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复杂微生物群落的 PCR-变性梯度凝胶电泳:解决凝胶间差异影响并允许在大型数据集上进行有效比较的两步法。

PCR-denaturing gradient gel electrophoresis of complex microbial communities: a two-step approach to address the effect of gel-to-gel variation and allow valid comparisons across a large dataset.

机构信息

Integrated Biology of GI Tract Programme, Institute of Food Research, Norwich Research Park, Colney, Norwich, NR4 7UA, UK.

出版信息

Microb Ecol. 2010 May;59(4):776-86. doi: 10.1007/s00248-009-9613-x. Epub 2009 Dec 3.

Abstract

Denaturing gradient gel electrophoresis (DGGE) is widely used in microbial ecology to profile complex microbial communities over time and in response to different stimuli. However, inherent gel-to-gel variability has always been a barrier toward meaningful interpretation of DGGE profiles obtained from multiple gels. To address this problem, we developed a two-step methodology to align DGGE profiles across a large dataset. The use of appropriate inter-gel standards was of vital importance since they provided the basis for efficient within- and between-gel alignment and a reliable means to evaluate the final outcome of the process. Pretreatment of DGGE profiles by a commercially available image analysis software package (TL120 v2006, Phoretix 1D Advanced) followed by a simple interpolation step in Matlab minimized the effect of gel-to-gel variation, allowing for comparisons between large numbers of samples with a high degree of confidence. At the same time, data were obtained in the form of whole densitometric curves, rather than as band presence/absence or intensity information, and could be readily analyzed by a collection of well-established multivariate methods. This work clearly demonstrates that there is still room for significant improvements as to the way large DGGE datasets are processed and statistically interrogated.

摘要

变性梯度凝胶电泳(DGGE)广泛应用于微生物生态学,用于随时间和对不同刺激反应的复杂微生物群落的分析。然而,凝胶间固有的可变性一直是对从多个凝胶获得的 DGGE 图谱进行有意义解释的障碍。为了解决这个问题,我们开发了一种两步法来对齐大型数据集的 DGGE 图谱。使用适当的胶间标准至关重要,因为它们为胶内和胶间对齐提供了基础,并且是评估该过程最终结果的可靠手段。使用市售的图像分析软件包(TL120 v2006,Phoretix 1D Advanced)对 DGGE 图谱进行预处理,然后在 Matlab 中进行简单的插值步骤,可以最小化凝胶间变化的影响,从而可以高度自信地对大量样本进行比较。同时,以全密度曲线的形式获得数据,而不是以带的存在/不存在或强度信息的形式,并且可以通过一系列成熟的多元方法轻松分析。这项工作清楚地表明,在处理和统计检验大型 DGGE 数据集的方式方面仍然有很大的改进空间。

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