Jacques'van'Heldenulb'ac'be

1
Transcriptome

• Bioinformatics

2
Transcriptome

• Bioinformatics

3
Measuring the expression of all the genes of a
genome

• In 1997, deRisi and co-workers develop a method
  to measure the level of transcription of all the
  genes of a genome.
• The method allows to compare the concentrations
  of mRNA of each gene between two experimental
  conditions
• Green channel reference
• Red channel test
• The intensity of a spot indicates the average
  concentration of the corresponding mRNA in the
  two samples.
• The color of a spot indicates regulation
• Red up-regulated in the test, relative to the
  reference condition
• Green down-regulated

deRisi et al. (1997). Science 278 680-686
4
DNA chip technology
Cell culture, tissue, ...
Sample 1
Sample 2
RNA
RNA
RNA extraction
cDNA
cDNA
Synthesis of fluorescent cDNA
Brightness ? Quantity Color ? Specificity

• yellowish not specific
• reddish sample 1 - specific
• greenish sample 2 - specific

DNA chip
Source deRisi et al., Science 1997
5
Scanning result
slide from Peter Sterk
6
Complete microarray
deRisi et al. (1997). Science 278 680-686
SourceDeRisi et al. (1997) Science, 278(5338),
680-6.
7
DNA chips raw measurements

• Raw measurements
• Red intensity
• Red background
• Green intensity
• Green background
• Intensity background level of expression
• Red in experimental conditions
• Green in control

8
DNA chips useful metrics

• The level of regulation is represented by the
  ratio

r gt1 ? up-regulated r lt 1 ? down-regulated

• The log-ratio provides a more convenient
  statistic (we will see why during the course)
• log2 is even more convenient because the scale is
  intuitive

R lt 0 ? down-regulated R gt 0 ? up-regulated
R gt 1 ? regulated by a factor of 2 R gt 2 ?
regulated by a factor of 4 R gt w ? regulated by
a factor of 2w
9
Time series

• At each time point, the expression level is
  compared to the control (log-ratio)
• Example Nitrogen depletion

Source Gasch et al (2000) Molecular Biology of
the Cell 114241-4257
10
Examples of experimental conditions

• Presence/absence of a metabolite
• gal vs glucose
• Transcription factor mutants
• Yap1p over-expression
• TUP1 deletion
• Massive environmental changes
• rich versus minimal medium
• diauxic shift (7 time points during the shift)
• Cell differentiation
• sporulation
• mating type
• Cell cycle

11
Temporal profiles of expression

• deRisi et al measured the level of expression of
  all the genes at 7 time points during the diauxic
  shit.
• The figure shows groups of genes show similar
  expression profiles,
• Some of these groups contain genes with similar
  function (e.g. coding for ribosomal proteins)
• Some of these groups have a common regulatory
  element in their promoter (e.g. stress response
  element).

deRisi et al. (1997). Science 278 680-686
12
Cell cycle

• In 1998, Spellman and colleagues measure the
  expression of all yeast genes during the cell
  cycle.
• They detect 800 genes showing periodical
  fluctuations of expression.
• These genes can be sorted according to the peak
  of expression, in order to group genes induced
  during the different phases of the cell cycle
  (G1, S, G2, M).

Spellman et al. (1998) Molecular Biology of the
Cell 93273-3297
13
Gene expression data hierarchical clustering

• On the image, genes are clustered according to
  expression profiles, using Michael Eisens
  software cluster (Eisen et al., PNAS 1998 95,
  14863-8).
• Strengths
• The profiles and the clusters are visible
  together
• Familiar to biologists (frequently used for
  phylogeny)
• Weaknesses
• Isomorphism each node of the tree can be
  permuted ? vertical distance between genes does
  not reflect the real distance
• Where to set the cluster boundaries ?
• The tree does not reflect the combinatorial
  aspect of regulation

Spellman et al. (1998). Mol Biol Cell 9(12),
3273-97.
14
Gasch (2000) - gene response to environmental
changes

• Gasch et al. (2000) measure the transcriptional
  response of yeast genes to various environmental
  changes
• 173 microarrays
• 6000 genes per microarray

15
Classification of cancer types

• Microarrays are also used to select genes which
  will serve as molecular signatures to classify
  cancer types.
• These genes can then be used to establish a
  diagnostic for new patients.