Detecting phenotype-driven transitions in regulatory network structure.

ABSTRACT: Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular and contain dense "communities" of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks and analyzing their topologies separately, there is a lack of robust methods for quantifying differences in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulatio

SUBMITTER: Padi M 

PROVIDER: S-EPMC5908977 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature