Hieff  NGS™ OnePot II DNA Library Prep Kit for MGI

Details:

Description

Hieff NGS™ OnePot II DNA Library Prep Kit for MGI is a new generation of library prepare kit specially designed for MGI sequencing platform, which contains high-quality enzyme for DNA fragmentation. This kit combines fragmentation, end-repair, and dA-tailing into one step, significantly reducing the time and cost of library preparation. The kit has a high library conversion rate and can be applied to most DNA samples from animals, plants, and microorganisms, including low-quality samples such as FFPE samples.

Features

  • Compatible with 10 ng-1 μg DNA samples, including gDNA and cDNA 
  • High-quality fragmentation enzyme which randomly fragments dsDNA without bias
  • Combining fragmentation, end-repair, and A-tailing in one step
  • High fidelity polymerase with high amplification efficiency, significantly increasing library quality and yield
  • Compatible with FFPE DNA samples
  • Stringent quality control and batch effect elimination

Applications

  • Construction of DNA library for MGI platform
  • Suitable for sequencing in tumor, reproductive genetics, genetic disease detection, and other fields

Specifications

Product Type Library Preparation Kit
Libraries Fragment Library
Fragmentation method Smearase
Input amount 10 ng-1 μg
For Use With (Equipment) MGI Platforms
Sample Type gDNA, cDNA and FFPE samples
Sequencing Type Genome & DNA Sequencing
Product Line DNA library constrction
For Use With (Application) NGS library preparation
Quantity 16/96 Reactions

Components

Components No. Name 13321ES16 13321ES96
13321-A Smearase™ Mix 160 μL 960 μL
13321-B Ligation Enhancer 480 μL 4×720 μL
13321-C Fast T4 DNA Ligase 80 μL 480 μL
13321-D 2×Ultima HF Amplification Mix 400 μL 4×600 μL
13321-E Primer Mix for MGI 80 μL 480 μL

Storage

-25℃ ~ -15℃ storage, valid for one year.

Citations & References:

[1] Liu K, Deng S, Ye C, et al. Mapping single-cell-resolution cell phylogeny reveals cell population dynamics during organ development. Nat Methods. 2021;18(12):1506-1514. doi:10.1038/s41592-021-01325-x(IF:28.547)

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