Co-expression pattern from DNA microarray experiments as a tool for operon prediction
Co-expression pattern from DNA microarray experiments as a tool for operon prediction
复制标题
DOI：
10.1093/nar/gkf388
复制
发表时间：
2002-07-01
影响因子：
14.9
通讯作者：
Liao, JC
中科院分区：
文献类型：
--
作者：
Sabatti, C;Rohlin, L;Liao, JC
操纵子是原核生物中最小的转录单位，对操纵子的预测是在全基因组水平重建调控网络的第一步。序列信息，特别是开放阅读框之间的距离，已被用于预测相邻的大肠杆菌基因是否在一个操纵子中。虽然这些预测取得了相当大的成功，但仍需要通过实验进行验证和完善。随着越来越多的大肠杆菌基因表达芯片实验数据的出现，我们研究了这些数据在多大程度上可用于改进和验证这些预测。为此，我们检查了大量已发表的微阵列数据。在贝叶斯分类方案中，利用相邻基因表达比率之间的相关性来预测这些基因是否在一个操纵子中。我们发现，对于数据集中在实验中表达水平发生显著变化的基因，目前可用的基因表达数据能够对基于序列的预测进行显著的完善。我们在大肠杆菌基因组图谱中报告了这些共表达相关性。然而，对于很大一部分基因对，所考虑的芯片实验集没有包含足够的信息来确定它们是否在同一个转录单元中。这并不是由于芯片数据本身不可靠，而是由于所分析的实验设计。一般来说，扰动大量基因的实验比有限扰动的实验为操纵子预测提供了更多的信息。这些结果为进行比较导致基因表达全局变化的条件的表达研究提供了理论依据。
The prediction of operons, the smallest unit of transcription in prokaryotes, is the first step towards reconstruction of a regulatory network at the whole genome level. Sequence information, in particular the distance between open reading frames, has been used to predict if adjacent Escherichia coli genes are in an operon. While appreciably successful, these predictions need to be validated and refined experimentally. As a growing number of gene expression array experiments on E.coli became available, we investigated to what extent they could be used to improve and validate these predictions. To this end, we examined a large collection of published microarry data. The correlation between expression ratios of adjacent genes was used in a Bayesian classification scheme to predict whether the genes are in an operon or not. We found that for the genes whose expression levels change significantly across the experiments in the data set, the currently available gene expression data allowed a significant refinement of the sequenced-based predictions. We report these co-expression correlations in an E.coli genomic map. For a significant portion of gene pairs, however, the set of array experiments considered did not contain sufficient information to determine whether they are in the same transcriptional unit. This is not due to unreliability of the array data per se, but to the design of the experiments analyzed. In general, experiments that perturb a large number of genes offer more information for operon prediction than confined perturbations. These results provide a rationale for conducting expression studies comparing conditions that cause global changes in gene expression.