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Common TFBS modules could partly explain expression similarity of the ZNF genes.
This could partly explain expression similarity, but regulatory elements from outside the proximal promoter regions might also contribute.
Another approach has constructed causal graphs from curated interactions in literature and used these graphs to explain expression profiles (Chindelevitch et al., 2012).
In patients, the exact place in tumor progression/growth is always different and thus this alone can explain expression differences, as opposed to the inherent character of genomic differences of the tumor.
We used these results to test whether ED parameters selected in the models for our data could also explain expression variance of the same clusters of genes in the PND.
However, the following problem remains to be investigated: Whether other TFs that bind together (or co-bind) with main factors, referred to as cofactors, can further explain expression patterns?
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Results were used in to identify codons most enriched in the evolution, and thus most critical for explaining expression.
Stochastic or genetic background-based epigenetic changes may be a superimposed regulatory element explaining expression differences.
The detected eQTLs for global MZDE genes well explained expression variations across RILs, 86 % on average.
However, instead of explaining expression similarity with cis-regulatory descriptors, transcription factors were found whose joint expression pattern was concordant with that of a cluster of genes.
It was reported previously that life-history variation governed by natural selection explains expression variation of 22% genes in marine species (Whitehead & Crawford, 2006b).
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