RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Open accessAbstract
Ultra-high-throughput sequencing is emerging as an attractive alternative to microarrays for genotyping, analysis of methylation patterns, and identification of transcription factor binding sites. Here, we describe an application of the Illumina sequencing (formerly Solexa sequencing) platform to study mRNA expression levels. Our goals were to estimate technical variance associated with Illumina sequencing in this context and to compare its ability to identify differentially expressed genes with existing array technologies. To do so, we estimated gene expression differences between liver and kidney RNA samples using multiple sequencing replicates, and compared the sequencing data to results obtained from Affymetrix arrays using the same RNA samples. We find that the Illumina sequencing data are highly replicable, with relatively little technical variation, and thus, for many purposes, it may suffice to sequence each mRNA sample only once (i.e., using one lane). The information in a single lane of Illumina sequencing data appears comparable to that in a single array in enabling identification of differentially expressed genes, while allowing for additional analyses such as detection of low-expressed genes, alternative splice variants, and novel transcripts. Based on our observations, we propose an empirical protocol and a statistical framework for the analysis of gene expression using ultra-high-throughput sequencing technology.
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Transcript abundance is directly proportional to the number of sequencing reads that map to a specific transcript, resulting in lower false-negative and false-positive discovery rates and a greater linear range.
“Transcript abundance is directly proportional to the number of sequencing reads that map to a specific transcript, resulting in lower falsenegative and false-positive discovery rates in addition to a greater linear range (Bainbridge et al., 2006;Cloonan et al., 2008;Marioni et al., 2008;Mortazavi et al., 2008;Nagalakshmi et al., 2008;Sultan et al., 2008;Wilhelm et al., 2008;Trapnell et al., 2010;W”
From An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex (2014) · cites this paper as Marioni et al., 2008
The Illumina sequencing data processing section says read counts are expected to be proportional to transcript length times mRNA expression level, under idealized assumptions—not simply to abundance alone. The Abstract reports 30% more differentially expressed genes than the array analysis at the same false discovery rate, but the copy does not establish lower false-positive rates or a greater linear range. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe choice of RNA-seq pipeline had an effect, with some tools requiring up to twice as many aligned fragments.
“The choice of RNA-seq pipeline also had an effect, with some tools requiring up to twice as many aligned fragments ( cf. TopHat2+Cufflinks, 23 Supplementary Figure 35).”
From A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium (2014) · cites this paper as [23]
The Illumina sequencing data processing section describes alignment with ELAND, while the RNA-seq study design section compares numbers of sequencing lanes. Neither reports a comparison of RNA-seq pipelines or says that any tool required twice as many aligned fragments. Searches for TopHat2, Cufflinks, and pipeline-dependent fragment requirements found no supporting passage in our copy; page numbers were unavailable. (LLM, conf=0.76)
Experimental — accuracy not yet measuredHigh within-study reproducibility is likely to reflect reproducible biases associated with use of a single measurement technique in addition to reproducible features of the biological sample.
“These data hint, coupled with similar observations in other biological systems [44], that high within-study reproducibility is likely to reflect reproducible biases associated with use of a single measurement technique in addition to reproducible features of the biological sample.”
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- Authors
- Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y.
- Affiliations
- University of Chicago (US)
- Yale University (US)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2008-06-11
- Volume
- 18
- Issue
- 9
- Pages
- 1509-1517
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC2527709
- OpenAlex
- W2121211805
- Cited by
- 2,771
- References
- 26
- Citations by year
- 2026: 29 · 2025: 45 · 2024: 67 · 2023: 68 · 2022: 96 · 2021: 117 · 2020: 129 · 2019: 130 · 2018: 163 · 2017: 199 · 2016: 208 · 2015: 242 · 2014: 282 · 2013: 273 · 2012: 266
- Type
- posted content
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- Genome Research (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Alfred P. Sloan Foundation
- Yale University
- University of Chicago
- National Institutes of Health (HG002585)
- National Human Genome Research Institute (HG002585, R01HG002585)
- National Institute of General Medical Sciences (GM077959, R01GM077959)
- National Institute of Neurological Disorders and Stroke (U24NS051869)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- RNA-seq, technical reproducibility, gene expression arrays, Affymetrix microarrays, differentially expressed genes, Illumina sequencing, low-expressed genes, alternative splicing, novel transcripts
- MeSH terms
- Humans, Male, Models, Biological, RNA, Messenger, Reproducibility of Results, Likelihood Functions, Sequence Analysis, RNA, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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