The large variety of publicly available gene regulation and expression data makes yeast an ideal model organism to test and validate our algorithm.

Loregic is also compatible with user-inputted, customized binary gene expression data [29].

BoolNet assigns Boolean values to expression data on the basis of modular co-expression patterns by k-means clustering across inputted samples and therefore accounts for differences in the dynamic ranges of expression among genes in the input data.

To test the robustness of Loregic to different binarization methods, we compared BoolNet with another method, ArrayBin [45], which uses an adaptive approach to binar-ize high-throughput gene expression data .

Loregic is also compatible with other discretization methods including using any custom-made binarized gene expression data as input.

We compared BoolNet with another method, ArrayBin [45], which uses an adaptive approach to binarize high-throughput gene expression data (Materials and Methods).

On a genome-wide scale ChIP-Seq provides regulatory information about wiring between RFs and targets, while RNA-Seq provides gene eXpression data ; by combining these two data types we are able to go beyond the regulatory activities of individual RFs and investigate the relationships between higher order RF groups.

The binarized gene eXpression data (1-on and O-off) is simple but useful in representing the network RFs’ activities on target genes.

the gate that best matches the binarized eXpression data for that triplet across all samples.

Step C. Query binarized gene expression data for each triplet;

Using a large collection of densely-sampled time series expression data we have examined the induction of the immediate-early response in unparalleled detail, across cell types and stimuli.

CAGE and miRNA expression data for these transcripts are presented together in Fig 5B where a lag between the rise in the CAGE signal and the recovery in the mature miRNA level for hsa-mir-320a can be seen, whereas the CAGE peak appears concurrent with the rise in mature hsa-mir-155 (none of these profiles satisfied our statistical criteria but significant changes occur between selected time points).

anRNA are typically eXpressed at very low levels (NEATl and MALATI being notable eXceptions), as are precursor miRNA, making their analysis problematic for methods that require more strictly thresholded eXpression data .

As CAGE data is obtained from the 5’ end of capped mRNA transcripts, it is expected to reflect the initial burst of overproduction of mRNA at promoters better than other expression data , and hence is well suited to explore the immediate-early response.