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All-in-one sequencing: an improved library preparation method for cost-effective and high-throughput next-generation sequencing

机译:一体化测序:改进的成本效益和高通量下一代测序的图书馆准备方法

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Next generation sequencing (NGS) has been widely used in biological research, due to its rapid decrease in cost and increasing ability to generate data. However, while the sequence generation step has seen many improvements over time, the library preparation step has not, resulting in low-efficiency library preparation methods, especially for the most time-consuming and labor-intensive steps: size-selection and quantification. Consequently, there can be bottlenecks in projects with large sample cohorts. We have described the all-in-one sequencing (AIO-seq) method, where instead of performing size-selection and quantification for samples individually, one sample one tube, up to 116 samples are pooled and analyzed in a single tube, ‘All-In-One’. The AIO-seq method pools libraries based on the samples’ expected data yields and the calculated concentrations of the size selected regions (target region), which can easily be obtained with the Agilent 2100 Bioanalyzer and Qubit Fluorometer. AIO-seq was applied to whole genome sequencing and RNA-seq libraries successfully, and it is envisaged that it could be applied to any type of NGS library, such as chromatin immunoprecipitation coupled with massively parallel sequencing, assays for transposase-accessible chromatin with high-throughput sequencing, and high-throughput chromosome conformation capture. We also demonstrated that for genetic population samples with low coverage sequences, like recombinant inbred lines (RIL), AIO-seq could be further simplified, by mixing the libraries immediately after PCR, without calculating the target region concentrations. The AIO-seq method is thus labor saving and cost effective, and suitable for projects with large sample cohorts, like those used in plant breeding or population genetics research.
机译:下一代测序(NGS)已广泛用于生物研究,因为它的成本迅速降低和增加了数据的能力。然而,虽然序列生成步骤随时间看出许多改进,但是图书馆准备步骤没有,导致低效的文库制备方法,特别是对于最耗时和劳动密集的步骤:尺寸 - 选择和量化。因此,可以有大型样本队列的项目中有瓶颈。我们已经描述了一体化排序(AIO-SEQ)方法,而不是单独对样品进行样品的尺寸选择和定量,汇集了116个样品,并在单个管中汇集并分析,'所有-in-one'。基于样本的预期数据产量和所计算的尺寸所选择的区域(目标区域)的计算浓度,可以容易地获得AIO-SEQ方法库,其可以用Agilent 2100 BioAnalyzer和Qubit荧光计可以容易地获得。成功地应用于全基因组测序和RNA-SEQ文库,并且设想它可以应用于任何类型的NGS文库,例如染色质免疫沉淀,与大规模平行测序,用于转座酶可接近的染色质的测定-Througupput测序,以及高通量染色体构象捕获。我们还证明,对于具有低覆盖序列的遗传群样品,如重组近亲(RIL),通过在PCR后立即混合文库,可以进一步简化AiO-SEQ,而无需计算目标区域浓度。因此,AIO-SEQ方法是劳动力节省和成本效益,适用于具有大型样品群体的项目,如植物育种或群体遗传学研究中使用的那些。

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