Koryachko Alexandr, Matthiadis Anna, Muhammad Durreshahwar, Foret Jessica, Brady Siobhan M, Ducoste Joel J, Tuck James, Long Terri A, Williams Cranos
Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina, United States of America.
Plant and Microbial Biology, North Carolina State University, Raleigh, North Carolina, United States of America.
PLoS One. 2015 Aug 28;10(8):e0136591. doi: 10.1371/journal.pone.0136591. eCollection 2015.
Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA). This algorithm was designed to process transcriptome data by first grouping genes based on stages of activity and then using similarities in gene expression to predict influential connections between individual genes. Regulatory relationships are assigned based on pairwise alignment scores generated using the expression patterns of two genes and some inferred delay between the regulator and the observed activity of the target. We applied the CDAA to an iron deficiency time course microarray dataset to identify regulators that influence 7 target transcription factors known to participate in the Arabidopsis thaliana iron deficiency response. The algorithm predicted that 7 regulators previously unlinked to iron homeostasis influence the expression of these known transcription factors. We validated over half of predicted influential relationships using qRT-PCR expression analysis in mutant backgrounds. One predicted regulator-target relationship was shown to be a direct binding interaction according to yeast one-hybrid (Y1H) analysis. These results serve as a proof of concept emphasizing the utility of the CDAA for identifying unknown or missing nodes in regulatory cascades, providing the fundamental knowledge needed for constructing predictive gene regulatory networks. We propose that this tool can be used successfully for similar time course datasets to extract additional information and infer reliable regulatory connections for individual genes.
时间进程转录组数据集通常用于预测与应激反应相关的关键基因调控因子，并探索基因功能。从高通量时间进程表达数据中提取基因间因果关系所开发的技术，受到低信号水平以及时间点上的噪声和稀疏性的限制。我们通过提出聚类和差异比对算法（CDAA）来处理这些限制。该算法旨在通过首先根据活性阶段对基因进行分组，然后利用基因表达的相似性来预测单个基因之间的影响连接，从而处理转录组数据。基于使用两个基因的表达模式生成的成对比对分数以及调控因子与目标观察活性之间的一些推断延迟来分配调控关系。我们将CDAA应用于缺铁时间进程微阵列数据集，以识别影响已知参与拟南芥缺铁反应的7个靶转录因子的调控因子。该算法预测，7个先前与铁稳态无关的调控因子影响这些已知转录因子的表达。我们在突变背景下使用qRT-PCR表达分析验证了超过一半的预测影响关系。根据酵母单杂交（Y1H）分析，一个预测的调控因子-靶标关系被证明是直接结合相互作用。这些结果作为概念验证，强调了CDAA在识别调控级联中未知或缺失节点方面的实用性，为构建预测性基因调控网络提供了所需的基础知识。我们提出，该工具可成功用于类似的时间进程数据集，以提取额外信息并推断单个基因的可靠调控连接。
