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根据大小和核苷酸序列组成对DNA二维显示进行定量预测。

Quantitative predictions for DNA two-dimensional display according to size and nucleotide sequence composition.

作者信息

Mercier Jean-François, Kingsburry Christine, Slater Gary W, Lafay Bénédicte

机构信息

Département de physique, Université d'Ottawa, Ottawa, Ontario, Canada.

出版信息

Electrophoresis. 2008 Mar;29(6):1264-72. doi: 10.1002/elps.200700407.

DOI:10.1002/elps.200700407
PMID:18288775
Abstract

2-D DNA display is a simple separation method that provides a fast and economical way of visualizing polymorphism and comparing genomes. The DNA fragments are separated first according to their size by standard gel electrophoresis and then according to their sequence composition using denaturing gradient gel electrophoresis. First developed by Fischer and Lerman (Cell 1979, 16, 191-200), this method has recently been used to distinguish strains within a bacterial species. The genomic restriction fragments are displayed as spots on a 2-D surface. Although most of the relevant physical mechanisms are understood, this technique is mostly empirical and remains essentially qualitative. In view of optimizing this procedure, we combine our understanding of the different physical mechanisms at play to develop a complete numerical model to predict the relative coordinates of the spots as a function of the corresponding DNA sequence and of the experimental conditions. We experimentally validate our model by predicting the outcome of a 2-D display of the lambda phage genome. It thus becomes possible to optimize in silico the experimental parameters, to predict whether specific mutations as well as yet undescribed genetic polymorphisms can be resolved, and to assist in interpreting the experimental data.

摘要

二维DNA展示是一种简单的分离方法,它提供了一种快速且经济的方式来可视化多态性并比较基因组。DNA片段首先通过标准凝胶电泳根据其大小进行分离,然后使用变性梯度凝胶电泳根据其序列组成进行分离。该方法最初由菲舍尔和勒曼开发(《细胞》,1979年,第16卷,第191 - 200页),最近已被用于区分细菌物种内的菌株。基因组限制性片段在二维表面上显示为斑点。尽管大多数相关物理机制已为人所知,但该技术大多基于经验,本质上仍为定性方法。为了优化此过程,我们结合对所涉及的不同物理机制的理解,开发了一个完整的数值模型,以预测斑点的相对坐标作为相应DNA序列和实验条件的函数。我们通过预测λ噬菌体基因组二维展示的结果对模型进行了实验验证。因此,有可能在计算机上优化实验参数,预测特定突变以及尚未描述的遗传多态性是否能够被分辨,并协助解释实验数据。

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