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When prior knowledge of the species composition and gene contents of the sequenced metagenome is unavailable, we can use as many reference gene sequences as possible (e.g. the entire set of genes from all available microbial genomes) to guide the inference of gene paths.
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How many reference genes should be used is dependent on the purpose of research.
Taken together, what and how many reference genes are sufficient for RT-qPCR data normalization varies on a case-by-case basis.
What and how many reference genes used for calculation of normalization factors (NF) in parallel samples is thus a crucial determinant of the accuracy of expression quantification.
This broad U-shape curve suggests that inclusion of either too few or too many reference genes may detriment the robustness of data normalization.
Unexpectedly the NF variation across samples does not exhibit a continuous decrease with pairwise inclusion of more reference genes, suggesting that either too few or too many reference genes may detriment the robustness of data normalization.
Many reference genes, assumed to have a stable expression have been used to normalize expression in transcriptomics.
In a subsequent step, the algorithm is able to indicate how many reference genes are optimally required to remove most of the technical variation.
In order to determine how many reference genes should be included, normalisation factors (NFn), based on the geometric mean of the expression levels of the n best reference genes, were calculated by inclusion of an extra, less stable, reference gene according to Vandesompele et al [ 9].
Briefly, geNorm ranks the genes according to the average pair-wise variation of a particular gene with all other genes, and also provides a measure of the minimum optimal number of reference genes to avoid the expense and 'noise' in the assay from using too many reference genes.
Also, now that an increasing number of detection methods are available, it proves impractical to use many different reference genes per crop.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com