Download - Gene Expression Analysis
![Page 1: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/1.jpg)
Gene Expression Analysis
![Page 2: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/2.jpg)
Gene Expression
2
proteinRNADNA
![Page 3: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/3.jpg)
Gene Expression
3
AAAAAAAAAAAAAA
AAAAAAAAAAAAAA
AAAAAAA
AAAAAAAAAAAAAA
AAAAAAAAAAAAAA
AAAAAAAAAAAAAA
AAAAAAAAAAAAAA
AAAAAAAAAAAAAA
AAAAAAAAAAAAAA
AAAAAAAmRNA gene1
mRNA gene2
mRNA gene3
![Page 4: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/4.jpg)
Studying Gene Expression 1987-2011
4
Spotted microarray
One channel microarray
RNA-seq (Next Generation Sequencing)
![Page 5: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/5.jpg)
Classical versus modern technologies to study gene expression
5
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
![Page 6: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/6.jpg)
6
1. Spotted Microarray
Two channel cDNA microarrays.
2. DNA Chips
One channel microarrays
(Affymetrix, Agilent),
Classical Methods
![Page 7: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/7.jpg)
http://www.bio.davidson.edu/courses/genomics/chip/chip.html
7
![Page 8: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/8.jpg)
8
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
![Page 9: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/9.jpg)
9
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
![Page 10: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/10.jpg)
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
![Page 11: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/11.jpg)
11
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
![Page 12: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/12.jpg)
12
Affymetrix Chip
![Page 13: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/13.jpg)
RNA-seq
13
![Page 14: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/14.jpg)
14
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
![Page 15: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/15.jpg)
15
Gene Expression Analysis
• Unsupervised -Hierarchical Clustering
-Partition MethodsK-means
• Supervised Methods-Analysis of variance-Discriminant analysis-Support Vector Machine (SVM)
![Page 16: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/16.jpg)
16
Clustering genes according to their expression profiles.
Gen
es
Experiments
![Page 17: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/17.jpg)
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?
![Page 18: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/18.jpg)
What does it mean for two genes to be close?
18
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
![Page 19: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/19.jpg)
Once we know this, how do we define groups?
19
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
![Page 20: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/20.jpg)
Internal nodes represent different functional Groups (A, B, C, D, E)
One genes may belong to more than one cluster
gene
s
![Page 21: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/21.jpg)
21
Clusters can be presented by graphs
![Page 22: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/22.jpg)
22
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
![Page 23: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/23.jpg)
23
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
![Page 24: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/24.jpg)
24
hnRNP A1 SRp40
EXAMPLE- hnRNP A1 and SRp40Gene with similar expression pattern tend to have common functions
![Page 25: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/25.jpg)
Are they regulated by the same transcription factor ?
25
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
![Page 26: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/26.jpg)
>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)
![Page 27: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/27.jpg)
Create a Multiple Sequence Alignment GGATAACAATTTCACATGTGAGCGGATAACAATGTGAGTTAGCTCACTTGTGATCTCTGTTACACGAGGATGAGAACACACTCGGTTTAGTTCACCTGTGACACAGTGCAAACCTGACGGAGTTCACAAGTGTCTATAATCACGTGGAATATCCATCACATGCAAAGGACGTCACGGGCGACCTGGGTCATGTGTGATGTGTATCGAATTTGAACCACATCGCAGGTGAGAGCCATCACATGTAAGCTGTGCCACGTTTATTCCATGTCACGTGTTATACACATCACTCGTGCTCCCACTCGCATGTGATTCGATTCACA
![Page 28: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/28.jpg)
Generate a PSSM
Find the transcription factor which bind the motif
![Page 29: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/29.jpg)
29
How can we use microarray for diagnostics?
![Page 30: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/30.jpg)
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?
![Page 31: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/31.jpg)
31
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
![Page 32: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/32.jpg)
32
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
![Page 33: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/33.jpg)
33
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.
![Page 34: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/34.jpg)
34
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.
![Page 35: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/35.jpg)
35
• 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.
?
![Page 36: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/36.jpg)
36
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.
![Page 37: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/37.jpg)
37
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.
![Page 38: Gene Expression Analysis](https://reader036.vdocument.in/reader036/viewer/2022081515/56813ac5550346895da2d8f3/html5/thumbnails/38.jpg)
38
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/