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Vega genomes datasets

Explore a demo dataset of HiFi genomes on the Vega benchtop system

Download the Vega human genome dataset      Vega system

Explore HiFi genome demo datasets on the Vega benchtop system

You can now bring highly accurate HiFi sequencing in-house with the power of the Vega system. HiFi long-read sequencing improves discovery of difficult-to-detect variants involved in human disease that are challenging or impossible to detect with short reads1. See what you might be missing by exploring the human HG002 (NA24385) dataset sequenced on the Vega system.

 

Short read sequencing vs. HiFi reads graph - PacBio

Key metrics for the demo dataset

Highlights

  • Industry-leading accuracy delivers long-read results you can trust
  • Powerful insights: 20x HiFi long-read human genome
  • Data equivalence: Ensures consistency between Vega and Revio systems

Industry-leading accuracy delivers long-read results you can trust

Built on proven HiFi technology and compatible with existing workflows, the Vega system brings powerful, reliable insights straight to your lab. The figure shows the read quality distribution with a median read quality of Q33 and with 90% of bases Q30 or higher. This level of accuracy is on par with standard short read technologies2 but with read lengths over 100 times longer, averaging 19.63 kb per HiFi read.

HiFi read quality distribution of the HG002 dataset generated on the Vega benchtop system showing a Q33 median read quality.

Structural variant (SV) calling performance against the GIAB HG002 telomere-to-telomere (T2T) genome benchmark v.1.04. PacBio HiFi data called with Sawfish5 is shown in magenta (F1-score) at each coverage titration point, while 60x Oxford Nanopore Technologies (ONT) R10.4 (blue)6 called with Sniffles2 and 30x Illumina DRAGENv4.2 (orange)7 are shown as horizontal lines.  20x HiFi genome data provides F1-score of 96.7%, relative to 93.12% for 60x ONT and 55% for 30x Illumina DRAGEN v4.2.

Powerful insights: 20x HiFi long-read genome

With best-in-class accuracy and long read lengths, HiFi sequencing delivers high-quality variant detection performance, even at just 20x coverage3—far surpassing other technologies like nanopore and SBS short-read sequencing, as shown in the figure. Want to explore more about what it means to generate a 20x HiFi long-read genome?

Small variant calling performance of HG002 at 20x coverage against the Genome in a Bottle (GIAB) v4.2.1 benchmark8 for data produced on Revio and Vega systems. Variant calling was performed using the PacBio HiFi human WGS WDL workflow9 v2.0.0rc4 and benchmarked using the hap.py framework10.

Vega shares same high-quality HiFi data as Revio

The Vega system delivers the same industry-leading accuracy as Revio but in a compact benchtop system. Shown in the figure, the HG002 human genome sequenced by the Vega and Revio systems, results in 90% of bases at Q30 or higher, with long reads that provide a full view from telomere to telomere. Whether you’re uncovering complex regions vital to disease research or detecting structural variants and repeat expansions, Vega is the essential tool you need for genomic exploration.

HiFi genome workflow

Sample prep

DNA extraction with Nanobind PanDNA kit (2 hrs)

Short fragment depletion with short read eliminator kits (SRE), if necessary (2.5 hrs)

DNA shearing to 15 – 20 kb length. (8 to 30 min)

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Library prep

SMRTbell prep kit 3.0 + polymerase binding (6 hrs)

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Sequencing

Vega system sequencing (24 hours)

On-instrument primary analysis:

  • HiFi base calling
  • 5mCpG calling
  • Demultiplexing secondary analysis

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Analysis

Secondary analysis using on-premise or cloud services:

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Explore other Vega datasets

Full-length transcript     PureTarget repeat expansion panels

References

  1. Höps W., et al. (2024). HiFi long-read genomes for difficult-to-detect clinically relevant variants. medRxiv, 2024-09.
  2. https://www.illumina.com/systems/sequencing-platforms/novaseq-x-plus/specifications.html
  3. Kolesnikov A., et al. (2024) Local read haplotagging enables accurate long-read small variant calling. Nature Communications,15, 5907.
  4. HG002 T2T draft genome benchmark: https://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/NIST_HG002_DraftBenchmark_defrabbV0.015-20240215/
  5. Saunders, C., et al. (2024) Sawfish: Improving long-read structural variant discovery and genotyping with local haplotype modeling. bioRxiv, https://doi.org/10.1101/2024.08.19.608674
  6. Oxford Nanopore Open Data. https://labs.epi2me.io/giab-2023.05/ Accessed September 2024.
  7. Behera, S., et al. (2024). Comprehensive and accurate genome analysis at scale using DRAGEN accelerated algorithms. bioRxiv.
  8. Wagner, J., et al. (2022). Benchmarking challenging small variants with linked and long reads. Cell Genomics, 2(5).
  9. https://github.com/PacificBiosciences/HiFi-human-WGS-WDL
  10. Illumina Hap.py framework: https://github.com/Illumina/hap.py

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