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KAPA HyperPlus Kits provide a streamlined workflow that includes fully automatable fragmentation and library preparation in a single tube. KAPA HyperPlus Kits offer complete library preparation solution with KAPA Adapters and KAPA Pure Beads. The kits are compatible with the Illumina sequencing platforms.
View data for higher conversion of input DNA by the KAPA HyperPlus Kit compared to Illumina TrueSeq Nano DNA Library Prep Kit or Covaris-sheared DNA with KAPA HyperPrep Kit.
Figure 1. The KAPA HyperPlus Kit offers industry-leading conversion rates. Conversion rate (% input DNA converted to sequencing-ready library) is an indication of library construction efficiency and ultimately determines library complexity. The efficiency of ligation-based library prep decreases with input. The integrated KAPA HyperPlus workflow enables improvements across the kit’s entire input range of 1 ng – 1 μg. Extremely high conversion rates, (≥80%) is possible at higher inputs (≥100 ng), thereby lowering the input requirements for PCR-free workflows. Data represents typical results from multiple experiments.
Tunable and low-bias enzymatic fragmentation
Library insert sizes adjustable from 150–800 bp by varying fragmentation time or temperature
Robust and reproducible fragmentation across a range of GC content and DNA input amounts and sample types
See performance data showing tunability and consistency in DNA library insert sizes with samples from different species, and comparative data showing minimal fragmentation bias by KAPA HyperPlus kits compared to Nextera Rapid Capture Exome Kit as well as KAPA HyperPrep Kit with Covaris shearing.
Figure 2. The KAPA fragmentation system enables tunable enzymatic fragmentation. Human genomic DNA (100 ng) was fragmented at 37°C for different periods of time (5 – 45 min) to achieve mode library insert sizes ranging from approximately 150 – 800 bp. The KAPA HyperPlus workflow was completed without any size selection using full-length adapters and 4 cycles of amplification. Final libraries were analyzed using a LabChip GX Touch HT instrument and HT DNA HiSens Reagent Kit (PerkinElmer).
Figure 3. Defined fragmentation parameters yield consistent library insert sizes across a range of species and genomic GC content. Genomic DNA (10 ng or 50 ng) from human (41% GC), Bordetella pertussis (68% GC), Clostridium difficile (29% GC), Escherichia coli (51% GC), and Plasmodium falciparum (20% GC) was fragmented at 37ºC for 5, 15, or 45 minutes, yielding average library insert sizes of ~700, ~350 or ~200 bp, respectively. Average fragment sizes for final, amplified libraries were determined with a Bioanalyzer 2100 instrument and High-Sensitivity DNA Assay (Agilent Technologies).
Figure 4. The KAPA HyperPlus workflow results in minimal fragmentation bias. Nucleotide content over a 40 bp window (-10 to +30 bp relative to read alignment start) for whole exome libraries were prepared from 50 ng of human gDNA (41% GC) with the KAPA HyperPlus Kit, the KAPA HyperPrep Kit with Covaris shearing, or the Nextera Rapid Capture Exome Kit (Illumina). Enzymatic fragmentation with the KAPA HyperPlus Kit resulted in slightly more start site bias than mechanical shearing, but much less bias than tagmentation. Paired-end sequencing (2 x 100 bp) was performed on an Illumina HiSeq 2500 and analysis performed with a custom python script.
Figure 5. The KAPA HyperPlus workflow results in minimal fragmentation bias. Nucleotide content over a 40 bp window (-10 to +30 bp relative to read alignment start) for whole exome libraries were prepared from 50 ng of human gDNA (41% GC) with the KAPA HyperPlus Kit, the KAPA HyperPrep Kit with Covaris shearing, or the Nextera Rapid Capture Exome Kit (Illumina). Enzymatic fragmentation with the KAPA HyperPlus Kit resulted in slightly more start site bias than mechanical shearing, but much less bias than tagmentation. Paired-end sequencing (2 x 100 bp) was performed on an Illumina HiSeq 2500 and analysis performed with a custom python script.
Figure 6. The KAPA HyperPlus workflow results in minimal fragmentation bias. Nucleotide content over a 40 bp window (-10 to +30 bp relative to read alignment start) for whole exome libraries were prepared from 50 ng of human gDNA (41% GC) with the KAPA HyperPlus Kit, the KAPA HyperPrep Kit with Covaris shearing, or the Nextera Rapid Capture Exome Kit (Illumina). Enzymatic fragmentation with the KAPA HyperPlus Kit resulted in slightly more start site bias than mechanical shearing, but much less bias than tagmentation. Paired-end sequencing (2 x 100 bp) was performed on an Illumina HiSeq 2500 and analysis performed with a custom python script.
Superior coverage uniformity and improved coverage of challenging regions
Higher coverage uniformity and generation of libraries with similar or better quality compared to libraries prepared with Covaris-fragmented DNA
View comparative data showing better sequence coverage uniformity and better coverage of GC-rich regions using KAPA HyperPlus Kit compared to HyperPrep Kits with Covaris shearing and Nextera Rapid Capture Exome Kit with Illumina.
Figure 7. Sequencing data were down-sampled to 27 million reads per technical replicate before sequence coverage calculation with Bedtools. KAPA libraries prepared from Covaris-sheared and enzymatically fragmented DNA yielded a similar and fairly narrow distribution around an overall higher average coverage depth than Nextera libraries, which contained regions of low coverage and coverage hotspots.
Figure 8. Thirty million reads from each library were aligned to the hg19 reference sequencing, using BWA. The Integrative Genomics Viewer (IGV) was used to visualize reads aligning in the 5' region of the human KLHL30 gene, which includes exons 1 and 2. Vertical lines depict the genomic GC content in a 20 bp sliding window (red = high %GC and blue = low %GC). Libraries were prepared from Covaris-sheared or enzymatically fragmented DNA. The KAPA HyperPrep chemistry exhibits good coverage in this GC-rich region. The Nextera data displays less uniform coverage of exon 1 and has no coverage for exon 2 (outlined in red), which contains a SNP of interest (yellow vertical line). Lower coverage uniformity requires more sequencing to cover all targets to the requisite depth, whereas gaps in coverage may have to be filled with alternative methodologies to obtain data for all variants of interest.
Figure 9. KAPA HyperPlus Kits enable improved coverage for FFPE samples. Libraries were prepared from 200 ng of FFPE DNA or 50 ng of matched normal controls (DNA from blood) using the KAPA HyperPrep Kit with Covaris® shearing, or the KAPA HyperPlus Kit. Two rounds of multiplexed target capture were performed with a custom SeqCap® EZ panel targeting regions of 42 genes associated with gastrointestinal cancer. Significantly lower duplication rates for the KAPA HyperPlus libraries and slightly higher on-target rates translated to a significant increase in coverage depth for the FFPE samples. Variants at an expected frequency ≥5% were detected with a high degree of correlation (R2 >0.96) between the libraries prepared from Covaris-sheared vs. enzymatically fragmented DNA. Paired-end sequencing (2 x 150 bp) was performed on an Illumina MiSeq and data analyzed with Picard and Illumina VariantStudio v2.2. Data courtesy of The Royal Marsden Hospital.
Figure 10. KAPA HyperPlus Kits enable improved coverage for FFPE samples. Libraries were prepared from 200 ng of FFPE DNA or 50 ng of matched normal controls (DNA from blood) using the KAPA HyperPrep Kit with Covaris® shearing, or the KAPA HyperPlus Kit. Two rounds of multiplexed target capture were performed with a custom SeqCap® EZ panel targeting regions of 42 genes associated with gastrointestinal cancer. Significantly lower duplication rates for the KAPA HyperPlus libraries and slightly higher on-target rates translated to a significant increase in coverage depth for the FFPE samples. Variants at an expected frequency ≥5% were detected with a high degree of correlation (R2 >0.96) between the libraries prepared from Covaris-sheared vs. enzymatically fragmented DNA. Paired-end sequencing (2 x 150 bp) was performed on an Illumina MiSeq and data analyzed with Picard and Illumina VariantStudio v2.2. Data courtesy of The Royal Marsden Hospital.
Figure 11. KAPA HyperPlus Kits enable improved coverage for FFPE samples. Libraries were prepared from 200 ng of FFPE DNA or 50 ng of matched normal controls (DNA from blood) using the KAPA HyperPrep Kit with Covaris® shearing, or the KAPA HyperPlus Kit. Two rounds of multiplexed target capture were performed with a custom SeqCap® EZ panel targeting regions of 42 genes associated with gastrointestinal cancer. Significantly lower duplication rates for the KAPA HyperPlus libraries and slightly higher on-target rates translated to a significant increase in coverage depth for the FFPE samples. Variants at an expected frequency ≥5% were detected with a high degree of correlation (R2 >0.96) between the libraries prepared from Covaris-sheared vs. enzymatically fragmented DNA. Paired-end sequencing (2 x 150 bp) was performed on an Illumina MiSeq and data analyzed with Picard and Illumina VariantStudio v2.2. Data courtesy of The Royal Marsden Hospital.
Figure 12. KAPA HyperPlus Kits enable improved coverage for FFPE samples. Libraries were prepared from 200 ng of FFPE DNA or 50 ng of matched normal controls (DNA from blood) using the KAPA HyperPrep Kit with Covaris® shearing, or the KAPA HyperPlus Kit. Two rounds of multiplexed target capture were performed with a custom SeqCap® EZ panel targeting regions of 42 genes associated with gastrointestinal cancer. Significantly lower duplication rates for the KAPA HyperPlus libraries and slightly higher on-target rates translated to a significant increase in coverage depth for the FFPE samples. Variants at an expected frequency ≥5% were detected with a high degree of correlation (R2 >0.96) between the libraries prepared from Covaris-sheared vs. enzymatically fragmented DNA. Paired-end sequencing (2 x 150 bp) was performed on an Illumina MiSeq and data analyzed with Picard and Illumina VariantStudio v2.2. Data courtesy of The Royal Marsden Hospital.
Minimal GC bias and higher coverage uniformity facilitating de novo whole genome assembly in microbial whole genome sequencing
Lower GC bias when compared to tagmentation and other enzymatic fragmentation methods
Higher coverage uniformity
Fewer contigs, longer N50 lengths and longer contigs
View coverage uniformity plots and GC-bias plots showing superior performance using KAPA Hyper Plus Kits compared to HyperPrep Kits, NEBNext Ultra DNA Library Prep Kit for Illumina and Nextera XT DNA Library Prep Kit.
Figure 13. KAPA HyperPlus outperforms NEBNext and Nextera workflows with respect to coverage uniformity and GC bias in microbial whole-genome sequencing.Libraries were prepared from 1 ng of genomic DNA from three bacteria with a range of genomic GC contents, using the KAPA HyperPlus Kit; KAPA HyperPrep Kit with Covaris shearing; NEBNext dsDNA Fragmentase and the NEBNext Ultra DNA Library Prep Kit for Illumina; or the Nextera XT DNA Library Prep Kit (Illumina). Paired-end sequencing (2 x 300 bp) was performed on an Illumina MiSeq.
Coverage uniformity plots. Data for all libraries were down-sampled to ~900,000 reads and coverage calculated using Bedtools. The HyperPrep and HyperPlus workflows yielded highly similar coverage profiles, with a sharp peak and negligible tails for all three bacteria, indicating uniform coverage. In contrast, the NEBNext and Nextera workflows yielded a broader distribution for the genomes with unbalanced GC content, and/or lower mode coverage depth.
Figure 14. KAPA HyperPlus outperforms NEBNext and Nextera workflows with respect to coverage uniformity and GC bias in microbial whole-genome sequencing.Libraries were prepared from 1 ng of genomic DNA from three bacteria with a range of genomic GC contents, using the KAPA HyperPlus Kit; KAPA HyperPrep Kit with Covaris shearing; NEBNext dsDNA Fragmentase and the NEBNext Ultra DNA Library Prep Kit for Illumina; or the Nextera XT DNA Library Prep Kit (Illumina). Paired-end sequencing (2 x 300 bp) was performed on an Illumina MiSeq.
Coverage uniformity plots. Data for all libraries were down-sampled to ~900,000 reads and coverage calculated using Bedtools. The HyperPrep and HyperPlus workflows yielded highly similar coverage profiles, with a sharp peak and negligible tails for all three bacteria, indicating uniform coverage. In contrast, the NEBNext and Nextera workflows yielded a broader distribution for the genomes with unbalanced GC content, and/or lower mode coverage depth.
Figure 15. KAPA HyperPlus outperforms NEBNext and Nextera workflows with respect to coverage uniformity and GC bias in microbial whole-genome sequencing.Libraries were prepared from 1 ng of genomic DNA from three bacteria with a range of genomic GC contents, using the KAPA HyperPlus Kit; KAPA HyperPrep Kit with Covaris shearing; NEBNext dsDNA Fragmentase and the NEBNext Ultra DNA Library Prep Kit for Illumina; or the Nextera XT DNA Library Prep Kit (Illumina). Paired-end sequencing (2 x 300 bp) was performed on an Illumina MiSeq.
Coverage uniformity plots. Data for all libraries were down-sampled to ~900,000 reads and coverage calculated using Bedtools. The HyperPrep and HyperPlus workflows yielded highly similar coverage profiles, with a sharp peak and negligible tails for all three bacteria, indicating uniform coverage. In contrast, the NEBNext and Nextera workflows yielded a broader distribution for the genomes with unbalanced GC content, and/or lower mode coverage depth.
Figure 16 GC-bias plots generated with Picard. Gray histograms represent the distribution of genomic GC content for each bacterium, calculated for the reference sequence in 100 bp bins. GC bias was assessed by plotting normalized coverage for each bin. If all sample-to-data processes (fragmentation, adapter addition, library amplification, cluster amplification, sequencing, and data analysis) were completely unbiased, all bins would be equally represented i.e., the plot for each workflow would be a horizontal distribution centered on a normalized coverage of 1. For C. difficile, near-perfect data were obtained for both the HyperPrep and HyperPlus workflows. In contrast, bins with a more balanced GC content (30 – 50% GC) were overrepresented in the NEBNext and Nextera C. difficile data at the expense of bins with extremely low GC content (<30% GC). The NEBNext and Nextera workflows generally performed better in balanced and slightly GC-rich regions (40 – 65% GC), as compared to AT-rich regions. All workflows performed poorly with respect to the extremely GC-rich (>70% GC) bins of B. pertussis, where limitations inherent to the sequencing technology start to dominate.
Figure 17 GC-bias plots generated with Picard. Gray histograms represent the distribution of genomic GC content for each bacterium, calculated for the reference sequence in 100 bp bins. GC bias was assessed by plotting normalized coverage for each bin. If all sample-to-data processes (fragmentation, adapter addition, library amplification, cluster amplification, sequencing, and data analysis) were completely unbiased, all bins would be equally represented i.e., the plot for each workflow would be a horizontal distribution centered on a normalized coverage of 1. For C. difficile, near-perfect data were obtained for both the HyperPrep and HyperPlus workflows. In contrast, bins with a more balanced GC content (30 – 50% GC) were overrepresented in the NEBNext and Nextera C. difficile data at the expense of bins with extremely low GC content (<30% GC). The NEBNext and Nextera workflows generally performed better in balanced and slightly GC-rich regions (40 – 65% GC), as compared to AT-rich regions. All workflows performed poorly with respect to the extremely GC-rich (>70% GC) bins of B. pertussis, where limitations inherent to the sequencing technology start to dominate.
Figure 18 GC-bias plots generated with Picard. Gray histograms represent the distribution of genomic GC content for each bacterium, calculated for the reference sequence in 100 bp bins. GC bias was assessed by plotting normalized coverage for each bin. If all sample-to-data processes (fragmentation, adapter addition, library amplification, cluster amplification, sequencing, and data analysis) were completely unbiased, all bins would be equally represented i.e., the plot for each workflow would be a horizontal distribution centered on a normalized coverage of 1. For C. difficile, near-perfect data were obtained for both the HyperPrep and HyperPlus workflows. In contrast, bins with a more balanced GC content (30 – 50% GC) were overrepresented in the NEBNext and Nextera C. difficile data at the expense of bins with extremely low GC content (<30% GC). The NEBNext and Nextera workflows generally performed better in balanced and slightly GC-rich regions (40 – 65% GC), as compared to AT-rich regions. All workflows performed poorly with respect to the extremely GC-rich (>70% GC) bins of B. pertussis, where limitations inherent to the sequencing technology start to dominate.
KAPA Single-Indexed Adapter Set A contains indices 2, 4, 5, 6, 7, 12, 13, 14, 15, 16, 18 and 19, whereas Set B contains indices 1, 3, 8, 9, 10, 11, 20, 21, 22, 23, 25, 27. All KAPA Single- and Dual-Indexed Adapter Kits contain KAPA Adapter Dilution Buffer. KAPA Dual-Indexed Adapter Kits also contain three additional sealing films to support multiple use.
KAPA Single-Indexed Adapter Set A contains indices 2, 4, 5, 6, 7, 12, 13, 14, 15, 16, 18 and 19, whereas Set B contains indices 1, 3, 8, 9, 10, 11, 20, 21, 22, 23, 25, 27. All KAPA Single- and Dual-Indexed Adapter Kits contain KAPA Adapter Dilution Buffer. KAPA Dual-Indexed Adapter Kits also contain three additional sealing films to support multiple use.
