Performance comparison of four exome capture systems for deep sequencing

Abstract

Background: Recent developments in deep (next-generation) sequencing technologies are significantly impacting medical research. The global analysis of protein coding regions in genomes of interest by whole exome sequencing is a widely used application. Many technologies for exome capture are commercially available; here we compare the performance of four of them: NimbleGen's SeqCap EZ v3.0, Agilent's SureSelect v4.0, Illumina's TruSeq Exome, and Illumina's Nextera Exome, all applied to the same human tumor DNA sample.

Results: Each capture technology was evaluated for its coverage of different exome databases, target coverage efficiency, GC bias, sensitivity in single nucleotide variant detection, sensitivity in small indel detection, and technical reproducibility. In general, all technologies performed well; however, our data demonstrated small, but consistent differences between the four capture technologies. Illumina technologies cover more bases in coding and untranslated regions. Furthermore, whereas most of the technologies provide reduced coverage in regions with low or high GC content, the Nextera technology tends to bias towards target regions with high GC content.

Conclusions: We show key differences in performance between the four technologies. Our data should help researchers who are planning exome sequencing to select appropriate exome capture technology for their particular application.

Figure 1

Venn diagram showing the overlap between different features. A) Overlap among Agilent, NimbleGen and Illumina capture targets. B) Overlap among RefSeq, CCDS, and ENSEMBL protein coding exon databases. Coverage of exome capture technology for C) CCDS coding exons, D) RefSeq coding exons, E) ENSEMBL coding exons, and F) RefSeq UTRs.

Figure 2

Read statistics. A) Bar plot showing percent of initial reads, mapped reads and reads left after filtering for four different technologies; each bar shows the number of reads in millions. B) Stacked bar plot showing subgroups of filtered reads.

Figure 3

Coverage efficiency comparison by technology. Coverage efficiency defined as the percent of the total targeted bases covered at particular depths. A) Coverage efficiency for intended targeted bases for each technology. B) Coverage efficiency for bases, which are shared, by all four technologies (26.2 MB). Smooth line indicates replicate 1, and dotted line indicates replicate 2.

Figure 4

Coverage efficiency as a function of number of reads. The percent of targeted bases covered at A) ≥10x, B) ≥20x, C) ≥30x, D) ≥40x, E) ≥50x, and F) ≥100x depths.

Figure 5

Density plots showing GC content against normalized mean read depth for A) Agilent, B) NimbleGen, C) TruSeq, and D) Nextera technologies.

Figure 6

SNV detection by technology as a function of increasing read counts on A) intended target region, B) regions common among technologies, C) CCDS exons, D) RefSeq exons, E) Ensembl exons, and F) UTRs. Solid-lines indicate technology specific SNVs, dashed-lines indicate total number of SNVs, and solid pink lines indicate the SNVs common between the four technologies.

Figure 7

Indels detection by technology as a function of increasing read counts on A) intended target region, B) regions common among the technologies, C) CCDS exons, D) RefSeq exons, E) Ensembl exons, and F) UTRs. Solid-lines indicate technology specific SNVs, dashed-lines indicate total number of SNVs, and solid pink lines indicate the SNVs common between four technologies.

Figure 8

Overview of the computational pipeline.