Synthetic spike-in standards for RNA-seq experiments (original) (raw)

1. Felix Schlesinger2,3,5,7,
2. Carrie A. Davis2,
3. Yu Zhang1,6,
4. Renhua Li1,
5. Marc Salit4,
6. Thomas R. Gingeras2 and
7. Brian Oliver1
8. 1Section of Developmental Genomics, Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA;
9. 2Cold Spring Harbor Laboratory, Genome Center, Woodbury, New York 11797, USA;
10. 3Cold Spring Harbor Laboratory, Watson School of Biological Sciences, Cold Spring Harbor, New York 11724, USA;
11. 4Biochemical Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
12. ↵5 These authors contributed equally to this work.

• ↵6 Present address: National Institute of Allergies and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA.

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 220 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.

Footnotes

• ↵7 Corresponding author.
  E-mail schlesin{at}cshl.edu.

• [Supplemental material is available for this article.]

• Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.121095.111.

• Received January 25, 2011.

• Accepted June 28, 2011.

Freely available online through the Genome Research Open Access option.