Analysis of variance components reveals the contribution of sample processing to transcript variation

The proper design of DNA microarray experiments requires knowledge of biological and technical variation of the studied biological model. For the filamentous fungus Aspergillus niger, a fast, quantitative real-time PCR (qPCR)-based hierarchical experimental design was used to determine this variation. Analysis of variance components determined the contribution of each processing step to total variation: 68% is due to differences in day-to-day handling and processing, while the fermentor vessel, cDNA synthesis, and qPCR measurement each contributed equally to the remainder of variation. The global transcriptional response to d-xylose was analyzed using Affymetrix microarrays. Twenty-four statistically differentially expressed genes were identified. These encode enzymes required to degrade and metabolize D-xylose-containing polysaccharides, as well as complementary enzymes required to metabolize complex polymers likely present in the vicinity of D-xylose-containing substrates. These results confirm previous findings that the d-xylose signal is interpreted by the fungus as the availability of a multitude of complex polysaccharides. Measurement of a limited number of transcripts in a defined experimental setup followed by analysis of variance components is a fast and reliable method to determine biological and technical variation present in qPCR and microarray studies. This approach provides important parameters for the experimental design of batch-grown filamentous cultures and facilitates the evaluation and interpretation of microarray data

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Bibliographic Details
Main Authors: van der Veen, D., Oliveira, J.M., van den Berg, W.A.M., de Graaff, L.H.
Format: Article/Letter to editor biblioteca
Language:English
Subjects:aspergillus-niger, cloning, gene-expression, growth, information, microarray experiments, nidulans, quantification, standards, time rt-pcr,
Online Access:https://research.wur.nl/en/publications/analysis-of-variance-components-reveals-the-contribution-of-sampl
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