NECorr, a Tool to Rank Gene Importance in Biological Processes using Molecular Networks and Transcriptome Data

The challenge of increasing crop yield while decreasing plants susceptibility to various stresses can be lessened by understanding plant regulatory processes in a tissue-specific manner. Molecular network analysis techniques were developed to aid in understanding gene inter-regulation. However, few tools for molecular network mining are designed to extract the most relevant genes to act upon. In order to find and to rank these putative regulator genes, we generated NECorr, a computational pipeline based on multiple-criteria decision-making algorithms. With the objective of ranking genes and their interactions in a selected condition or tissue, NECorr uses the molecular network topology as well as global gene expression analysis to find hub genes and their condition-specific regulators. NECorr was applied to Arabidopsis thaliana flower tissue and identifies known regulators in the developmental processes of this tissue as well as new putative regulators. NECorr will accelerate translational research by ranking candidate genes within a molecular network of interest.