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
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.
Project description:RNA-Seq on libraries made from External RNA Controls Consortium (ERCC) external RNA controls, and a mixture of mRNA from Drosophila melanogaster S2 cell and ERCC mRNAs. We evaluated performance of RNA-Seq on known synthetic PolyA+ mRNAs from the External RNA Controls Consortium (ERCC) alone and in mixtures with PolyA+ mRNA from Drosophila S2 cells. ERCC mRNAs were obtained under Phase V testing from the National Institutes of Standards and Technology (NIST). The ERCC pool contained 96 species of mRNA of various lengths and GC content covering a 2^20 concentration range. Libraries were constructed using 100ng S2 mRNA with 5ng, 2.5ng, or 1ng ERCC mRNAs, and using 50ng ERCC mRNA without S2 cell mRNA. Our data shows an outstanding linear fit between RNA-Seq read density and known input amounts.
Project description:RNA-Seq on libraries made from External RNA Controls Consortium (ERCC) external RNA controls, and a mixture of mRNA from Drosophila melanogaster S2 cell and ERCC mRNAs. We evaluated performance of RNA-Seq on known synthetic PolyA+ mRNAs from the External RNA Controls Consortium (ERCC) alone and in mixtures with PolyA+ mRNA from Drosophila S2 cells. ERCC mRNAs were obtained under Phase V testing from the National Institutes of Standards and Technology (NIST). The ERCC pool contained 96 species of mRNA of various lengths and GC content covering a 2^20 concentration range. Libraries were constructed using 100ng S2 mRNA with 5ng, 2.5ng, or 1ng ERCC mRNAs, and using 50ng ERCC mRNA without S2 cell mRNA. Our data shows an outstanding linear fit between RNA-Seq read density and known input amounts. We made libraries with 100ng S2 mRNA with 5ng, 2.5ng or 1ng ERCC mRNAs and with 50ng ERCC mRNAs only. For each library, one lane was sequenced for a 36bp run and around 15 million reads were obtained for each lane.
Project description:RNA-Seq on libraries made from serial dilutions of mRNA from Drosophila melanogaster S2 cell and the External RNA Controls Consortium (ERCC) external RNA controls. We evaluated performance of RNA-Seq by serially diluting a complex pool of known synthetic PolyA+ mRNAs from the External RNA Controls Consortium (ERCC) and PolyA+ mRNA from Drosophila S2 cells. ERCC mRNAs were obtained under Phase V testing from the National Institutes of Standards and Technology (NIST). The ERCC pool contained 96 species of mRNA of various lengths and GC content covering a 2^20 concentration range. Libraries were constructed with 100ng to 10pg of input mRNA. Our data shows an outstanding linear fit between RNA-Seq read density and known input amounts.
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.
Project description:We explored the ability of External RNA Control Consortium (ERCC) standards to detect technical biases between batches of sequencing experiments and to set detection thresholds. We compared three normalization methods (FPKM, DESeq2 and RUV) to identify the optimal analysis approach.
