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YEASEN’s OnePot Enzymatic Fragmentation Enables Rapid Constructions of High-quality Libraries

The focused acoustic shearing method is currently the most commonly used fragmentation approach in NGS library construction. However, there has been an emerging demand for alternative methods as mechanical shearing is always associated with sample damage/loss, high costs, and scalability challenges.



The OnePot series DNA library preparation kits developed for the Illumina platform utilize a patent non-restrictive endonuclease which fragments the input DNA to targeted sizes in a time-dependent manner. This kit enables the single-step reaction of fragmentation, end-repair, and A-tailing without the requirement of beads clean-up, significantly reducing material loss, experimental costs, and hands-on time.



1. Simplified Workflow: combining fragmentation, end repair, and A-tailing in one single step



2. Controllable Fragmentation: time-dependent fragmentation, which is highly tolerant to EDTA and introduces little bias 


Figure 1: 200 ng of human genomic DNA NA12878 enzymatically fragmented with the Hieff NGS ® OnePot Pro fragmentation mix, with reaction time ranging from 8 to 16 minutes. 


Figure 2: 200 ng of human genomic DNA NA12878 enzymatically fragmented with the Hieff NGS ® OnePot Pro fragmentation mix at 30 C for 8 mins. The blue line represents a 1x TE reaction system (with 1mM EDTA), and the red line represents a reaction system with 1.125 mM EDTA. 


Figure 3: Sequencing data of the libraries constructed with the Hieff NGS ® OnePot Pro DNA Library Prep Kit has low fragmentation bias.


3. Compatible with FFPE samples of different degrees of degradation



4. Broad applications: enabling library construction from ultra-low input samples, including sputum and ascitic fluid samples




With our recently upgraded formulas, the Hieff NGS ® OnePot DNA Library Prep Kits exhibit even higher efficiencies in end-repair, A-tailing, and adapter ligation. The high-fidelity polymerases used in these upgraded kits significantly reduce error rates and GC bias, resulting in more accurate and even represented sequencing data.


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