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CAGER: classification analysis of gene expression regulation using multiple information sources
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Many classification approaches have been applied to analyzing transcriptional regulation of gene expressions. These methods build models that can explain a gene's expression level from the regulatory elements (features) on its promoter sequence. Different types of features, such as experimentally verified binding motifs, motifs discovered by computer programs, or transcription factor binding data measured with Chromatin Immunoprecipitation (ChIP) assays, have been used towards this goal. Each type of features has been shown successful in modeling gene transcriptional regulation under certain conditions. However, no comparison has been made to evaluate the relative merit of these features. Furthermore, most publicly available classification tools were not designed specifically for modeling transcriptional regulation, and do not allow the user to combine different types of features.
In this study, we use a specific classification method, decision trees, to model transcriptional regulation in yeast with features based on predefined motifs, automatically identified motifs, ChlP-chip data, or their combinations. We compare the accuracies and stability of these models, and analyze their capabilities in identifying functionally related genes. Furthermore, we design and implement a user-friendly web server called CAGER (Classification Analysis of Gene Expression Regulation) that integrates several software components for automated analysis of transcriptional regulation using decision trees. Finally, we use CAGER to study the transcriptional regulation of Arabidopsis genes in response to abscisic acid, and report some interesting new results.
Models built with ChlP-chip data suffer from low accuracies when the condition under which gene expressions are measured is significantly different from the condition under which the ChIP experiment is conducted. Models built with automatically identified motifs can sometimes discover new features, but their modeling accuracies may have been over-estimated in previous studies. Furthermore, models built with automatically identified motifs are not stable with respect to noises. A combination of ChlP-chip data and predefined motifs can substantially improve modeling accuracies, and is effective in identifying true regulons. The CAGER web server, which is freely available at http://cic.cs.wustl.edu/CAGER/, allows the user to select combinations of different feature types for building decision trees, and interact with the models graphically. We believe that it will be a useful tool to facilitate the discovery of gene transcriptional regulatory networks.
KeywordsPositive Instance Decision Tree Model Negative Instance Positive Gene ChIP Data
List of abbreviations
transcription factor. ChIP: Chromatin Immunoprecipitation. GO: gene ontology. ABA: abscisic acid. ABRE: ABA-responsive element. CE: coupling element. ORF: open reading frame. DEG: differentially expressed gene. SS: sensitivity. SP: specificity. FDR: false discovery rate. TP: true positive. FP: false positive. TN: true negative. FN: false negative.
A major challenge in computational biology is to reveal the cis-regulatory logics of gene expression through analysis of high-throughput genomic data, for example, genomic sequences and microarray gene expression data. A common practice is to first identify putatively co-regulated genes by clustering gene expression patterns [1, 2, 3], and then search for common motifs from the promoter sequences of these genes [4, 5, 6]. However, motif finding methods are often sensitive to noises and usually do not consider combinatorial nature of cis-regulation. Furthermore, these methods by themselves do not reveal the actual transcription factors (TFs) that bind to particular sequence motifs.
Recently, many researchers attempted to build quantitative or qualitative models to associate a gene's expression level with regulatory motifs on its promoter sequence. Pilpel et al. [7] explicitly analyzed the combinatorial effects of motif pairs on gene expression profiles and identified many significant motif combinations. Bussemaker et al. [8] and others [9, 10] modeled the expression levels of genes as a linear regression of putative binding motifs, and applied feature selection techniques to find the most significant motifs. Hu et al. [11] used decision trees to find motif combinations that best separate two sets of genes. Phuong et al. [12] applied multivariate regression trees to model the transcriptional regulation of gene expressions over several time points simultaneously. Middendorf et al. [13] used an ensemble of decision trees to model gene expression levels by combining putative binding motifs and the expression levels of putative TFs. Simonis et al. [14] combined a string-based motif finding method and linear discriminant analysis to identify motif combinations that can separate true regulons from false ones. Segal et al. [15] and Beer and Tavazoie [16] built probabilistic graphical models, e.g., Bayesian networks, to explain gene expression patterns from motifs. In these models, the predictors (features) are the matching scores of promoter sequences to putative binding motifs, and the predictions (responses) can be continuous or discrete gene expression levels or categorical cluster labels.
The features used in these models generally come from one of the following sources. First, one can use computer programs to automatically find motifs from the promoters of the genes to be modeled [10, 11, 14, 15, 16]. Second, predefined motifs can be obtained independently from sources such as databases of experimentally verified or putative motifs [12, 13]. Third, one can enumerate all words up to a certain length as features [8, 9]. In addition, TF binding data derived from Chromatin Immunoprecipitation (ChIP) assays [17] have been used as a substitution of motif scores. For example, Banerjee and Zhang [18] directly applied the method of Pilpel et al. [7] to ChIP-chip data to identify TF combinations; Gao et al. [19] replaced the variables in the linear model of Bussemaker et al. [8] with ChIP-chip data and identified significant regulators for many experimental conditions. We recently applied a decision tree method to S. cerevisiae ChIP-chip data and identified all known TFs and many interesting TF combinations for yeast cell cycle [20].
Each type of the features discussed above (motifs or binding data) has its advantages and disadvantages in modeling gene transcriptional regulations. As to our knowledge, no comparison has been made to evaluate their relative merits. Modeling accuracy is largely affected by the type of features considered in model construction. If all relevant features are included correctly, many modeling algorithms may have equally high accuracies. On the other hand, if most significant features are omitted, no model can achieve satisfactory accuracy. Furthermore, the inclusion of many irrelevant features may significantly decrease modeling accuracy. Therefore, a comparative study can identify limitations of these feature types, and provide some guidelines and justifications.
Although many classification tools are publicly available (for example, WEKA [21]), most of them are not designed specifically for modeling transcriptional regulation, and are not convenient for biological applications. The only related software is a web server called REDUCE [22], which combines linear regression and feature selection methods to identify significant motifs for specific biological events. It uses enumerated words up to a certain length as features, but does not allow other types of features such as position specific weight matrices [23] or ChIP-chip data to be used. Moreover, the linear model used in REDUCE assumes that each motif contributes linearly to gene expressions, and therefore is unable to represent complex cis-regulatory logics such as AND and OR relations [16, 24].
In this study, we apply a well-studied classification method, decision tree [25, 26, 27], to model significantly up-or down-regulated genes in each of 250 microarray experiments of S. cerevisiae. The utilization of decision trees in modeling transcriptional regulation has been explored previously by others [11, 12, 13] and in our own research [20]. Here we focus on analyzing the extent to which the expression of these genes can be predicted using different features. We compare the cross-validation accuracies of the models built with different features or feature combinations. We also compare the robustness of these models by introducing noises into training data. Furthermore, we analyze the enrichment of functional categories of the genes that can pass model tests comparing to those fail the tests, and show that decision tree models can be used to detect true regulons. Finally, we present the design and implementation of a user-friendly web interface that combines multiple information sources for automated analysis of gene transcriptional regulations using decision trees. As an example, we also present a case study on the transcriptional regulation of genes in Arabidopsis thaliana in response to abscisic acid (ABA) treatment.
Results and discussion
Modeling gene transcriptional regulation with decision trees
- 1.
Examine every possible binary split of the genes in the node based on each feature i, such that all genes in one subset have f i <x and those in the other subset have f i ≥ x.
- 2.
Select the best split, and create two child nodes that contain the two subsets of genes respectively.
Steps 1 and 2 are then recursively applied to each of the child nodes until no split is possible, or until all genes in the current node have the same label. Finally, some branches of the tree may be pruned to prevent over-fitting. Nodes with or without child node are called internal nodes or leaf nodes, respectively. For examples and biological interpretations of decision trees, see Figure 4 and Figure 5.
Each entry f i in the feature vector of a gene corresponds to the matching score of the gene's promoter to the i th binding motif, or the binding data of the i th TF to the gene's promoter, depending on the type of features used. A split is equivalent to a test for a gene in the form of, for example, "is the matching score of the gene's promoter to motif A greater than x?" or "is the binding affinity of the gene's promoter to TF B greater than y?" The exact split point x or y is determined by maximizing an objective function that reflects the purity of the child nodes. Information gains and gain ratios are two frequently used objective functions [26]. Here we used information gains (see Methods).
The class label of a gene represents a property of the gene that we want to model. For example, one can cluster genes according to expression patterns, and then assign the same label to genes in the same group. Labels can also be derived from other sources such as functional annotations. In this work, we assign labels to genes according to the change of their expressions under certain conditions relative to some reference state (see Experimental setup), and focus our attention on the comparison of different features. This modeling approach is based on the assumption that co-regulated genes very often share common regulatory elements on their promoters. This approach, on the other hand, will not capture post-transcriptional modifications, and will ignore genes that share no regulatory elements with other genes. Note that the underlying assumption may not always be met, since not all co-expressed genes are co-regulated. Moreover, genes may be mislabeled due to noises in their expression data. The purpose is, therefore, to identify rules of thumb for the regulation of the majority genes, while tolerating to some extent the failure in modeling particular genes. As we will see later, decision tree models can indeed be used to detect the true regulons from putatively co-regulated genes.
Experimental setup
We collected data for 250 microarray experiments on yeast S. cerevisiae, of which 77 were for cell cycle [28, 29] and 173 were for responses to various stress conditions [30]. We built decision trees for each condition with two classes of instances: positive genes that are differentially expressed (up- or down-regulated) with respect to the reference state, negative genes that are neither up- nor down-regulated. For each experiment, we selected up to 50 up- or down-regulated genes as positive instances and sampled negative instances from non-differentially expressed genes. (See next subsection for selecting candidate genes). Once the genes were selected, we modeled the regulation of up- and down-regulated genes separately. There may be common regulatory motifs shared by up- and down-regulated genes, which may not be revealed if they were modeled together.
Since most genes are not differentially expressed, the number of negative instances is far greater than the number of positive instances. Previous researches have shown that class distribution is an important factor for successful modeling [31]. In general, a skewed class distribution will lead to a degraded modeling accuracy. Therefore, for a set of n positive instances, we randomly sampled μn instances from all possible negative instances to obtain a desired class distribution. The sampling was repeated 10 times, and each set of sampled negative instances was combined with the set of positive instances to build a decision tree. The accuracy of each model was estimated with a ten-fold cross-validation and measured by the kappa statistic (see Methods). As shown in Figure 1A, the highest accuracy was achieved when μ = 3, which is consistent with the results of Simonis et al. [14]. Therefore, we used μ = 3 in all subsequent analysis.
In general, the inclusion of a large number of irrelevant features in training data decreases the accuracy of most classification algorithms. Therefore, feature selection methods are usually applied to reduce the number of features. Most feature selection methods can be categorized as wrappers [33] or filters [34]. A wrapper method searches for a subset of features that maximize the cross-validation accuracy of a given classification algorithm. This strategy is guaranteed to improve the classification accuracy if the same algorithm is used in feature selection and model training. However, it may over-fit to the specific classification algorithm. Furthermore, the method is computationally expensive since many iterations of the classification algorithm need to be executed. In a filter method, features are selected independently of any classification algorithm. Individual features or feature subsets are ranked according to certain scoring functions, and the top ones are selected. This approach is efficient in removing a large number of irrelevant features, but may sometimes eliminate low-ranked, nevertheless important, features. In this study, we used a filter method because of its efficiency in computation. The method ranks individual features according to their information gains and selects the top d features (see Methods and [35]). As shown in Figure 1B, the best kappa was achieved with as few as 5 features. This agrees with the fact that a transcriptional regulation only involves a few transcription factors in general. Careful inspections on individual decision trees show that with 5 features, the performance of some trees may be worse than those with more features, due to the loss of some significant features. With 10 features, the modeling accuracies were almost never worse than those with more features. Therefore we used d = 10 in all subsequent analysis. The accuracy of each classification model was estimated using a ten-fold cross-validation procedure. First, a training set was randomly divided into ten equal-sized subsets. Each subset was then used in turn as a validation set to test the accuracy of the model built with the other nine subsets. We calculated kappa [36] to measure model accuracies (see Methods). The kappa statistic, written κ, measures the agreement between the class labels and the predictions made by the classifier, corrected by the amount of agreement that may be achieved by chance. Therefore, it reflects the extent to which the differential expression between positive and negative genes can truly be explained by the classification model. For example, given a data set containing 20 positive and 80 negative genes, a model that simply guesses all genes as negative agrees with the true labels on 80% of cases, as does another model that makes five mistakes in positive and 15 in negative genes. Taking into account the amount of agreement that we would expect by chance, the value of κ is 0.0 for the former model, while 0.47 for the latter.
It is known that κ depends on the class distribution and the number of categories in the test data [37]. This, however, was not a problem in our case, since the models in our test all had binary classes and the same class distributions. Another difficulty associated with κ is its lack of interpretability, although some relations between κ and model quality have been suggested [36]. Therefore, in addition to κ, we calculated sensitivity (SS) and specificity (SP) for each model (see Methods). SS is the proportion of positive genes that are correctly predicted by the model, i.e., the proportion of up- or down-regulated genes that can be explained by the regulatory elements identified. SP is the proportion of negative genes that are correctly predicted by the model, and 1 - SP represents the proportion of negative genes that cannot be separated from the positive genes based on the regulatory elements.
Methods for identifying DEG candidates
Microarray data are noisy and often measured with limited or no replication, which makes the identification of differentially expressed genes (DEGs) difficult. In most early microarray analysis, a fixed fold-change threshold (generally two-fold) was used to identify DEGs, while more sophisticated methods have emerged recently [38, 39, 40, 41]. Although it is not the focus of this paper, we considered and compared several different DEG identification methods to show that our conclusions on the classification models are unlikely to be affected by the specific DEG identification method used.
For the first method (referred to as the vanilla method), we downloaded the transformed and normalized log ratios for the 250 microarray experiments. The data set had been corrected for background noises, and globally normalized by constant factors such that the mean log2(cy 5/cy 3) value is zero within each slide [40]. For expression data in cell cycle conditions, the log ratios were further normalized such that the mean log ratio for each gene across all cell cycle conditions is zero [28]. Several rules were also applied to remove spurious data points [28]. For each column (condition) of this data set, we selected the genes with log2(cy 5/cy 3) ≥ 1 (more than two-fold induction) as up-regulated genes. In cases there were more than 50 up-regulated genes, we selected the top 50 with the highest fold changes, or until ties were broken. Likewise, down-regulated genes with log2(cy 5/cy 3) ≤ - 1 were selected. Genes with |log2(cy 5/cy 3)| ≤ 0.6 (less than 1.5 fold expression change) were considered as non-DEGs and were used to sample negative instances. Note that we intentionally used two different thresholds for DEGs and non-DEGs, in order to exclude genes whose labels may be ambiguous.
For the second to fifth methods, we downloaded the raw intensity data. However, we only found raw data for 216 of the 250 conditions, and it was sometimes unclear how to match the name of a raw data file with a column in the log ratio data. The intensity data were background corrected, without any other normalization or transformation. We removed low quality data points that were annotated by the authors with failed status or non-zero flags.
The second approach (referred to as the global normalization method [40]) was similar to the vanilla approach, except that no per-gene normalization was made. The third approach (referred to the lowess normalization method) was similar to the second approach, except that within-slide normalization factors were intensity dependent, obtained through a locally weighted regression approach [40]. The fourth method (referred to as the vsn method) transformed the intensities with a generalized logarithm function, in order to stabilize the variances which were originally intensity dependent [39]. We applied the same thresholds as in the vanilla approach to the transformed data. The fifth method (referred to as the EDGE method) quantified the heteroscedasticity as a function of intensity and then taking it into account to identify DEGs [41]. The EDGE method assigned each gene a p-value based on its distance from the line of equivalence (cy 5 = cy 3), corrected for multiple tests. We ranked genes according to their corrected p-values, and selected up to 50 up-regulated or down-regulated genes with false discovery rate (FDR) < 0.002 [42]. The FDR threshold was used to ensure that the number of DAGs selected by EDGE was approximately equal to those chosen by the other approaches. It had no impact for most experiments, where all top 50 genes had FDRs less than 0.002. Genes with FDR > 0.5 were considered as non-DEGs. The programs for vsn and EDGE were obtained from their original authors.
Statistics of DEG sets identified by different methods. The ranges represent one standard deviation from the means.
Number of down-regulated sets
Number of down-regulated sets
Number of genes per set
48 ± 3
47 ± 8
45 ± 6
49 ± 7
46 ± 6
A comparison of the prediction power of different features
We compared the cross-validation accuracies of the models built with three types of features that we discussed early: ChIP-chip data, predefined motifs and auto motifs. The combination of ChIP-chip data with predefined motifs was also tested. For each type of features or feature combinations, 446 × 10 = 4460 decision tree models were built (446 sets of DEGs and 10 sets of negative genes randomly sampled for each set of DEGs). We randomly exchanged the labels for positive and negative genes to serve as controls. Note that we carried out two types of cross-validations for models built with auto motifs. In the first method, promoter sequences of genes in both training data and test data were combined to find motifs. In other words, motifs were identified from all positive genes, and the same set of motifs was used to train models for each fold in a cross-validation. In the second method, genes were first divided into ten subsets without constructing the actual feature vectors. A subset was chosen for testing, and the other nine subsets were used for motif finding. In other words, motifs were identified from only the training genes, and a different set of motifs was used to train models for each fold in a cross-validation. The second method provided a more stringent estimation of the generalization accuracy of a model, since it completely hided the test data from the learning algorithm until they were tested. The first method, however, was used in several previous studies [11, 16], probably because it is simple to implement and convenient to test. Here we analyzed the results of both cross-validation methods to compare auto motifs with other feature types. In the next two subsections, we used only the first method to show other aspects of the models based on auto motifs.
Stability of decision tree models. Up: the models built for up-regulated genes. Down: the models built for down-regulated genes. Noise: the number of noisy instances added into the training set. TP: the number of true positive genes predicted by models built on the original data. TP': the number of true positive genes predicted by models built on the noisy data. Loss: the number of positive instances correctly classified in the original data but mis-classified in the noisy data. Rescue: the number of positive instances correctly classified in the noisy data but mis-classified originally. FP: the number of newly added noise genes classified as positive. Each value is an average across 223 up-regulated or 223 down-regulated gene sets. The standard errors for loss, rescue and FP are all less than 0.2.
Predefined motifs
Auto motifs
As shown in Figure 3A, when the first cross-validation method is used, the models using auto motifs have the highest kappa values (~0.53) among the three individual feature types. However, it is important to note that these models also have the highest kappa values on randomly selected genes (~0.4).
Furthermore, the accuracies measured by the second cross-validation method are much lower: the average kappa values are 0.15 for models in cell cycle and 0.22 for models in stress response experiments, and are approximately zero for models of randomly selected genes. Therefore, the first method considerably over-estimates the accuracies of the models built with auto motifs. This is because that, with the first cross-validation method, the feature set contains some information about the test instances, even though the models are built only on training instances. Consequently, although the results reported in previous studies utilizing automatically identified motifs [11, 16] may still be valid qualitatively, the exact accuracies may need to be re-evaluated. Nevertheless, an apparent advantage of using automatically identified motifs is that it may be able to discover new features not included in predefined motifs and ChIP data.
In cell cycle experiments, the models using ChIP data or predefined motifs have similar kappa values (0.27 and 0.29, respectively; p-value = 0.15 in a paired t-test). In contrast, in stress response experiments, the models using ChIP data alone have very low kappa values than that using predefined motifs (0.15 vs. 0.33, p-value = 10-47 in a paired t-test). The ChIP data used in this study were measured under normal cell growth conditions. However, it is known that TF binding may change with environmental conditions [44]. While the cell cycle expression data were measured under conditions relatively similar to normal cell growth conditions, stress treatment dramatically changes the environmental conditions and thereby alters the binding of TFs. It is thus expected to observe lower prediction accuracies in stress response experiments than in cell cycle experiments when using ChIP data alone.
Figure 3B and Figure 3C show the mean SS and 1 - SP for the models built with different features.
Figure 3B for SS shows almost identical trends as Figure 3A, which means that with combined features, the models can better explain the co-regulation of the genes. On the other hand, Figure 3C shows that models built with ChIP data have higher specificity than those built with predefined or auto motifs (p-value = 10-68 in a paired t-test between ChIP data and predefined motifs). This can be explained by the fact that ChIP data are more reliable than motif scores as an indicator of co-regulations, since ChIP data explicitly measure the binding affinities of gene promoters to TFs. There are also other advantages in using ChIP-chip data as features. For example, the number of features is well bounded by the number of TFs, which is estimated to be around 200 in yeast [44], comparing to thousands of putative binding motifs. As a result, the correlations and redundancies among features are low in ChIP-chip data, which makes it possible to build simple models for better interpretability. Furthermore, the models built with ChIP-chip data directly suggest regulatory relations between TFs and genes, which can be used to construct regulatory networks. Our results indicate that, however, the conditions of microarray experiments and ChIP assays must be considered with special care.
In cell cycle experiments, the models built with a combination of predefined motifs and ChIP data have substantially better kappa values than those with ChIP data or predefined motifs alone (Figure 3D). The p-value is 10-14 in a paired t-test for results of ChIP data (0.27) and combined features (0.34), and 10-12 for predefined motifs (0.29) and combined features (0.34). In stress experiments, the kappa values for models based on combined features (0.35) are only marginally better than that for predefined motifs (0.33), due to limited usage of ChIP data as pointed out above. Nevertheless, the models using combined features perform significantly better (improvement of kappa > 0.1) in 33 cases, while the models using predefined motifs are never better by more than 0.1 in kappa (Figure 3E). A paired t-test yields a p-value 10-5, which is still statistically significant. Putative binding motifs and ChIP data represent two distinct and complementary sources of evidence of regulation. Therefore, a combination of them can provide a better discriminative power than either of them does.
Since the role of decision tree models is exploratory, there is no need to be restricted to any specific type of features. Indeed, one should try a variety of them to identify the most relevant features for a particular set of genes. A good strategy in choosing feature types is probably as follows: first use a combination of TF binding data and predefined motifs for modeling; if a model with good cross-validation accuracy can not be found, then consider using automatically identified motifs. Obviously when predefined motifs or TF binding data are unavailable or insufficient, using automatically identified motifs as features is the only option.
Stability of models
To analyze the stability of decision tree models, we introduced some noises into positive training data and tested whether the models can separate the true positives from the noises. For each microarray experiment we randomly selected 5 to 50 negative genes and deliberately mislabeled them as positive. The original data and the additional fake positive instances were then combined to build a decision tree model. We compared the results of the new models to the results of the original models in ten-fold cross-validations and counted the numbers of losses, rescues and false positives (FPs). According to [14], a loss is a positive instance that is mis-classified in the noisy data but correctly classified in the original data. A rescue is a positive instance that is correctly classified in the noisy data but mis-classified originally. An FP corresponds to a newly added noise gene that is classified as a (false) positive gene.
As shown in Table 2, the models built with predefined motifs are more robust than the models built with auto motifs. Even at 100% noise level (50 randomly selected genes added), almost 90% of the introduced noises Open image in new window can be correctly filtered out by the models built with predefined motifs. In comparison, the models built with auto motifs can only filter out ≈ 75% of the noises. This is because the motif finding program was distracted by the wrongly labeled genes so that the discovered motifs became less effective to discriminate the true positives from false ones.
Furthermore, the predictions made by the models built with predefined motifs are more stable, as reflected by the smaller values of loss + rescue in Table 2. The addition of a small amount of noises may result in, dramatic change to the predictions by the models built with auto motifs. When as few as 5 noisy instance were added, the models based on auto motif changed their predictions by Open image in new window ≈ 58%, while the models based on predefined motif changed their predictions by only Open image in new window ≈ 30%.
Functional enrichment in correctly classified positive genes
Functional enrichment in correctly predicted positive genes. Predefined motifs: the models built with predefined motifs. Auto motifs: the models built with auto motifs. Up: the models built for up-regulated genes. Down: the models built for down-regulated genes. #Gp: the number of Gp genes (positive genes that are predicted as positive by a model). #Gn: the number of Gn genes (positive genes that are predicted as negative by a model). Open image in new window : the number of enriched functional categories in the set of Gp genes. Open image in new window : the number of enriched functional categories in the set of Gn genes. Open image in new window : the number of enriched functional categories in the set of Gp' genes (see text). The sample sizes for up- and down-regulated genes in cell cycle are 56 and 59, respectively, and 167 and 164 in stress conditions. The ranges represent individual 95% confidence interval for the means.
Predefined motifs
Cell cycle
17.2 ± 2.2
27.1 ± 2.7
15.1 ± 2.9
8.2 ± 2.6
12.3 ± 3.5
14.0 ± 2.1
31.0 ± 2.5
14.5 ± 3.7
9.6 ± 3.4
8.4 ± 3.3
16.4 ± 1.0
33.1 ± 1.1
13.4 ± 2.3
10.6 ± 1.8
10.3 ± 1.9
21.0 ± 2.0
30.7 ± 1.7
17.1 ± 2.4
7.8 ± 1.7
14.7 ± 2.3
Auto motifs
Cell cycle
24.9 ± 1.8
19.3 ± 1.8
16.9 ± 3.2
7.9 ± 2.5
13.9 ± 3.2
23.6 ± 1.9
21.4 ± 1.8
15.6 ± 3.7
8.4 ± 2.9
11.8 ± 3.5
27.0 ± 0.8
22.6 ± 0.8
16.2 ± 2.3
9.1 ± 1.9
15.2 ± 2.4
29.4 ± 1.3
22.2 ± 1.1
18.2 ± 2.3
6.6 ± 1.5
16.4 ± 2.4
Furthermore, we tested whether a similar degree of functional enrichment can be achieved without decision tree models. Suppose a decision tree model predicted 25 out of 50 positive genes as true positives, and GO analysis showed that these 25 genes were functionally more coherent than the remaining 25 genes. We want to know whether there is another way to select 25 genes that have higher degree of functional enrichment than those predicted by decision trees. For example, we may simply pick genes that are top ranked according to their expressions. We used the following procedure to test this hypothesis. For each set of up-or down-regulated genes, A, which has p predicted true positive genes, we selected p top ranked genes from A based on the absolute log ratios of their expressions. We denoted this set of genes as Gp', which has the same number of genes as Gp, and counted the number of enriched GO categories in it. The average results over all microarray experiments are shown in the Open image in new window column of Table 3. As shown, the genes in Gp contain more enriched functional categories than genes in Gp'( Open image in new window ). A paired t-test between Open image in new window and Open image in new window combining all cases has a p-value 10-16. Therefore, we can conclude that both models built with auto motifs and with predefined motifs are more effective in selecting functionally related genes than the naive differential expression model.
CAGER web server
Several previous studies have shown that decision tree is a valuable tool in analyzing transcriptional regulation of gene expressions [11, 12, 13, 20]. Although there are many publicly available software packages for building decision trees (for example, [21, 25, 26]), they are not specifically designed for biological applications, and are not convenient for biologists to use. Therefore, to make a good use of the results from this study, we designed and implemented a user-friendly web server and interface for building decision trees to analyze transcriptional regulation. The server integrates several software components that allow the user to select from different types of features and to interact with the constructed models.
The interface for user inputs is shown in Figure 4A. To submit a job to CAGER, the user is first asked to provide positive and negative genes as either ORF identifies for one of the supported organisms, which currently includes yeast S. cerevisiae and Arabidopsis thaliana, or promoter sequences in FASTA format. These can be copy-pasted to the web form or uploaded from files in the user's local computer. Given ORF identifiers, the promoter sequences in a supported genome are retrieved automatically from a local database. Second, the user specifies some feature sets, which may be ChIP-chip data for yeast S. cerevisiae [17], predefined motifs from Pilpel et al. [7], motifs automatically identified from the promoter sequences by AlignACE [5], or a combination of them. The user can also specify whether features should be identified from negative instances as well as from positive instances, whether feature filters should be used, and the minimum number of instances per node. Finally, the user fills in his or her email address and submits the job. After a job is completed, the user will be notified by email for instructions about how to access the results.
On the output page, a decision tree is displayed as a portable network graph (PNG), along with related statistics for the tree in training and cross-validation processes (Figure 4B). The text inside an internal node of the tree gives the name of a feature, and the text inside a leaf node shows the predicted label for genes inside the node, as well as the number of supporting and counter-instances for the prediction. Each node of the decision tree can be clicked to show some details. For example, if an internal node contains a feature derived from ChIP-chip data for a TF in yeast, clicking on it leads the user to SGD [46] for detailed information about the TF. If the feature is a binding motif, a click opens a new window to display the sequence logo [47] and the position specific weight matrix of the motif. A click on a leaf node brings up a window for displaying the identifiers of the positive and negative genes in the leaf.
Application to ABA-responsive genes in Arabidopsis
Here we show an example of using the web server to study the regulation of genes expressed in response to abscisic acid (ABA) in Arabidopsis. ABA is a phytohormone that plays important roles in many stages of plants, such as seed development and stress responses (see [48] for a review). Seki et al. [49] identified about 250 genes in Arabidopsis that are induced by at least 5-fold after ABA treatment. Since Arabidopsis is one of the supported organisms in our current server, its promoter sequences are available in our database.
Therefore, we provided as positive instances the list of ORF names corresponding to the up-regulated genes, and as negative instances a list of randomly selected genes that are not up-regulated. Promoter sequences were retrieved for 152 of the positive genes. We used auto motifs identified from these promoters as features. The decision tree and the sequence logos for the most interesting motifs are shown in Figure 5. AlignACE identified a total of 37 motifs with default parameters, five of which were selected by the decision tree (Figure 5A). Three motifs, ace_m2, ace_m9, and ace_18 (Figure 5B) together correctly classified 35 (= 13 + 6 + 22 - 6) positive genes (the rightmost three leaves labeled with 'p'), while as many as 107 positive genes were classified as negative. This may be due to the fact that ABA triggers a lot of down-stream responsive genes, many of which are not co-regulated with direct targets of ABA. The motif ace_m2 has a conserved CACGTG core, which is very close to the known ABA responsive elements (ABREs) identified in many other plants [50, 51, 52, 53, 54, 55, 56]. It is known that an ABRE often functions together with a coupling element (CE), but the consensus sequence of CE in Arabidopsis is elusive. Here, the decision tree suggests that ace_m18 and ace_m9 may be two possible CEs. Motif ace_m18 has a CGTGTG core which partially resembles the CE in rice OSEM gene (GACGCGTG TC) [57], and CE3 in barley HVA1 gene (ACGCGTGTC CTC) [55]. Note that ace_m18 has a weak second copy of the GTGTG core, whicl may be important for enhanced binding activity. Motif ace_m18 is also remotely similar to CE3 in maize RAB28 gene (ACGCGCC TCCTC) [53], although in maize the CGTGTG core is replaced by CGCGCC. Motif ace_m9 is a weak but significant motif that consists of a series of G's separated by one or two A's. This motif is not similar to any known motif in the database of Plant Cis-acting Regulatory DNA Elements (PLACE) [58], and therefore may be a new motif for Arabidopsis.
In this research, we compared the effect of using different features to study transcriptional regulation of gene expressions by classification methods. We considered features based on ChIP-chip data, predefined motifs, automatically identified motifs, and their combinations. We found that TF binding data from ChIP assays are effective in modeling gene expressions only under the same conditions where ChIP-chip experiments were conducted. Our results also indicate that many previous studies may have over-estimal the cross-validation accuracies of models built with automatically identified motifs. Furthermore, the models built with automatically identified motifs are not robust with respect to noises, comparing to those built with predefined motifs. A combination of ChIP-chip data with predefined motifs seems to be superior to either one of them applied separately.
We also showed that the positive genes correctly predicted by decision tree models are more functionally related than those that are not correctly predicted. Therefore, decision tree models can be used to refine putative regulons and detect new genes of a regulon. Simonis et al. [14] have showed this by testing on known regulons, while we confirmed this through analyzing the functional enrichment of predicted regulons. We presented a web service that integrates motif finding and decision tree learning for analyzing transcriptional regulation of gene expressions. Its usefulness was illustrated with an example of studying the regulation of ABA-responsive genes in Arabidopsis. We identified two motifs that are similar to known ABA-responsive elements (ABREs) and coupling elements (CEs), and suggested a new CE, which may deserve further studies. As demonstrated by the example, the web interface combines a number of software components and hides most specific parameters from the user, while still allows some flexibilities. The graphical representation of a decision tree makes it easy to visualize and extract significant regulatory rules. We believe that it can significantly reduce the learning curve for those who are interested in applying classification methods to analyzing transcriptional regulation, and will be a useful tool to facilitate the discovery of transcriptional regulatory networks by combining multiple information sources.
Yeast gene expression data were downloaded from from Expression Connection [59]. ABA-induced gene expression data for Arabidopsis were obtained from [49]. ChIP-chip data for yeast transcription factors were downloaded from [60]. Putative binding motifs for yeast genome were obtained from [61]. Upstream sequences of yeast and Arabidopsis ORFs were obtained from RSA-tools [62] by retrieving up to 800 and 1500 bases, respectively, from translation start sites. Overlaps with other ORFs were truncated, and upstream sequences shorter than 100 bases were discarded. For yeast ORFs, the upstream sequences were used as promoters. For Arabidopsis ORFs, the upstream sequences were used as inputs to a promoter prediction program, TSSP [63], to predict transcription start sites (TSSs). Promoters of Arabidopsis ORFs were defined as from 350 bases upstream to 50 bases downstream relative to the predicted TSSs, where most known ABREs and CEs were discovered.
Feature filters and decision tree learning
Given a training set, features were ranked according to the restricted information gains that can be achieved by using individual features to separate positive and negative instances. Suppose that there are p positive instances and n negative instances, and by selecting a splitting point x on feature f i , the numbers of positive and negative instances on the left (f i <x) and right (f i ≥ x) child nodes are p1, n1, p2, and n2, respectively. The restricted information gain Open image in new window due to feature f i with respect to this split can be calculated by:
where I gain is the normal information gain computed from entropies [26]. The restriction to the information gain calculation ensures that a selected feature is more over-represented in positive instances than in negative instances, which is necessary since negative instances were chosen randomly and therefore should not be co-regulated. Features were ranked by Open image in new window and the top d features were selected for decision tree building (d is denned by the user and is default to 10 in this study). Decision trees were built with C4.5 algorithm [26], except that Open image in new window instead of I gain were used.
Measuring model accuracy
Let TP, TN, FP, and FN be the numbers of true positive, true negative, false positive and false negative predictions made by a binary classifier, respectively, and N = TP + TN + FP + FN. Sensitivity is defined as Open image in new window . Specificity is defined as Open image in new window . The kappa static κ of the classifier is defined as Open image in new window , where Open image in new window is the percentage of correctly predicted instances, and C is the expected percentage of instances that a classifier can predict correctly by chance.
Note that when A = 100% and C ≠ 100%, κ = 1.0, corresponding to a perfect classifier; when A ≤ C, κ ≤ 0, meaning that the classifier does not perform better than random guessing.
GO functional enrichment
GO annotations were retrieved from SGD (version September 2004) [46]. Go functional enrichment were calculated with accumulative hyper-geometric distribution. GO::TermFinder perl module [64] was used to search for significantly enriched functional categories with a false discovery rate (FDR) < 0.05 [42].
Software and implementation
Motifs were identified with AlignACE [5] and scanned against promoter sequences with ScanACE [5]. Decision trees were built with the J48 program, which is a java implementation of the C4.5 decision tree learning algorithm [26], included in the WEKA machine learning package [21]. Decision trees were drawn with the dot program in Graphviz 1.0 [65] and displayed with webdot in the same package. Sequence logos of motifs were drawn with the seqlogo program [47]. The CAGER web service was implemented in perl and run on an apache web server with dual AMD Athlon 1600 MHz CPUs and 2 GB of RAM.
This research was supported in part by NSF grants IIS-0196057 and EIA-0113618 under the ITR program, and a grant from Monsanto Corporation. We thank two anonymous reviewers for their very useful comments. JR also wishes to thank Alexander V Loguinov for the EDGE software.
Supplementary material
- 1.Eisen M, Spellman P, Brown P, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998, 95: 14863–8. 10.1073/pnas.95.25.14863PubMedCentralCrossRefPubMedGoogle Scholar
- 2.Tavazoie S, Hughes J, Campbell M, Cho R, Church G: Systematic determination of genetic network architecture. Nat Genet 1999, 22: 281–5. 10.1038/10343CrossRefPubMedGoogle Scholar
- 3.Tamayo P, Slonirn D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander E, Golub T: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 1999, 96: 2907–12. 10.1073/pnas.96.6.2907PubMedCentralCrossRefPubMedGoogle Scholar
- 4.Bailey T, Elkan C: Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol 1994, 2: 28–36.PubMedGoogle Scholar
- 5.Roth F, Hughes J, Estep P, Church G: Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat Biotechnol 1998, 16: 939–45. 10.1038/nbt1098-939CrossRefPubMedGoogle Scholar
- 6.van Helden J, Andre B, Collado-Vides J: Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J Mol Biol 1998, 281(5):827–42. 10.1006/jmbi.1998.1947CrossRefPubMedGoogle Scholar
- 7.Pilpel Y, Sudarsanam P, Church G: Identifying regulatory networks by combinatorial analysis of promoter elements. Nat Genet 2001, 29: 153–9. 10.1038/ng724CrossRefPubMedGoogle Scholar
- 8.Bussemaker H, Li H, Siggia E: Regulatory element detection using correlation with expression. Nat Genet 2001, 27: 167–71. 10.1038/84792CrossRefPubMedGoogle Scholar
- 9.Keles S, van der Laan M, Eisen M: Identification of regulatory elements using a feature selection method. Bioinformatics 2002, 18: 1167–75. 10.1093/bioinformatics/18.9.1167CrossRefPubMedGoogle Scholar
- 10.Conlon E, Liu X, Lieb J, Liu J: Integrating regulatory motif discovery and genome-wide expression analysis. Proc Natl Acad Sci USA 2003, 100: 3339–44. 10.1073/pnas.0630591100PubMedCentralCrossRefPubMedGoogle Scholar
- 11.Hu Y, Sandmeyer S, McLaughlin C, Kibler D: Combinatorial motif analysis and hypothesis generation on a genomic scale. Bioinformatics 2000, 16(3):222–32. 10.1093/bioinformatics/16.3.222CrossRefPubMedGoogle Scholar
- 12.Phuong T, Lee D, Lee K: Regression trees for regulatory element identification. Bioinformatics 2004, 20(5):750–7. 10.1093/bioinformatics/btg480CrossRefPubMedGoogle Scholar
- 13.Middendorf M, Kundaje A, Wiggins C, Freund Y, Leslie C: Predicting genetic regulatory response using classification. Bioinformatics 2004, 20(Suppl 1):I232-I240. 10.1093/bioinformatics/bth923CrossRefPubMedGoogle Scholar
- 14.Simonis N, Wodak S, Cohen G, van Helden J: Combining pattern discovery and discriminant analysis to predict gene co-regulation. Bioinformatics 2004, 20(15):2370–9. 10.1093/bioinformatics/bth252CrossRefPubMedGoogle Scholar
- 15.Segal E, Yelensky R, Koller D: Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics 2003, 19(Suppl 1):i273–82. 10.1093/bioinformatics/btg1038CrossRefPubMedGoogle Scholar
- 16.Beer M, Tavazoie S: Predicting gene expression from sequence. Cell 2004, 117(2):185–98. 10.1016/S0092-8674(04)00304-6CrossRefPubMedGoogle Scholar
- 17.Lee T, Rinaldi N, Robert F, Odom D, Bar-Joseph Z, Gerber G, Hannett N, Harbison C, Thompson C, Simon I, Zeitlinger J, Jennings E, Murray H, Gordon D, Ren B, Wyrick J, Tagne J, Volkert T, Fraenkel E, Gilford D, Young R: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 2002, 298: 799–804. 10.1126/science.1075090CrossRefPubMedGoogle Scholar
- 18.Banerjee N, Zhang M: Identifying cooperativity among transcription factors controlling the cell cycle in yeast. Nucleic Acids Res 2003, 31: 7024–31. 10.1093/nar/gkg894PubMedCentralCrossRefPubMedGoogle Scholar
- 19.Gao F, Foat B, Bussemaker H: Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data. BMC Bioinformatics 2004, 5: 31. 10.1186/1471-2105-5-31PubMedCentralCrossRefPubMedGoogle Scholar
- 20.Ruan J, Zhang W: Discovering transcriptional regulatory rules from gene expression and TF-DNA binding data by decision tree learning. Tech. Rep. 43, Department of Computer Science and Engineering, Washington University in St Louis 2004.Google Scholar
- 21.Witten I, Frank E: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. San Mateo, CA: Morgan Kaufmann; 1999.Google Scholar
- 22.Roven C, Bussemaker H: REDUCE: An online tool for inferring cis-regulatory elements and transcriptional module activities from microarray data. Nucleic Acids Res 2003, 31(13):3487–90. 10.1093/nar/gkg630PubMedCentralCrossRefPubMedGoogle Scholar
- 23.Stormo G: DNA binding sites: representation and discovery. Bioinformatics 2000, 16: 16–23. 10.1093/bioinformatics/16.1.16CrossRefPubMedGoogle Scholar
- 24.Buchler N, Gerland U, Hwa T: On schemes of combinatorial transcription logic. Proc Natl Acad Sci USA 2003, 100: 5136–41. 10.1073/pnas.0930314100PubMedCentralCrossRefPubMedGoogle Scholar
- 25.Breiman L, Friedman J, Stone ROC: Classification and Regression Trees. Belmont, CA: Wadsworth Int. Group; 1984.Google Scholar
- 26.Quinlan R: C4–5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann; 1993.Google Scholar
- 27.Murthy SK: Automatic construction of decision trees from data: a multi-disciplinary survey. Data Mining and Knowledge Discovery 1998, 2(4):345–389. 10.1023/A:1009744630224CrossRefGoogle Scholar
- 28.Spellman P, Sherlock G, Zhang M, Iyer V, Anders K, Eisen M, Brown P, Botstein D, Futcher B: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998, 9: 3273–97.PubMedCentralCrossRefPubMedGoogle Scholar
- 29.Cho R, Campbell M, Winzeler E, Steinmetz L, Conway A, Wodicka L, Wolfsberg T, Gabrielian A, Landsman D, Lockhart D, Davis R: A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell 1998, 2: 65–73. 10.1016/S1097-2765(00)80114-8CrossRefPubMedGoogle Scholar
- 30.Gasch A, Spellman P, Kao C, Carmel-Harel O, Eisen M, Storz G, Botstein D, Brown P: Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 2000, 11(12):4241–57.PubMedCentralCrossRefPubMedGoogle Scholar
- 31.Weiss G, Provost F: The effect of class distribution on classifier learning: an empirical study. Tech. Rep. ML-TR-44, Department of Computer Science, Rutgers University 2001.Google Scholar
- 32.Mewes H, Amid C, Arnold R, Frishman D, Guldener U, Mannhaupt G, Munsterkotter M, Pagel P, Strack N, Stumpflen V, Warfsmann J, Ruepp A: MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res 2004, 32(Databaseissue):D41–4. 10.1093/nar/gkh092PubMedCentralCrossRefPubMedGoogle Scholar
- 33.Kohavi R, John G: Wrappers for feature subset selection. Artificial Intelligence 1997, 97: 273–324. 10.1016/S0004-3702(97)00043-XCrossRefGoogle Scholar
- 34.Jain A, Zonker D: Feature selection: Evaluation, application, and small sample performance. IEEE Trans, on Pattern Analysis and Machine Intelligence 1997, 19: 153–158. 10.1109/34.574797CrossRefGoogle Scholar
- 35.Xing E, Jordan M, Karp R: Feature selection for high-dimensional genomic microarray data. Proceedings of the Eighteenth International Conference on Machine Learning 2001, 601–608.Google Scholar
- 36.Landis J, Koch G: The measurement of observer agreement for categorical data. Biometrics 1977, 33: 159–74.CrossRefPubMedGoogle Scholar
- 37.Altman DG: Practical Statistics for Medical Research. Chapman & Hall/CRC; 1991.Google Scholar
- 38.Quackenbush J: Microarray data normalization and transformation. Nat Genet 2002, 32(Suppl):496–501. 10.1038/ng1032CrossRefPubMedGoogle Scholar
- 39.Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M: Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002, 18(Suppl 1):S96–104.CrossRefPubMedGoogle Scholar
- 40.Yang Y, Dudoit S, Luu P, Lin D, Peng V, Ngai J, Speed T: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 2002, 30(4):el5. 10.1093/nar/30.4.e15CrossRefGoogle Scholar
- 41.Loguinov A, Mian I, Vulpe C: Exploratory differential gene expression analysis in microarray experiments with no or limited replication. Genome Biol 2004, 5(3):R18. 10.1186/gb-2004-5-3-r18PubMedCentralCrossRefPubMedGoogle Scholar
- 42.Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society 1995, 57: 289–300.Google Scholar
- 43.Supplementary Website[http://cic.cs.wustl.edu/CAGER/suppl.htm]
- 44.Harbison C, Gordon D, Lee T, Rinaldi N, Macisaac K, Danford T, Hannett N, Tagne J, Reynolds D, Yoo J, Jennings E, Zeitlinger J, Pokholok D, Kellis M, Rolfe P, Takusagawa K, Lander E, Gifford D, Fraenkel E, Young R: Transcriptional regulatory code of a eukaryotic genome. Nature 2004, 431: 99–104. 10.1038/nature02800PubMedCentralCrossRefPubMedGoogle Scholar
- 45.Harris M, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, Richter J, Rubin G, Blake J, Bult C, Dolan M, Drabkin H, Eppig J, Hill D, Ni L, Ringwald M, Balakrishnan R, Cherry J, Christie K, Costanzo M, Dwight S, Engel S, Fisk D, Hirschman J, Hong E, Nash R, Sethuraman A, Theesfeld C, Botstein D, Dolinski K, Feierbach B, Berardini T, Mundodi S, Rhee S, Apweiler R, Barrell D, Camon E, Dimmer E, Lee V, Chisholm R, Gaudet P, Kibbe W, Kishore R, Schwarz E, Sternberg P, Gwinn M, Hannick L, Wortman J, Berriman M, Wood V, de la Cruz N, Tonellato P, Jaiswal P, Seigfried T, White R: The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 2004, 32(Database issue):D258–61.PubMedGoogle Scholar
- 46.Dwight S, Balakrishnan R, Christie K, Costanzo M, Dolinski K, Engel S, Feierbach B, Fisk D, Hirschman J, Hong E, Issel-Tarver L, Nash R, Sethuraman A, Starr B, Theesfeld C, Andrada R, Binkley G, Dong Q, Lane C, Schroeder M, Weng S, Botstein D, Cherry J: Saccharomyces genome database: underlying principles and organisation. Brief Bioinform 2004, 5: 9–22. 10.1186/1471-2105-5-9PubMedCentralCrossRefPubMedGoogle Scholar
- 47.Crooks G, Hon G, Chandonia J, Brenner S: WebLogo: a sequence logo generator. Genome Res 2004, 14(6):1188–90. 10.1101/gr.849004PubMedCentralCrossRefPubMedGoogle Scholar
- 48.Leung J, Giraudat J: Abscisic acid signal transduction. Annu Rev Plant Physiol Plant Mol Biol 1998, 49: 199–222. 10.1146/annurev.arplant.49.1.199CrossRefPubMedGoogle Scholar
- 49.Seki M, Ishida J, Narusaka M, Fujita M, Nanjo T, Umezawa T, Kamiya A, Nakajima M, Enju A, Sakurai T, Satou M, Akiyama K, Yamaguchi-Shinozaki K, Carninci P, Kawai J, Hayashizaki Y, Shinozaki K: Monitoring the expression pattern of around 7,000 Arabidopsis genes under ABA treatments using a full-length cDNA microarray. Fund Integr Genomics 2002, 2(6):282–91. 10.1007/s10142-002-0070-6CrossRefGoogle Scholar
- 50.Ono A, Izawa T, Chua N, Shimamoto K: The rab16B promoter of rice contains two distinct abscis acid-responsive elements. Plant Physiol 1996, 112(2):483–91. 10.1104/pp.112.2.483PubMedCentralCrossRefPubMedGoogle Scholar
- 51.Hattori T, Totsuka M, Hobo T, Kagaya Y, Yamamoto-Toyoda A: Experimentally determined sequenc requirement of ACGT-containing abscisic acid response element. Plant Cell Physiol 2002, 43: 136–40. 10.1093/pcp/pcf014CrossRefPubMedGoogle Scholar
- 52.Garcia-Garrido J, Menossi M, Puigdomenech P, Martinez-Izquierdo J, Delseny M: Characterization of a gene encoding an abscisic acid-inducible type-2 lipid transfer protein from rice. FEBS Lett 1998, 428(3):193–9. 10.1016/S0014-5793(98)00529-8CrossRefPubMedGoogle Scholar
- 53.Busk P, Pages M: Protein binding to the abscisic acid-responsive element is independent of VIVIPAROUS1 in vivo. Plant Cell 1997, 9(12):2261–70. 10.1105/tpc.9.12.2261PubMedCentralCrossRefPubMedGoogle Scholar
- 54.Busk P, Jensen A, Pages M: Regulatory elements in vivo in the promoter of the abscisic acid responsive gene rab17 from maize. Plant J 1997, 11(6):1285–95. 10.1046/j.1365-313X.1997.11061285.xCrossRefPubMedGoogle Scholar
- 55.Shen Q, Zhang P, Ho T: Modular nature of abscisic acid (ABA) response complexes: composite promoter units that are necessary and sufficient for ABA induction of gene expression in barley. Plant Cell 1996, 8(7):1107–19. 10.1105/tpc.8.7.1107PubMedCentralCrossRefPubMedGoogle Scholar
- 56.Guiltinan M, WR JM, Quatrano R: A plant leucine zipper protein that recognizes an abscisic ack response element. Science 1990, 250(4978):267–71.CrossRefPubMedGoogle Scholar
- 57.Hobo T, Asada M, Kowyama Y, Hattori T: ACGT-containing abscisic acid response element (ABRE) and coupling element 3 (CE3) are functionally equivalent. Plant J 1999, 19(6):679–89. 10.1046/j.1365-313x.1999.00565.xCrossRefPubMedGoogle Scholar
- 58.Higo K, Ugawa Y, Iwamoto M, Korenaga T: Plant cis-acting regulatory DNA elements (PLACE) database. Nucleic Acids Res 1999, 27: 297–300. 10.1093/nar/27.1.297PubMedCentralCrossRefPubMedGoogle Scholar
- 59.Expression Connection[http://db.yeastgenome.org/cgi-bin/expression/expressionConnection.pl]
- 60.Saccharomyces cerevisiae ChlP-chip data[http://web.wi.mit.edu/young/regulator_network/]
- 61.Known and putative regulatory motifs[http://genepath.med.harvard.edu/~tpilpel/MotifList.html]
- 62.van Helden J: Regulatory sequence analysis tools. Nucleic Acids Res 2003, 31(13):3593–6. 10.1093/nar/gkg567PubMedCentralCrossRefPubMedGoogle Scholar
- 63.Solovyev V: Statistical approaches in Eukaryotic gene prediction. In Handbook of Statistical genetic. Edited by: DB. John Wiley and Sons; 2001:83–127.Google Scholar
- 64.Boyle E, Weng S, Gollub J, Jin H, Botstein D, Cherry J, Sherlock G: GO::TermFinder – open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics 2004, 20: 3710–3715. D258–61 10.1093/bioinformatics/bth123PubMedCentralCrossRefPubMedGoogle Scholar
- 65.Graphviz – Graph Visualization Software[http://www.research.att.com/sw/tools/graphviz/]
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text: On Thu, Apr 01, 1999 at 11:10:35AM -0600, John Hasler wrote: > BTW, you say that the library is "public domain". What exactly does the > license say? See the reply in -legal. -- Pedro Guerreiro (aka digito) (email@example.com) -------------------------------------------------------- Diplomacy: the art of letting someone have your own way.

subset: commoncrawl


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Golden Triangle Tour with Mata Vaishno Devi
Golden Triangle Tour with Mata Vaishno Devi
Destinations Covered : Delhi » Agra » Jaipur » Jammu » Katra Mata Vaishno Devi » Katra » Delhi
Price: On Request
Duration: 9 Nights 10 Days
Tour Description:
Golden Triangle tour's this package will cover Delhi, Agra, and Jaipur along with Mata Vaishno Devi Temple (Katra). Golden Triangle Tour is considering the most popular route in the country with covering three royal cities along with three historical and pilgrimage places. First you will explore Delhi, the capital of India, where you will come to know about Mughal and colonial monuments. After Delhi, you will be going to Agra, where you will see two world heritage sites and the symbol of love Taj Mahal. After this, you will move to Jaipur, known for Rajput architecture and mystical forts. After this, we will take you to
Mata Vaishno Devi Temple
(Katra). Here you will get to enjoy the natural beauty of mountains. At the end of your journey, you will come back to Delhi and head towards airport for your departure.
Day 01, Arrival in Delhi:
Golden Triangle Tour's representative will receive you at the airport and drive you to hotel where you will get a traditional welcome. You will be tired after your flight journey so you can take rest or order a coffee. In the evening you will go to see the Light and Sound Show program at Red Fort. After this you will have dinner at a famous restaurant of Delhi and stay overnight at hotel.
Day 02, Delhi Sightseeing:
Next day of the tour, you will go for sightseeing in Delhi after breakfast. You will also get a guide if you want. The guide will help you to see and know the history about the places you are going to visit.
In Delhi, you will come to see the two sides of Delhi called Old Delhi and New Delhi. Old Delhi connects you with the history, tradition and culture of India. On the other side, New Delhi shows the face of developing and modern culture in India.
In old Delhi, you will get to see the sights that include Red Fort (majestic red stone architecture), Jama Masjid, Chandi Chowk and old market of Delhi. Whereas in New Delhi, you will visit to India Gate, Laxmi Narayan Temple, Humayun's Tomb, Qutub Minar, and Akshardham Temple, and National Art Gallery.
In evening you can go to visit your own, wherever you like to go. After have mouth-watering, stay overnight at hotel.
Day 03, Delhi - Agra:
On second morning after breakfast, we will again take you to visit places in Delhi that you might have left previous day. You will also enjoy the rickshaw ride which will take you to explore the old markets of Delhi.
In the afternoon, you will leave for Agra. On the way, you will have delicious lunch by road side and also will visit famous place like Mathura & Vrindavan. After reaching Agra, our representative will guide you to the hotel. You can have some rest or order coffee in the hotel. In the evening you will get to enjoy the magic show that is performed by great magicians of Agra.
In the evening, you will enjoy a special Mughlai dinner at a famous restaurant in Agra. Stay overnight at hotel.
Day 04, Agra-Jaipur (250 km):
Today morning after having your breakfast, you will visit Taj Mahal to explore the changing color of marble in Taj Mahal during sunrise. The Taj was built by Mughal Emperor Shah Jahan for his beloved wife Mumtaj. Taj Mahal signifies the purity and symbol of love.
After that, Golden Triangle Tour will guide you to Agra Fort and Itmad-Daula Tomb. In Agra fort, Shah Jahan spent his last days of life imprisoned by his son, Aurangzeb.
After having the lunch of your choice, you will leave for Jaipur, also known as pink city. On the way to pink city, you will visit Fatehpur Sikari. This place is known as world heritage place which has some masterpieces of Mughal architecture. Once you reach Jaipur, our representative will guide you to check-in at hotel. Here you will enjoy the traditional dinner and stay overnight.
Day 05, Jaipur Sightseeing:
Today you are heading for sightseeing tour of Jaipur with Golden Triangle Tour. You can also take a guide along with you if you want. First you will explore Amber fort and then Jaigarh fort. Here at Amber fort, you will enjoy the elephant ride which surely will be thrilling experience for you. At Jaigarh fort, you will get to explore the largest canon on Wheels.
After enjoy visiting forts, you will come back to city for lunch and start your tour again in the places like Hawa Mahal, City Palace, Albert Hall Museum, Astronomical observatory and the temples and gardens. Here in Jaipur, you can enjoy the endless shopping in buying things like jewelry, carpets, stone carvings, paintings and many more.
The most important, you will enjoy a Camel ride near Jal Mahal palace. You tour is not complete without Camel ride.
After all this you will go to a theme restaurant called 'Choki Dhani' in the evening. This place is for you to feel the vast art and culture of Rajasthan. Here one can enjoy the folk dance and music with typical Rajasthani cuisine. After that you will come back to hotel and will stay overnight.
Day 06, Jaipur - Delhi (260 km):
On the fifth day of your tour, you will get up from asleep according to your mood. After having the breakfast you will move to Delhi. On the way to Delhi, you will witness many historical places and also come to know the village culture in India. After arrival at Delhi, our representative will guide you to the hotel where you can rest for while before exploring evening in Delhi.
In the evening you will go to see the Light and Sound Show program at Red Fort. After this you will have dinner at a famous restaurant of Delhi and stay overnight at hotel.
Day 07, Delhi-Jammu-Katra:
On the seventh day of your tour, after breakfast we will transfer you to Delhi airport where you will catch a flight for Jammu. Once you reach Jammu, our representative will pick you up and drive to Katra (Vaishno Devi). On the way towards Katra, you can enjoy the lunch at Dhabha. After reaching Katra, you will take the hotel and stay overnight. You can also start your journey by foot for another morning Darshan of Mata Vaishno Devi. Many people take overnight by foot journey to reach Mata Vaishno Devi temple.
Day 08, Mata Vaishno Devi Darshan:
Today get up in the morning and get ready to catch the flight by helicopter to reach Mata Vaishno Devi Temple. It will take you 10 minutes to reach Vaishno Devi Temple from Katra. After that you can take a horse and enjoy the horse ride to Bhairo Prasad Baba temple. Then come to helipad by horse to fly for Katra. On arrival at Katra, enjoy the evening with natural beauty of trees and snow glad mountains around you. After dinner, stay overnight at hotel.
Day 09, Katra to Jammu to Delhi:
Get up according to your comfort. After breakfast, we will drive you to Jammu. Reaching Jammu you will have lunch at the famous restaurant known as Havelli. After enjoying the lunch, our representative will drop you to the airport where you catch the flight for Delhi. Take hotel at Delhi and stay overnight.
Day 10, departure from Delhi:
Here is the last day of your tour with
Golden Triangle Tour
. After breakfast, our representative will drop you to Delhi airport where you will catch the flight to go home. We hope you will have an awesome trip with
Golden Triangle Tour
in exploring India.
Note: We will use Ac Dzire / Etios for 02-03 heads and for 04 – 06 heads we will use Ac Innova.


text:
Designed for nursing educators and students interested in the field of nursing education, Integrating Technology in Nursing Education: Tools for the Knowledge Era provides valuable, easy-to-use strategies on incorporating technology into the classroom. The text examines the increased role of technology in healthcare and its transformational impact on that field, allowing nurses to understand current and future trends and thus, integrate technology into nursing education in order to effectively prepare students for a new, technologically-driven healthcare environment. Also featured are topics on learning theories, the instructional design process, changes in higher education, and variations in learning environments. Using case studies, critical-thinking exercises, weblinks, and more, the text challenges nurses to think critically and formulate compelling teaching strategies.
Written by a renowned nursing educator, instructional designer, informaticist, and simulation expert, Integrating Technology in Nursing Education: Tools for the Knowledge Era gives nurses the tools they need to step boldly into the technology era.
Kathleen Mastrian, PhD, RN-Associate Professor and Program Coordinator for Nursing, Pennsylvania State University, Shenango
Dr. Kathleen Mastrian is an Associate Professor of Nursing and Program Coordinator for the Shenango Campus of Penn State. She has a PhD in Sociology with an emphasis on Medical Sociology, A Master's degree in Nursing from Pitt and a BS in Nursing from Penn State. She has been involved in the education of non-traditional aged students for 36 years in the RN to BS nursing program. She is one of the founding proposers to place the RN to BS program online for the Penn State's World Campus, has developed five of the courses for that curriculum, and teaches at least one section per semester for the World Campus RN to BS program. She has extensive experience teaching informatics, research and sociology for the Shenango campus and has taught online for the World Campus for over 10 years. She is SR. managing Editor of the Online Journal of Nursing Informatics, now a publication of HIMSS.
Dr. Mastrian has presented at numerous national,conferences , co-authored two text books and written more than 30 articles. An additional book written for nurse educators to help them integrate technology to enhance teaching and learning, Integrating Technology into Nursing Education: Tools for the Knowledge Era, was AJN's 2010 Technology Book of the Year, and this text won second place in the AJN's Technology Division in 2015. Drs. Mastrian and McGonigle were each named to the "15 Leading Professors of Nursing Informatics" 2015 list compiled by Medical Technology Schools.
Dr. Mastrian won the 2012 Shirley Novosel Distinguished Nursing Alumni Award and was recently nominated for an Athena Leadership Award.
Dee McGonigle, PhD, RN, CNE, FAAN, ANEF-Director, Virtual Learning Experiences (VLE), Professor, Graduate Programs, Chamberlain College of Nursing
Dr. McGonigle brings more than 40 years of experience in nursing and nursing informatics to this position. She is a Certified Distance Education Instructor and a Certified Nurse Educator.
After being introduced to computers enhancing nursing education while working towards her master's degree, Dr. McGonigle defined informatics in 1991. She co-founded the Online Journal of Nursing Informatics (OJNI), a professional, scholarly, peer-reviewed journal in 1996 for which she was the Editor in Chief for 17 years through 2013. OJNI is currently published by the Healthcare Information and Management Systems Society (HIMSS). In 2014, Dr. McGonigle was the first one honored as a Platinum Award recipient from the Online Journal of Nursing Informatics. She developed the Foundation of Knowledge Model and the ETHICAL Model used to handle ethical challenges brought about by the volatile information age.
Dr. McGonigle has received over $870,000.00 in funding, presented internationally and nationally, authored workbooks, book chapters and more than 130 publications. She led the development of an online self-assessment tool for level 3 and level 4 nursing informatics competencies, NICA L3/L4, which was cited in ANIA's Nursing Informatics Today in 2014 and ANA's NI Scope and Standards document. Dr. McGonigle was a research team member for the development of an online self-assessment tool, TANIC, for basic level 1 and level 2 nursing informatics competencies based on the TIGER initiatives.
Dr. McGonigle has co-authored three text books. The nursing informatics text, Nursing Informatics and the Foundation of Knowledge is in its 3rd edition and took second place as AJN's 2014 Information Technology/Social Media Book of the Year. The text written for nurse educators to help them assimilate technology to enhance teaching and learning, Integrating Technology into Nursing Education: Tools for the Knowledge Era, was AJN's 2010 first place Technology Book of the Year. This text, Informatics for Health Professionals, released in 2016. She is a member of the Expert Panel on Informatics and Technology for the American Academy of Nursing, a fellow in the American Academy of Nursing and a Fellow in the NLN Academy of Nursing Education. She is also a member of the Serious Gaming and Virtual Environments Special Interest Group for the Society for Simulation in Healthcare.
Wendy L. Mahan, PhD-Instructional Designer, College of Health and Human Development, Penn State University
Dr. Mahan is an instructional designer with the College of Health and Human Development and the School of Nursing at the Pennsylvania State University. She has over six years' experience in the design, development, implementation and assessment of online and blended learning courses for traditional, graduate and adult students. Her specific areas of interest include:
Brett Bixler, PhD-Education Technology Services (ETS), Teaching and Learning with Technology, Information Technology Services, Pennsylvania State University
Dr. Bixler has over 25 years of experience in the instructional design field. He is the Lead Instructional Designer with Education Technology Services, part of Information Technology Services (ITS) at Penn State. In his current position Brett is working with the latest educational technologies and learning theories to produce learner-centered active and collaborative learning environments. Brett works with peers from across Penn State and other universities to discuss common issues and provide advice and guidance on a wide range of issues. He is responsible for a variety of other tasks, including:
Brett is actively investigating the use of games, simulations, and virtual worlds for educational purposes. He is in charge of the Educational Gaming Commons (see http://gaming.psu.edu) to support collaboration and initiatives in these areas throughout Penn State.
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SOURCE: Ckrush, Inc.
October 10, 2007 08:00 ET
Network Comprised of LiveMansion.com, AudioStreet.net, MixStreet.net Offers Advertisers Cost-Effective Soultion Across Ckrush Properties
NEW YORK, NY--(Marketwire - October 10, 2007) - Ckrush Digital Media, Inc., a wholly owned
subsidiary of Ckrush, Inc. (OTCBB: CKRU), announced today that it has
created the Ckrush Digital Network to streamline advertising sales across
its web sites. The Ckrush Digital Network will initially be comprised of
advertising inventory from the company's LiveMansion.com, AudioStreet.net,
and MixStreet.net properties. Advertisers will now have the opportunity to
efficiently purchase banner advertisements, rich media, sponsorships,
interactive promotions and contests across the growing Ckrush platform. The
Ckrush Digital Network at inception will offer advertisers access to over
700,000 registered members.
Ckrush's subsidiary Trisoft Digital Media LLC will provide turnkey creative
and technical solutions to participating advertisers, including rich media
ads, web "skins," and interactive contests. Ckrush will manage the sale of
the Digital Network inventory while continuing to work with existing
affiliates and other interactive agencies.
"This is the next step in the Ckrush Digital business plan," commented
Ckrush CEO Jeremy Dallow. "As the Ckrush sites continue to grow and the
value of our audience to advertiser's increases, we want to provide an
efficient means to meet the goals of advertisers. With the formation of the
Ckrush Digital Network we can do just that."
About Ckrush, Inc.
Ckrush is an innovative entertainment and interactive media group at the
forefront of the convergence of entertainment content with social media,
online communities and digital technology. Ckrush produces feature films
and other content and develops and maintains web sites, including online
communities, primarily geared towards young adults 18-34. Ckrush's fully
interactive online communities include "LiveMansion.com"
(http://www.livemansion.com/), "AudioStreet.net"
(http://www.audiostreet.net/) and "MixStreet.net"
(http://www.mixstreet.net/). Ckrush's feature film productions include
"Beer League," starring Artie Lange; "National Lampoon's TV the Movie,"
starring Steve O and Wee Man of "Jackass" fame; and "National Lampoon's
Pledge This," starring Paris Hilton. Ckrush services also include website
design, development, hosting, advertising and media placement. For
additional information, please visit www.ckrush.net.
Forward-Looking Statements
This release contains forward-looking statements within the meaning of the
Private Securities Litigation Reform Act of 1995, Section 27A of the
Securities Act of 1933, as amended, and Section 21E of the Securities
Exchange Act of 1934, as amended. These statements are based on
management's current expectations or beliefs, and are subject to
uncertainty and changes in circumstances. Actual results may vary
materially from those expressed or implied by the statements herein due to
changes in economic, business, competitive, technological and/or regulatory
factors; acquisitions of dispositions of business assets; and the potential
impact of future decisions by management. More detailed information about
these factors may be found in filings by Ckrush, Inc. with the Securities
and Exchange Commission. Ckrush, Inc. is under no obligation to, and
expressly disclaims any such obligation to, update or alter its
forward-looking statements, whether as a result of new information, future
events, or otherwise.
CONTACT:Jeremy DallowCkrush, Inc.212-564-1111Ckrush, Inc. Website: http://www.ckrush.net
Market News First - News AgentAngela email@example.com
See all RSS Newsfeeds


text:
Brian Hughes
About Brian
Brian Hughes has been a resident of NYC for 10 Years after having relocated from the Pacific Northwest in Washington State.
He uses his 7 years of sales experience in the Broadcast industry and past hospitality positions to create real, personal, and lasting relationships with his clients. Working hard to understand their specific needs and how to best serve them.
His outgoing personality is warm, inviting, and extremely welcoming to clients who are often used to just the opposite in their past real estate endeavors.
The opportunity to serve and fulfill your real estate needs is something he takes very seriously and looks forward to every day.
Whether you are renting, buying, or selling Brian will continually strive to provide the best possible service to you. In addition to residential services Brian also functions as a commercial leasing / retail agent also dealing in acquisitions/sales and investment properties.


text:
MARGARET centers on a 17-year-old New York City high-school student who feels certain that she inadvertently played a role in a traffic accident that has claimed a woman's life. In her attempts to set things right she meets with opposition at every step. Torn apart with frustration, she begins emotionally brutalizing her family, her friends, her teachers, and most of all, herself. She has been confronted quite unexpectedly with a basic truth: that her youthful ideals are on a collision course against the realities and compromises of the adult world.
|Run time
|149 Minutes
|Theater Release Date
|September 30, 2011
|DVD and Blu-ray Release Date
|July 10, 2012
|Anna Paquin, Matt Damon, Mark Ruffalo, Sarah Steele, Matthew Broderick, Jean Reno, J. Smith-Cameron
|Directed by
|Kenneth Lonergan
|Fox Searchlight
|R (Rated R for strong language, sexuality, some drug use and disturbing images)
|Watch Movie Trailer
|Margaret Trailer
|Where to Rent
|Netflix Redbox
the trailer is very dramatic realistic cant wait to see!


text:
First Airbus final assembly line outside Europe inaugurated in Tianjin, China
Airbus officially opened its first final assembly line outside of Europe. The Chinese Premier Wen Jiabao, together with the Partys Secretary of Tianjin, Zhang Gaoli and Airbus President and CEO Tom Enders inaugurated the Airbus A320 Family Final Assembly Line in Tianjin, China (FALC) at a grand ceremony with over 600 guests.
The final assembly line is a joint venture between Airbus and a Chinese consortium comprising Tianjin Free Trade Zone (TJFTZ) and China Aviation Industry Corporation (AVIC).
The Tianjin final assembly line for Airbus A320 Family aircraft has been set as a paradigm of Sino-European friendship and cooperation. Based on mutual trust and close-knit cooperation, the two parties have commenced the final assembly of the first aircraft within only one year and three months from the beginning of the facility construction till present and created a miracle in the history of this giant project, said Huang Xingguo, mayor of Tianjin.
The opening of the FALC is an historic event for Airbus and for the aviation industry, said Tom Enders, President and CEO of Airbus. The first Airbus final assembly line outside Europe highlights the importance we attach to growing our partnership with China, one of the most important aviation markets today and certainly tomorrow.
The FALC in Tianjin is based on the latest state-of-the-art Airbus single aisle final assembly line in Hamburg, Germany. The aircraft will be assembled and delivered in China to the same standards as those assembled and delivered in Europe.
The first aircraft assembled in the FALC will be delivered to Sichuan Airlines through Dragon Aviation Leasing in mid 2009. The FALC will ramp up production to four aircraft a month by 2011.
The FALC is a demonstration of Airbus commitment to forging a long-term strategic partnership with China. Six Chinese manufacturers are already involved in manufacturing parts, such as wing components, emergency-exit doors and assembly and transportation tools for Airbus aircraft. In 2007 alone, Airbus sourced $70 million worth of high quality components and materials from Chinese companies. By 2010, Airbus purchasing from the Chinese aviation industry will almost treble to $200 million, before more than doubling again to $450 million by 2015.
By the end of August, there were 351 Airbus A320 Family aircraft in operation with 12 operators in China and over 400 on order.
The A320 Family, which includes the A318, A319, A320 and A321, is recognized as the benchmark single-aisle aircraft family. Each aircraft features fly by wire controls and all share a unique cockpit and operational commonality across the range. More than 6,300 Airbus A320 Family aircraft have been sold and more than 3,500 delivered to some 280 customers and operators worldwide, making it the worlds best selling commercial jetliner ever. With proven reliability and extended servicing periods, the A320 Family has the lowest operating costs of any single aisle aircraft. Uniquely, the A320 Family offers a containerized cargo system, which is compatible with the world wide standard wide-body system.
This news content was configured by WebWire editorial staff. Linking is permitted.
News Release Distribution and Press Release Distribution Services Provided by WebWire.


text:
3 hot dogs chopped into 1 inch pieces
1 onion chopped
1 sweet pepper chopped
1 tbspn ketchup
1 tspn pepper sauce
black pepper
For those who only associate hot dogs with a bun this easy to make snack is a real treat.
Fry the onions and sweet peppers down until very soft and then add the hot dog pieces and cook for a minute or two to heat. Then add salt and pepper and ketchup and pepper sauce and stir in. Serve with toast for a scrumptious and easy breakfast snack. Serves 2. Add a poached egg if you want to make it more "breakfasty"


text:
Heya! I'm Ben (I am not a cat).
I am a UX and Web Designer in Cincinnati, Ohio. I attended the University of Cincinnati's DAAP design school for Digital Design where I had the opportunity to study a number of disciplines including UX, Web and Motion Design. I interned at a number of places including Crew Creative Advertising in Los Angeles, Lands' End in Wisconsin, and MAYA Design in Pittsburgh before starting my post-graduation career as a Front End User Experience Specialist at Hobsons in Cincinnati. I have since also worked for Top Gun Sales Performance, a small sales training company in Mason, Ohio, as a Web Designer and most recently in the Commercial Banking Division of Fifth Third Bank as a Contract User Experience Designer.
Outside of work, I am an avid gamer currently enjoying firing off rockets in Overwatch and replaying Final Fantasy XIII and Legend of Zelda: Twilight Princess. I play board games like Dominion, Pandemic, and Carcassonne. I read the novels of George R R Martin, Chuck Palahniuk, and Stephen King. I enjoy movies like Star Wars, Frozen, and Brazil and watch TV shows like Game of Thrones, The X-Files, Doctor Who, and The Americans. I also just adopted a cat. His name is Hamish. That's him on the left.
I would love to hear from you with any awesome opportunities for a skilled, creative, and insightful UX and Web Designer. If you'd like to know more, just email me at email@example.com. And do check out my LinkedIn page for my experience and my Instagram page for more cat photos linked in the footer below.


text:
The 'data tapping' dispute between HTC and Apple is back in play. Back in December, the ITC sided with Apple in its "data tapping" patent infringement complaint against HTC. The patent itself is a "system and method [which] causes a computer to detect and perform actions on structures identified in computer data," which covers how Android interacts with the UI, automatically identifying a phone number, email address or hyperlink and presenting the user with multiple execution options when tapped.
The ITC gave HTC until April 19, 2012 to update the software on its phones before an exclusion order would prevent all infringing devices from being imported in the the United States. After a short holdup and inspection by U.S. Customs, all HTC handsets were cleared and said to be in compliance with the ITC's ruling.
Unfortunately, Apple was not satisfied with the U.S. Customs stamp of approval and has issued another complaint with the ITC regarding the same "data tapping" patent. Apple claims that HTC's software workaround still infringes on Apple's #5,946,647 patent even though HTC's tweaks change the way the OS interacts with links, phone numbers and emails by allowing users to select application associations within the phone's settings. At the time of the ITC's initial ruling, HTC claimed "the '647 patent is a small UI experience and HTC will completely remove it from all of our phones soon."
Apple's complaint shows images of the functionality within the Gmail application which they claim infringes on its patent. Upon inspection, Apple is showing off Android's menu which shows up when long-pressing a link which gives users the option to open the link in the browser, share it with another application, or copy the URL – which are present on all modern-day computer operating systems and most likely do not fall under Apple's #5,946,647 patent.
Do you think Apple's new complaint against HTC will get any traction from the ITC or will Apple have to resign to the fact that its "data tapping" patent is trivial and can easily be avoided by HTC and other OEMs.


text:
Visualise a Smart City
If you have an idea, a business or just a desire to make Sydney and our other great cities more sustainable, liveable, and basically intelligent you can't afford to miss this.
You will get an exclusive opportunity to connect to smart city VIPs — across transport, planning, environment, and government. Help us join the dots in creating a healthy, living and breathing, clever and connected metropolis!
Data61 (NICTA) is running 'Smart Cities for Startups'. This will be a networking event where problem owners, subject matter experts and startups can give quick 'shout outs' about their issues/opportunities and connect collaboratively exploring ways technology can improve our cities.
With our big cities set to double in size by 2050 and transport and other infrastructure struggling to cope we are calling for creative minds to help us improve how our cities work. The event is envisaged as the 'getting to know you' phase of a co-innovation process. The goals are to identify and match opportunity to innovators and plan for a follow up event in early December. The latter could be in the form of a hackathon.
Data61 has invited and will be featuring innovators across the urban design and transport sectors – companies like Arup plus all levels of government including NSW Department of Industry, Transport for NSW, NSW Department of Planning and Environment, NSW Roads and Maritime Services and thought leaders like the Committee for Sydney, MarcoPlan Dimasi and UrbanGrowth NSW.
WHERE: NICTA, National ICT Australia, Level 5, 13 Garden St, Eveleigh NSW 2015
COST: General Admission (must register before) 28/10/2015
5:30 – 6:00pm – Registration
6:00pm – 7:00pm – Shout Outs
7:00pm – 7:30pm – Networking


text:
W. H. Auden Quotes About JoyQuotes about: Joy
Acting Affection Age Aging Art Authority Belief Bones Books Character Children Choices Cooking Country Criticism Critics Culture Dance Dancing Death Desire Dogs Drama Dreams Duty Earth Ego Evil Eyes Fate Fear Feelings Food Funeral Funny Giving God Greek Grief Happiness Healing Heart Hell Helping Others History Homosexuality Honor Hope Hunger Imagination Inspirational Joy Judgment Labor Language Life Listening Love Lying Mankind Mathematics Mistakes Money Mothers Mountain Mourning Music Nature Opinions Parties Passion Past Pleasure Poetry Politicians Politics Prayer Quality Reality Relationships Religion Running Science Sexuality Silence Sin Sleep Songs Sorrow Soul Study Success Suffering Time Today Torture Waiting Water Wife Winning Work Writing Youth
Most poetry is the utterance of a man in some state of passion, love, joy, grief, rage, etc., and no doubt this is as it should be. But no man is perpetually in a passion and those states in which he is amused and amusing, detached and irreverent, if less important, are no less amusing. If there were no poets who, like Byron, express these states, Poetry would lack something.
W. H. Auden
- Born: February 21, 1907
- Died: September 29, 1973
- Occupation: Poet
- Cite this Page: Citation


text:
Sr. Applications Support Analyst
Information Technology
Phone: 404-712-8824
Email: email@example.com
I've been with the Emory SOM-ITS since November, 2007 and spent approximately 4 years at the Grady campus providing multimedia support and consulting to the Emory, Grady and Morehouse School of Medicine community.
As a Sr. Applications Support Analyst with the SOM-ITS' Curricular Support team, I provide dedicated technical support and consulting to the Emory Center for Experiential Learning's (ExCEL) OSCE and Simulation centers. My focus is the support and development of innovative multimedia technologies that assist SOM faculty with evaluating student performance in structured clinical examination scenarios and simulated medical events using pediatric, adult and organ-specific medical simulators.
With over 9 years of experience with digital video production, post-production and video-based consulting, I've executed digital multimedia, networked AV systems and Mac/PC support in the realms of higher education, web-based companies, art museums/galleries, and network television.
I obtained my B.F.A. in Digital Video from the Atlanta College of Art and pursued advanced study in Georgia State University's Mass Communication M.A. program and the Rhode Island School of Design's M.F.A. program in Digital + Media. I'm a Certified Technology Specialist - General (CTS) in AV media from Infocomm International.
I enjoy documentary filmmaking, experimental cinema, philosophy, art history, politics, satire, Latin & Italian language studies, travel, music & literature.