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NimbleGen 微阵列的杂交热力学。

Hybridization thermodynamics of NimbleGen microarrays.

机构信息

WWTF Chair of Bioinformatics, Boku University Vienna, Muthgasse 18, 1190 Vienna, Austria.

出版信息

BMC Bioinformatics. 2010 Jan 19;11:35. doi: 10.1186/1471-2105-11-35.

DOI:10.1186/1471-2105-11-35
PMID:20085625
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2823707/
Abstract

BACKGROUND

While microarrays are the predominant method for gene expression profiling, probe signal variation is still an area of active research. Probe signal is sequence dependent and affected by probe-target binding strength and the competing formation of probe-probe dimers and secondary structures in probes and targets.

RESULTS

We demonstrate the benefits of an improved model for microarray hybridization and assess the relative contributions of the probe-target binding strength and the different competing structures. Remarkably, specific and unspecific hybridization were apparently driven by different energetic contributions: For unspecific hybridization, the melting temperature Tm was the best predictor of signal variation. For specific hybridization, however, the effective interaction energy that fully considered competing structures was twice as powerful a predictor of probe signal variation. We show that this was largely due to the effects of secondary structures in the probe and target molecules. The predictive power of the strength of these intramolecular structures was already comparable to that of the melting temperature or the free energy of the probe-target duplex.

CONCLUSIONS

This analysis illustrates the importance of considering both the effects of probe-target binding strength and the different competing structures. For specific hybridization, the secondary structures of probe and target molecules turn out to be at least as important as the probe-target binding strength for an understanding of the observed microarray signal intensities. Besides their relevance for the design of new arrays, our results demonstrate the value of improving thermodynamic models for the read-out and interpretation of microarray signals.

摘要

背景

虽然微阵列是基因表达谱分析的主要方法,但探针信号变化仍然是一个活跃的研究领域。探针信号是序列依赖性的,受探针-靶标结合强度以及探针和靶标中探针-探针二聚体和二级结构的竞争形成的影响。

结果

我们展示了一种改进的微阵列杂交模型的优势,并评估了探针-靶标结合强度和不同竞争结构的相对贡献。值得注意的是,特异性和非特异性杂交显然由不同的能量贡献驱动:对于非特异性杂交,熔点 Tm 是信号变化的最佳预测因子。然而,对于特异性杂交,充分考虑竞争结构的有效相互作用能是探针信号变化的两倍强预测因子。我们表明,这主要归因于探针和靶分子中二级结构的影响。这些分子内结构的预测能力已经与熔点或探针-靶标双链体的自由能相当。

结论

这项分析说明了考虑探针-靶标结合强度和不同竞争结构的影响的重要性。对于特异性杂交,探针和靶分子的二级结构对于理解观察到的微阵列信号强度至少与探针-靶标结合强度一样重要。除了对新阵列设计的相关性之外,我们的结果还证明了改进用于读取和解释微阵列信号的热力学模型的价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf27/2823707/cad502fdf90d/1471-2105-11-35-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf27/2823707/edc96a2cdde8/1471-2105-11-35-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf27/2823707/18a22e6ca6f0/1471-2105-11-35-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf27/2823707/afc84ebfdcef/1471-2105-11-35-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf27/2823707/cad502fdf90d/1471-2105-11-35-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf27/2823707/edc96a2cdde8/1471-2105-11-35-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf27/2823707/18a22e6ca6f0/1471-2105-11-35-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf27/2823707/afc84ebfdcef/1471-2105-11-35-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf27/2823707/cad502fdf90d/1471-2105-11-35-4.jpg

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