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

通用前端可改善下一代测序和重新组装的结果

• 批准号: 7853052
• 负责人: Annelise Emily Barron
• 金额: $ 73.27万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7853052
• 关键词: Algorithms; Animal Model; Bar Codes; Cloning; Complex; Computing Methodologies; DNA; DNA Sequence; Data; Data Set; Development; Devices; Digestion; Encapsulated; Escherichia coli; Frequencies; Gel; Genes; Genetic; Genetic Variation; Genome; Genomics; Goals; Human Genome; Human Resources; Hydrogels; Individual; Informatics; Label; Laws; Length; Manufacturer Name; Maps; Medicine; Metagenomics; Methods; Microbe; Microcapsules drug delivery system; Microfluidic Microchips; Microfluidics; Modification; Oligonucleotides; Organism; Outcome; Preparation; Process; Protocols documentation; Reading; Reagent; Relative (related person); Research; Route; Sampling; Shotguns; Solid; System; Technology; Testing; Time; base; computer infrastructure; cost; design; genome sequencing; improved; instrument; metagenomic sequencing; microorganism; new technology; next generation; novel; prototype; public health relevance

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.

描述（由申请人提供）：DNA测序目前处于破坏性技术转变的中间，有454，Illumina和固体为我们提供了巨大的吞吐量增加，每个基数的成本大幅下降。大量并行技术每周提供几英镑的序列，作为短片段或读取。启用了测序的新应用：个人基因组测序，对'汤的“元基因组学”分析，其中包含几种，对数百种独特的生物，最后是对复杂生物体新颖基因组的从头测序。无论测序如何完成，都必须在计算中组装读取，如果它们有用。鉴于新工具的读取长度和读取质量局限性以及生成的大量数据，计算组装问题变得至关重要，计算基础架构和人员的成本超过了试剂和仪器相关的成本。此外，组装的结果目前远非理想。例如，由于重复的比例很高，许多人类基因组仍然看不见。 我们建议将新的“前端”开发到下一代测序仪以进行DNA制备，即“读取云方法”，该方法可以将基因组组装的计算成本降低2-3个数量级，产生更完整和准确的基因组，并使元组学拖拉。我们提出了一种分层测序方法，而无需细菌克隆。我们将通过在微流体设备上处理具有高冗余性的单个DNA分子来实现这一目标。我们将设计，原型和彻底测试技术，以（i）剪切基因组DNA到具有较窄尺寸分布的200 kBp片段中； （ii）在多孔的凝胶微牙本质中随机放大每个个体，分别分离出200-kbp DNA，该微胶质体将在微电位内形成DsDNA分子周围形成； （iii）将微封装的DNA消化成可调尺寸的小片段； （iv）bar用12mer寡核苷酸代码每个200-kbp DNA的后代，以识别每个读数与特定的200-kbp DNA相关联。将制造平面微流体设备，以使一个独特的条形码序列钝化到两个DNA末端。汇总条编码的DNA，并进行下一代测序。结果是一个高度还原的数据集。该方法和算法普遍适用于下一代平台。 PIS（Batzoglou，Barron，Shaqfeh，Quake）将合作，在微流体设备中采用有效的方法来进行分层测序。 公共卫生相关性：项目叙事基因测序对医学很重要。我们的DNA测序方法具有通过数量级来降低计算成本的潜力，同时使组装基因组明显更完整和准确。此步骤的关键是使用微流体处理技术将基因组DNA细分为200KBP片段，然后将其彼此隔离并彼此分离，并标记为独特的标签以形成一个高度还原的基因组组装数据集。