Comparative transcriptomics method to infer gene coexpression networks and its applications to maize and rice leaf transcriptomes.

ABSTRACT: Time-series transcriptomes of a biological process obtained under different conditions are useful for identifying the regulators of the process and their regulatory networks. However, such data are 3D (gene expression, time, and condition), and there is currently no method that can deal with their full complexity. Here, we developed a method that avoids time-point alignment and normalization between conditions. We applied it to analyze time-series transcriptomes of developing maize leaves under light-dark cycles and under total darkness and obtained eight time-ordered gene coexpression networks (TO-GCNs), which can be used to predict upstream regulators of any genes in the GCNs. One of the eight TO-GCNs is light-independent and likely includes all genes involved in the development of Kranz

SUBMITTER: Chang YM 

PROVIDER: S-EPMC6386681 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature