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Both approaches agreed about a negative correlation between length and expression for highly expressed genes, but they disagreed about the trends for moderately and lowly expressed genes.
Currently, the main approach to identify reference genes involves the mining of expression microarray data for highly expressed and relatively constant transcripts across a sample set.
Analysis of dominant transcript expression indicates that the GENCODE Basic set is enriched for highly expressed transcripts (see Additional file 13: Dominant expression analysis).
Our interpretation is that Affymetrix technology can be saturated for highly expressed genes, becoming insensitive to subtle expression changes.
This finding is consistent with others, as most of the previous expression measures have considered those as representative standards for highly expressed genes in their calculation.
These results indicate that microarray data for highly expressed markers are consistent with the literature; however, genes with lower gene expression levels exhibit greater variability.
Almost all proteins were detected for highly expressed genes, whereas this proportion covered by proteomics decreases with decreasing gene expression level (see Figure 4).
Importantly, as defined here, the measure of expression consistency is expected to be biased toward generating higher estimates for highly expressed and long genes.
A similar level of difference in neutral mutations between the polyploid and diploids was found for highly expressed genes.
For highly expressed genes intergenic regions were compact with only a small region completely excised from the mRNA (Figure 1).
The sequence evolution rate for highly expressed proteins is constrained at the translational level (the translational hypothesis).
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