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Applications and characterization of mRNA expression compendia in inference of genetic association networks.

机译:mRNA表达纲要在遗传关联网络推断中的应用和表征。

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摘要

Large mRNA expression compendia, comprised of an organism's genome-wide responses to a range of experimental conditions, potently enable inference of genetic association networks. Here, we contribute to two applications of this strategy: perturbation target prediction and transcriptional regulatory network inference (TRNI).;A key challenge in analyzing an organism's expression response to a perturbation is distinguishing the genes directly affected from the many indirectly affected genes. To address this, we developed a genome-wide model of mRNA expression and applied sparse inference methods to construct a network model of gene interaction effects. This inferred network model was then used to filter the expression profile of a perturbation of interest and predict the genes directly targeted by that perturbation. Our method performed well in identifying targets of genetic perturbations in microarray compendia with up to a 32% improvement in sensitivity, compared to the next best method. The overall performance of our network-filtering method shows promise for identifying the direct targets of genetic dysregulation in cancer and disease from expression profiles.;In applying the method above, we observed evidence of structure within the compendia. We sought to explicitly characterize this structure and resulting dependency in an Escherichia coli microarray compendium ( n=376 experiments), and illustrate its consequences in performance and statistical testing for edge selection in TRNI. We observed structure across experimental condition-based groupings of experiments and, consequently, a substantially reduced effective sample size neff=14.7. Furthermore, we found that neff of select subsets of the data exceeded neff of the full compendium, and observed improved performance in TRNI using these subsets. Finally, using the RegulonDB truth-set, we demonstrated that false discovery rates derived using neff yielded accurate thresholds for edge selection in TRNI, while using n vastly overestimated the number of true edges. These results recommend neff as a potent, specific descriptor of microarray compendia and highlight a TRNI method that is readily applicable to compendia from any species, even when a truth-set is not available to guide edge selection.;These findings demonstrate improvement in perturbation target prediction and promote a more refined construction and utilization of microarray compendia in TRNI and related problems.
机译:大型的mRNA表达纲要由生物体对一系列实验条件的全基因组范围内的反应组成,有力地推论了遗传关联网络。在此,我们为该策略的两个应用做出了贡献:扰动目标预测和转录调控网络推论(TRNI)。分析生物体对扰动的表达反应的关键挑战是将直接受影响的基因与许多间接受影响的基因区分开来。为了解决这个问题,我们开发了一个全基因组的mRNA表达模型,并应用了稀疏推理方法来构建基因相互作用效应的网络模型。然后,使用这种推断的网络模型过滤感兴趣的扰动的表达谱,并预测该扰动直接靶向的基因。与次优方法相比,我们的方法在识别微阵列概要中的遗传扰动目标方面表现出色,灵敏度提高了32%。我们的网络过滤方法的整体性能显示了从表达谱中鉴定癌症和疾病中遗传失调的直接靶标的前景。在应用上述方法时,我们观察到了纲目中结构的证据。我们试图在大肠杆菌微阵列纲要中明确表征该结构及其所产生的依赖性(n = 376实验),并说明其在TRNI边缘选择的性能和统计测试中的后果。我们观察到了基于实验条件的实验分组的结构,因此,有效样本量neff = 14.7大大减少了。此外,我们发现数据的选定子集的neff超过了完整纲要的neff,并观察到使用这些子集的TRNI的性能有所提高。最后,使用RegulonDB真值集,我们证明了使用neff得出的错误发现率在TRNI中产生了准确的边缘选择阈值,而使用n则大大高估了真实边缘的数量。这些结果推荐neff作为微阵列补遗的有力,特定的描述符,并强调了TRNI方法,即使没有真相集可以指导边缘选择,该方法也适用于任何物种的补遗;这些发现表明摄动目标得到了改善。预测并促进TRNI及其相关问题中微阵列汇编的更完善的构建和利用。

著录项

  • 作者

    Cosgrove, Elissa J.;

  • 作者单位

    Boston University.;

  • 授予单位 Boston University.;
  • 学科 Engineering Biomedical.;Biology Bioinformatics.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 157 p.
  • 总页数 157
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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