Summarizing and correcting the GC content bias in high-throughput sequencing

By Yuval Benjamini, Terence P. Speed. Nucleic Acids Research, 2012

914 citations recorded by the source corpus.

GC content bias describes the dependence between fragment count (read coverage) and GC content found in Illumina sequencing data. This bias can dominate the signal of interest for analyses that focus on measuring fragment abundance within a genome, such as copy number estimation (DNA-seq). The bias is not consistent between samples; and there is no consensus as to the best methods to remove it in a single sample. We analyze regularities in the GC bias patterns, and find a compact description for this unimodal curve family. It is the GC content of the full DNA fragment, not only the sequenced read, that most influences fragment count. This GC effect is unimodal: both GC-rich fragments and AT-rich fragments are underrepresented in the sequencing results. This empirical evidence strengthens the hypothesis that PCR is the most important cause of the GC bias. We propose a model that produces predictions at the base pair level, allowing strand-specific GC-effect correction regardless of the downstream smoothing or binning. These GC modeling considerations can inform other high-throughput sequencing analyses such as ChIP-seq and RNA-seq.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/08e848d1a2fba5b5a19cc25a3fb2a61b88dbb2ff

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Citations in the corpus

CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing (2016, background): “DNA regions with extreme GC content are less accessible to hybridization and amenable to amplification during library preparation [26, 27].”
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).”
ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions (2019, background): “This, in part, is due to the elevated error rates of such regions in sequencing data (Benjamini and Speed 2012; Dolzhenko et al. 2017).”
Measurement error in Lasso: impact and likelihood bias correction (2012, methodology): “, Benjamini and Speed (2012) and the references cited therein).”
Special features of RAD Sequencing data: implications for genotyping (2012, methodology): “Adapter-ligated restriction fragments are sheared to a size suitable for Illumina sequencing (typically 300 – 700 bp), and sheared fragments containing restriction site overhangs are ampliﬁed using polymerase chain reaction (PCR) and sequenced, typically using Illumina sequencing-by-synthesis.”
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions (2019, background): “…two-way interaction terms are needed to detect possible epistasis between genes (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).”
Comparative performance of the BGISEQ-500 vs Illumina HiSeq2500 sequencing platforms for palaeogenomic sequencing (2017, background): “The relationship between read abundance in a given genomic region and its GC content, is well known and characterized for the Illumina platform[98].”
Oligotyping: Differentiating between closely related microbial taxa using 16S rRNA gene data (2013, background): “…even the most effective quality filtering (Qu, Hashimoto & Morishita 2009; Schroder et al. 2009; Bravo & Irizarry 2010; Leek et al. 2010; Meacham et al. 2011; Minoche, Dohm & Himmelbauer 2011; Quince et al. 2011; Benjamini & Speed 2012; Victoria et al. 2012) will not produce error-free data sets.”