Integrating many co-splicing networks to reconstruct splicing regulatory modules.
Integrating many co-splicing networks to reconstruct splicing regulatory modules.
复制标题
整合多个共剪接网络重构剪接调控模块
DOI：
10.1186/1752-0509-6-s1-s17
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发表时间：
2012
影响因子：
--
通讯作者：
Zhou XJ
中科院分区：
文献类型：
--
作者：
Dai C;Li W;Liu J;Zhou XJ
背景选择性剪接是一种普遍存在的基因调控机制，它极大地增加了蛋白质组的复杂性。然而，调节选择性剪接的机制知之甚少，协调剪接调节的研究仅限于个别情况。为了研究全基因组剪接调控，我们整合了许多人类RNA-seq数据集来识别剪接模块，我们将其定义为一组由相同剪接因子共同调控的盒式外显子。ResultsWe设计了一种基于张量的方法来识别在多种条件下频繁出现的共剪接簇，因此很可能代表剪接模块-剪接调控网络中的一个单元。特别是，我们将每个RNA-seq数据集建模为共剪接网络，其中节点代表外显子，边缘通过外显子包含率曲线之间的相关性进行加权。我们将基于张量的方法应用于来自人类RNA-seq数据集的38个共剪接网络，并识别出频繁共剪接簇的图谱。我们证明，这些确定的集群代表潜在的剪接模块验证对四个生物知识数据库。一个频繁的共剪接簇具有生物学意义的可能性随着其在多个数据集上的重复出现而增加，突出了整合方法的重要性。ConclusionsCo-splicing簇揭示了不能通过共表达簇识别的新功能组，特别是它们可以赋予与转录后调控相关的功能的新见解，并且相同的外显子可以动态地参与不同的途径，这取决于不同的条件和共剪接的不同的其它外显子。我们建议，通过识别剪接模块，剪接调控网络中的一个单元可以作为一个重要的步骤来破译剪接密码。
BackgroundAlternative splicing is a ubiquitous gene regulatory mechanism that dramatically increases the complexity of the proteome. However, the mechanism for regulating alternative splicing is poorly understood, and study of coordinated splicing regulation has been limited to individual cases. To study genome-wide splicing regulation, we integrate many human RNA-seq datasets to identify splicing module, which we define as a set of cassette exons co-regulated by the same splicing factors.ResultsWe have designed a tensor-based approach to identify co-splicing clusters that appear frequently across multiple conditions, thus very likely to represent splicing modules - a unit in the splicing regulatory network. In particular, we model each RNA-seq dataset as a co-splicing network, where the nodes represent exons and the edges are weighted by the correlations between exon inclusion rate profiles. We apply our tensor-based method to the 38 co-splicing networks derived from human RNA-seq datasets and indentify an atlas of frequent co-splicing clusters. We demonstrate that these identified clusters represent potential splicing modules by validating against four biological knowledge databases. The likelihood that a frequent co-splicing cluster is biologically meaningful increases with its recurrence across multiple datasets, highlighting the importance of the integrative approach.ConclusionsCo-splicing clusters reveal novel functional groups which cannot be identified by co-expression clusters, particularly they can grant new insights into functions associated with post-transcriptional regulation, and the same exons can dynamically participate in different pathways depending on different conditions and different other exons that are co-spliced. We propose that by identifying splicing module, a unit in the splicing regulatory network can serve as an important step to decipher the splicing code.