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Target enrichment with hybridization capture

Hybridization capture, targets regions of the genome specific to researchers’ objectives using probes to hybridize directly to regions of interest. This method results in a greater number of reads in concentrated areas, providing more data for variant identification.

xGen™ NGS—made to work.

Overview

  • Expanded target region of interest
  • Targets higher amount of total gene content
  • Rare variant detection increased
  • High yield and library complexity even with degraded samples

Hybridization capture, often called target enrichment, lets researchers reliably target large numbers of genes—especially regions of the genome specific to their research objectives—by using probes to hybridize directly to those regions of interest.

This method allows for the discovery of novel variants compared to other methods such as PCR or Sanger sequencing. Because hybrid capture targets a higher amount of total gene content, the subsequent profiling of the variant types is comprehensive and provides the basis for thorough characterization of the variants found.

When would you choose hybridization capture over other methods?

Hybridization capture works well for genotyping and rare variant identification. Since capture with hybridization probes does not require PCR primer design, it is less likely to miss mutations and performs better with respect to sequence complexity. Hybridization capture’s capacity for mutation discovery makes it particularly suited to cancer research. Both its sequence complexity and scalability make it an excellent choice for exome sequencing. Depending on your sample type or experimental goals, you can use UMIs (unique molecular identifiers), sometimes called ‘molecular barcodes’, these complex indexes can be added to sequencing libraries before the PCR amplification steps to enable the accurate bioinformatic identification of PCR duplicates. UMI sequence information along with alignment coordinates allow grouping of sequence data into read families that represent individual sample DNA or RNA fragments.

For a deeper look into how the method is performed, see our introduction to hybridization capture.

Method data

xGen cfDNA & FFPE DNA Library Prep Kit paired with UMIs helps to enable complexity in discovery

We extracted DNA from biobank sourced material which included matched FFPE-tumor, adjacent fresh-frozen normal, and plasma samples from three donors (Figure 1). The AnaPrep FFPE DNA Extraction Kit (BioChain) and the cfPure® V2 Cell-Free DNA Extraction Kit (BioChain) were used to perform the extraction. Depending on the sample, one of several methods were used to assess its quality:

  • Fluorometric quantification—Qubit™ dsDNA BR Assay Kit (Thermo Fisher Scientific)
  • Capillary electrophoresis—Bioanalyzer HS DNA chip (Agilent)
  • qPCR—Kapa hgDNA Quantification and QC Kit (Roche)

Sequencing libraries were generated with 100 ng of DNA extracted from the FFPE samples and adjacent fresh-frozen normal samples of the three donors. Despite input sample quality, the xGen cfDNA & FFPE DNA Library Prep Kit generated high-yield libraries. These libraries were captured in singleplex with a custom xGen pan-cancer hyb panel and sequenced. Picard was used to evaluate library preparation and hybrid capture performance including HS library size (Figure 2), duplicate rate, and coverage after standard start-stop deduplication. Our experimental goal was to identify as many variants as possible.

High-yield libraries were generated with 25 ng of cfDNA from each of the trios and captured with subject-matched custom xGen Custom Hyb Panels. Incorporation of unique dual indexes (UDIs) ensured accuracy and prevented sample misassignment. Despite the small size of these panels, we obtained high on-target rates and achieved a sequence depth sufficient to reach duplication rates of >80%, which is recommended for collapsed read analysis to enable error correction (Figure 3A). Collapsing reads uses unique molecular identifiers (UMIs) to remove sample-prep, library-prep, and sequencing errors, allowing reliablevariant calling of ultra low frequency variants.

Mapped reads were used to generate collapsed single and combined read families, as outlined in the xGen cfDNA and FFPE DNA Library Prep Kit Analysis Guidelines. The combination of the xGen cfDNA & FFPE DNA Library Prep Kit with xGen Custom Hyb Panels capture resulted in high conversion rates, complexity, and coverage for cfDNA (Figure 3B).

Figure 1. Overview of the research workflow. Fresh-frozen normal tissue, tumor-derived FFPE tissue, and plasma cfDNA were extracted from three biobank samples. The fresh-frozen normal and FFPE tissues were used for hybridization capture to identify tumor-associated variants. The results from that sequencing were used to design subject-specific xGen Custom Hyb panels for use in targeted deep sequencing of the plasma cfDNA.

Figure 2. High-quality sequencing libraries from tumor and normal samples. Libraries derived from fresh-frozen normal and FFPE-tumor biobank samples were generated with the xGen cfDNA & FFPE DNA Library Prep Kit from 100 ng of input material. Libraries were captured in singleplex with a custom 2.2 Mb xGen Pan-Cancer Hybridization Panel. Libraries were pooled and sequenced on NextSeq™ 500 (Illumina) instrument. Reads were downsampled to 140 M reads per library and mapped using BWA (0.7.15). Libraries were then deduplicated based on start-stop position using Picard (2.18.9) or fgbio (0.7.0) as described in the xGen cfDNA & FFPE DNA Library Prep Kit Analysis Guidelines. HS library size was calculated using Picard [1].

Figure 3. High complexity and coverage sequencing data from cfDNA. Libraries were generated with the xGen cfDNA & FFPE DNA Library Prep Kit from 25 ng of cfDNA material. Libraries were captured in singleplex with custom subject-specific xGen Custom Hyb Panels. Libraries were pooled and sequenced on a NextSeq™ 500 (Illumina) instrument. Reads were downsampled to 40 M reads per library and mapped using BWA (0.7.15). Libraries were then deduplicated based on start-stop position using Picard (2.18.9) or error-corrected with combined read families using fgbio (0.7.0) as described in the xGen cfDNA & FFPE DNA Library Prep Kit Analysis Guidelines. (A) On-target rate was calculated using Picard percent selected bases. HS library size, and (B) coverage were also calculated using Picard [1].

Ordering

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References

  1. "Picard Toolkit". Broad Institute, GitHub repository: Broad Institute; 2019. https://github.com/broadinstitute/picard

*RUO—For research use only. Not for use in diagnostic procedures. Unless otherwise agreed to in writing, IDT does not intend for these products to be used in clinical applications and does not warrant their fitness or suitability for any clinical diagnostic use. Purchaser is solely responsible for all decisions regarding the use of these products and any associated regulatory or legal obligations.

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