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Total RNA isolated from adipose tissue, liver, muscle, and pancreas of a subset of F2 mice (n = 16) were used for microarray analysis, requiring 64 arrays.
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However, the standard procedure for cDNA microarray analysis requires 5 μg of good quality total RNA as starting material for each target preparation reaction.
However, cDNA microarray analysis requires a large amount of total RNA (5 μg to 100 μg).
Since genes extracted from the microarray analysis require verification by other biochemical experiments, false-positive genes will be recognized and can be reclassified.
The methods in this article provide a framework for thoroughly investigating whether microarray data analysis requires robust approaches, or whether we may safely rely on Gaussian assumptions.
In contrast, the labeling protocols for microarray analysis frequently require ≥5 μg of total RNA [ 15].
The use of microarray chips in gene expression analysis requires an enormous amount of data to be analysed and often, while at the same time, selecting the most informative genes from different gene sets.
If the analysis requires the entire transcript as in the case of WT (whole-transcript) microarray platforms where individual exons are analyzed, random primers are required.
Analysis of the larger complement of CYP genes for maternal transcript in unfertilized eggs requires microarray analysis, and is ongoing.
Recently, Zender et al. identified that eIF-5A2 is amplified in human cancer using representational oligonucleotide microarray analysis (ROMA), and is required for proliferation of XPO4-deficient tumor cells and promotes hepatocellular carcinoma in mice [ 11].
In addition, data normalization fundamentally differs between microarray analysis and qPCR, the former requiring global normalization, while the latter generally utilizes the expression of one or more reference genes against which all other gene expression is calibrated.
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