1. FINAL PROJECT- Key dates
9.1 –last day to decided on a project *
18,23,24/1- Presenting a proposed project in small groups
A very short presentation (Max 5 minutes)
Title- Background
Main question
Major tools you are planning to use to answer the questions
6.3 Final submission

2. Gene Expression Analysis

3. Gene Expression
DNA
RNA
protein

4. Gene Expression mRNA gene1 mRNA gene2 mRNA gene3 AAAAAAA AAAAAAA

5. Studying Gene Expression 1987-2010
Spotted microarray
One channel microarray
RNA-seq (Next Generation Sequencing)

6. 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

7. Different types of microarray technologies
Classical Methods
Different types of microarray technologies
Spotted Microarray
Two channel cDNA microarrays.
DNA Chips
One Channel microarrays
(Affymetrix, Agilent),

8. Microarray Experiment

9. 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

10. Expression Data Format
Experiments
cold normal hot
uch
gut
fip
msh
vma
meu
git
sec7b
apn
wos
Genes / mRNAs

11. RNA-seq

12. Gene Expression Analysis
Unsupervised
-Hierarchical Clustering
-Partition Methods
K-means
Supervised Methods
-Analysis of variance
-Discriminant analysis
-Support Vector Machine (SVM)

13. Clustering genes according to their expression profiles.
Experiments
Genes

14. 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?
Notice we do this ourselves all the time: divide people by race, divide animals into families, etc…

15. What does it mean for two genes to be close?
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 2
Euclidean distance= Sqrt of Sum of (E1i -E2i)2, i=1,…,N

16. Once we know this, how do we define groups?
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
Hierarchical Clustering
Gene Cluster
Genes
Experiments

17. Internal nodes
represent different
functional
Groups (A, B, C, D, E)
genes
One genes may belong
to more than one cluster

18. Clusters can be presented by graphs

19. 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

20. EXAMPLE- hnRNP A1 and SRp40
Gene with similar expression pattern tend to have common functions
HnRNPA1 and SRp40
have a similar gene expression pattern in
different tissues

21. EXAMPLE- hnRNP A1 and SRp40
Gene with similar expression pattern tend to have common functions
hnRNP A1
SRp40

22. Are they regulated by the same transcription factor ?
1. Extract their promoter regions
2. Find a common motif in both sequences (MEME)
hnrnpA1
SRp40
gene
Promoter
Common motif
3. Identify the transcription factor related to the motif

23. Extract the promoters of the genes in the cluster and find a
common motif (using MEME)
>GGATAACAATTTCACAAGTGTGTGAGCGGATAACAA
>AAGGTGTGAGTTAGCTCACTCCCCTGTGATCTCTGTACATAG
>ACGTGCGAGGATGAGAACACAATGTGTGTGCTCGGTTTAGTCACC
>TGTGACACAGTGCAAACGCGCCTGACGGAGTTCACA
>AATTGTGAGTGTCTATAATCACGATCGATTTGGAATATCCATCACA
>TGCAAAGGACGTCACGATTTGGGAGCTGGCGACCTGGGTCATG
>TGTGATGTGTATCGAACCGTGTATTTATTTGAACCACATCGCAGGTGAGAGCCATCACAG
>GAGTGTGTAAGCTGTGCCACGTTTATTCCATGTCACGAGTGT
>TGTTATACACATCACTAGTGAAACGTGCTCCCACTCGCATGTGATTCGATTCACA

24. Create a Multiple Sequence Alignment
GGATAACAATTTCACA
TGTGAGCGGATAACAA
TGTGAGTTAGCTCACT
TGTGATCTCTGTTACA
CGAGGATGAGAACACA
CTCGGTTTAGTTCACC
TGTGACACAGTGCAAA
CCTGACGGAGTTCACA
AGTGTCTATAATCACG
TGGAATATCCATCACA
TGCAAAGGACGTCACG
GGCGACCTGGGTCATG
TGTGATGTGTATCGAA
TTTGAACCACATCGCA
GGTGAGAGCCATCACA
TGTAAGCTGTGCCACG
TTTATTCCATGTCACG
TGTTATACACATCACT
CGTGCTCCCACTCGCA
TGTGATTCGATTCACA

25. Generate a PSSM
Find the transcription factor which bind the motif

26. How can we use microarray for diagnostics?

27. Gene-Expression Profiles in Hereditary Breast Cancer
cDNA Microarrays
Parallel Gene Expression Analysis
Breast tumors studied:
BRCA1
BRCA2
sporadic tumors
Log-ratios measurements of genes for each tumor after initial data filtering
RESEARCH QUESTION
Can we distinguish BRCA1 from BRCA2– cancers based solely on their gene expression profiles?

28. + - How can microarrays be used as a basis for diagnostic ?
5 Breast Cancer Patient
Patient 1
patient 2
patient 3
patient4
patient 5
Gen1 + -
Gen2
Gen3
Gen4
Gen5

29. + - How can microarrays be used as a basis for diagnostic ? BRCA1
patinet1
patient 2
patient4
patient 3
patient 5
Gen1 + -
Gen3
Gen4
Gen2
Gen5
Informative
Genes

30. Specific Examples Cancer Research Hundreds of genes
that differentiate between
cancer tissues in different
stages of the tumor were found.
The arrow shows an example
of a tumor cells which
were not detected correctly by
histological or other
clinical parameters.
Ramaswamy et al, 2003
Nat Genet 33:49-54

31. Supervised approaches for 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.

32. 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.

33. Using SVMs to diagnose tumors based on expression data
Each 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.

34. How do SVM’s work with expression data?
In this example red dots can be primary tumors and blue are
from 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.

35. Gene Expression Databases and Resources on the Web
GEO Gene Expression Omnibus -
List of gene expression web resources
Another list with literature references
Cancer Gene Anatomy Project
Stanford Microarray Database