Ontology highlight
ABSTRACT:
PROVIDER: PRJNA295386 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:On the relevance of technical variation due to building pools in microarray experiments [Mouse]
| PRJNA295205 | ENA
Project description:On the relevance of technical variation due to building pools in microarray experiments [Honey bee]
| PRJNA295389 | ENA
Project description:An EST database from immune tissues was used to design the first high density turbot (Scophthalmus maximus) oligo-microarray with the aim of identifying candidate genes for tolerance to pathogens. Specific oligonucleotides (60mers) were successfully designed for 2716 out of 3482 unique sequences of the database. The performance of the microarray and the sources of variation along microarray analysis were examined on spleen pools of controls and Aeromonas salmonicida challenged fish at 3 days post-infection. An asymmetric hierarchical design was employed to ascertain the noise associated with biological and technical (RNA extraction, labeling, hybridization, slide and dye bias) factors using one-colour (1C) and two-colour (2C) -labeling approaches. The high correlation coefficient between replicates at most factors tested demonstrated the high reproducibility of the signal. An analysis of random effects variance revealed that technical variation was mostly negligible and biological variation represented the main factor, even using pooled samples. One-colour approach performed at least as well as 2C. A relevant proportion of genes turn out to be differentially labelled depending on fluorophore, which alerts for the likely need of swapping replication in 2C experiments. A set of differentially expressed genes and enriched functions related to immune/defence response were detected at three days post-challenging.
2009-08-04 | GSE17357 | GEO
Project description:Prior to generation of microarray data for a Top-down Systems analysis of influenza A infection, we evaluated the degree and origin of technical variation in gene expression microarray data from mouse lungs and compared this to inter-animal variation. Technical variation in these microarray data in the context of inter-animal variation supported a choice of biological rather than technical replicates.
2013-07-03 | GSE42521 | GEO
Project description:Knowledge of the biological and technical variation for fermentor-grown Aspergillus niger cultures is needed to design DNA microarray experiments properly. We cultured A. niger in batch-operated fermentor vessels and induced with D-xylose. Transcript profiles were followed in detail by qPCR for 8 genes. A variance components analysis was performed on these data to determine the origin and magnitude of variation within each process step for this experiment. 6 Fermentor cultures were selected to determine technical and biological variation for all 14554 ORFs present on this array type. Keywords: Validation of microarrays; variation analysis; experimental design
2008-12-31 | GSE11405 | GEO
Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
2014-01-15 | GSE54068 | GEO
Project description:Background: RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results: Independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions: Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.
2014-01-15 | GSE54067 | GEO
Project description:We investigated the effects of the variation in intracellular adenylic nucleotides pools due to the absence of the adenylate kinase Adk1p on the global yeast transcriptome. In this work, global transcription analyses were combined to measurements of intracellular nucleotides pools by HPLC. Keywords: Comparative genomic transcription on total RNA from cells containing or not Adk1p.
2008-04-17 | GSE9553 | GEO
Project description:The objective of the study was to assess the technical error due to blending of individual samples into pools in different experimental data sets. The blending error variance component corresponds to random effects for inaccuracies, which were modeled on the logarithmic scale of normalized gene expression. It's estimation based on a linear mixed model, fitted for each transcript.
2015-09-11 | GSE72943 | GEO
Project description:The objective of the study was to assess the technical error due to blending of individual samples into pools in experimental data. The blending error variance component corresponds to random effects for inaccuracies, which were modeled on the logarithmic scale of normalized gene expression. It's estimation based on a linear mixed model, fitted for each transcript.
2015-09-10 | GSE72861 | GEO