Mohamed et al (2020) Integrated transcriptome, DNA methylome and chromatin state accessibility landscapes reveal regulators of Atlantic salmon maturation
This repository y contains awk scripts to perform master regulator analysis and gene co-expression analysis based on PCIT. Genes selected for the network analysis originated from different omics analysis (transcriptome, DNA methylome and Chromatin accessibility). the PCIT_based_GRN_analyses.awk script describe steps to construct gene networks: genes were used as nodes and significant connections (edges) between them were identified using the Partial Correlation and Information Theory (PCIT) algorithm (Reverter and Chan, 2008), considering all 48 samples. PCIT determinates the significance of the correlation between two nodes after accounting for all the other nodes in the network.
differential connectivity: two networks were created; one using 12 samples at T1 (pre-maturation) and a second using 36 samples at T2, T3 and T4 (post-maturation). The number of connections of each gene in each network was computed, making it possible to compare the same gene in the two networks to identify differentially connected genes (DCGs). A series of subnetworks were explored based on the top trio genes and the top regulators (TFs) based on their differential connectivity between pre-and post-maturation
the RIF_analyses.awk script desribes the Master regulator analysis erformed using regulatory impact factor (RIF) metrics (Reverter et al., 2010) to identify key regulators contributing to the differential expression in the T4 vs T1 comparison in each tissue. Data for potential transcription factors (TFs) in Atlantic salmon were retrieved (Mohamed et al., 2018). RIF exploits the differential co-expression concept where regulators were contrasted against unique lists of genes that were differentially expressed at T4 in each tissue