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GC-Content Normalization for RNA-Seq Data
Open accessAbstract
Background: Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof. Results: We focus on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. We propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs. Our methods are compared to state-of-the-art normalization procedures in terms of bias and mean squared error for expression fold-change estimation and in terms of Type I error and p-value distributions for tests of differential expression. The exploratory data analysis and normalization methods proposed in this article are implemented in the open-source Bioconductor R package EDASeq. Conclusions: Our within-lane normalization procedures, followed by between-lane normalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression. Such results are crucial for the biological interpretation of RNA-Seq experiments, where downstream analyses can be sensitive to the supplied lists of genes.
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![Figure 6 Yeast dataset: Proportion of DE genes vs. GC-content. Here, a gene is declared DE between the three growth conditions if its nominal unadjusted p-value from the negative binomial LRT is below the threshold of 10-5 (corresponding to a nominal Bonferroni family wise error rate of 0.057 and Benjamini & Hochberg [37] false discovery rate of 4.22 × 10-5). There is a clear trend towards more detected differential expression at higher GC-content with all within-lane normalization procedures but the full-quantile.](/paper-figures/133c4095c1b84462ae53508ba5e7f521/gc-content-normalization-for-rna-seq-data-figure-6.jpg)
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“commonly used methods fornormalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [15]
Experimental — accuracy not yet measuredIn the citing paper
“These effects can be minimized by appropriate experimental design [51] or, alternatively, removed by batch-correction methods such as COMBAT [52] or ARSyN [20, 53].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [20]
In the citing paper
“Filtering out genes that are expressed at low levels prior to differential expression analysis reduces the severity of the correction and may improve the power of detection [20].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [20]
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 [20]
In the citing paper
“SAMseq performs well in terms of FDR but presents an acceptable sensitivity when the number of replicates is relatively high, at least 10 [20, 55, 209].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [20]
In the citing paper
“Notably, neither applying GC bias correction tools (cqn14 and EDASeq15) to individual sites nor changing read counts to only use 3′-UTRs was effective at decreasing the number of inter-site false positive DEGs (Fig. 1b and Supplementary Fig. 3); in most cases, these methods actually increased the number of false positives.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [15]
In the citing paper
“The application of GC content bias correction packages including cqn14 and EDASeq15 could not remove these false positives, likely because GC content bias is not the only source that contributes to bias in gene expression data.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [15]
In the citing paper
“This confirms the impact of two already recognized RNA-seq latent variables, GC-content and insert size15,22, and it also identifies two more relevant contributions to technical variation, gene coverage variation and error rate.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [15]
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Metadata
- Authors
- Risso, D., Schwartz, K., Sherlock, G., & Dudoit, S.
- Affiliations
- University of Padua (IT)
- Stanford University (US)
- University of California, Berkeley (US)
- Corresponding author
- Dudoit, Sandrine
- Venue
- BMC Bioinformatics
- Published
- 2011-12
- Volume
- 12
- Issue
- 1
- Pages
- 480-480
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC3315510
- OpenAlex
- W2168342464
- Cited by
- 867
- References
- 35
- Citations by year
- 2026: 29 · 2025: 58 · 2024: 83 · 2023: 80 · 2022: 89 · 2021: 97 · 2020: 74 · 2019: 55 · 2018: 60 · 2017: 49 · 2016: 59 · 2015: 38 · 2014: 48 · 2013: 30 · 2012: 18
- FWCI
- 12.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Collection of Biostatistics Research Archive (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- Università degli Studi di Padova
- National Institutes of Health
- National Human Genome Research Institute (R01 HG03468, R01HG003468)
- National Institute of Allergy and Infectious Diseases (R01AI077737)
- 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, gene expression bias, transcriptome sequencing
- MeSH terms
- Base Composition, Saccharomyces cerevisiae, Sequence Analysis, RNA, Gene Expression Profiling, Transcriptome
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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