the challenge of bioinformatics chris glasbey biomathematics & statistics scotland
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The challenge of bioinformatics
Chris Glasbey
Biomathematics & Statistics Scotland
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Talk plan
1. DNA
2. mRNA
3. Protein
4. Genetic networks
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1. DNA
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1. DNA
Frank Wright et al
BioSS
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1.DNA
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1. DNA
TOPALi
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2. mRNAPrepare cDNA targets
Label withfluorescent dyes
Combine Equal Amounts
Hybridise for 5 -12 hours
Scanning
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2. mRNA• Scanner’s PMT setting is one
of the sources of contamination.
• Scanner’s setting is to be raised to a certain level to make the weakly expressed genes visible.
• This may cause highly expressed genes to get censored (at 216–1= 65535) expression values.
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2. mRNA
Censored spot
Imputed values
0
65535
With GTI (Edinburgh)
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2. mRNA
Scan-1 intensity data
Scans 1
to 4
inte
nsity d
ata
0 10000 20000 30000 40000 50000
020000
40000
60000
Scan-1 vs. Scan-1Scan-2 vs. Scan-1Scan-3 vs. Scan-1Scan-4 vs. Scan-1
Multiple scans
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Estimated gene expression
Obs
erve
d pi
xel m
ean
/ bet
a
0 10000 20000 30000 40000
010
000
3000
050
000
Scan-1Scan-2Scan-3Scan-4
Array-2 data
Mizan Khondoker
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2. mRNA
Jim McNicol
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3. Proteins
Electrophoresis gel
Lars Pedersen DTU, Denmark
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3. Proteins
Protein separation by
1. pH
2. Mol. Wt.
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3. Proteins
gel 1 gel 2
How to compare gels 1 and 2?
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3. Proteins
John Gustafsson, Chalmers University, Sweden
WARP
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3. Proteins
Two gels superimposed (in different colours)
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3. Proteins
Statistical Design
3 complete reps of 15 treatment combinations. (3 ecotypes by 5 heavy metals)
Maximum of 1400 protein spots per gel
Statistical Analyses
Filter data – remove spots with low intensity values and low quality scores (leaving ~290 spots)
Individual proteins – ANOVA, main effects and interactions
1E-16
1E-14
1E-12
1E-10
1E-08
1E-06
0.0001
0.01
1
1 26 51 76 101 126 151 176 201 226 251 276
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3. Proteins
Principal Components Analysis
Identify groups of proteins that are affected in a consistent manner by treatments
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
1 25 49 73 97 121 145 169 193 217 241 265 289
Protein identity
Loadin
gs
Jim McNicol
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4. Genetic networks
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4. Genetic networks
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4. Genetic networks
Is it possible to infer the network from gene expression data such as these?
Dirk Husmeier
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4. Genetic networks
Bayesian network
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4. Genetic networks
truth inferred
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“I genuinely believe that we are living through the greatest intellectual moment in human history.” (Matt Ridley, Genome, 1999)
“Grand Unified Systems Biology”