GC-Content Normalization for RNA-Seq Data

By Davide Risso, Katja Schwartz, Gavin Sherlock, Sandrine Dudoit. BMC Bioinformatics, 2011

801 citations recorded by the source corpus.

BackgroundTranscriptome 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.ResultsWe 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.ConclusionsOur 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2174477a13f1d0b4ccffc11da1bfbdc3ed2eea0f

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Citations in the corpus

Selecting between‐sample RNA‐Seq normalization methods from the perspective of their assumptions (2016, methodology): “For applications requiring both between-sample and within-sample normalization, performing both types of normalization may be necessary; for example, Risso et al. recommend using within-sample GC-content normalization in combination with between-sample normalization [6].”
RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics (2012, methodology): “Since it has been shown that the GC content can have a substantial impact on the read abundances in a RNA-Seq data set (21–23) we incorporated the GC content bias correction methods implemented in the EDASeq package (24).”
A survey of best practices for RNA-seq data analysis (2016, methodology): “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].”
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions (2019, background): “enes (Aschard, 2016; Slim et al., 2018) and to appropriately account for the site- and sample-speciﬁc effects of GC content on genomic and other types of sequencing data (Benjamini & Speed, 2012; Risso et al., 2011). In economics and clinical trials, pairwise interactions between covariates and ”
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, positive): “, using cqn [12] or EDASeq [13]), which may differ from gene to gene.”
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package (2015, methodology): “Technology biases, such as the transcript length (12), GC content (13), PCR artifacts, uneven transcript read coverage, contamination by off-target transcripts, or large differences in transcript distributions (14), are factors that interfere in the linear relationship between transcript abundance a”
TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data (2015, methodology): “TCGAanalyze Normalization allows users to normalize mRNA transcripts and miRNA using the EDASeq package (28).”
variancePartition: interpreting drivers of variation in complex gene expression studies (2016, positive): “3c), and recapitulates the known role of GC content with reproducibility of RNA-seq data [8, 41–43].”
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024, positive): “Observation-specific library sizes can be used to implement non-linear normalization in edgeR [ 11 , 12 , 27–29 ] or to allow for transcript length effects [ 30 ]. edgeR v4 also allows observation-specific dispersions by where the w gi are known GLM weights.”
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor (2013, methodology): “In general, we recommend starting from a sample metadata table that contains”
Normalization of RNA-seq data using factor analysis of control genes or samples (2014, background): “…last few years, several normalization strategies have been proposed to correct for between-sample distributional differences in read counts, such as differences in total counts (i.e., sequencing depths) 1,4 , and within-sample gene-specific effects, such as gene length or GC-content effects 2,5 .”
Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series (2014, methodology): “…to gene expression quantitation and, therefore, dedicated normalization methods exist for RNA-seq to correct for sequencing depth, transcript length (Mortazavi et al., 2008), GC content (Risso et al., 2011) and non-uniform transcript distributions (Bullard et al., 2010; Robinson and Oshlack, 2010).”
Computational analysis of bacterial RNA-Seq data (2013, background): “We did not explore more complex relationships, beyond correlation, between GC-content and expression, though it may well be the case that GC content biases are unimodal, with both high GC content and low GC content transcripts being under-represented by sequencing reads (44).”
A general and flexible method for signal extraction from single-cell RNA-seq data (2017, positive): “Negative binomial fit For the V1 dataset, after full-quantile normalization (function betweenLaneNormalization from the R package EDASeq [38]), we used the R package edgeR [39] (Version 3.”