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In this first excercise we will perform few basic operations with VESPUCCI. We will create a Module starting from few genes and then automatically extend it by adding more genes. We will also have a look at gene and samples annotations. The use case Notebook is hosted in Google Colab
The second exercise tries to show a more general data analysis pipeline that relies on VESPUCCI for the data but integrates other tools and an packages, such as Scikit-learn, used to fit a Gaussian Mixture Model and GOAtools used for the Gene Ontology Enrichment. Everything is seamlessly integrated in one analysis workflow that ca be explored in this Notebook hosted in Google Colab.
In this use case, we have queried the database with a short list of genes putatively involved in pollen development with the final goal of retrieving additional genes with the same function. This Notebook hosted in Google Colab.
In this use case the main aim is the characterization of a grapevine gene family. Specific objetives are to verify if the different gene members have a tissue-specific expression and to identify the members those expression is significantly modulated upon biotic stresses. This Notebook hosted in Google Colab.
In this final exercise we start from few thousand of genes related to a Myb transcription factor and try to determine which are tissue-specific. After the initial filtering we attached the result with a GO enrichment analysis using GOAtools. This Notebook hosted in Google Colab.