Dataset Information

Integrating many co-splicing networks to reconstruct splicing regulatory modules.

ABSTRACT:

Background

Alternative 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.

Results

We 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.

Conclusions

Co-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.

SUBMITTER: Dai C

PROVIDER: S-EPMC3403501 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrating many co-splicing networks to reconstruct splicing regulatory modules.

Dai Chao C Li Wenyuan W Liu Juan J Zhou Xianghong Jasmine XJ

BMC systems biology 20120716

<h4>Background</h4>Alternative 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 facto ...[more]

PMID: 23046974

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Project description:Current experimental evidence indicates that functionally related genes show coordinated expression in order to perform their cellular functions. In this way, the cell transcriptional machinery can respond optimally to internal or external stimuli. This provides a research opportunity to identify and study co-expressed gene modules whose transcription is controlled by shared gene regulatory networks.We developed and integrated a set of computational methods of differential gene expression analysis, gene clustering, gene network inference, gene function prediction, and DNA motif identification to automatically identify differentially co-expressed gene modules, reconstruct their regulatory networks, and validate their correctness. We tested the methods using microarray data derived from soybean cells grown under various stress conditions. Our methods were able to identify 42 coherent gene modules within which average gene expression correlation coefficients are greater than 0.8 and reconstruct their putative regulatory networks. A total of 32 modules and their regulatory networks were further validated by the coherence of predicted gene functions and the consistency of putative transcription factor binding motifs. Approximately half of the 32 modules were partially supported by the literature, which demonstrates that the bioinformatic methods used can help elucidate the molecular responses of soybean cells upon various environmental stresses.The bioinformatics methods and genome-wide data sources for gene expression, clustering, regulation, and function analysis were integrated seamlessly into one modular protocol to systematically analyze and infer modules and networks from only differential expression genes in soybean cells grown under stress conditions. Our approach appears to effectively reduce the complexity of the problem, and is sufficiently robust and accurate to generate a rather complete and detailed view of putative soybean gene transcription logic potentially underlying the responses to the various environmental challenges. The same automated method can also be applied to reconstruct differentially co-expressed gene modules and their regulatory networks from gene expression data of any other transcriptome.

Project description:Simple Summary The growing evidence suggested that competing endogenous RNAs (ceRNAs) have significant associations with tumor occurrence and progression, yet the regulatory mechanism of them in lung adenocarcinoma remains unclear. Identification of the regulatory modules for lung adenocarcinoma is a critical and fundamental step towards understanding the regulatory mechanisms during carcinogenesis. Deep neural network (DNN) models have become a powerful tool to intelligently recognize the sophisticated relationships of ceRNAs appropriately. In this paper, multiple deep neural network models were constructed using the expression data to identify regulatory modules for lung adenocarcinoma in biological networks. Three identified regulatory modules association with lung adenocarcinoma were validated from three aspects, i.e., literature review, functional enrichment analysis, and an independent dataset. The regulatory relationships between RNAs were validated in various datasets, including CPTAC, TCGA and an expression profile from the GEO database. Our study will contribute to improving the understanding of regulatory mechanisms in the carcinogenesis of lung adenocarcinoma and provide schemes for identifying novel regulatory modules of other cancers. Abstract Lung adenocarcinoma is the most common type of primary lung cancer, but the regulatory mechanisms during carcinogenesis remain unclear. The identification of regulatory modules for lung adenocarcinoma has become one of the hotspots of bioinformatics. In this paper, multiple deep neural network (DNN) models were constructed using the expression data to identify regulatory modules for lung adenocarcinoma in biological networks. First, the mRNAs, lncRNAs and miRNAs with significant differences in the expression levels between tumor and non-tumor tissues were obtained. MRNA DNN models were established and optimized to mine candidate mRNAs that significantly contributed to the DNN models and were in the center of an interaction network. Another DNN model was then constructed and potential ceRNAs were screened out based on the contribution of each RNA to the model. Finally, three modules comprised of miRNAs and their regulated mRNAs and lncRNAs with the same regulation direction were identified as regulatory modules that regulated the initiation of lung adenocarcinoma through ceRNAs relationships. They were validated by literature and functional enrichment analysis. The effectiveness of these regulatory modules was evaluated in an independent lung adenocarcinoma dataset. Regulatory modules for lung adenocarcinoma identified in this study provided a reference for regulatory mechanisms during carcinogenesis.

Dataset Information

Integrating many co-splicing networks to reconstruct splicing regulatory modules.

Background

Results

Conclusions

Publications

Integrating many co-splicing networks to reconstruct splicing regulatory modules.

Similar Datasets

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