A Universal Front End to Improve Assembly Outcomes for Next-Gen Sequencing and Re

描述（由申请人提供）：DNA测序目前正处于颠覆性的技术转变之中，454、Illumina和Solid为我们提供了巨大的吞吐量增加和单位碱基成本的大幅降低。大规模并行技术每周以短片段或读段的形式提供几Gbp的序列。测序的新应用最近才被认为是不切实际的：个人基因组测序，“宏基因组学”分析的“汤”含有几个，数百个独特的生物体，最后，从头测序的新基因组的复杂的生物体。无论测序是如何完成的，读段必须通过计算组装，如果它们是有用的。考虑到新仪器的读取长度和读取质量限制以及产生的大量数据，计算组装问题变得至关重要，计算基础设施和人员的成本超过试剂和仪器相关成本。此外，目前组装的结果远非理想;例如，由于高百分比的重复，大部分人类基因组仍然不可见。 我们建议开发一种新的“前端”，以下一代测序仪的DNA制备，“读云方法”，它可以减少基因组组装的计算成本2-3个数量级，产生更完整和准确的基因组，使宏基因组学易于处理。我们提出了一种分层测序方法，而不需要任何细菌克隆。我们将通过在微流控设备上处理单个DNA分子来实现这一目标，这些DNA分子以高冗余度平铺在基因组中。我们将设计，原型，并彻底测试技术（i）剪切基因组DNA成200 kbp的片段与窄尺寸分布;（ii）随机扩增每个人，200 kbp的DNA隔离，在多孔凝胶微容器，将形成周围的dsDNA分子在一个微型装置;（iii）消化微囊化DNA成小片段，可调大小;（iv）用12聚体寡核苷酸对每个200-kbp DNA的后代进行条形码化，以将每个读段鉴定为与特定的200-kbp DNA相关。将制造平面微流体装置以允许一个独特的条形码序列平端连接到两个DNA末端。将条形码DNA合并，并进行下一代测序。结果是一个高度可简化的数据集。该方法和算法对下一代平台具有普遍适用性。PI（Batzoglou，巴伦，Shaqfeh，地震）将合作，使一个有效的方法，在微流控设备的分层测序。 公共卫生相关性：项目叙述基因测序对医学很重要。我们的DNA测序方法有可能降低计算成本的数量级，同时使组装的基因组显着更完整和准确。这一步骤的关键是使用微流体处理技术将基因组DNA细分为200 kbp的片段，然后将其彼此隔离扩增并标记，以形成用于基因组组装的高度可简化的数据集。

DESCRIPTION (provided by applicant): DNA sequencing is currently in the midst of disruptive technological shifts, with 454, Illumina, and Solid providing us with enormous throughput increases and large reductions in cost per base. Massively parallel technologies deliver a few Gbp of sequence per week as short fragments, or reads. New applications of sequencing only recently considered impractical are enabled: personal genome sequencing, "metagenomics" analysis of 'soups' containing several, to hundreds of unique organisms, and finally, de novo sequencing of novel genomes of complex organisms. No matter how the sequencing is done, reads must be assembled computationally, if they are to be useful. Given the read length and read quality limitations of new instruments and the massive volume of data generated, the computational assembly problem is becoming critical, with the cost of computational infrastructure and personnel exceeding reagent and instrument-related costs. Moreover, the results of assembly are currently far from ideal; for example, much of the human genome remains invisible due to high percentage of repeats. We propose to develop a new "front end" to next-gen sequencers for DNA preparation, the "Read-Cloud Method", which can reduce computational cost of genome assembly by 2-3 orders of magnitude, produce more complete and accurate genomes, and make metagenomics tractable. We propose a hierarchical sequencing approach, without any need for bacterial cloning. We will achieve this by handling single DNA molecules, tiled across the genome with high redundancy, on microfluidic devices. We will design, prototype, and thoroughly test technology to (i) shear genomic DNA into 200- kbp fragments with narrow size distributions; (ii) randomly amplify each individual, 200-kbp DNA in isolation, within a porous gel microcontainer that will be formed around the dsDNA molecule within a microdevice; (iii) digest micro-encapsulated DNA into small fragments, of tunable size; (iv) bar-code the progeny of each 200-kbp DNA with a 12mer oligonucleotide, to identify each read as associated with a particular 200-kbp DNA. A planar microfluidic device will be fabricated to allow one unique bar- code sequence to be blunt-end-ligated to both DNA termini. Bar-coded DNA is pooled, and next-gen sequencing is done. The results are a highly reducible data set. The method and algorithm are applicable universally, to next-generation platforms. The PIs (Batzoglou, Barron, Shaqfeh, Quake) will collaborate to make an efficient approach to hierarchical sequencing in microfluidic devices. PUBLIC HEALTH RELEVANCE: Project Narrative Gene sequencing is important to medicine. Our DNA sequencing method has the potential for reducing computational cost by orders of magnitude while making the assembled genomes significantly more complete and accurate. The key to this step is using microfluidic handling technologies to subdivide genomic DNA into 200kbp fragments, which are then amplified in isolation from each other and uniquely-labeled to form a highly reducible dataset for genomic assembly.