Exact(7)
DQ performed microarray data, promoter, gene expression, and protein-DNA interaction analyses, and drafted the manuscript.
Attempts have also been made to infer regulatory networks from a combined analysis of gene expression data, promoter regions and TF binding site data [ 24- 26].
In a previous study, Chawade et al. [ 10] constructed putative cold regulatory networks by integrating data from co-expressed microarray data, promoter sequences and known promoter binding sites.
Many algorithms have been developed to identify cooperative TF pairs in yeast by integrating multiple high-throughput data sources such as gene expression data, ChIP-chip data, protein-protein interaction data, promoter sequence data, etc. [ 1- 15].
From these data, promoter expression (CPS/OD) and transcription factor activity for AraC-arabinose and CRP-cAMP was calculated according to equation 2. In the diauxic shift experiments, the exhaustion of glucose varied between experiments.
Among these, Network Component Analysis (NCA) [ 3] and generalized NCA (gNCA) [ 2] provide a robust framework for deducing TFAs based on DNA microarray data, promoter connectivity, and genetic regulatory constraints imposed by regulatory knock-out experiments.
Similar(53)
As shown, microarray data and promoter sequence are used for the promoter scan.
According to our data, the promoter region controlling flower/leaf expression is within 1 kb of the promoter.
(Also, there is no data on promoter transcription factor binding sites, only on cis regulatory elements).
We have demonstrated how to infer the activity modification of transcription factors in the long-lived mutants with respect to wild-type yeast by integrating microarray expression data with promoter sequence data and ChIP-chip data.
We applied log base-2 of average enrichment ratio with nucleosome-normalizing for each of 22 histone modification data in promoter and open reading frame (ORF) regions of genes.
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