Dataset Information

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.

SUBMITTER: Chilamakuri CS

PROVIDER: S-EPMC4092227 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Performance comparison of four exome capture systems for deep sequencing.

Chilamakuri Chandra Sekhar Reddy CS Lorenz Susanne S Madoui Mohammed-Amin MA Vodák Daniel D Sun Jinchang J Hovig Eivind E Myklebost Ola O Meza-Zepeda Leonardo A LA

BMC genomics 20140609

<h4>Background</h4>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 th ...[more]

PMID: 24912484

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Project description:BackgroundAdvances in single-cell sequencing provide unprecedented opportunities for clinical examination of circulating tumor cells, cancer stem cells, and other rare cells responsible for disease progression and drug resistance. On the genomic level, single-cell whole exome sequencing (scWES) started to gain popularity with its unique potentials in characterizing mutational landscapes at a single-cell level. Currently, there is little known about the performance of different exome capture kits in scWES. Nextera rapid capture (NXT; Illumina, Inc.) has been the only exome capture kit recommended for scWES by Fluidigm C1, a widely accessed system in single-cell preparation.ResultsIn this study, we compared the performance of NXT following Fluidigm's protocol with Agilent SureSelectXT Target Enrichment System (AGL), another exome capture kit widely used for bulk sequencing. We created DNA libraries of 192 single cells isolated from spheres grown from a melanoma specimen using Fluidigm C1. Twelve high-yield cells were selected to perform dual-exome capture and sequencing using AGL and NXT in parallel. After mapping and coverage analysis, AGL outperformed NXT in coverage uniformity, mapping rates of reads, exome capture rates, and low PCR duplicate rates. For germline variant calling, AGL achieved better performance in overlap with known variants in dbSNP and transition-transversion ratios. Using calls from high coverage bulk sequencing from blood DNA as the golden standard, AGL-based scWES demonstrated high positive predictive values, and medium to high sensitivity. Lastly, we evaluated somatic mutation calling by comparing single-cell data with the matched blood sequence as control. On average, 300 mutations were identified in each cell. In 10 of 12 cells, higher numbers of mutations were identified using AGL than NXT, probably caused by coverage depth. When mutations are adequately covered in both AGL and NXT data, the two methods showed very high concordance (93-100% per cell).ConclusionsOur results suggest that AGL can also be used for scWES when there is sufficient DNA, and it yields better data quality than the current Fluidigm's protocol using NXT.

Project description:BackgroundNext-generation sequencing technology is developing rapidly and target capture sequencing has become an important technique. Several different platforms for library preparation and target capture with different bait types respectively are commercially available. Here we compare the performance of the four platforms with different bait types to find out their advantages and limitations. The purpose of this study is to help investigators and clinicians select the appropriate platform for their particular application and lay the foundation for the development of a better target capture platform for next-generation sequencing.ResultsWe formulate capture efficiency as a novel parameter that can be used to better evaluations of specificity and coverage depth among the different capture platforms. Target coverage, capture efficiency, GC bias, AT Dropout, sensitivity in single nucleotide polymorphisms, small insertions and deletions detection, and the feature of each platform were compared for low input samples. In general, all platforms perform well and small differences among them are revealed. In our results, RNA baits have stronger binding power than DNA baits, and with ultra deep sequencing, double stranded RNA baits perform better than single stranded RNA baits in all aspects. DNA baits got better performance in the region with high GC content and RNA baits got lower AT dropout suggesting that the binding power is different between DNA and RNA baits to genome regions with different characteristics.ConclusionsThe platforms with double stranded RNA baits have the most balanced capture performance. Our results show the key differences in performance among the four updated platforms with four different bait types. The better performance of double stranded RNA bait with ultra deep sequencing suggests that it may improve the sensitivity of ultra low frequent mutation detection. In addition, we further propose that the mixed baits of double stranded RNA and single stranded DNA may improve target capture performance.

Project description:BACKGROUND:Next Generation Sequencing (NGS) has become a valuable tool for molecular landscape characterization of cancer genomes, leading to a better understanding of tumor onset and progression, and opening new avenues in translational oncology. Formalin-fixed paraffin-embedded (FFPE) tissue is the method of choice for storage of clinical samples, however low quality of FFPE genomic DNA (gDNA) can limit its use for downstream applications. METHODS:To investigate the FFPE specimen suitability for NGS analysis and to establish the performance of two solution-based exome capture technologies, we compared the whole-exome sequencing (WES) data of gDNA extracted from 5 fresh frozen (FF) and 5 matched FFPE lung adenocarcinoma tissues using: SeqCap EZ Human Exome v.3.0 (Roche NimbleGen) and SureSelect XT Human All Exon v.5 (Agilent Technologies). RESULTS:Sequencing metrics on Illumina HiSeq were optimal for both exome systems and comparable among FFPE and FF samples, with a slight increase of PCR duplicates in FFPE, mainly in Roche NimbleGen libraries. Comparison of single nucleotide variants (SNVs) between FFPE-FF pairs reached overlapping values >90 % in both systems. Both WES showed high concordance with target re-sequencing data by Ion PGM™ in 22 lung-cancer genes, regardless the source of samples. Exon coverage of 623 cancer-related genes revealed high coverage efficiency of both kits, proposing WES as a valid alternative to target re-sequencing. CONCLUSIONS:High-quality and reliable data can be successfully obtained from WES of FFPE samples starting from a relatively low amount of input gDNA, suggesting the inclusion of NGS-based tests into clinical contest. In conclusion, our analysis suggests that the WES approach could be extended to a translational research context as well as to the clinic (e.g. to study rare malignancies), where the simultaneous analysis of the whole coding region of the genome may help in the detection of cancer-linked variants.

Project description:BackgroundTo promote the clinical application of next-generation sequencing, it is important to obtain accurate and consistent variants of target genomic regions at low cost. Ion Proton, the latest updated semiconductor-based sequencing instrument from Life Technologies, is designed to provide investigators with an inexpensive platform for human whole exome sequencing that achieves a rapid turnaround time. However, few studies have comprehensively compared and evaluated the accuracy of variant calling between Ion Proton and Illumina sequencing platforms such as HiSeq 2000, which is the most popular sequencing platform for the human genome. The Ion Proton sequencer combined with the Ion TargetSeq Exome Enrichment Kit together make up TargetSeq-Proton, whereas SureSelect-Hiseq is based on the Agilent SureSelect Human All Exon v4 Kit and the HiSeq 2000 sequencer.ResultsHere, we sequenced exonic DNA from four human blood samples using both TargetSeq-Proton and SureSelect-HiSeq. We then called variants in the exonic regions that overlapped between the two exome capture kits (33.6 Mb). The rates of shared variant loci called by two sequencing platforms were from 68.0 to 75.3% in four samples, whereas the concordance of co-detected variant loci reached 99%. Sanger sequencing validation revealed that the validated rate of concordant single nucleotide polymorphisms (SNPs) (91.5%) was higher than the SNPs specific to TargetSeq-Proton (60.0%) or specific to SureSelect-HiSeq (88.3%). With regard to 1-bp small insertions and deletions (InDels), the Sanger sequencing validated rates of concordant variants (100.0%) and SureSelect-HiSeq-specific (89.6%) were higher than those of TargetSeq-Proton-specific (15.8%).ConclusionsIn the sequencing of exonic regions, a combination of using of two sequencing strategies (SureSelect-HiSeq and TargetSeq-Proton) increased the variant calling specificity for concordant variant loci and the sensitivity for variant loci called by any one platform. However, for the sequencing of platform-specific variants, the accuracy of variant calling by HiSeq 2000 was higher than that of Ion Proton, specifically for the InDel detection. Moreover, the variant calling software also influences the detection of SNPs and, specifically, InDels in Ion Proton exome sequencing.

Dataset Information

Performance comparison of four exome capture systems for deep sequencing.

Background

Results

Conclusions

Publications

Performance comparison of four exome capture systems for deep sequencing.

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