This article is part of a special issue on exome sequencing.

Open Access Highly Accessed Research

Comprehensive comparison of three commercial human whole-exome capture platforms

Asan23*, Yu Xu1, Hui Jiang1, Chris Tyler-Smith4, Yali Xue4, Tao Jiang1, Jiawei Wang1, Mingzhi Wu1, Xiao Liu1, Geng Tian1, Jun Wang1, Jian Wang1, Huangming Yang1* and Xiuqing Zhang1*

Author Affiliations

1 Beijing Genomics Institute at Shenzhen, 11F, Bei Shan Industrial Zone, Yantian District, Shenzhen 518083, China

2 Beijing Institute of Genomics, Chinese Academy of Sciences, No.7 Beitucheng West Road, Chaoyang District, Beijing 100029, China

3 Graduate University of Chinese Academy Sciences, 19A Yuquanlu, Beijing 100049, China

4 The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK

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Genome Biology 2011, 12:R95  doi:10.1186/gb-2011-12-9-r95

Published: 28 September 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.