Difference between revisions of "Module 1: Is the hourglass model for gene expression really supported by the data?"
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* Key claim of the paper: "Gene expression follows the so-called hour-glass pattern observed for morphological features of development, which are most similar to each other in the phylotypic stage in mid-development." | * Key claim of the paper: "Gene expression follows the so-called hour-glass pattern observed for morphological features of development, which are most similar to each other in the phylotypic stage in mid-development." | ||
− | * Data and Code | + | * Data and Code: [[Media:danio_age_index.txt.zip]] [[Media:ProcessedMicroarrayData.txt.zip]] |
* Schedule: | * Schedule: | ||
− | + | ** H1: General introduction to the paper/motivation (slides: [[Media:IntroductionM1.pptx]]) | |
− | ** H1: General introduction to the paper/motivation | ||
** H2-3: Write code to import the data and start computing transcriptome age index (TAI) | ** H2-3: Write code to import the data and start computing transcriptome age index (TAI) | ||
+ | ** H4-6: Aim to reproduce figure 1 of the paper – help/scripts will be given if needed. | ||
+ | ** H7: Discussion: “Are you convinced of this result? What might have gone wrong?” | ||
+ | ** H8: Redo analysis using log-transformed data | ||
+ | ** H9: Wrap-up | ||
− | * | + | * Key bioinformatics concept of this module: "Data normalization is important and can impact the results of subsequent analyses!" |
− | * | + | * back to [[UNIL MSc course: "Case studies in bioinformatics"]] |
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Latest revision as of 15:26, 15 November 2017
- Title: "Is the hourglass model for gene expression really supported by the data?"
- Paper to be examined: “A phylogenetically based transcriptome age index mirrors ontogenetic divergence patterns”, Nature 9;468(7325):815-8 (2010)[1]
- Key claim of the paper: "Gene expression follows the so-called hour-glass pattern observed for morphological features of development, which are most similar to each other in the phylotypic stage in mid-development."
- Schedule:
- H1: General introduction to the paper/motivation (slides: Media:IntroductionM1.pptx)
- H2-3: Write code to import the data and start computing transcriptome age index (TAI)
- H4-6: Aim to reproduce figure 1 of the paper – help/scripts will be given if needed.
- H7: Discussion: “Are you convinced of this result? What might have gone wrong?”
- H8: Redo analysis using log-transformed data
- H9: Wrap-up
- Key bioinformatics concept of this module: "Data normalization is important and can impact the results of subsequent analyses!"