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Discovery of complex regulatory modules from expression genetics data.

机译：从表达遗传学数据中发现复杂的调控模块。

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摘要

Mapping of strongly inherited classical traits have been immensely helpful in understanding many important traits including diseases, yield and immunity. But some of these traits are too complex and are difficult to map. Taking into consideration gene expression, which mediates the genetic effects, can be helpful in understanding such traits. Together with genetic variation data such data-set is collectively known as expression genetics data. Presence of discrete and continuous variables, observed and latent variables, availability of partial causal information, and under-specified nature of the data make expression genetics data computationally challenging, but potentially of great biological importance.;In this dissertation the underlying regulatory processes are modeled as Bayesian networks consisting of gene expression and genetic variation nodes. Due to the under-specified nature of the data, inferring the complete regulatory network is impractical. Instead, the following techniques are proposed to extract interesting subnetworks with high confidence.;The network motif searching technique is used to recover instances of a known regulatory mechanism. The local network inference technique is used to identify immediate neighbors of a given transcript. Application of these two techniques often results in identification of hundreds of individual networks. The network aggregation technique extracts the most common subnetwork from those networks, and identifies its immediate neighbors by collapsing them into a common network.;In all the above tasks, simulation studies were carried out to estimate the robustness of the proposed methods and the results suggest that these techniques are capable of recovering the correct substructure with high precision and moderate recall. Moreover, manual biological review shows that the recovered regulatory network substructures are typically biologically sensible.
机译：强烈继承的经典性状的作图对理解许多重要性状（包括疾病，产量和免疫力）非常有帮助。但是其中一些特征过于复杂且难以绘制。考虑介导遗传效应的基因表达可能有助于理解这些性状。这些数据集与遗传变异数据一起统称为表达遗传学数据。离散变量和连续变量的存在，观测变量和潜在变量的存在，部分因果信息的可获得性以及数据的特定性不足，使得表达遗传学数据在计算上具有挑战性，但可能具有重要的生物学意义。作为由基因表达和遗传变异节点组成的贝叶斯网络。由于数据的指定性质不足，因此无法推断出完整的监管网络。取而代之的是，提出了以下技术来以高置信度提取有趣的子网。网络主题搜索技术用于恢复已知调节机制的实例。本地网络推断技术用于识别给定成绩单的直接邻居。这两种技术的应用通常会导致识别数百个单独的网络。网络聚合技术从这些网络中提取最常见的子网，并通过将它们折叠成一个通用网络来识别其直接邻居。在上述所有任务中，进行了仿真研究，以评估所提出方法的鲁棒性，结果表明这些技术能够以高精度和中等召回率恢复正确的子结构。此外，人工生物学审查显示，回收的调节网络亚结构通常具有生物学敏感性。

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  • 作者

    Jagalur, Manjunatha N.;

  • 作者单位

    University of Massachusetts Amherst.;

  • 授予单位 University of Massachusetts Amherst.;
  • 学科 Biology Biostatistics.;Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 99 p.
  • 总页数 99
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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