A rice protein interaction network reveals high centrality nodes and candidate pathogen effector targets.

ABSTRACT: Network science identifies key players in diverse biological systems including host-pathogen interactions. We demonstrated a scale-free network property for a comprehensive rice protein-protein interactome (RicePPInets) that exhibits nodes with increased centrality indices. While weighted k-shell decomposition was shown efficacious to predict pathogen effector targets in Arabidopsis, we improved its computational code for a broader implementation on large-scale networks including RicePPInets. We determined that nodes residing within the internal layers of RicePPInets are poised to be the most influential, central, and effective information spreaders. To identify central players and modules through network topology analyses, we integrated RicePPInets and co-expression networks repres

SUBMITTER: Mishra B 

PROVIDER: S-EPMC9062363 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature