The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Synthetic spike-in standards for RNA-seq experiments
Open accessAbstract
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.
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“The ERCC spike-in control (Jiang et al., 2011) was used to adjust for sequencing batch effect.”
From Cholesterol and matrisome pathways dysregulated in astrocytes and microglia (2022) · cites this paper as Jiang et al., 2011
In the citing paper
“Including exogenous reference transcripts (‘spike-ins’) is useful both for quality control [1, 197] and for library-size normalization [198].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [197]
In the citing paper
“Across a set of cells, this manifests as bimodality of gene expression (with one mode at zero). While in some cases such bimodality is due to stochastic (‘bursty’) gene expression40 or more stable variation between cell types and states3, in many others it is dominated by false negatives70,85.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [70]
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Cites
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 12,082
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181
Global analysis of protein expression in yeast
Ghaemmaghami, S., Huh, W.-K., Bower, K., Howson, R. W., Belle, A., Dephoure, N., O'Shea, E. K., & Weissman, J. S. (2003) · Nature · doi:10.1038/nature02046 · Cited by 3,750
Nonparametrics: Statistical Methods Based on Ranks
Mergenthaler, M. J., & Lehmann, E. L. (1979) · Technometrics · doi:10.2307/1268531 · Cited by 3,353
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771
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A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794
Normalization of RNA-seq data using factor analysis of control genes or samples
Risso, D., Ngai, J., Speed, T. P., & Dudoit, S. (2014) · Nature Biotechnology · doi:10.1038/nbt.2931 · Cited by 2,052
Single-cell mRNA quantification and differential analysis with Census
Qiu, X., Hill, A. J., Packer, J. S., Lin, D., Ma, Y.-A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4150 · Cited by 1,904
Comparative Analysis of Single-Cell RNA Sequencing Methods
Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494
The Technology and Biology of Single-Cell RNA Sequencing
Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.005 · Cited by 1,487
Metadata
- Authors
- Jiang, L., Schlesinger, F., Davis, C. A., Zhang, Y., Li, R., Salit, M., Gingeras, T. R., & Oliver, B.
- Affiliations
- National Institutes of Health (US)
- Cold Spring Harbor Laboratory (US)
- National Institute of Standards and Technology (US)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2011-08-04
- Volume
- 21
- Issue
- 9
- Pages
- 1543-1551
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3166838
- OpenAlex
- W2009458183
- Cited by
- 653
- References
- 44
- Citations by year
- 2026: 18 · 2025: 35 · 2024: 50 · 2023: 36 · 2022: 53 · 2021: 45 · 2020: 58 · 2019: 54 · 2018: 51 · 2017: 67 · 2016: 64 · 2015: 38 · 2014: 40 · 2013: 25 · 2012: 15
- Type
- posted content
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Genome Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institutes of Health
- National Institute of Standards and Technology
- National Human Genome Research Institute (U54HG004557)
- National Institute of Diabetes and Digestive and Kidney Diseases (ZIADK015600)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- RNA-seq, transcriptome quantification, GC content bias, calibration curve, isoform abundance, ENCODE, modENCODE
- MeSH terms
- Animals, Humans, Quality Control, RNA, Sensitivity and Specificity, Reproducibility of Results, Gene Library, Bias, Sequence Analysis, RNA, Gene Expression Profiling, High-Throughput Nucleotide Sequencing
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