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

Authors: LISERON-MONFILS, CHRISTOPHE - Cold Spring Harbor Laboratory; OLSON, ANDREW - Cold Spring Harbor Laboratory; Ware, Doreen

Submitted to: bioRxiv
Publication Type: Other
Publication Acceptance Date: 5/21/2018
Publication Date: 5/21/2018
Citation: Liseron-Monfils, C., Olson, A.J., Ware, D. 2018. NECorr, a Tool to Rank Gene Importance in Biological Processes using Molecular Networks and Transcriptome Data. bioRxiv. 1-11. https://doi.org/10.1101/326868.
DOI: https://doi.org/10.1101/326868

Interpretive Summary: Integration of data types provides more predictive power, than a single data type alone. In this manuscript we describe a method to integrate gene regulatory maps and gene expression profiles to provide a rank list of the genes that are likely to impact the fitness of the plant. We applied this method to data from the model plant arabidopsis. We believe the method will accelerate research to rank candidate genes associated with agronomic gain.

Technical Abstract: 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.