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Differential expression in RNA-seq: A matter of depth
Open accessAbstract
Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach—NOISeq—that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.
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In the citing paper
“Previous studies demonstrated that inferences about DE are generally more accurate for highly expressed genes19,20.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [20]
Experimental — accuracy not yet measuredIn the citing paper
“Experimental results suggest that deep sequencing improves quantification and identification but might also result in the detection of transcriptional noise and off-target transcripts [10].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“Saturation curves can be used to assess the improvement in transcriptome coverage to be expected at a given sequencing depth [10].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“For example, rRNA and small RNAs should not be present in regular polyA longRNA preparations [10, 19].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“In such cases, simpler methods based on the Poisson distribution, such as DEGseq [62], or on empirical distributions (NOISeq [10]) can be an alternative”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“Increasing sequencing depth also can improve statistical power for lowly expressed genes [10, 194], and for any given sample there exists a level of sequencing at which power improvement is best achieved by increasing the number of replicates [195].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
In the citing paper
“NOISeq and NOISeqBIO (the adaptation of NOISeq for biological replication) are more efficient in avoiding false positive calls at the cost of some sensitivity but perform well with different numbers of replicates [10, 20, 212].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [10]
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Metadata
- Authors
- Tarazona, S., García-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A.
- Affiliations
- Centro de Investigacion Principe Felipe (ES)
- Universitat Politècnica de València (ES)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2011-09-08
- Volume
- 21
- Issue
- 12
- Pages
- 2213-2223
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3227109
- OpenAlex
- W2161106528
- Cited by
- 1,632
- References
- 42
- Citations by year
- 2026: 45 · 2025: 61 · 2024: 90 · 2023: 70 · 2022: 104 · 2021: 171 · 2020: 156 · 2019: 162 · 2018: 151 · 2017: 129 · 2016: 131 · 2015: 128 · 2014: 123 · 2013: 71 · 2012: 37
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Funders
- Generalitat Valenciana
- Ministerio de Ciencia e Innovación (BIO2008-04212)
- Instituto de Salud Carlos III (RD06/0020)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- 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
- Keywords
- RNA-seq, differential gene expression, sequencing depth, false discovery rate, nonparametric statistics, transcript detection, sequencing noise, replication, gene expression profiling
- MeSH terms
- Algorithms, Gene Expression Regulation, Humans, Models, Genetic, Expressed Sequence Tags, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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