PO.ET02.07 · 实验与分子治疗
NEBNext UltraShear® Long Read:用于临床相关样本长读长测序的酶法DNA片段化
NEBNext UltraShear ® Long Read: Enzymatic DNA fragmentation for long read sequencing of clinically relevant samples
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
长读长测序技术的进步显著提高了准确性和通量,使其适用于临床应用和个性化医疗。由高质量提取产生的超长DNA分子,如果保持完整,通常难以高效转化为文库并在长读长平台上测序。因此,强烈建议进行片段化以用于高通量长读长测序。长读长测序上游的DNA片段化通常还能在不同样本和实验间产生可比的片段大小,具有更高且一致的N50长度,以及相比超长DNA更高的测序产量。目前的DNA片段化方法包括机械剪切和酶法片段化。机械剪切(例如Covaris® g-TUBE和Megaruptor®)是长读长文库制备上游DNA片段化的金标准方法。这些方法的缺点包括使用昂贵的耗材和不利于自动化的仪器,并导致样本损失。相比之下,酶法DNA片段化不需要昂贵的仪器且利于自动化,然而现有的片段化参数可能并非长读长测序的最佳选择。
为解决这些限制,我们开发了一种新型酶法片段化解决方案NEBNext UltraShear® Long Read(UltraShear LR),其快速、可调且利于自动化。酶法片段化具有时间依赖性,可用于生成适合不同应用和测序平台的宽范围DNA片段大小(2至30 kb)。UltraShear LR在宽范围的gDNA输入量(250至5,000 ng)以及不同的gDNA样本和物种(例如动物、植物和人类)中均表现稳健。在此,我们通过在Oxford Nanopore™ Technologies和PacBio®平台上测序,证明UltraShear LR片段化可生成具有可调读长的高质量文库。UltraShear LR文库保留碱基修饰(包括CpG甲基化),并在相同读数下比机械剪切文库鉴定出更多的CpG。此外,我们使用Twist癌症热点长读长测序捕获panel,应用配对的正常和肿瘤样本来识别拷贝数变异并进行变异检出,证明了UltraShear LR对临床相关样本的适用性。
UltraShear LR系统以稳健且经济高效的方式生成可重现的时间依赖性DNA片段大小。UltraShear LR通过简化样本处理、提高通量和保留碱基修饰,克服了机械剪切方法的诸多局限。这些优势共同改善了长读长测序文库制备中的可用性和数据质量,使该方法非常适合临床应用和个性化医疗。
查看英文原文 English abstract
Advances in long read sequencing technologies have significantly improved accuracy and throughput, making them useful for clinical applications and personalized medicine. Ultra-long DNA molecules resulting from high quality extractions, if left intact, are generally inefficiently converted into libraries and sequenced on long-read platforms. Therefore, fragmentation is strongly recommended for high-throughput long read sequencing. DNA fragmentation upstream of long read sequencing typically also results in comparable fragment sizes across samples and experiments with higher and consistent N50 lengths as well as improved sequencing yields compared to ultra-long DNA. Current methods for DNA fragmentation include mechanical shearing and enzymatic fragmentation. Mechanical shearing (e.g., Covaris ® g-TUBE and Megaruptor ® ) is the gold standard method for DNA fragmentation upstream of long read library preparation. Drawbacks to these methods include the use of expensive consumables and instruments that are not automation-friendly and result in sample loss. In contrast, enzymatic DNA fragmentation does not require expensive instruments and is automation friendly, however existing fragmentation parameters may not be optimal for long read sequencing.
To address these constraints, we developed a novel enzymatic fragmentation solution, NEBNext UltraShear ® Long Read (UltraShear LR), that is quick, tunable, and automation friendly. Enzymatic fragmentation is time-dependent and can be used to generate a wide-range of DNA fragment sizes (2 to 30 kb) suitable for different applications and sequencing platforms. UltraShear LR is robust across a wide range of gDNA input amounts (250 to 5,000 ng) as well as different gDNA samples and species (e.g., animal, plant and human). Here we demonstrate that UltraShear LR fragmentation generates high quality libraries with tunable read lengths by sequencing on Oxford Nanopore™ Technologies and PacBio ® platforms. UltraShear LR libraries retain base modifications (including CpG methylation) and identify more CpGs than mechanically sheared libraries at the same read count. Additionally, we applied the Twist cancer hotspot long read sequencing capture panel using matched normal and tumor samples to identify copy number variations and perform variant calling, demonstrating the applicability of UltraShear LR for clinically relevant samples.
The UltraShear LR system generates time-dependent DNA fragment sizes that are reproducible in a robust and cost-effective manner. UltraShear LR overcomes many limitations of mechanical shearing methods by simplifying sample processing, increasing throughput, and preserving base modifications. These advantages collectively improve usability and data quality in long read sequencing library preparation, making the approach well-suited for clinical applications and personalized medicine.
利益披露 Disclosure
K. Krishnan, None..
B. S. Sexton, None..
M. Angel, None..
K. McKay, None..
J. Sanford, None..
R. Moulton, None..
L. Williams, None..
B. W. Langhorst, None..
P. Liu, None..
V. Ponnaluri, None.