Summarizing and correcting the GC content bias in high-throughput sequencing.
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.
Department of Statistics, University of California, Berkeley, CA 94720, USA. yuvalb@stat.berkeley.edu
Yuval
Y
Benjamini
Terence P
TP
Speed
D001482:Base Composition; D004247:DNA / Q000737:chemistry; D015894:Genome, Human; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D016012:Poisson Distribution; D017422:Sequence Analysis, DNA / Q000379:methods*
D004247:DNA