ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks.

ABSTRACT:

Background

Uncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge. However, a vast amount of data from public microarray experiments is available, which can be subject to inference algorithms in order to recover reliable TRN architectures.

Results

In this study we present a simple bioinformatics methodology that uses public, carefully curated microarray data and the mutual information algorithm ARACNe in order to obtain a database of transcriptional interactions. We used data from Arabidopsis thaliana root samples to show that the transcriptional regulatory networks derived from this database successfully recover previously identified root transcriptional modules and to propose new transcription factor

SUBMITTER: Chavez Montes RA 

PROVIDER: S-EPMC4021103 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature