To explain the cooperativity within a module, we performed multiple-mutant cycle analysis of cluster 2 resulting in a high-resolution energy map of this module.
To test the association between responders characteristics and response mode in the second module, we performed logistic regressions using a dichotomous outcome (online vs. paper-pencil).
For strand preference of motif pairs within a motif module, we performed analysis similar to the analysis of the order preference.
For each predicted regulatory module, we performed functional analysis of its targets plus direct interaction partners of those targets in the protein interaction network.
Having identified an integrase module, we performed experiments to determine whether site-specific integration of pPDL2 takes place at an artificially created attB site cloned in temperature-sensitive plasmid pDSP8.
Next, in the 'CEGsFuncs' module, we performed an enrichment analysis of integrated CEGs (union of CEGs) of these two lncRNAs in the KEGG pathways by using the 'Lung (GEO Seo JS et al).' dataset.
Then, in order to test if the enrichment was dependent on the definition of the pleiotropic module, we performed a correlation analysis: we correlated the fraction of disease susceptibility genes and the number of disease modules for each gene and found a strong significant positive PCC for both GWAS (PCC = 0.91, P < 4 × 10-4) and cancer (PCC = 0.94, P < 2 × 10-4) genes.
To explore and analyze functional relevance of our identified GS BC modules, we performed pathway enrichment analysis from three aspects including GO terms, functional pathways and diseases using Gene Ontology (GO, KEGGG pathways, and OMIM diseases databases, respectively.
To evaluate the robustness of the identified modules, we performed module stability analysis from bootstrapped networks (see Methods).
To functionally characterize genes in the brown and blue modules, we performed GO analysis.
