Gene Expression Analysis

Gene Expression

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proteinRNADNA

Gene Expression

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AAAAAAAmRNA gene1

mRNA gene2

mRNA gene3

Studying Gene Expression 1987-2011

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Spotted microarray

One channel microarray

RNA-seq (Next Generation Sequencing)

Classical versus modern technologies to study gene expression

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Classical Methods (Spotted microarray, DNA chips)-Require prior knowledge on the RNA transcriptGood for studying the expression of known genes

New generation RNA sequencing-Do not require prior knowledge Good for discovering new transcripts

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1. Spotted Microarray

Two channel cDNA microarrays.

2. DNA Chips

One channel microarrays

(Affymetrix, Agilent),

Classical Methods

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

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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

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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

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

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One channel DNA chips

• Each sequence is represented by a probe set colored with one fluorescent dye

• Target hybridizes to complimentary probes only• The fluorescence intensity is indicative of the

expression of the target sequence

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Affymetrix Chip

RNA-seq

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Applications

• Identify gene function– Similar expression can infer similar function

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

• Diagnostics and Therapy– Different genes expression can indicate a disease

state– Genes which change expression in a disease can be

good candidates for drug targets

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Gene Expression Analysis

• Unsupervised -Hierarchical Clustering

-Partition MethodsK-means

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

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Clustering genes according to their expression profiles.

Gen

es

Experiments

Clustering

Clustering organizes things that are close into groups.

- What does it mean for two genes to be close?

- Once we know this, how do we define groups?

What does it mean for two genes to be close?

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We need a mathematical definition of distance between the expression of two genes

Gene 1

Gene 2

Gene1= (E11, E12, …, E1N)’Gene2= (E21, E22, …, E2N)’

For example distance between gene 1 and 2Euclidean distance= Sqrt of Sum of (E1i -E2i)2, i=1,…,N

Once we know this, how do we define groups?

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Michael Eisen, 1998 : Generate a tree based on similarity(similar to a phylogenetic tree)

Each gene is a leaf on the treeDistances reflect similarity of expression

Hierarchical Clustering

Gen

es

Experiments

Gene Cluster

Internal nodes represent different functional Groups (A, B, C, D, E)

One genes may belong to more than one cluster

gene

s

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Clusters can be presented by graphs

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

• Similar expression between genes

– The genes have similar function

– One gene controls the other in a pathway

– All genes are controlled by a common regulatory genes

• Clusters can help identify regulatory motifs

– Search for motifs in upstream promoter regions of all the genes in a cluster

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EXAMPLE- hnRNP A1 and SRp40Gene with similar expression pattern tend to have common functions

HnRNPA1 and SRp40have a similar gene expression pattern in different tissues

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hnRNP A1 SRp40

EXAMPLE- hnRNP A1 and SRp40Gene with similar expression pattern tend to have common functions

Are they regulated by the same transcription factor ?

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hnrnpA1

SRp40

1. Extract their promoter regions

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

3. Identify the transcription factor related to the motifhttp://jaspar.cgb.ki.se/

Promoter gene

Common motif

>GGATAACAATTTCACAAGTGTGTGAGCGGATAACAA>AAGGTGTGAGTTAGCTCACTCCCCTGTGATCTCTGTACATAG>ACGTGCGAGGATGAGAACACAATGTGTGTGCTCGGTTTAGTCACC>TGTGACACAGTGCAAACGCGCCTGACGGAGTTCACA>AATTGTGAGTGTCTATAATCACGATCGATTTGGAATATCCATCACA>TGCAAAGGACGTCACGATTTGGGAGCTGGCGACCTGGGTCATG>TGTGATGTGTATCGAACCGTGTATTTATTTGAACCACATCGCAGGTGAGAGCCATCACAG>GAGTGTGTAAGCTGTGCCACGTTTATTCCATGTCACGAGTGT>TGTTATACACATCACTAGTGAAACGTGCTCCCACTCGCATGTGATTCGATTCACA

Extract the promoters of the genes in the cluster and find a common motif (using MEME)

Create a Multiple Sequence Alignment GGATAACAATTTCACATGTGAGCGGATAACAATGTGAGTTAGCTCACTTGTGATCTCTGTTACACGAGGATGAGAACACACTCGGTTTAGTTCACCTGTGACACAGTGCAAACCTGACGGAGTTCACAAGTGTCTATAATCACGTGGAATATCCATCACATGCAAAGGACGTCACGGGCGACCTGGGTCATGTGTGATGTGTATCGAATTTGAACCACATCGCAGGTGAGAGCCATCACATGTAAGCTGTGCCACGTTTATTCCATGTCACGTGTTATACACATCACTCGTGCTCCCACTCGCATGTGATTCGATTCACA

Generate a PSSM

Find the transcription factor which bind the motif

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How can we use microarray for diagnostics?

Gene-Expression Profiles in Hereditary Breast Cancer

• Breast tumors studied: BRCA1 BRCA2sporadic tumors

• Log-ratios measurements of 3226 genes for each tumor after initial data filtering

cDNA MicroarraysParallel Gene Expression Analysis

RESEARCH QUESTIONCan we distinguish BRCA1 from BRCA2– cancers based solely on their gene expression profiles?

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How can microarrays be used as a basis for diagnostic?

Patient 1

patient 2

patient 3

patient4

patient 5

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

5 Breast Cancer Patient

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How can microarrays be used as a basis for diagnostic?

patinet1

patient 2

patient4

patient 3

patient 5

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

InformativeGenes

BRCA1 BRCA2

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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.

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Supervised approachesfor 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.

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• Using this training set, an SVM would learn to differentiate 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.

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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.

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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.

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

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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/