Using a seed-network to query multiple large-scale gene expression datasets from the developing retina in order to identify and prioritize experimental targets.
Using a seed-network to query multiple large-scale gene expression datasets from the developing retina in order to identify and prioritize experimental targets.
复制标题
DOI：
10.4137/bbi.s417
复制
发表时间：
2008-02-01
影响因子：
5.8
通讯作者：
Greenlee MH
中科院分区：
文献类型：
--
作者：
Hecker LA;Alcon TC;Honavar VG;Greenlee MH
了解在发育中的视网膜中协调视网膜祖细胞分化为光感受器的基因网络是重要的，不仅因为其在治疗视网膜变性中的治疗应用，而且因为发育中的视网膜提供了研究CNS发育的极好模型。虽然有几项研究已经描述了正常视网膜发育过程中基因表达的变化，但这些研究充其量只能为专注于较小基因子集的功能研究提供一个起点。在相对较少的时间点分析的大量基因使得从基因表达数据集可靠地推断基因网络变得极其困难。我们描述了一种新的方法来识别和优先考虑从多个基因表达数据集，一个小的基因子集，可能是很好的候选人进行进一步的实验研究。我们报告了解决这个问题的进展，使用一种新的方法来查询多个大规模的表达数据集，使用一个“种子网络”组成的一小组基因，所涉及的已发表的研究在视杆细胞分化。我们使用种子网络来识别和排序一系列基因，这些基因的表达水平与五个基因表达数据集中的至少两个中的多个种子网络基因的表达水平高度相关。事实上，在这个列表中的几个基因已被证明，通过文献中报道的实验研究，是重要的视杆细胞的功能提供了支持，这种方法的效用，优先实验目标进一步的实验研究。基于基因本体论和KEGG途径注释的基因列表中获得的其他信息的背景下，在文献中，我们确定了七个基因或基因组可能包括在基因网络中参与视网膜祖细胞分化成视杆光感受器。我们的方法来查询多个基因表达数据集，使用种子网络构建从特定的感兴趣的基因之间的已知相互作用提供了一个有前途的策略，集中假设驱动的实验，使用大规模的“组学”数据。
Understanding the gene networks that orchestrate the differentiation of retinal progenitors into photoreceptors in the developing retina is important not only due to its therapeutic applications in treating retinal degeneration but also because the developing retina provides an excellent model for studying CNS development. Although several studies have profiled changes in gene expression during normal retinal development, these studies offer at best only a starting point for functional studies focused on a smaller subset of genes. The large number of genes profiled at comparatively few time points makes it extremely difficult to reliably infer gene networks from a gene expression dataset. We describe a novel approach to identify and prioritize from multiple gene expression datasets, a small subset of the genes that are likely to be good candidates for further experimental investigation. We report progress on addressing this problem using a novel approach to querying multiple large-scale expression datasets using a ‘seed network’ consisting of a small set of genes that are implicated by published studies in rod photoreceptor differentiation. We use the seed network to identify and sort a list of genes whose expression levels are highly correlated with those of multiple seed network genes in at least two of the five gene expression datasets. The fact that several of the genes in this list have been demonstrated, through experimental studies reported in the literature, to be important in rod photoreceptor function provides support for the utility of this approach in prioritizing experimental targets for further experimental investigation. Based on Gene Ontology and KEGG pathway annotations for the list of genes obtained in the context of other information available in the literature, we identified seven genes or groups of genes for possible inclusion in the gene network involved in differentiation of retinal progenitor cells into rod photoreceptors. Our approach to querying multiple gene expression datasets using a seed network constructed from known interactions between specific genes of interest provides a promising strategy for focusing hypothesis-driven experiments using large-scale ‘omics’ data.