Exercise 5 - Task 2

Gene regulation networks / Gene ontology

• Use the Cytoscape BiNGO plugin to find statistically overrepresented GO terms of the "Biological Process" category in the network (Top/Bottom 100 sig/fc from 4.1).
  Hint: install the Bingo plugin first
  
  
  • Which are the 15 most prominent/significant (before correction) "Biological Processes"?
    saved table, can be imported into Excel
  • Show the categories as hierarchical tree
  • Which pathways could be affected by the MEKi treatment?



• Use the Cytoscape BiNGO plugin to find statistically overrepresented GO terms of the "Molecular Function" category in the network
• Which genes are molecular functions that are significantly affacted by the MEKi treatment?

• Map the gene expression data from Exercise 4 to the human p38 MAPK Signaling pathway.
  
  Hint: install the "WikiPathways" plugin first
  
  
  • Import the human p38 MAPK signaling pathway from the WikiPathways database (File -> Import -> Network from public Databases…). Search for human “p38 MAPK signaling” (use double quotes), which should be affected by the MEKi treatment
    
    
    
  • Map the gene expression data (log2FoldChange) to the pathway
    
    Data File: DESeq_result_significant.tsv
    
    
    • File -> Import -> Table -> File...
      Attention: define a mapping from "gene_name" to "shared name"
    
    
  • display the Node expression values as colors (remember 5.1.1).
    
  • Make a filter that selects all 2 fold (up or down) regulated genes in any time point, which are in the pathway.
  • Export a list of the genes that are regulated in the pathway


• Map the gene expression data from Exercise 4 to the known human CollecTRI transcription factor - target interactions and find all 1st level targets of the MYC transcription factor that are regulated upon MEKi treament. Remember: our GO anlysis identified the DDIT3 gene as strongly upregulated transcription factor.
  
  Hint: install the "OminPath" plugin first
  
  
  • Import the human CollecTRI TF-target interactions from the OmniPath (TF confidence level A,B, Directed interactions only)
    
    
  • Map the gene expression data to the network
    
    Data File: DESeq_result_significant.tsv
    
    
    • File -> Import -> Table -> File...
      Attention: define a mapping from gene_name to gene_symbol (Key Column for Networks <-> gene_name)
    
    Hint: Use filters
    • Filter for "Node: gene_symbol" MYC and build a "Chain" using the "Node Adjacency Transformer" to add adjacent nodes where the edges are outgoing and the expression is < -log2(1.5) or > log2(1.5)
    • Create a new sub-network from the seleced nodes and edges
  • display the Node expression values as colors (remember 5.1.1).
    
  • Adjust the Style to display gene_symbols as node names and arrows for interactions.
  • Adjust the Style to display degree as node size.
  • Use Edge-weighted Spring Embedded Layout (is_directed)
  • Export a list of these target genes