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Transcriptome

Bioinformatics. Transcriptome. Bioinformatics. Transcriptome. 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.

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Transcriptome

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  1. Bioinformatics Transcriptome Jacques van Helden Jacques.van-Helden@univ-amu.fr Aix-Marseille Université (AMU), France Lab. Technological Advances for Genomics and Clinics (TAGC, INSERM Unit U1090) http://tagc.univ-mrs.fr/ FORMER ADDRESS (1999-2011) Université Libre de Bruxelles, Belgique Bioinformatique des Génomes et des Réseaux (BiGRe lab) http://www.bigre.ulb.ac.be/

  2. Bioinformatics Transcriptome Jacques.van.Helden@ulb.ac.be Université Libre de Bruxelles, Belgique Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe) http://www.bigre.ulb.ac.be/

  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 yellowish not specific reddish sample 1 - specific greenish sample 2 - specific Cell culture, tissue, ... Sample 1 Sample 2 RNA RNA RNA extraction cDNA cDNA Synthesis of fluorescent cDNA Brightness  Quantity Color  Specificity 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 Source:DeRisi 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 >1  up-regulated r < 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 < 0  down-regulated R > 0  up-regulated |R| > 1  regulated by a factor of 2 |R| > 2  regulated by a factor of 4 |R| > 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 11:4241-4257

  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 9:3273-3297

  13. Gene expression data: hierarchical clustering Alpha cdc15 cdc28 Elu MCM CLB2 SIC1 MAT CLN2 Y' MET • On the image, genes are clustered according to expression profiles, using Michael Eisen’s 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 Cell9(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 Golub et al. (1999). Science 286: 531-537 • 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.

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