Article

The External RNA Controls Consortium: a progress report

Illumina, Inc., 9885 Towne Centre Drive, San Diego, California 92121, USA.
Nature Methods (Impact Factor: 25.95). 11/2005; 2(10):731-4. DOI: 10.1038/nmeth1005-731
Source: PubMed

ABSTRACT Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

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Available from: Elizabeth Ann Wagar, Apr 11, 2014
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    • "Validation and standardization of qPCR measurements [16] [30] and RNA processing [31] is receiving increasing attention from regulatory bodies such as the US Food and Drug Administration (FDA) [32], the Environment Protection Agency (EPA) [33], and standardization organizations such as the Clinical and Laboratory Standards Institute (CLSI, earlier called NCCLS) [34] "
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    ABSTRACT: We have examined the imprecision in the estimation of PCR efficiency by means of standard curves based on strategic experimental design with large number of technical replicates. In particular, how robust this estimation is in terms of a commonly varying factors: the instrument used, the number of technical replicates performed and the effect of the volume transferred throughout the dilution series. We used six different qPCR instruments, we performed 1–16 qPCR replicates per concentration and we tested 2–10 μl volume of analyte transferred, respectively. We find that the estimated PCR efficiency varies significantly across different instruments. Using a Monte Carlo approach, we find the uncertainty in the PCR efficiency estimation may be as large as 42.5% (95% CI) if standard curve with only one qPCR replicate is used in 16 different plates. Based on our investigation we propose recommendations for the precise estimation of PCR efficiency: (1) one robust standard curve with at least 3–4 qPCR replicates at each concentration shall be generated, (2) the efficiency is instrument dependent, but reproducibly stable on one platform, and (3) using a larger volume when constructing serial dilution series reduces sampling error and enables calibration across a wider dynamic range.
    03/2015; 10. DOI:10.1016/j.bdq.2015.01.005
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    • "Validation and standardization of qPCR measurements [16] [30] and RNA processing [31] is receiving increasing attention from regulatory bodies such as the US Food and Drug Administration (FDA) [32], the Environment Protection Agency (EPA) [33], and standardization organizations such as the Clinical and Laboratory Standards Institute (CLSI, earlier called NCCLS) [34] "
    01/2015;
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    • "applications (Baker et al. 2005; ERCC 2005; Devonshire et al. 2010). Here we present Illumina GAII–generated RNA-seq data from several modENCODE and ENCODE experiments that contain the Phase IV test set of ERCC RNA standards. "
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    ABSTRACT: High-throughput sequencing of cDNA (RNA-seq) is a widely deployed transcriptome profiling and annotation technique, but questions about the performance of different protocols and platforms remain. We used a newly developed pool of 96 synthetic RNAs with various lengths, and GC content covering a 2(20) concentration range as spike-in controls to measure sensitivity, accuracy, and biases in RNA-seq experiments as well as to derive standard curves for quantifying the abundance of transcripts. We observed linearity between read density and RNA input over the entire detection range and excellent agreement between replicates, but we observed significantly larger imprecision than expected under pure Poisson sampling errors. We use the control RNAs to directly measure reproducible protocol-dependent biases due to GC content and transcript length as well as stereotypic heterogeneity in coverage across transcripts correlated with position relative to RNA termini and priming sequence bias. These effects lead to biased quantification for short transcripts and individual exons, which is a serious problem for measurements of isoform abundances, but that can partially be corrected using appropriate models of bias. By using the control RNAs, we derive limits for the discovery and detection of rare transcripts in RNA-seq experiments. By using data collected as part of the model organism and human Encyclopedia of DNA Elements projects (ENCODE and modENCODE), we demonstrate that external RNA controls are a useful resource for evaluating sensitivity and accuracy of RNA-seq experiments for transcriptome discovery and quantification. These quality metrics facilitate comparable analysis across different samples, protocols, and platforms.
    Genome Research 08/2011; 21(9):1543-51. DOI:10.1101/gr.121095.111 · 13.85 Impact Factor
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