Extracting transcriptional events from temporal gene expression patterns during Dictyostelium development
Extracting transcriptional events from temporal gene expression patterns during Dictyostelium development
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DOI：
10.1093/bioinformatics/18.1.61
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发表时间：
2002-01-01
期刊：
影响因子：
5.8
通讯作者：
Loomis, WF
中科院分区：
文献类型：
--
作者：
Sásik, R;Iranfar, N;Loomis, WF
动机：DNA微阵列技术可以产生描述基因表达时间进程的大量数据。这些数据，如果解释得当，可以产生大量关于发育过程中差异基因表达的信息。目前生物信息学中的许多工作都致力于基因表达数据的分析，通常是通过在某个抽象的高维空间中对原始数据进行聚类分析。在这里，我们描述了一种方法，其中我们首先使用一个简单的基于生物学的基因表达动力学模型来处理原始的时间进程数据。这使我们能够将大量的数据简化为表征每个表达谱的几个重要属性，例如，发育调节基因表达的开始和停止时间。然后，这些重要的属性可以通过肉眼观察进行简单的聚类，以揭示生物学上的显著影响。结果：我们应用了这种方法来处理在盘基网柄菌24小时发育过程中每隔2小时分离一次的样本的微阵列表达数据。50个发育基因的mRNA积累模式符合动力学模型，p值在0.05或更高。这些基因的转录似乎在发育过程中明确定义的时期以一种依赖序列的方式突然启动。这种方法可以应用于其他时间基因表达模式的分析，包括细胞周期的那些。
Motivation: The DNA microarray technology can generate a large amount of data describing the time-course of gene expression. These data, when properly interpreted, can yield a great deal of information concerning differential gene expression during development. Much current effort in bioinformatics has been devoted to the analysis of gene expression data, usually via some 'clustering analysis' on the raw data in some abstract high dimensional space. Here, we describe a method where we first 'process' the raw time-course data using a simple biologically based kinetic model of gene expression. This allows us to reduce the vast data to a few vital attributes characterizing each expression profile, e.g. the times of the onset and cessation of the expression of the developmentally regulated genes. These vital attributes can then be trivially clustered by visual inspection to reveal biologically significant effects.Results: We have applied this approach to microarray expression data from samples isolated every 2 h throughout the 24 h developmental program of Dictyostelium discoideum. mRNA accumulation patterns for 50 developmental genes were found to fit the kinetic model with a p-value of 0.05 or better. Transcription of these genes appears to be initiated in bursts at well-defined periods during development, in a manner suggestive of a dependent sequence. This approach can be applied to analyses of other temporal gene expression patterns, including those of the cell cycle.