Bioinformatics Tools

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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
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Gene Expression Analysis
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Gene Expression
protein
RNA
DNA
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Gene Expression
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mRNA gene1
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mRNA gene2
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mRNA gene3
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Studying Gene Expression 1987-2010
Spotted microarray
One channel microarray
RNA-seq (Next Generation Sequencing)
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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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Classical Methods

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

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Microarray Experiment
http//www.bio.davidson.edu/Courses/genomics/chip/
chip.html
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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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Expression Data Format
cold normal hot uch1 -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
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RNA-seq
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Gene Expression Analysis

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

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Clustering genes according to their expression
profiles

• .

Experiments
Genes
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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?

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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,
i1,,N
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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
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Internal nodes represent different functional
Groups (A, B, C, D, E)
genes
One genes may belong to more than one cluster
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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 SRp40 Gene with similar
expression pattern tend to have common functions
HnRNPA1 and SRp40 have a similar gene expression
pattern in different tissues
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EXAMPLE- hnRNP A1 and SRp40 Gene with similar
expression pattern tend to have common functions
hnRNP A1
SRp40
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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 http//jaspar.cgb.ki.se/
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Extract the promoters of the genes in the
cluster and find a common motif (using MEME)
gtGGATAACAATTTCACAAGTGTGTGAGCGGATAACAA gtAAGGTGTGAGT
TAGCTCACTCCCCTGTGATCTCTGTACATAG gtACGTGCGAGGATGAGAA
CACAATGTGTGTGCTCGGTTTAGTCACC gtTGTGACACAGTGCAAACGCG
CCTGACGGAGTTCACA gtAATTGTGAGTGTCTATAATCACGATCGATTTG
GAATATCCATCACA gtTGCAAAGGACGTCACGATTTGGGAGCTGGCGACC
TGGGTCATG gtTGTGATGTGTATCGAACCGTGTATTTATTTGAACCACAT
CGCAGGTGAGAGCCATCACAG gtGAGTGTGTAAGCTGTGCCACGTTTATT
CCATGTCACGAGTGT gtTGTTATACACATCACTAGTGAAACGTGCTCCCA
CTCGCATGTGATTCGATTCACA
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Create a Multiple Sequence Alignment
GGATAACAATTTCACA TGTGAGCGGATAACAA TGTGAGTTAGCTCAC
T TGTGATCTCTGTTACA CGAGGATGAGAACACA CTCGGTTTAGTTCA
CC TGTGACACAGTGCAAA CCTGACGGAGTTCACA AGTGTCTATAATC
ACG TGGAATATCCATCACA TGCAAAGGACGTCACG GGCGACCTGGGT
CATG TGTGATGTGTATCGAA TTTGAACCACATCGCA GGTGAGAGCCA
TCACA TGTAAGCTGTGCCACG TTTATTCCATGTCACG TGTTATACAC
ATCACT CGTGCTCCCACTCGCA TGTGATTCGATTCACA
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Generate a PSSM
Find the transcription factor which bind the motif
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How can we use microarray for diagnostics?
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Gene-Expression Profiles in Hereditary Breast
Cancer

• Breast tumors studied
• BRCA1
• BRCA2
• sporadic tumors
• Log-ratios measurements of 3226 genes for each
  tumor after initial data filtering

RESEARCH QUESTION Can 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 ?
5 Breast Cancer Patient
Patient 1 patient 2 patient 3 patient4 patient 5
Gen1 - -
Gen2 - -
Gen3 - -
Gen4 - -
Gen5 - - -
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How can microarrays be used as a basis for
diagnostic ?
BRCA1
BRCA2
patinet1 patient 2 patient4 patient 3 patient 5
Gen1 - -
Gen3 - -
Gen4 - -
Gen2 - -
Gen5 - - -
Informative Genes
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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 3349-54
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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 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.
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How do SVMs 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.
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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/