Technical controls (mock communities, blanks) associated with unfractionated (>0.2 uM) seawater samples from GEOTRACES GA03/GP13 transects
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA658608 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Unfractionated (>0.2 uM) seawater samples from GEOTRACES GA03 transect Targeted loci environmental
| PRJNA658384 | ENA
Project description:Unfractionated (>0.2 uM) seawater samples from GEOTRACES GP13 transect Targeted loci environmental
| PRJNA658385 | ENA
Project description:Estrogen signaling pathway is critical for breast cancer development and has remained the major adjuvant therapeutic target for this disease. Tamoxifen has been used in clinic for many years to treat ER-positive breast cancer. However a great many (30%) suffer relapse due to drug resistance. In this study, the bromodomain inhibitor JQ1 was found to down-regulate ERalpha gene expression and have anti-tumor effect in cultured tamoxifen-resisant breast cancer cells. We used microarrays to detail the global programme of gene expression in tamoxifen-resistant MCF7 cells treated with the bromodomain inhibitor JQ1. Tamoxifen-resistant breast cancer MCF7 cells were treated with DMSO (vehicle) or JQ1 (0.2 uM) for 24 hours before total RNA was purified for microarray. Each sample was triplicated.
2014-03-01 | E-GEOD-49124 | biostudies-arrayexpress
Project description:Technical snow production chain Raw sequence reads
| PRJNA1336629 | ENA
Project description:2-4-8 cell porcine embryo technical repeat dye-swap for Agilent EMPV1 (4x44k Format)
2013-02-25 | GSE34157 | GEO
Project description:Top-Down proteomics pilot experiment of unfractionated Bovine Heart Mitochondria (BHM) using ultra high resolution Q-ToF tandem mass spectrometry (maXis 4G ETD, Bruker Daltonics).
2018-04-05 | PXD005350 | Pride
Project description:RNA-seq: technical variability and sampling
| PRJNA234515 | ENA
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