Homo sapiens
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ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE47774
PROVIDER: PRJNA208822 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for sequence discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcriptlevel profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings. The well-characterized reference RNA samples A (pooled cell lines) and B (human brain) from the MAQC consortium, adding spike-ins of synthetic RNA from the External RNA Control Consortium (ERCC). Samples C and D were then constructed by combining A and B in known mixing ratios, 3:1 and 1:3, respectively. All samples were distributed to several independent sites for RNA-Seq library construction and profiling by Illumina HiSeq 2000 and LifeTech SOLiD 5500 platforms. Also, vendors created their own cDNA libraries that were then distributed to each test site, in order to examine the degree of a M-bM-^@M-^\site effectM-bM-^@M-^] that was independent of the library preparation process. To support an assessment of gene models, samples A and B were also sequenced at independent sites by the Roche 454 platform, providing longer reads. For comparison to other technologies, these data were also compared to the MAQC-I Affymetrix U133 Plus2 microarray, several current microarray platforms, and also assessed by 20,801 PrimePCR reactions.
2014-08-08 | E-GEOD-56457 | biostudies-arrayexpress
Project description:Targeted assay to detect 22 selected ERCC spike-in mRNA molecules for method sensitivity testing purposes
2018-03-01 | GSE108759 | GEO
Project description:Comparison to RNA-Seq showed different strengths and weaknesses for different regimes of expression strength. At sufficient read-depth, both platforms seemed comparable for gene-level expression profiling. Comparison to RNA-Seq showed comparable accuracy and precision with microarrays for ERCC spike-ins at medium to high expression levels. At low expression levels, the array showed signal attenuation but better precision, while RNA-Seq maintained accuracy albeit with highly inflated variance. In summary, both platforms can be meaningfully applied in a similar range of expression strength. Assessment of (precision, accuracy, reproducibility, mutual information, titration order consistency, and known mixing ratio recovery) by measurement of known-ratio mixtures of two RNA reference samples. The array probes 776 complex genes representative of the AceView gene model annotation, as well as 92 ERCC spike-in controls.
2015-04-03 | E-GEOD-48016 | biostudies-arrayexpress
Project description:A large number of computational methods have been recently developed for analyzing differential gene expression (DE) in RNA-seq data. We report on a comprehensive evaluation of the commonly used DE methods using the SEQC benchmark data set and data from ENCODE project. We evaluated a number of key features including: normalization, accuracy of DE detection and DE analysis when one condition has no detectable expression. We found significant differences among the methods. Furthermore, computational methods designed for DE detection from expression array data perform comparably to methods customized for RNA-seq. Most importantly, our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth. The Sequencing Quality Control Consortium generated two datasets from two reference RNA samples in order to evaluate transcriptome profiling by next-generation sequencing technology. Each sample contains one of the reference RNA source and a set of synthetic RNAs from the External RNA Control Consortium (ERCC) at known concentrations. Group A contains 5 replicates of the Strategene Universal Human Reference RNA (UHRR), which is composed of total RNA from 10 human cell lines, with 2% by volume of ERCC mix 1. Group B includes 5 replicate samples of the Ambion Human Brain Reference RNA (HBRR) with 2% by volume of ERCC mix 2. The ERCC spike-in control is a mixture of 92 synthetic polyadenylated oligonucleotides of 250-2000 nucleotides long that are meant to resemble human transcripts.
2013-08-20 | E-GEOD-49712 | biostudies-arrayexpress
Project description:A bead supsension and a solution of ERCC spike-ins at a concentration of ~100,000 molecules per droplet was used in Drop-Seq, a novel technology for high-throughput single cell mRNAseq An estimated 84 beads were selected for amplification.
2015-05-20 | E-GEOD-66694 | biostudies-arrayexpress
Project description:The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and analysis issues. We demonstrate the consistency of results within a platform across test sites as well as the high level of cross-platform concordance in terms of genes identified as differentially expressed. The MAQC study provides a rich resource that will help build consensus on the use of microarrays in research, clinical and regulatory settings. Manuscripts related to the MAQC project have been published in Nature Biotechnology, 24(9), September, 2006. More information about the MAQC project can be found at http://edkb.fda.gov/MAQC/.<br><br>Expression data from two distinct reference RNA samples (A and B) in four titration pools were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Sample A = Stratagene Universal Human Reference RNA (UHRR, Catalog #740000), Sample B = Ambion Human Brain Reference RNA (HBRR, Catalog #6050), Sample C = Samples A and B mixed at 75%:25% ratio (A:B); and Sample D = Samples A and B mixed at 25%:75% ratio (A:B). In general, each microarray platform was tested at three sites and each sample was tested in five replicates at each test site. Samples (hybridizations) were named according to the following convention: Platform_Testsite_SampleRelicate. For example, AFX_2_B1 represents the hybridization (array) from platform AFX processed by test site 2 for the first replicate of sample B. Assignment of platform code: ABI = Applied Biosystems (microarray); AFX = Affymetrix; AG1 = Agilent one-color; AGL = Agilent two-color; GEH = GE Healthcare; ILM = Illumina; NCI = NCI two-color (Operon oligos); EPP = Eppendorf; TAQ = TaqMan (Applied Biosystems); QGN = QuantiGene (Panomics); GEX = StaRT-PCR (Gene Express); H25K = TeleChem two-color; H25K1 = TeleChem one-color; BIO = CapitalBio two-color (Operon oligos); BIO1 = CapitalBio one-color (Operon oligos); OPN = Operon two-color (Operon oligos); NMC = Norwegian Microarray Consortium two-color (Operon oligos).
2006-12-04 | E-TABM-132 | biostudies-arrayexpress
Project description:(i) RNA transcripts of Raji iBZLF1 cells (https://www.biorxiv.org/content/10.1101/573659v1) were compared between non-induced cells and cells induced with doxycycline (100 ng/ ml) for 6 h. Human (Hg19) as well as viral (EBV, Raji KF717093.1) transcripts were analyzed. Artificial ERCC Spike-ins (Thermo Scientific) were added to selected samples to control for global changes of RNA levels. (ii) For control purposes, the RNA transcripts in Raji cells equipped with a doxycycline-controlled truncated BZLF1 allele lacking its transactivation domain (Raji iBZLF1 AD-truncated) were prior to and after induction for 6 h. (iii) For another control, the RNA transcripts of parental, unmodified Raji cells were analyzed prior to and after doxycycline induction for 6 h. All experiments were performed as triplicates.
2019-01-01 | E-MTAB-7820 | biostudies-arrayexpress
Project description:We report the ERCC spike-in controlled RNA-seq in human hippocampus subject to normal aging and Alzheimer's disease.
2020-07-07 | GSE153873 | GEO
Project description:This study aims to assess the reliability of spike-in normalization for analyzing single-cell RNA sequencing data. This is done by performing mixture experiments where two different sets of spike-in RNA (ERCC and SIRV) are added separately to a single mouse cell (416B or trophoblast stem cells (TSCs)), followed by generation of sequencing libraries using a modified version of the Smart-seq2 protocol. The aim is to measure the variance of the log-ratio of the total counts between the two spike-in sets. This will quantify how precisely the spike-in RNA was added to each well. As a control, addition was also performed with a premixed solution of both spike-ins, to quantify the variability in the log-ratios due to the experimental protocol. The same data can also be used to measure the well-to-well variability in the differences in behaviour between the two spike-in sets. The data contain four batches of libraries (block), using different batches of cells that were processed and sequenced separately. Each batch contains libraries with all three types of spike-in addition (ERCC+SIRV, SIRV+ERCC or Premixed). The 416B cells also contain a CBFB-MYH11 oncogene, which is expressed in half of the cells (Induced) and silent in the other half (Control).
2017-05-31 | E-MTAB-5522 | biostudies-arrayexpress
Project description:Difference in RNA content of different cell types introduces bias to gene expression deconvolution methods. If ERCC spike-ins are introduced into samples, predicted proportions of deconvolution methods can be corrected
2019-04-03 | GSE129240 | GEO