Regression trees for regulatory element identification

Phương, Từ Minh; Lee, Doheon; Lee, Kwang Hyung

doi:10.1093/bioinformatics/btg480

Cited by 32 publications

(41 citation statements)

References 21 publications

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“…Many bioinformatic methods have been proposed to identify synergistic pairs of TFs (5)(6)(7)(8)(9)(10). Some of these methods (9,10) assume that a pair of TFs is synergistic if genes regulated by both TFs show stronger coexpression patterns than the expression patterns of genes regulated by either TF alone.…”

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confidence: 99%

Statistical methods for identifying yeast cell cycle transcription factors

Tsai

2005

Proc. Natl. Acad. Sci. U.S.A.

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Knowing transcription factors (TFs) involved in the yeast cell cycle is helpful for understanding the regulation of yeast cell cycle genes. We therefore developed two methods for predicting (i) individual cell cycle TFs and (ii) synergistic TF pairs. The essential idea is that genes regulated by a cell cycle TF should have higher (lower, if it is a repressor) expression levels than genes not regulated by it during one or more phases of the cell cycle. This idea can also be used to identify synergistic interactions of TFs. Applying our methods to chromatin immunoprecipitation data and microarray data, we predict 50 cell cycle TFs and 80 synergistic TF pairs, including most known cell cycle TFs and synergistic TF pairs. Using these and published results, we describe the behaviors of 50 known or inferred cell cycle TFs in each cell cycle phase in terms of activation͞repression and potential positive͞negative interactions between TFs. In addition to the cell cycle, our methods are also applicable to other functions.cell cycle regulators ͉ microarray data ͉ synergistic interactions T o understand how cell cycle genes are regulated, it is useful to identify transcription factors (TFs) that are cell cycle regulators. In the yeast Saccharomyces cerevisiae, a number of such TFs have already been identified through various approaches (1-4). A recent powerful tool is the chromatin immunoprecipitation (ChIP)-chip technique, which utilizes ChIP to isolate DNA bound by a TF and applies microarrays to precipitated DNAs to identify genes bound by the TF. Using this technique, Lee et al. (4) identified 11 cell cycle TFs. Assuming that genes coordinately bound are coordinately expressed, they also determined several TFs that might have combinatorial or synergistic regulations.Many bioinformatic methods have been proposed to identify synergistic pairs of TFs (5-10). Some of these methods (9, 10) assume that a pair of TFs is synergistic if genes regulated by both TFs show stronger coexpression patterns than the expression patterns of genes regulated by either TF alone. This type of method requires data collected over multiple time points to calculate the degree of coexpression, and some of these methods ignore the additive effects of the two TFs (5, 8-10). Also, a pair of TFs may interact only under certain conditions, whereas these methods consider all time points, which may introduce noise.In this study, we propose two methods to find, respectively, individual TFs and synergistic pairs of TFs that are cell cycle regulators in yeast. The essential idea is that if a TF is a cell cycle regulator, then genes regulated and not regulated by it should, on average, have significantly different expression levels during one or more phases of the cell cycle (5). The target genes of TFs are collected from four TF databases (11-14) and ChIP-chip data (15), and the expression data of yeast genes are gathered from the microarray data of Spellman et al. (16). In this study, the majority of known cell cycle-related TFs and synergistic pairs are i...

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confidence: 99%

Statistical methods for identifying yeast cell cycle transcription factors

Tsai

2005

Proc. Natl. Acad. Sci. U.S.A.

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“…More recently, models that account for cooperativity between TFs during transcription regulation have been developed (6)(7)(8)(9)(10). However, all of these models are limited by one or more of the following factors.…”

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confidence: 99%

“…However, all of these models are limited by one or more of the following factors. Some of these methods (6)(7)(8), like expression coherence (EC) score approach (6,7), require data from multiple time points, which are not always available. Methods based on regression trees (8), on the other hand, cannot take proper account of additive effects.…”

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confidence: 99%

“…Some of these methods (6)(7)(8), like expression coherence (EC) score approach (6,7), require data from multiple time points, which are not always available. Methods based on regression trees (8), on the other hand, cannot take proper account of additive effects. In other cases (9, 10), we found either the known pairs of motifs are not correctly predicted or the accuracy of the regression model does not improve significantly (Ϸ5-10%) when interacting pairs are introduced in the model, which is inconsistent with the biological notion of synergistic gene regulation.…”

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confidence: 99%

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Interacting models of cooperative gene regulation

Das

Banerjee

Zhang

2004

Proc. Natl. Acad. Sci. U.S.A.

109

134

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Cooperativity between transcription factors is critical to gene regulation. Current computational methods do not take adequate account of this salient aspect. To address this issue, we present a computational method based on multivariate adaptive regression splines to correlate the occurrences of transcription factor binding motifs in the promoter DNA and their interactions to the logarithm of the ratio of gene expression levels. This allows us to discover both the individual motifs and synergistic pairs of motifs that are most likely to be functional, and enumerate their relative contributions at any arbitrary time point for which mRNA expression data are available. We present results of simulations and focus specifically on the yeast cell-cycle data. Inclusion of synergistic interactions can increase the prediction accuracy over linear regression to as much as 1.5-to 3.5-fold. Significant motifs and combinations of motifs are appropriately predicted at each stage of the cell cycle. We believe our multivariate adaptive regression splines-based approach will become more significant when applied to higher eukaryotes, especially mammals, where cooperative control of gene regulation is absolutely essential.cooperativity ͉ correlation ͉ expression data ͉ transcription regulation R egulation of gene transcription in eukaryotes is complex and is inherently combinatorial in nature (1, 2). Transcriptional synergy is a key element of such combinatorial control in gene regulation networks. It requires cooperative binding of multiple transcription factors (TFs) and is intrinsically nonlinear in nature (2). Taking adequate account of such synergy in computational models is extremely important to have an accurate view of the underlying biology.Conventional computational methods (3) have focused on identifying motifs upstream of the clusters of coexpressed genes. However, many genes fail to cluster and, therefore, regulatory elements of a large number of genes are unknown. Recent work (4, 5) has attempted to overcome this problem by correlating the frequency of DNA motifs with the logarithm of expression levels by using multivariate linear regression. Despite the success in identifying many known important motifs, this method does not account for the synergistic effects and nonlinearities present during transcription regulation. When applied to the yeast cell-cycle data, we found that these methods can explain only 10% of the variations in the data on an average (noise level accounts for Ϸ50%; ref. 4).More recently, models that account for cooperativity between TFs during transcription regulation have been developed (6-10). However, all of these models are limited by one or more of the following factors. Some of these methods (6-8), like expression coherence (EC) score approach (6, 7), require data from multiple time points, which are not always available. Methods based on regression trees (8), on the other hand, cannot take proper account of additive effects. In other cases (9, 10), we found either the known pairs of motifs ...

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“…Sample preparation [18],qualitative phytochemical analysis [19], in vitro antidiabetic activities namely α-amylase [20] and α-glucosidase [20] inhibitory activity and in vivo antidiabetic activity namely evaluation of alloxan induced diabetic rats were carried out following the methods reported previously [21].…”

Section: Methodsmentioning

confidence: 99%

Antidiabetic Potential of the Oyster Mushroom Pleurotus Florida (Mont.) Singer

Prabu

Kumuthakalavalli

2017

Int J Curr Pharm Sci

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Objective: The present investigation comprises, in vitro antidiabetic activity such as α-amylase and α-glucosidase inhibitory activities and in vivo antidiabetic activity of methanolic extract of Pleurotus florida. Methods:The fruiting bodies of Pleurotus florida were obtained from Mushroom Unit, Department of Biology, Gandhigram Rural Institute-Deemed University, Gandhigram, Dindigul, Tamil Nadu, India. Sample preparation, qualitative phytochemical analysis, in vitro antidiabetic activities namely α-amylase and α-glucosidase inhibitory activity and in vivo antidiabetic activity namely evaluation of alloxan induced diabetic rats were carried out following the methods reported previously. Results:In vitro and in vivo antidiabetic activity of P. florida exhibited significant results for its α-amylase (94.93±1.75 % at 1000 µg/ml) and α-glucosidase inhibitory activity (84.90±0.42 % at 1000 µg/ml) in a dose-dependent manner. The extract also showed significant antidiabetic activity on in vivo (p<0.05) at the tested dose level (200 mg/kg b. w) this was comparable to Glibenclamide, a standard antidiabetic drug. Conclusion:The presence of phytochemicals namely phenols, flavonoids, saponins, tannins and terpenoids may be responsible for such antidiabetic activity. These results reveal that P. florida can be used as a potential antidiabetic agent.

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Regression trees for regulatory element identification

Abstract: http://if.kaist.ac.kr/~phuong/RegTree

Cited by 32 publications

References 21 publications

Statistical methods for identifying yeast cell cycle transcription factors

Statistical methods for identifying yeast cell cycle transcription factors

Interacting models of cooperative gene regulation

Antidiabetic Potential of the Oyster Mushroom Pleurotus Florida (Mont.) Singer

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