Transcriptome of Dp1Tyb and wild-type mouse embryonic fibroblasts [ERCC spike-ins]
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ABSTRACT:
PROVIDER: PRJNA508970 | ENA |
REPOSITORIES: ENA
Similar Datasets
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
2015-05-20 | GSE66694 | GEO
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:Purpose: to identify the effects of the Dp1Tyb mutation on the transcriptome of mouse embryonic fibroblasts
2019-03-29 | GSE123514 | 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:This SuperSeries is composed of the following subset Series: GSE20555: RNA-Seq on libraries made from serial dilutions of Drosophila melanogaster S2 cell mRNA and ERCC external RNA controls GSE20579: RNA-Seq on libraries made from ERCC external RNA controls, and a mixture of D. melanogaster S2 cell mRNA and ERCC mRNAs Refer to individual Series
2010-04-18 | E-GEOD-20580 | biostudies-arrayexpress
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.
2015-04-03 | GSE48016 | GEO
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: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:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being M-bM-^@M-^\recalibratedM-bM-^@M-^] (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units M-BM- at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration. Four human RNA samples with equimolar ERCC spike-in standards were sequenced on Illumina. Two human brain/liver/muscle RNA mixtures with dynamic range of ERCC spike-in standards were sequenced on SOLiD.
2012-03-03 | E-GEOD-36217 | biostudies-arrayexpress