Abstract
Whole Exome Sequencing (WES) is a powerful clinical diagnostic tool for discovering the genetic basis of many diseases. A major shortcoming of WES is uneven coverage of sequence reads over the exome targets contributing to many low coverage regions, which hinders accurate variant calling. In this study, we devised two novel metrics, Cohort Coverage Sparseness (CCS) and Unevenness (UE) Scores for a detailed assessment of the distribution of coverage of sequence reads. Employing these metrics we revealed non-uniformity of coverage and low coverage regions in the WES data generated by three different platforms. This non-uniformity of coverage is both local (coverage of a given exon across different platforms) and global (coverage of all exons across the genome in the given platform). The low coverage regions encompassing functionally important genes were often associated with high GC content, repeat elements and segmental duplications. While a majority of the problems associated with WES are due to the limitations of the capture methods, further refinements in WES technologies have the potential to enhance its clinical applications.
Highlights
Whole Exome Sequencing (WES) is a high throughput genomic technology that sequences coding regions of the genome selectively captured by target enrichment strategies[1,2,3]
We evaluated the sequence content and characteristics of the genomic regions contributing to systematic biases in exome sequencing using WES data from a total of 169 individuals obtained from three different platforms
To characterize the distribution of sequence reads along the exome, we developed two metrics, Cohort Coverage Sparseness (CCS) and Unevenness (UE) scores
Summary
Whole Exome Sequencing (WES) is a high throughput genomic technology that sequences coding regions of the genome selectively captured by target enrichment strategies[1,2,3]. While the basic sample preparation protocols are similar among these platforms, major differences lie in the design of the oligonucleotide probes, including selection of target genomic regions, sequence features and lengths of probes, and the exome capture mechanisms[21,22,23,24]. Studies examining the overall quality of WES data have focused on comparing the performance of a single DNA sample or a small number (n ≤ 6) of samples in different capture technologies[22, 23, 27] While these studies have focused on the GC content and overall coverage differences between different platforms, the intra-platform variation in sequence coverage, characteristics of the low-coverage regions, and variation of coverage across the exome have not been quantitatively evaluated. Our study provides quantitative metrics for systematic analysis of different parameters that could potentially impact WES analysis, and confirms the association between low coverage regions and occurrence of duplicated sequences and high GC content
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