High-resolution analysis of condition-specific regulatory modules in Saccharomyces cerevisiae

Lee, Hun-Goo; Lee, Hyo-Soo; Jeon, Seung Ho; Chung, Tae-Hoon; Lim, Young-Sung; Huh, Won-Ki

doi:10.1186/gb-2008-9-1-r2

Cited by 6 publications

(6 citation statements)

References 136 publications

(61 reference statements)

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“…While it is recognized that not all binding sites found on a promoter will be functional elements, it is also recognized that functional sites are not activated simultaneously or independently of condition, or environment, since the cooperation of TFs is highly dependent on context [102][103][104][105][106]. Human RANTES/CCL5, a member of the CC-or β-subfamily chemotactic cytokines for instance, appears to have six functionally characterized short regulatory elements on its promoter that mediate its transcription initiation.…”

Section: Context-specific Searchmentioning

confidence: 99%

“…The term 'context' here is used in a way that implicitly refers to a set of potentially co-regulated genes e.g. genes that appear either to exhibit correlation in their expression patterns or to be involved in similar functions in a specific condition and/or tissue [103,108,109]. Two elements become critical in this direction: (i) knowledge of the set of potentially co-regulated genes, and (ii) the context-specific nature of functionality.…”

Section: Context-specific Searchmentioning

confidence: 99%

“…Specifically, recent studies have shown that genes sharing similar expression patterns can participate in a number of different biological functions and/or genes in the same pathway can exhibit different patterns of expression [122,123]. Moreover, the underlying gene regulation is shown to be tissue and/or condition specific and the TFs that drive the gene expression are very flexible in function and activity under different conditions [103][104][105][106]. Therefore, defining the context in which a set of genes are more likely to be co-regulated poses a formidable challenge to researchers.…”

Section: Context-specific Searchmentioning

confidence: 99%

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Recent Advances in the Computational Discovery of Transcription Factor Binding Sites

Androulakis

2009

Algorithms

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The discovery of gene regulatory elements requires the synergism between computational and experimental techniques in order to reveal the underlying regulatory mechanisms that drive gene expression in response to external cues and signals. Utilizing the large amount of high-throughput experimental data, constantly growing in recent years, researchers have attempted to decipher the patterns which are hidden in the genomic sequences. These patterns, called motifs, are potential binding sites to transcription factors which are hypothesized to be the main regulators of the transcription process. Consequently, precise detection of these elements is required and thus a large number of computational approaches have been developed to support the de novo identification of TFBSs. Even though novel approaches are continuously proposed and almost all have reported some success in yeast and other lower organisms, in higher organisms the problem still remains a challenge. In this paper, we therefore review the recent developments in computational methods for transcription factor binding site prediction. We start with a brief review of the basic approaches for binding site representation and promoter identification, then discuss the techniques to locate physical TFBSs, identify functional binding sites using orthologous information, and infer functional TFBSs within some context defined by additional prior knowledge. Finally, we briefly explore the opportunities for expanding these approaches towards the computational identification of transcriptional regulatory networks

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Section: Context-specific Searchmentioning

confidence: 99%

Section: Context-specific Searchmentioning

confidence: 99%

Section: Context-specific Searchmentioning

confidence: 99%

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Recent Advances in the Computational Discovery of Transcription Factor Binding Sites

Androulakis

2009

Algorithms

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“…To distinguish between these hypotheses, we analyzed published gene expression profiles (Gasch et al 2000) to identify SBTFs whose subtelomeric target genes had conditionspecific regulatory behaviors (McCord et al 2007;Lee et al 2008) (see Methods). We discovered a strong correlation between the binding and upregulation of targets of Aft2p in response to oxidative stress.…”

Section: Dynamic Subtelomeric Binding Is Correlated With Gene Expressionmentioning

confidence: 99%

Dynamic reprogramming of transcription factors to and from the subtelomere

Mak¹,

Pillus²,

Ideker³

2009

Genome Res.

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Transcription factors are most commonly thought of as proteins that regulate expression of specific genes, independently of the order of those genes along the chromosome. By screening genome-wide chromatin immunoprecipitation (ChIP) profiles in yeast, we find that more than 10% of DNA-binding transcription factors concentrate at the subtelomeric regions near to chromosome ends. None of the proteins identified were previously implicated in regulation at telomeres, yet genomic and proteomic studies reveal that a subset of factors show many interactions with established telomere binding complexes. For many factors, the subtelomeric binding pattern is dynamic and undergoes flux toward or away from the telomere as physiological conditions shift. We find that subtelomeric binding is dependent on environmental conditions and correlates with the induction of gene expression in response to stress. Taken together, these results underscore the importance of genome structure in understanding the regulatory dynamics of transcriptional networks.

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“…This limitation was first confronted by Segal et al (2003) who applied a probabilistic model on gene expression data to identify the modules, their regulators and the conditions under which this regulation takes place. Since then, numerous studies incorporated heterogeneous experimental data such as motif information (Kundaje et al, 2005; Hu et al, 2008; Lee et al, 2008), chromatin immunoprecipitation (ChiP-chip) (Gao et al, 2004; Imoto et al, 2004; Xu et al, 2004; Youn et al, 2010), and protein interaction data (Tornow and Mewes, 2003; Maraziotis et al, 2008) in order to infer gene functional modules and associated networks.…”

Section: Introductionmentioning

confidence: 99%

Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response

Manioudaki¹,

Poirazi²

2013

Front. Genet.

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Over the last decade, numerous computational methods have been developed in order to infer and model biological networks. Transcriptional networks in particular have attracted significant attention due to their critical role in cell survival. The majority of network inference methods use genome-wide experimental data to search for modules of genes with coherent expression profiles and common regulators, often ignoring the multi-layer structure of transcriptional cascades. Modeling methodologies on the other hand assume a given network structure and vary significantly in their algorithmic approach, ranging from over-simplified representations (e.g., Boolean networks) to detailed -but computationally expensive-network simulations (e.g., with differential equations). In this work we use Artificial Neural Networks (ANNs) to model transcriptional regulatory cascades that emerge during the stress response in Saccharomyces cerevisiae and extend in three layers. We confine the structure of the ANNs to match the structure of the biological networks as determined by gene expression, DNA-protein interaction and experimental evidence provided in publicly available databases. Trained ANNs are able to predict the expression profile of 11 target genes across multiple experimental conditions with a correlation coefficient >0.7. When time-dependent interactions between upstream transcription factors (TFs) and their indirect targets are also included in the ANNs, accurate predictions are achieved for 30/34 target genes. Moreover, heterodimer formation is taken into account. We show that ANNs can be used to (1) accurately predict the expression of downstream genes in a 3-layer transcriptional cascade based on the expression of their indirect regulators and (2) infer the condition- and time-dependent activity of various TFs as well as during heterodimer formation. We show that a three-layer regulatory cascade whose structure is determined by co-expressed gene modules and their regulators can successfully be modeled using ANNs with a similar configuration.

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High-resolution analysis of condition-specific regulatory modules in Saccharomyces cerevisiae

Cited by 6 publications

References 136 publications

Recent Advances in the Computational Discovery of Transcription Factor Binding Sites

Recent Advances in the Computational Discovery of Transcription Factor Binding Sites

Dynamic reprogramming of transcription factors to and from the subtelomere

Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response

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