Flowchart of MAGI. Given PPI and coexpression networks and case and control mutations, MAGI detects highly connected modules that are enriched for mutations in cases. The first phase calculates a score for each gene in the networks and selects seed pathways with high scores based on an extension of the color-coding algorithm (Alon et al. 1995). In the second phase, MAGI merges the seeds into modules using a random-walk approach and improves each one of them by applying a local search. The output consists of the best module detected, as well as a set of suboptimal modules. Each gene is assigned a “confidence score” according to its frequency within suboptimal modules.