Beyond gel electrophoresis: microfluidic separations, fluorescence burst analysis, and DNA stretching.
Beyond gel electrophoresis: microfluidic separations, fluorescence burst analysis, and DNA stretching.
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DOI：
10.1021/cr3002142
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发表时间：
2013-04-10
期刊：
影响因子：
62.1
通讯作者：
Tree, Douglas R.
中科院分区：
文献类型：
--
作者：
Dorfman, Kevin D.;King, Scott B.;Olson, Daniel W.;Thomas, Joel D. P.;Tree, Douglas R.
本文综述了直接从未扩增的基因组长度DNA中获得序列信息的方法。我们的通用起始点是一大块DNA，它包含数千个碱基对（碱基对，kbp），甚至数百万个碱基对（碱基对，Mbp）。我们想确定给定序列的两个重复之间的基因组距离，如图1中螺旋DNA分子上的红点所示。这种类型的大规模基因组作图是测序的重要补充。(1)例如，考虑获得“医学级”人类基因组的挑战（2），该基因组包含患者的完整二倍体基因组序列，具有准确的结构变异信息，如拷贝数多态性和基因重排。理想情况下，每个患者的基因组都应该独立地从头组装，特别是在具有高度混乱结构重排的癌症基因组中。不幸的是，即使测序成本正在降低，高通量测序仍然受到限制（3，4），例如（i）有限的读取长度，这阻碍了从头基因组组装并阻碍了大倒位的检测，（5）和（ii）串联重复序列和端粒区域的困难。(5-7)即使新兴测序技术兑现了它们的承诺（8-10）并实现了长时间、准确的测序读数，医学级基因组的测序方法仍然会遇到试图存储、注释和比较大量基因组的“数据泛滥”问题。(11)此外，我们需要比较每一个碱基来做出有用的诊断，这一点也不明显。事实上，许多大的基因组重排的范围内的β-内酰胺酶的兆碱基对是重要的决定因素的表型和疾病状态。（12）在这种情况下，基因组作图是检测这种大规模重排的关键工具，我们将在这里看到对单个DNA分子进行高通量操作的方法。在我们将在第3节讨论的基因组作图的许多应用中，一个特别值得注意的例子是检测拷贝数变异。(13)这些很容易被视为图1中红点之间距离的变化，但很难从短测序读数中获得。映射和排序之间的联系类似于使用放大/缩小功能探索Google地图。虽然非常局部化的街景（即，来自下一代测序的短读段）是有用的，但如果没有任何上下文位置信息，特别是当房屋看起来相似时（即，从基因组图谱获得的拷贝数扩增和重复），人们会迷路。
This Review addresses methods for obtaining sequence information directly from unamplified genomic length DNA. Our generic starting point is a large piece of DNA that contains many thousands of base pairs (kilobase pairs, kbp) or even millions of base pairs (megabase pairs, Mbp). We would like to determine the genomic distance between two repeats of a given sequence, indicated by the red dots on the coiled DNA molecule in Figure 1. This type of large-scale genomic mapping is an important complement to sequencing.(1) For example, consider the challenges in obtaining a “medical grade” human genome (2) that contains a complete diploid genome sequence for a patient with accurate structural variation information, such as copy number polymorphism and gene rearrangements. Ideally, every patient genome should be de novo assembled independently, especially in a cancer genome with highly scrambled structural rearrangements. Unfortunately, even though sequencing costs are decreasing, high-throughput sequencing still suffers from limitations (3, 4) such as (i) limited read lengths, which hinders de novo genome assembly and hampers detection of large inversions,(5) and (ii) difficulty with tandem repeats and telomeric regions.(5-7) Even if emerging sequencing technologies deliver on their promises (8-10) and enable long, accurate sequencing reads, a sequencing approach to the medical grade genome would still suffer from the “data deluge” problem of trying to store, annotate, and compare vast numbers of genomes.(11) Moreover, it is not at all obvious that we need to compare every base to make a useful diagnosis. Indeed, many large genomic rearrangements in the kilobase to megabase pair range are important determinants of phenotype and disease states.(12)In this context, genome mapping is a key tool for detecting such large-scale rearrangements, and we will see methods here that operate with high-throughput on single molecules of DNA. Of the many applications of genome mapping that we will discuss in section 3, a particularly notable example is detecting copy number variants.(13) These are easily seen as changes in the distance between red dots in Figure 1 but very hard to obtain from short sequencing reads. The connection between mapping and sequencing is analogous to exploring Google Maps with the zoom in/out functions. While the very localized street view (ie, short reads from next generation sequencing) is useful, one would get lost without any contextual location information, especially when the houses look alike (ie, copy number amplifications and repeats obtained from genome mapping).
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