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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes.
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Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes.

机译：比较时间序列表达谱的连续表示以鉴定差异表达的基因。

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摘要

We present a general algorithm to detect genes differentially expressed between two nonhomogeneous time-series data sets. As increasing amounts of high-throughput biological data become available, a major challenge in genomic and computational biology is to develop methods for comparing data from different experimental sources. Time-series whole-genome expression data are a particularly valuable source of information because they can describe an unfolding biological process such as the cell cycle or immune response. However, comparisons of time-series expression data sets are hindered by biological and experimental inconsistencies such as differences in sampling rate, variations in the timing of biological processes, and the lack of repeats. Our algorithm overcomes these difficulties by using a continuous representation for time-series data and combining a noise model for individual samples with a global difference measure. We introduce a corresponding statistical method for computing the significance of this differential expression measure. We used our algorithm to compare cell-cycle-dependent gene expression in wild-type and knockout yeast strains. Our algorithm identified a set of 56 differentially expressed genes, and these results were validated by using independent protein-DNA-binding data. Unlike previous methods, our algorithm was also able to identify 22 non-cell-cycle-regulated genes as differentially expressed. This set of genes is significantly correlated in a set of independent expression experiments, suggesting additional roles for the transcription factors Fkh1 and Fkh2 in controlling cellular activity in yeast.
机译：我们提出了一种通用算法来检测两个非均匀时间序列数据集之间差异表达的基因。随着越来越多的高通量生物学数据的获得，基因组和计算生物学的主要挑战是开发用于比较来自不同实验来源的数据的方法。时间序列全基因组表达数据是特别有价值的信息来源，因为它们可以描述正在发展的生物学过程，例如细胞周期或免疫应答。但是，时间序列表达数据集的比较由于生物学和实验上的不一致性而受到阻碍，例如采样率的差异，生物学过程的时间变化以及缺少重复序列。我们的算法通过对时间序列数据使用连续表示并将单个样本的噪声模型与全局差异度量相结合来克服了这些困难。我们介绍了一种相应的统计方法，用于计算此差异表达量度的重要性。我们使用我们的算法来比较野生型和基因敲除酵母菌株中细胞周期依赖性基因的表达。我们的算法确定了56个差异表达基因的集合，这些结果通过使用独立的蛋白质DNA结合数据进行了验证。与以前的方法不同，我们的算法还能够识别22个非细胞周期调控的基因差异表达。这组基因在一组独立的表达实验中具有显着的相关性，表明转录因子Fkh1和Fkh2在控制酵母细胞活动中的其他作用。

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