结合基因表达分析和先验知识鉴定参与循环过程的基因。

Wentao Zhao, Erchin Serpedin, Edward R Dougherty
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引用次数: 9

摘要

基于基因的时间序列表达,通过谱分析和统计周期性检测测试可以识别出循环基因。这些循环基因通常与循环的生物过程有关,例如细胞周期和昼夜节律。通过将检测到的周期性表达基因与实验验证的参与循环过程的基因进行比较,可以实际测量方案的功率。然而,在上述过程中,有价值的先验知识只是作为一个评价基准,在算法的实现中没有得到充分的利用。此外,部分数据集由于其非平稳性也被忽略。本文提出了一种将先验知识与基因表达分析相结合的循环过程相关基因识别算法。该算法分别应用于酿酒酵母(Saccharomyces cerevisiae)和果蝇(Drosophila melanogaster)对应的数据集。生物学证据证实了所发现的基因在细胞周期和昼夜节律中的作用。树形图是用来聚类已鉴定的基因和揭示表达模式。这证实了所提出的新的识别方案提供了一个有价值的技术,揭示与循环过程相关的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Identifying genes involved in cyclic processes by combining gene expression analysis and prior knowledge.

Identifying genes involved in cyclic processes by combining gene expression analysis and prior knowledge.

Identifying genes involved in cyclic processes by combining gene expression analysis and prior knowledge.

Identifying genes involved in cyclic processes by combining gene expression analysis and prior knowledge.

Based on time series gene expressions, cyclic genes can be recognized via spectral analysis and statistical periodicity detection tests. These cyclic genes are usually associated with cyclic biological processes, for example, cell cycle and circadian rhythm. The power of a scheme is practically measured by comparing the detected periodically expressed genes with experimentally verified genes participating in a cyclic process. However, in the above mentioned procedure the valuable prior knowledge only serves as an evaluation benchmark, and it is not fully exploited in the implementation of the algorithm. In addition, partial data sets are also disregarded due to their nonstationarity. This paper proposes a novel algorithm to identify cyclic-process-involved genes by integrating the prior knowledge with the gene expression analysis. The proposed algorithm is applied on data sets corresponding to Saccharomyces cerevisiae and Drosophila melanogaster, respectively. Biological evidences are found to validate the roles of the discovered genes in cell cycle and circadian rhythm. Dendrograms are presented to cluster the identified genes and to reveal expression patterns. It is corroborated that the proposed novel identification scheme provides a valuable technique for unveiling pathways related to cyclic processes.

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