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Selection for unpreferred codon usage, and potentially reduced translational efficiency, is likely to be more common than previously thought among Eukaryotes, and is a factor that must be considered in extrapolating ultimate gene expression levels from the vast amounts of transcription data now available.
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In essentially every experimental system - including our system of choice, the nematode C. elegans - the ultimate product of gene expression can be directly visualized in the form of a fluorescent-protein reporter, such as GFP, attached to the gene of interest [1].
Similarly, the ultimate goal in gene expression analysis is to have an evolutionary model for gene expression.
mRNA sequences, such as from EST libraries, can help to map transcribed genes but experimentally verified proteins are still the ultimate affirmation of gene expression.
The nature of the estrogenic ligand is, therefore, the driving force of the composition of co-regulatory complexes, and ultimate ligand-specific gene expression and biological response ensues (Nilsson et al., 2011).
Thus, although there is good evidence that regulatory changes underlie morphological divergence among species of Darwin's finches, the ultimate link between gene expression and genetically based reproductive isolation (= speciation) is yet to be made.
The ultimate goal is that gene expression profiles of a new patient's tumor could be analyzed in the context of a database of gene expression profiles from patients with known outcomes.
Nonetheless, the ultimate "gold standards" for gene expression during bovine pre-implantation development can only be established using naturally ovulated/developed oocytes/embryos.
Nevertheless, assuming that an organism's ultimate phenotype determined by gene expression levels (and subsequent post-transcriptional regulation), the conserved expression levels among parental species during earlier developmental stages is probably a compelling validation of Von Baer's third law.
At this time, it is difficult to predict how these interactions may affect CBF and COR gene expression and ultimate freezing tolerance.
Our ultimate selection strategy also incorporated gene expression data, an RNAi screen, and a network analysis to focus the list on the most likely driver genes within CNAs.
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