Comprehensive comparison of three commercial human whole-exome capture platforms - PubMed (original) (raw)

Comparative Study

doi: 10.1186/gb-2011-12-9-r95.

Yu Xu, Hui Jiang, Chris Tyler-Smith, Yali Xue, Tao Jiang, Jiawei Wang, Mingzhi Wu, Xiao Liu, Geng Tian, Jun Wang, Jian Wang, Huangming Yang, Xiuqing Zhang

Affiliations

Comparative Study

Comprehensive comparison of three commercial human whole-exome capture platforms

Asan et al. Genome Biol. 2011.

Abstract

Background: Exome sequencing, which allows the global analysis of protein coding sequences in the human genome, has become an effective and affordable approach to detecting causative genetic mutations in diseases. Currently, there are several commercial human exome capture platforms; however, the relative performances of these have not been characterized sufficiently to know which is best for a particular study.

Results: We comprehensively compared three platforms: NimbleGen's Sequence Capture Array and SeqCap EZ, and Agilent's SureSelect. We assessed their performance in a variety of ways, including number of genes covered and capture efficacy. Differences that may impact on the choice of platform were that Agilent SureSelect covered approximately 1,100 more genes, while NimbleGen provided better flanking sequence capture. Although all three platforms achieved similar capture specificity of targeted regions, the NimbleGen platforms showed better uniformity of coverage and greater genotype sensitivity at 30- to 100-fold sequencing depth. All three platforms showed similar power in exome SNP calling, including medically relevant SNPs. Compared with genotyping and whole-genome sequencing data, the three platforms achieved a similar accuracy of genotype assignment and SNP detection. Importantly, all three platforms showed similar levels of reproducibility, GC bias and reference allele bias.

Conclusions: We demonstrate key differences between the three platforms, particularly advantages of solutions over array capture and the importance of a large gene target set.

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Figures

Figure 1

Figure 1

Normalized per-base sequencing-depth distribution on targets. For the purpose of comparison among the three platforms, we selected a set of reads with an average coverage of approximately 30-fold from each replicate. The depth and the frequency (the fraction of a certain depth-level bases for certain sequencing depth-coverage in the total sequencing data) were normalized by the average coverage depth of each replicate on targets. NA-r1 and NA-r2, NS-r1 and NS-r2, and AS-r1 and AS-r2 represent each of two replicates for NimbleGen Sequence Capture Arrays, NimbleGen SeqCap EZ and Agilent SureSelect, respectively.

Figure 2

Figure 2

Genotype sensitivity. (a) Genotype sensitivity of six replicates at 30× sequencing depth. (b) Genotype sensitivity as a function of sequencing depth. For the analyses, subsets of reads from two combined replicate datasets for each platform were randomly extracted at different average depths. NA, NS and AS represent NimbleGen Sequence Capture Arrays, NimbleGen SeqCap EZ and Agilent SureSelect, respectively, while r1 and r2 are two replicate experiments for each platform.

Figure 3

Figure 3

Correlation of sequencing depth and coverage rate on consensus targeted CCDSs. The graph shows pair-wise Pearson correlation coefficients for both sequencing depth (top-left triangle) and coverage rate (bottom-right triangle) based on the 182,259 CCDSs targeted by both Agilent and NimbleGen. NA, NS and AS represent NimbleGen Sequence Capture Arrays, NimbleGen SeqCap EZ and Agilent SureSelect, respectively, while r1 and r2 are two replicate experiments for each platform.

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