Box 1. Interactome Dysregulation Enrichment Analysis (IDEA)

An overview of the proposed network-based analysis to characterize oncogenic mechanisms and pharmacological interventions. (A) In step 1, a comprehensive network of interactions is generated for B cells using a Bayesian evidence integration approach, including predictions of post-translational modifications. In this diagram, transcription factors are shown in red, non-transcription factors in gray, and modulators are shown in blue. Directed arrows indicate protein–DNA (P-D) interactions, and undirected indicate protein–protein (P-P) interactions or modulation events. Evidences, or clues, include curated databases, literate mining, orthologous interactions from model organisms, and reverse engineering algorithms. (B) In step 2, each interaction is analyzed to determine which show aberrant behavior in a specific phenotype (P); that is, interactions that show correlation in all samples except P (TF1 and T1), or interactions that are not correlated in any samples except P (TF1 and T2). These dysregulated interactions are classified as LoC or GoC, respectively, for every edge in the BCI. (C) In step 3, these dysregulated interactions are pooled together and a statistical enrichment is calculated which identifies genes having an unusually high number of these interactions in its neighborhood, either through direct or modulated links.