Conceptualization of molecular findings by mining gene annotations

Chen, Vicky; Lu, Xinghua

doi:10.1186/1753-6561-7-s7-s2

Cited by 4 publications

(10 citation statements)

References 28 publications

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“…The overall framework of this study is shown in Fig 2, which consists of the following steps to search for a set of mutually exclusive driver SGAs that affect a common signal: 1) We first identified differentially expressed genes from each tumor, and then grouped genes into non-disjoint functional sets according to their Gene Ontology [18] (GO) annotation using the methods previous developed by our group [19][20][21], such that functions of the genes in a set are coherently related to each other and are summarized by a GO term from the Biological Process Domain of the GO (Fig 2A). 2) After selecting the differentially expressed genes annotated with a common GO term across tumors, we constructed a bipartite graph consisting of tumors on one side and the genes on the other side, and we then searched for a densely connected subgraph (Fig 2B).…”

Section: Methodsmentioning

confidence: 99%

“…To find such modules among the cancer tumors, we employed a knowledge-driven data mining approach, developed in our previous studies [19][20][21], which consists of two major procedures: 1) identifying functionally coherent gene subsets among the differentially expressed genes in each tumor, such that each gene subset is annotated by a GO term that summarizes the function of the genes; and 2) further identifying the gene subsets that are differentially expressed in multiple Overall scheme. A) Use GO structure and semantic information to categorize differentially expressed genes in each tumor into functionally coherent subgroups.…”

Section: Identifying Gene Modules As Signal-response Unitsmentioning

confidence: 99%

“…To identify functionally coherent gene subsets, we first found a tumor's differentially expressed genes and grouped them into non-disjoint subsets by mining their annotations [19][20][21]. This was achieved by representing the hierarchical structure of GO terms as a directed acyclic graph and searching for genes annotated with closely related GO terms.…”

Section: Identifying Gene Modules As Signal-response Unitsmentioning

confidence: 99%

“…We first associated genes to the GO terms according to annotations of the genes. We then iteratively merged highly specific GO terms and their associated genes to their parent GO terms according to a procedure [19] that strives to minimize the loss of semantic information during the process. In this fashion, we can group genes annotated with closely related terms into a set annotated with a more general GO term that retains the information of the original annotations.…”

Section: Identifying Gene Modules As Signal-response Unitsmentioning

confidence: 99%

“…In this fashion, we can group genes annotated with closely related terms into a set annotated with a more general GO term that retains the information of the original annotations. We stop the procedure if a further merge leads to a non-coherent gene set, according to a quantitative metric that assesses the statistical significance of functional coherence of the gene set [19,21]. This procedure enabled us to partition differentially expressed genes from each tumor into non-disjoint, functionally coherent subsets.…”

Section: Identifying Gene Modules As Signal-response Unitsmentioning

confidence: 99%

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Identifying driver genomic alterations in cancers by searching minimum-weight, mutually exclusive sets

Cheng³

et al. 2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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An important goal of cancer genomic research is to identify the driving pathways underlying disease mechanisms and the heterogeneity of cancers. It is well known that somatic genome alterations (SGAs) affecting the genes that encode the proteins within a common signaling pathway exhibit mutual exclusivity, in which these SGAs usually do not co-occur in a tumor. With some success, this characteristic has been utilized as an objective function to guide the search for driver mutations within a pathway. However, mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways. Here, we propose a novel, signal-oriented framework for identifying driver SGAs. First, we identify the perturbed cellular signals by mining the gene expression data. Next, we search for a set of SGA events that carries strong information with respect to such perturbed signals while exhibiting mutual exclusivity. Finally, we design and implement an efficient exact algorithm to solve an NP-hard problem encountered in our approach. We apply this framework to the ovarian and glioblastoma tumor data available at the TCGA database, and perform systematic evaluations. Our results indicate that the signal-oriented approach enhances the ability to find informative sets of driver SGAs that likely constitute signaling pathways. Author SummaryAn important goal of studying cancer genomics is to identify critical pathways that, when perturbed by somatic genomic alterations (SGAs) such as somatic mutations, copy number alterations and epigenomic alterations, cause cancers and underlie different clinical phenotypes. In this study, we present a framework for discovering perturbed signaling pathways in cancers by integrating genome alteration data and transcriptomic data from the Cancer Genome Atlas (TCGA) project. Since gene expression in a cell is regulated by cellular signaling systems, we used transcriptomic changes to reveal perturbed cellular signals in each tumor. We then combined the genomic alteration data to search for SGA events across multiple tumors that affected a common signal, thus identifying the candidate members of cancer pathways. Our results demonstrate the advantage of the signaloriented pathway approach over previous methods.

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Section: Methodsmentioning

confidence: 99%

Section: Identifying Gene Modules As Signal-response Unitsmentioning

confidence: 99%

Section: Identifying Gene Modules As Signal-response Unitsmentioning

confidence: 99%

Section: Identifying Gene Modules As Signal-response Unitsmentioning

confidence: 99%

Section: Identifying Gene Modules As Signal-response Unitsmentioning

confidence: 99%

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Identifying driver genomic alterations in cancers by searching minimum-weight, mutually exclusive sets

Cheng³

et al. 2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Distinct Signaling Roles of Ceramide Species in Yeast Revealed Through Systematic Perturbation and Systems Biology Analyses

et al. 2013

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Ceramide, the central molecule of sphingolipid metabolism, is an important bioactive molecule participating in cellular regulatory events and having implications for disease. A challenge in deciphering ceramide signaling emanates from the myriad of ceramide species that exist and the possibility that many of them may have distinct functions. Here, we applied systems biology and molecular approaches to perturb ceramide metabolism in the yeast (Saccharomyces cerevisiae) and inferred causal relationships between ceramide species and their potential targets by combining lipidomic, genomic, and transcriptomic analyses. We find that during heat stress distinct metabolic mechanisms control the abundance of different groups of ceramide species. Additionally, distinct groups of ceramide species regulated different sets of functionally related genes, indicating that specific sub-groups of lipids participated in different regulatory pathways. These results indicate a previously unrecognized complexity and versatility of lipid-mediated cell regulation.

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Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model

et al. 2016

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BackgroundA living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program. An important and yet challenging task in systems biology is to reconstruct cellular signaling system in a data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks in learning and representing the hierarchical organization of yeast transcriptomic machinery.ResultsWe have designed a sparse autoencoder model consisting of a layer of observed variables and four layers of hidden variables. We applied the model to over a thousand of yeast microarrays to learn the encoding system of yeast transcriptomic machinery. After model selection, we evaluated whether the trained models captured biologically sensible information. We show that the latent variables in the first hidden layer correctly captured the signals of yeast transcription factors (TFs), obtaining a close to one-to-one mapping between latent variables and TFs. We further show that genes regulated by latent variables at higher hidden layers are often involved in a common biological process, and the hierarchical relationships between latent variables conform to existing knowledge. Finally, we show that information captured by the latent variables provide more abstract and concise representations of each microarray, enabling the identification of better separated clusters in comparison to gene-based representation.ConclusionsContemporary deep hierarchical latent variable models, such as the autoencoder, can be used to partially recover the organization of transcriptomic machinery.

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Conceptualization of molecular findings by mining gene annotations

Cited by 4 publications

References 28 publications

Identifying driver genomic alterations in cancers by searching minimum-weight, mutually exclusive sets

Identifying driver genomic alterations in cancers by searching minimum-weight, mutually exclusive sets

Distinct Signaling Roles of Ceramide Species in Yeast Revealed Through Systematic Perturbation and Systems Biology Analyses

Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model

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