs.

25

EXAMPLE

HnRNPA1 and SRp40have a similar gene expression pattern in different tissues

Are they regulated by the same transcription factor ?

26

hnrnpA1 promoter

SRp40 promoters

Common motif

1. Extract their promoter regions

2. Find a common motif in both sequences (MEME)

3. Identify the transcription factor related to the motif

27

How can we use microarray for diagnostics?

28

How can microarrays be used as a basis for diagnostic?

patient 1

patient 2

patient 3

patient4

patient 5

Gen1 + - - + +Gen2 + + - + -Gen3 - + + + -Gen4 + + + - -Gen5 - - + - +

Informative Genes

29

Differentially expressed in the two classes.

Goal Identifying (statistically significant) informative

genes

30

How can microarrays be used as a basis for diagnostic?

patinet1

patient 2

patient4

patient 3

patient 5

Gen1 + - + - +Gen3 - + + + -Gen4 + + - + -Gen2 + + + - -Gen5 - - - + +

InformativeGenes

31

Specific Examples

Cancer Research

Ramaswamy et al, 2003Nat Genet 33:49-54

Hundreds of genesthat differentiate betweencancer tissues in differentstages of the tumor were found.The arrow shows an exampleof a tumor cells which were not detected correctly byhistological or other clinical parameters.

32

Supervised approchesfor predicting gene function based on microarray data

• SVM would begin with a set of genes that have a common function (red dots), In addition, a separate set of genes that are known not to be members of the functional class (blue dots) are specified.

33

• Using this training set, an SVM would learn to discriminate between the members and non-members of a

given functional class based on expression data.

• Having learned the expression features of the class, the SVM could recognize new genes as members or as non-members of the class based on their expression data.

?

34

Using SVMs to diagnose tumors based on expression dataEach dot represents a vector of the expression pattern taken from a microarray experiment . For example the expression pattern of all genes from a cancer patients.

35

How do SVM’s work with expression data?In this example red dots can be primary tumors and blue arefrom metastasis stage.The SVM is trained on data which was classified based on histology.

?

After training the SVM we can use it to diagnose the unknown tumor.

36

Gene Expression Databasesand Resources on the Web

• GEO Gene Expression Omnibus- http://www.ncbi.nlm.nih.gov/geo/

• List of gene expression web resources– http://industry.ebi.ac.uk/~alan/MicroArray/

• Another list with literature references– http://www.gene-chips.com/

• Cancer Gene Anatomy Project– http://cgap.nci.nih.gov/

• Stanford Microarray Database– http://genome-www.stanford.edu/microarray/