EDCC-CNG

Cis-regulatory elements (CREs) and cis-regulatory modules (CRMs) play an important role in temporal and spatial regulation of gene expression, which is a common process in eukaryotic organisms.

We developed two programs that serve as exploratory tools in the analysis of CRM-mediated control of gene expression: “Exploration of Distinctive CREs and CRMs” (EDCC) and “CRM Network Generator” (CNG). EDCC correlates the presence and positions of CREs/CRMs with gene expression data and identifies candidate regulatory elements for further functional analysis. CNG provides an unbiased neural network approach to assess the importance of positional features that were determined by EDCC.

To sustain a high computational performance even for large datasets, the mostly in Python 3 written programs use k-mer based indexing, parallelization and a neural network approach for categorization.

For further information please refer to the publication: doi.org/10.1371/journal.pone.0190421