Scientific ReportsGenomicsPeer-reviewed journal article
Systematic dissection of biases in whole-exome and whole-genome sequencing reveals major determinants of coding sequence coverage
Yury A. BarbitoffiD, Dmitrii E. PoleviD, Andrey Sergeyevich GlotoviD, Elena A. SerebryakovaiD, Irina V. Shcherbakova, Artem M. KiseleviD, Anna Aleksandrovna KostarevaiD, Oleg S. GlotoviD, Alexander V. PredeusiD
Abstract
Next generation DNA sequencing technologies are rapidly transforming the world of human genomics. Advantages and diagnostic effectiveness of the two most widely used resequencing approaches, whole exome (WES) and whole genome (WGS) sequencing, are still frequently debated. In our study we developed a set of statistical tools to systematically assess coverage of CDS regions provided by several modern WES platforms, as well as PCR-free WGS. Using several novel metrics to characterize exon coverage in WES and WGS, we showed that some of the WES platforms achieve substantially less biased CDS coverage than others, with lower within- and between-interval variation and virtually absent GC-content bias. We discovered that, contrary to a common view, most of the coverage bias in WES stems from mappability limitations of short reads, as well as exome probe design. We identified the ~ 500 kb region of human exome that could not be effectively characterized using short read technology. We also showed that the overall power for SNP and indel discovery in CDS region is virtually indistinguishable for WGS and best WES platforms. Our results indicate that deep WES (100x) using least biased technologies provides similar effective coverage (97% of 10x q10+ bases) and CDS variant discovery to the standard 30x WGS, suggesting that WES remains an efficient alternative to WGS in many applications. Our work could serve as a guide for selection of an up-to-date resequencing approach in human genomic studies.
- organism: human
- assay: whole-exome sequencing
- assay: whole-genome sequencing
- research focus: variant calling
- approach: statistical model
- Citations
- 143
- FWCI
- 12.00
- Field percentile
- top 1%
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