Kundaje Anshul, Xin Xiantong, Lan Changgui, Lianoglou Steve, Zhou Mei, Zhang Li, Leslie Christina
Department of Computer Science, Columbia University, New York, New York, United States of America.
PLoS Comput Biol. 2008 Nov;4(11):e1000224. doi: 10.1371/journal.pcbi.1000224. Epub 2008 Nov 14.
Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included.
通过高通量表达数据分析来解读基因调控机制是一个具有挑战性的计算问题。以往的计算研究使用大型表达数据集来解析共表达的精细模式，从而产生潜在共调控基因的聚类或模块。这些方法通常在聚类后的单独步骤中检查启动子序列信息，如DNA基序或转录因子占据数据。我们需要一种替代的、更综合的方法，使用一个小型的扰动实验数据集来研究酿酒酵母中的氧调控网络。氧感应和调控机制是许多生理和病理过程的基础，而在以往的研究中只鉴定出了少数几种氧调节因子。我们使用一种名为MEDUSA的新机器学习算法，利用全基因组表达变化来揭示氧调控网络的详细信息，这些变化是对氧、血红素、Hap1和Co2+水平扰动的响应。MEDUSA整合了mRNA表达、启动子序列和ChIP-chip占据数据，以学习一个能准确预测保留数据中靶基因差异表达的模型。我们使用一种基于边际的新分数来提取显著条件特异性调节因子，并构建氧感应和调控网络的全局图谱。该网络包括已知的氧和血红素调节因子，如Hap1、Mga2,、Hap4和Upc2，以及许多新的候选调节因子。MEDUSA还鉴定出许多与先前实验鉴定的转录因子结合位点一致的DNA基序。由于MEDUSA的调控程序通过启动子序列将调节因子与靶基因关联起来，我们通过对缺氧特异性诱导基因OLE1的启动子活性进行实验分析，直接测试了预测的调节因子。在每种情况下，候选调节因子的缺失都导致了对启动子活性的预测效应，证实MEDUSA鉴定出的几种新调节因子确实参与了氧调控。MEDUSA可以从小型数据集中揭示重要信息，并生成可用于进一步实验分析的可测试假设。包含补充数据。
