Network Visualization and Analysis

As discussed in Methods below, classifier performance is measured using leave-one-out cross-validation. Since 50 classifiers are trained for each TF each using a different randomly chosen negative set, the reported accuracy is an average over 50 trials. The cross-validation accuracy in training sets for all binding targets (over all classifiers) is 76%. New predictions are the result of averaging the assigned Platt scores from each of the 50 classifiers for a TF and applying a threshold of 0.95.

We note here that the resulting predictions indicate the association of TF and promoter DNA, which is the quantity being measured in ChIP-chip studies. As such, with a binding prediction one cannot necessarily conclude that the bound TF drives expression of the target gene in vivo, meaning that the rate of biological false positives will need to be determined later in the context of specific experimental conditions.

One of the origins of network structure is combinatorial regulation; i.e. single TFs often regulate more than one target, and particular targets are often regulated by more than one TF. Networks can be inferred using a combination of experimental and computational methods and displayed as a repertoire of connections (the cell's network repertoire), different subsets of which are selected by particular environments. The repertoire is available at the VisAnt [20, 21] website and can be accessed in the methods table as "TFSVM." Visualization in VisAnt allows predictions to be integrated with many other large scale genomic datasets including protein-protein interaction, gene expression, GO functional annotation, and genetic interaction. VisAnt also allows adjustment of the network based on a user-defined likelihood threshold for accepting an association. Thus predictions can be embedded in networks mined at any specified degree of stringency for further analysis.

The full set of predictions for 163 TFs in S. cerevisiae are available our webserver [28]. Users may query either a transcription factor, returning the list of predicted targets, or a gene, returning a list of possible regulators. The Platt scores may also be set by the user, providing an adjustable threshold on the predictions for each TF. In addition, the cross-validated accuracies for all classifiers have been posted online, as well as the top 50 ranked features for each TF-classifier.

Global Network Properties

In any regulatory network it is of interest to know which genes are most heavily under transcriptional control, and which TFs exert the most control by regulating large numbers of genes. When analyzing global network properties, we limit our analysis to high quality predictions by including only TFs that have a leave-one-out cross-validated classification accuracy greater than 0.6 (there are 130 such TFs), and targets that have a Platt score ≥ 0.95.

The resulting network includes a large number of genes that are under strong regulatory control. In particular 125 genes are regulated by 12 or more transcription factors. These genes show statistical enrichment (p ≤ 0.05) in several GO biological process categories. The enriched categories are mainly involved in carbon metabolism and energy generation (see Additional File 1), which are crucial functions expected to be under tight control. Other important processes include DNA damage checkpoint, DNA recombination, DNA damage response, acetyl-CoA catabolism, NADP(H) metabolism, and telomere maintenance. With respect to breadth of control, 13 TFs regulate more than 300 genes (known + predicted) each at high Platt score cutoffs (P ≥ 0.95). This set of TFs includes the pervasive regulators Abf1 and Reb1, as well as TFs involved in the cell cycle, growth, and the stress response (see Additional File 2).

Local Structure: Swi6 Case Study

Our results include a large number of subnetworks. As an example we consider known and predicted targets of the cell cycle regulator Swi6, subsets of which (18 out of 142, and 15 out of 138, respectively) are shown in Figure 2. The average cross-validated prediction accuracy in training sets is 83%. The new predictions are highly connected to known targets by a variety of the experimental and computational methods which are available for download in the VisAnt system. Both gene groups in Figure 2 contain cell cycle genes, and the most common connection is phylogenetic profiling, suggesting considerable functional similarity. The network perspective supports the prediction of common regulation of these highly interacting genes, and makes it easier to formulate testable hypotheses about the relationships of regulatory targets.

Among the new targets of Swi6 is YLR176C (Rfx1), which codes for a transcription factor that is predicted to regulate a previously known Swi6 target, YMR279C. This arrangement is a feedforward loop, suggesting that YMR279C is under combinatorial control by these two factors (Figure 2a). It should be noted that although our method independently predicts that Rfx1 is a regulator of YMR279C, Rfx1 was reported to bind the promoter of YMR279C in an early ChIP-chip study [29]. An updated analysis of those results by the same group removed YMR279C from the dataset (can be downloaded from [30]), and a subsequent ChIP-chip experiment did not show significant regulation [22]. Due to this ambiguity the Rfx1-YMR279C interaction is not in our positive set; nonetheless, it is predicted by the classifier for Rfx1.

Rfx1 is a repressor involved in the cell-cycle DNA damage checkpoint. Inactivation of Rfx1 occurs in response to DNA damage and causes the induction of many genes [31] (by release of repression by Rfx1). In addition to our prediction that Rfx1 is regulated by Swi6, an analysis of the expression of the two TFs during the alpha-factor arrested cell cycle time course [32] shows that their transcripts are correlated. Figure 3 shows the expression of Swi6, Rfx1, and two reference genes which show expression peaks in G1 and S-phase. Across the 18 experiments in the time course, which spans 2 cell cycles, the two factors show a correlation coefficient of 0.6.

Since Swi6 is known to be important for the G1/S transition, the expression in these specific experiments was examined more closely (noted in Figure 3). Using prototypical G1 and S phase reference genes, eight time points spanning the peak of G1 through the peak of S phase were selected. In these eight experiments Swi6 and Rfx1 show a stronger correlation of 0.73, which is statistically significant at p = 0.04. Interestingly, Swi6 may bind several genes in the DNA damage response pathway including Dun1 (known), Rad53 (new, P = 0.96), and Mec1 (potential target, P = 0.76). These targets are kinases known to phosphorylate and thereby activate Rfx1 in the DNA damage response pathway [31].

Finally, it has been shown that the kinase Rad53 phosphorylates Swi6, delaying progression of the cell cycle into S-phase [33]. The ultimate target of the new feed forward loop is YMR279C, which is an uncharacterized gene showing sequence similarity to membrane transport proteins. This gene shows a 9-fold induction in response to DNA damage [34, 35]. Taken together, this evidence suggests that Swi6 is involved in the DNA damage response at the end of G1, and that YMR279C plays a role in DNA damage response and perhaps in cell cycle progression.

Deletion mutants of YMR279C are viable [36] but it is not known how this deletion affects the cell cycle or DNA damage response. Examining these mutants for deficiencies in growth and DNA damage response may shed some light on the true function of YMR279C. Detailed experiments including reporter assays would be needed to determine how closely interlinked Swi6, Rfx1, and YMR279C are on the transcriptional level. As a working hypothesis, it appears that Swi6 is available to activate DNA damage response genes such as Rfx1, ensuring they are present at crucial times in the cell cycle. Normally Rfx1 is repressing its targets and YMR279C will not be activated. In times of DNA damage Rfx1 is inactivated by a phosphorylation [31] allowing response genes to be induced by Swi6 and resulting in cell cycle arrest. The prediction of Swi6 binding to the Rfx1 promoter may not be of much consequence, perhaps activating Rfx1 to insure its presence during G1/S-phase. YMR279C is already known to be bound by Swi6, so the prediction that Rfx1 also binds is significant, suggesting that YMR279C will be repressed by Rfx1 until DNA damage releases it to be activated, possibly by Swi6 during the critical G1/S checkpoint.

Prediction Analysis and Feature Rank: More on Swi6

In order to examine the regulatory implications of our model, Swi6 is again used as an example. First, the functional enrichment of Swi6 targets is examined. Swi6 interacts with Mbp1 and Swi4 during the cell cycle (G1/S transition) and in meiosis [37, 38]. The known targets of Swi6 and the new predictions (throughout the range of Platt scores P > 0.5 → P > 0.95) are significantly enriched (p < 0.05) in the expected GO biological processes including cell cycle, regulation of cell cycle, mitotic cell cycle, DNA repair, etc. Some of the new categories for which targets at P > 0.95 show enrichment include chromatin assembly/disassembly (p = 1e-10), septin checkpoint (p = 2.7e-3), and lipid metabolism (p = 8.4e-4). These new targets fit with the current knowledge about Swi6 regulation and suggest possible new roles of action.

As described in Methods, the features for each classifier are ranked using an expanded variation of the basic SVM-RFE (Support Vector Machine – Recursive Feature Elimination) algorithm [39]. Ranked features can be used to reveal interesting biological aspects of regulation and suggest directions for future experiments. Only a relatively small number of features show the highest discriminative ability and consistency across many subsamples of the training set (Figure 4). The first 10 features in Figure 4 have a high rank in more than 65% of the resamplings while the remaining features fall off sharply in reliability. The top ranked features for each transcription factor are available on our website (features listed in order of decreasing importance).

The most important feature for identifying known targets of Swi6 is a microarray experiment measuring expression of genes in an Mbp1 deletion mutant. This makes sense since Swi6/Mbp1 interact and function as coactivators at many promoters [37]. By t-test the observed expression change is significant between the negative set and the known positives (p = 3.7e-25), and between the negative set and the genes at P = 0.95 (p = 9.14e-27, 280 genes-known and new).

The next several highest ranked features identify the k-mer ACGCG/CGCGT as being important for classification by conservation and overrepresentation (features ranked 2–4, 7, and 9 indicate this k-mer). The k-mer overlaps highly with known binding sites for Swi6; for example, Swi6 binds CGCGAAA in the Cln2 promoter [24]. The overrepresentation of this sequence in the positive training set can be seen by examining the calculated significance values [40], which are the scores used to determine the statistical importance of each k-mer (see Methods). Viewing this score in the negative set genes as compared to the positives is informative, and Figure 5 plots the distribution of significance scores in the genes of the negative set, positive set, and the predicted targets at Platt scores P > 0.95. The graph was generated by placing the genes in each set (i.e., positive, negative, and predicted) into 5 equally spaced bins based on significance score. The known and predicted targets clearly show enrichment of ACGCG as compared to the negative genes.

The conservation of ACGCG is ranked more highly than overrepresentation, indicating that this sequence is preserved in promoter alignments which include 7 Saccharomyces species. Figure 6a shows such an alignment in the promoter region of the Isc1 gene, a newly predicted target of Swi6 (Figure 6b shows a similar alignment in the Sur2 promoter). Two occurrences (highlighted in red) of the indicated k-mer appear in close proximity in a highly conserved segment of the Isc1 promoter. The conservation scores in Figure 6 are the output of the PhastCons algorithm [41] and are pre-calculated on the UCSC Genome Browser website (see Methods). The sequences flanking the k-mer in Figure 6 also show strong conservation. The SVM model does not indicate the position or which specific instances of ACGCG are conserved, but only that the k-mer occurs more often in conserved regions in bound promoters as opposed to non-bound promoters. This knowledge can be useful, as seen in Isc1, for narrowing down the specific sequences which may be bound.

Isc1 is an important gene since it is the only member of the sphingomyelinase (SMase) gene family present in the yeast genome. These SMases are responsible for generating ceramides, which are bioactive lipids known to modulate a variety of cellular processes including cell growth, apoptosis, and the cell cycle in yeast [42]. They also contain a newly discovered P-loop-like domain, which is conserved in the gene family from yeast to humans [43]. Isc1 is the closest yeast homolog to the human SMase2 gene and has thus become the model for exploring the functions of SMase genes in general.

Isc1 has been implicated in general cell growth [42], fermentative growth, and sexual reproduction [44]. Recent experiments in human and mouse models have demonstrated ceramide enhanced cancer cell death, indicating that ceramide could act synergistically with other chemotherapeutic agents [45]. Indeed, several therapeutic compounds are currently under development which modulate ceramide metabolism. The prediction that Isc1 is regulated by Swi6 (Mbp1) is interesting since it provides a direct link between cell cycle regulation and the generation of bioactive lipids via the hydrolytic pathway. Further investigation will be necessary to determine the biological validity of this link and whether the human ortholog, SMase2, shows a similar connection to the cell cycle. It is possible that ceramide production via SMase2 could serve as a target for anti-cancer therapy which can be easily studied in yeast models.

Because predicted targets of Swi6 include SMase and are enriched in genes functioning in cellular lipid metabolism, Swi6 may have a greater role than previously appreciated in controlling lipid metabolism and its coupling to cell growth, apoptosis, and reproduction.

Additional Examples of Feature Ranking

As a further example of the usefulness of SVMs coupled to our feature ranking method, we briefly explore the TFs Gzf3 (YJL110C), and Ash1 (YKL185W). The feature ranking for the factor Gzf3 (YJL110C) indicates that the melting temperature at window positions -274 to -286 (see Methods) in target promoter regions is different than that in non-target promoters. Figure 7 shows a plot of the average melting temperature in sets of yeast promoters averaged within a moving 20 bp window. Known targets clearly have a reduced melting temperature at the identified positions as compared to negative set or average genes. The relationship is also present in the targets which show a Platt score > 0.95 (known and new). This group contains 72 targets, 27 of which are new predictions. Although it is unclear how the melting temperature and helix stability in this region affects regulation by Gzf3, it is possible that Gzf3 or other factors induce changes in DNA compaction or stability which alter regulation at these promoters. Binding sites of Gzf3 do not appear to be concentrated in the affected region, possibly implicating the activity of other factors at this site. In any case, feature ranking has identified specific nucleotide positions which can be tested experimentally for their role in transcriptional regulation.

The classifier for Ash1 has an average (cross validation) prediction accuracy of 88%. The most predictive feature is gene expression in She4 deletion mutants. Expression in these mutants of known targets and genes at P = 0.95 is significantly different than in the negative set (p = 5.4e^-15 and p = 6.5e^-8 respectively). This gene expression difference in She4 mutants is consistent with known biology since several She genes are responsible for controlling the localization, and hence appropriate regulation, of Ash1 [46–48].

General Results and Comparison with Other Methods

As mentioned previously the cross-validation accuracy in training sets for detecting yeast binding sites (over all classifiers) is 76%. An obvious question relevant to validation is how noise in ChIP-chip data (false positives) affects assessment of the method. There are several sources of a noise. One is that the TF of interest does not contact DNA directly but interacts with a second factor which binds to the promoter. In principle this does not affect our results since the SVMs are searching for TF-promoter associations, not for specific binding sites. Other types of noise that affect allocation are more fundamental. If we consider a worst-case scenario in which the ChIP-chip targets for a regulator are near-random or too noisy, the SVM classifiers will also perform no better than random and, as we show below, our system will produce no new predictions for the TF (at the 0.95 threshold). Finally, it is important to distinguish promoter binding from transcriptional activity. Like ChIP-chip data itself, the method is determining binding rather than biological activity, the latter being inferred from a positive binding prediction. Because of the very low false positive binding rate, and because many predictions are functionally consistent with the biology of known targets we expect that the predictions made by SVM are biologically relevant.

Randomized Controls

Here we compare TF-target classifiers to randomized data for several transcription factors. As with the actual classifiers, predictions for a randomized control must pass the 0.95 threshold across 50 separate classifiers. To illustrate the stringency implied by this requirement, the prediction procedure was implemented for three control TFs chosen according to the number of available targets (Dat1 – small, 17 targets, Yap5 – medium,72 targets, Swi6 – large, 142 targets). For each factor, the positive and negative training labels were shuffled (over all 50 random negative sets), and classifiers were built and applied to the genome. In all cases the shuffled classifiers made no predictions which passed the 0.95 threshold. Leave-one-out cross validation was also performed to determine whether the accuracy and PPV of the actual classifiers was significantly different than random. In all cases the Accuracy and PPV were significantly better than random with p-values less than 10^-28. This can be seen for PPV in Figure 8, which gives box-plots of the PPVs of actual and random classifiers for the three TFs mentioned. Because these are notched-box-plots, the medians of two populations may be considered significantly different (with 95% confidence) if the notches on the boxes do not overlap.

SVM training, parameter tuning, and validation

The SVM is a statistical learning method originally developed by Vapnik [18, 19, 52]. SVMs are based on rigorous statistical principles and show excellent performance when making predictions on many types of large genomic datasets. The algorithm seeks a maximal separation between two groups of binary labelled (e.g., 0, 1 or negative, positive) training examples [53]. The training examples are feature vectors x of individual genes, each vector populated by measurements taken on genome scale datasets (see below). These measurements are the attributes of the data. A single classifier based on these features is then constructed to predict targets for each TF. Positives are genes which are known targets of the TF, and negatives are a randomly chosen subset of genes (equal in size to the positive set) which are least likely to be targets. The positives are taken from ChIP-chip experiments [23, 54], Transfac 6.0 Public [24], and a list curated by Young et al., from which we have excluded indirect evidence such as sequence analysis and expression correlation [25]. ChIP-chip interactions of p-value ≤ 10^-3 are considered positives [54] and are not filtered using any other criteria. Since some TFs are tested under multiple experimental conditions, the negatives will be the targets with highest average p-values under all experiments (and they must not be targets under any condition).

The separation of targets from non-targets is accomplished by an optimization which finds a hyperplane separating the two classes [18, 19]. Aside from the number of features on which to base the classifier, only one parameter must be set in our framework before training the SVM, the C parameter, which controls the tradeoff between the so-called margin and the classification error. Since it would be computationally expensive to choose these parameters during the training of every classifier (which would also be more likely to over-fit the data), they are first optimized on the classifier for one TF. The learned values are then applied to the remaining classifiers.

The C parameter adjusts the tolerance of the algorithm for misclassifications. Lower values indicate higher tolerance for errors or noisy data, with a compensating increase in the strictness of the error margin around the SVM separating hyperplane. As with feature selection (described below), the classifier for YIR018W (Yap5) was used as the prototype for parameter selection since it is known to regulate ~70 genes, which is close to the average for all TFs being analyzed. Grid selection was performed on the training set for YIR018W using many values of C, and classifier accuracy was measured with 5-fold cross validation. The SVM was seen to be insensitive to the choice of C, with most values less than 1 showing similar performance. Tested values include: [2^-7 2^-5 2^-3 2^-1 1 1.5 2 2² 2³ 2⁴ 2⁵ 2⁶]. The value 0.0078 was chosen as this was the value reported by the SPIDER machine learning package [55] as having the best performance. In tests with other transcription factors the same value was chosen in most cases.

In previous work [17] we experimented with various varieties of SVM and found the linear SVM to be largely superior with the datasets examined here (unpublished work). By reanalyzing that original data we see that in one initial experiment where the type of SVM was allowed to vary between linear, quadratic, and cubic, the linear SVM had a higher cross-validation accuracy in 90 out of 104 cases. Similarly, using cross-validation on 104 TF classifiers, the PPV obtained by linear SVM was significantly higher by t-test than those using RBF (p = 3.62e-4) or Gaussian (p = 8.5e-68) SVMs. The formulas for RBF and Gaussian are quite similar and can be seen in [17]. They are used as implemented in the SPIDER machine learning toolbox [55].

The scheme used here for classifier construction is outlined in Figure 1. To illustrate the construction and validation more concretely, a short outline is provided below. For a particular TF

1. Assemble positive set of n known targets. Sample n genes randomly from the negative pool (see above) to construct the negative set

2. Split the data for LOOCV (n-1 genes in training set, 1 gene in test set).

3. Use SVM-RFE to rank all features in the training set.

4. Construct SVM classifier on top 1500 features. Save full feature ranking.

5. Classify left out gene.

6. Repeat steps 2–5 to complete LOOCV. Save all feature rankings.

7. Calculate performance statistics (Accuracy, PPV, etc.)

8. Repeat steps 1–7 50 times.

9. Calculate final performance statistics (i.e., mean Accuracy, mean PPV).

A new gene can be classified by applying all 50 TF-specific classifiers to the feature vector for that gene in balanced genomic test sets. Each classification produces a Platt score (see below), and the mean of all 50 scores is used. If the mean P > 0.95, classify the gene as a target of the TF. The full set of feature rakings on every training set is used to calculate the final feature rank (see below).

Genomic feature selection and ranking

The SVM algorithm can be used to select and rank data features. An important output from the algorithm is the vector w, which contains the learned weights of each feature. This vector points in a direction perpendicular to the hyperplane, and thus defines its orientation. Features with higher components in w are more useful in separating the positive and negative classes. The SVM recursive feature elimination (SVM-RFE) algorithm uses w to select features useful for classification [39]. The original SVM-RFE algorithm trains an SVM on a training set, and the components (attributes) of the feature vector x which have smallest weights are discarded [39]. The w vector is recalibrated and the process is repeated until the desired number of attributes remains. If feature selection were only performed one time for each TF prior to cross validation there would naturally be a risk of over-fitting, since both training and test information would be used to choose the best features. Instead we perform feature selection on each training set during cross-validation, ensuring that any over-fitting would be detected as low accuracy on the test set.

In our study, features for all genomic datasets are concatenated to produce large attribute sets for each gene. SVM-RFE is used to select relevant features during classifier training and various feature subset sizes are tested using a leave-one-out cross validation. Thus we allow the datasets to adjust, automatically selecting the most important features, irrespective of the data sets from which they originated. Once again Yap5 was used as a prototype. Figure 9 shows the effect that changes in feature number have on Yap5 classifier accuracy. Although as few as five features achieve 70% accuracy, the addition of more features continues to improve accuracy until 1500 features are selected, where accuracy is approximately 85%. 1500 is then the number chosen for the remaining TFs. The specific 1500 features are, of course, chosen individually for each TF. A more optimal procedure would be to also choose the best number of features for each classifier as this may vary from TF to TF. Such a procedure would be computationally prohibitive. In our procedure half of the features are removed during each iteration of SVM-RFE until at least 1550 features remain. Features are then removed one at a time until the target of 1500 is reached

It is important to appreciate the difference between the feature selection performed during classifier construction and the feature ranking which is later used to assess the overall usefulness of features for predicting targets of a TF. Feature selection for each classifier is simply an application of the SVM-RFE procedure. The feature rankings mentioned in the Discussion are created by compiling the individual rankings from all 50 TF classifiers. This provides a more accurate selection of features by choosing those which are ranked highly in a majority of training sets.

The final feature rank is achieved in the following way. After a single application of the SVM-RFE algorithm, the w-vector for the top 1500 features is used to determine the rank of the features in that training set, with higher weighted features having higher rank. These rankings are accumulated over every training set during cross validation of all 50 classifiers created for a TF. The result is a large set of feature rankings for a particular factor. The top 40 features (the number 40 was chosen arbitrarily) from each ranking are collected into a list, and a count is taken of the number of times each feature appears. The final rank is established by sorting the features based on the frequency of their appearance. Therefore, features which are consistently ranked high during all cross-validation trials are given a high rank. Clearly, features high on this list are reliably important for separation and robust to changes in the training set. In keeping with our example of Swi6, there are a total of 7100 feature rankings available for Swi6 features (142 examples times 50 cross validation repetitions). Figure 4 shows a plot of the features for Swi6 sorted by their occurrence in the top 40 ranked features within the 7100 rankings.

Classifying new targets and prediction significance

As described in [27], SVMs can provide a probabilistic output which in this case measures the likelihood that any given gene is a target. Here this output is referred to simply as a Platt score or enrichment score. The intuition of this method of assigning scores is that data points which are deeper in the positive region (i.e., further from negative examples) are the most likely to be true positives.

Because the prior probabilities of each class (positive and negative) for a transcription factor is unknown, we choose each class to be of equal size. Thus, the Platt probabilities are strictly accurate only on the training set rather than the entire genome, where the classes would be very imbalanced. Using a balanced training set has an advantage in that some classifiers trained on balanced data often have better performance (as measured by ROC analysis) than classifiers trained on imbalanced data. This can vary, however, depending on the data sampling technique used. See [56] for an insightful discussion and comparison of sampling strategies. Thus, it should be kept in mind when evaluating the results herein that, when using balanced datasets, an Accuracy of 50% is random. As discussed above, randomized controls may be used to assess the significance of any single classifier.

Platt scores can nevertheless still be used to rank new predictions, and only those genes with a score of ≥ 0.95 are considered as potential targets in this study. The fact that our framework requires that any new target achieve an average score of ≥ 0.95 across 50 classifiers partly offers an intrinsic correction and increases the confidence in the predictions made. Randomized simulations can then be used determine whether any classifier performs significantly better than random.

where p is the p-value (1-Platt score) in the sample and p_full is the p-value for the genome. As an example, if we see that a TF is predicted to bind a gene with a Platt score of 0.99, this conditional probability is equivalent to an uncorrected p-value of 0.01. Thus, the correction above would transform the p-value of 0.01 to approximately a p_full of 0.1. Note that this is a very conservative correction since it does not take into account the fact that our Platt score is the averageover 50 classifiers.

Here and elsewhere we refer to the average, uncorrected Platt output using the upper case P (e.g., P > 0.99), whereas p-values measured by other means are shown in lower case (e.g., p < 0.01).

Feature Datasets

Eight different types of features were used to describe genes. The first six feature sets have been used previously and their full descriptions along with many relevant references can be found in [17]. The remaining two datasets have been modified or are novel. All together these datasets comprise 15516 features. The k-mer based kernels are inspired by the spectrum kernel [57], the (g, k)-gappy kernel [58] and the mismatch kernel [59] which have been proposed for sequence classification. In cases were computations were made on promoter regions (datasets 1–3, 5, 7,8) the promoters were defined as the 800 base pairs upstream of the coding region (translation start cite) and S. cerevisiae promoters were downloaded from RSA tools [60]. Before analysis all features are normalized to 0 mean and standard deviation of 1. Each of the datasets is available for download at [28].

1. k-mers (KMER) – Feature vectors are formed by enumerating all possible strings of nucleotides of length 4, 5, and 6. The number of occurrences of each string is counted in a gene's promoter region, and this string of counts is the gene's feature vector. K-mer counting which was used in part for datasets 1, 2 and 8 were performed using code modified from a script kindly provided by Dr. William Stafford Noble of Washington University.

2. k-mers with Mismatch (M01) – Similar to k-mer counts, occurrences of all strings of length 4, 5, and 6 are counted. In addition, any string which contains only one mismatch is also considered a hit, but is given a count of 0.1 rather than 1.

3. Melting Temperature Profile (MT) – It is possible that TF binding is facilitated by conformational adjustments in promoter DNA, which depends on the stability of the helix. Some recent evidence shows correlation between sites of promoter melting, regulatory sites, and transcription initiation sites [61]. The EMBOSS [62] toolbox is used to calculate the melting temperature profiles of all yeast promoters using a sliding window of 20 bp. The feature vectors are the same as described in [17]. A temperature is calculated for each 20 bp window in the promoter, and this vector of window temperatures comprises the MT profile. 20 bp is the default window size in the EMBOSS tool.

4. Homolog Conservation (HC) – [63] BLASTP is used to compare proteins in yeast to those in 180 prokaryotic genomes. The best hit E-values to each genome are discretized by placing them into one of six bins using empirically determined E-value cut-offs. Bin numbers range from 0 (no significant hit) to 5 (very significant). Each gene then has 180 features, each for a different genome, with values ranging from 0–5, signifying the strength of the best BLASTP hit of that gene's protein to another genome.

5. k-mer Median Positions (Kpo) – For each possible k-mer (k = 4, 5, and 6) we record its median distance from the transcription start in each gene.

6. Expression (EXP) – Normalized log2 ratios for each gene across 1011 experiments [64] are used as features. Each gene's expression profile is normalized to a mean of 0 and standard deviation of 1. For each gene a vector 1011 long (one feature for each expression experiment) is included in the data set.

7. k-mer Overrepresentation (Kev) – This method counts the number of each k-mer appearing in a promoter and calculates the significance of its occurrence. This method is the same as that reported in our previous work [17], except that the binomial distribution is used to calculate p-values rather than the Poisson distribution. This is in line with the calculations described in RSA tools [40, 60]. A higher Significance corresponds to a more relevant k-mer.

8. Conserved k-mers – This method for constructing a k-mer conservation matrix is based on output generated by the PhastCons algorithm [41, 65]. PhastCons is a two state phylogenetic hidden Markov model. The underlying idea is that conserved elements evolve more slowly than non-conserved elements. Thus, it has a "slow" state for conserved DNA and a "fast" state for non-conserved, more rapidly changing sites. Given DNA sequence alignments from multiple species, PhastCons outputs a probability score for each base pair in the alignment indicating from which state the sequence arises. This probability can be interpreted as the likelihood that the base pair is part of a conserved element. Genomic alignments for seven yeast species are used to generate the probability scores, which are available for download from the USC genome browser website [66, 67]. Note that the conservation scores shown in Figure 6 are from the PhastCons algorithm and were taken directly from the UCSC Genome Brower website.

where P _c is the average PhastCons score for a particular k-mer. β is an adjustable parameter which controls how much the conservation of a k-mer increases its count. In this study we choose β = 0.75, so that an element with a maximum conservation of 1 has a count of 4. An element which shows no conservation has the default count of 1. Increasing β will further emphasize the effect of conservation. This method based on PhastCons is inspired by the "marginalized motif kernel for phylogenetic shadowing" introduced in [68]. Their method uses promoter alignments and a probabilistic model of fast and slow evolution to assess conserved elements. While their method can be considered more robust when good sequence alignments are available, we adopt the approach described here so that all yeast sequences may be included in our analysis.

Functional Analysis, Software, and Expression Data for the Swi6 Analysis

Statistical enrichment of GO biological process terms in gene sets was performed using the GO Term Finder on the Saccharomyces Genome Database website [69, 70]. Most of the analysis was performed in MATLAB [71] using custom scripts along with the SPIDER software package [55].

Expression data for Swi6 was taken from [32] and their associated website [72]. Expression data for YMR279C showing cell cycle induction was from [34] and was explored using the Expression Connection [35] at the SGD website.

VisAnt Networks

The networks (such as Figure 3) created with the VisAnt toolkit[20, 21] show links which have come from many publications. Any particular type of link (e.g., protein-protein interaction) may represent a collection of data from several genomic datasets. Each link type is referred to in VisAnt as a "method" and each method has a unique identifier. The method IDs for the link types in this paper include: M0037(phylogenetic profile), M0013(copurification), M0040(screened yeast-2-hybrid), M0031(other biophysical), M0046(Bayesian Predicted Interaction), M0045(affinity chromatography). Complete references and datasets are available in the VisAnt suite, accessible from the VisAnt website [73].

Reviewer's report 1

Igor Jouline (Zhulin), Joint Institute for Computational Sciences, The University of Tennessee – Oak Ridge National Laboratory, Oak Ridge, TN Reviewer comments:

This study is an extension of a previously published work on machine learning for regulatory analysis and transcription factor target prediction in yeast by the same authors. Novel aspects of this work include the inclusion of new features in SVM classifiers, expansion of the transcription factor list and a case study of one of them, where some new biological insights can be gained. Overall, this is a straightforward work, which potentially can help uncovering useful biological information. Personally, I found the "principal finding #4" most interesting and appealing to a broader audience, although it is clear that it wouldn't be there without other findings reported in this study.

I do not have any major concerns. I am not impressed with the way this work is presented, perhaps because it is difficult to evaluate the true biological significance of the method. Why the Swi6 story is told in a great detail and two other transcription factors were "briefly explored"? This is out of 163 transcription factors for which the developed formalism was applied to identify their targets. Clearly, it is difficult to produce in-depth analyses for all of them, but what was the choice of the few based upon? In the absence of such explanation, one usually thinks (ignoring the presumption of innocence rule) that in the case of "the case study" biological insights were obtained, whereas in other cases it was not that impressive. Hopefully, authors can prove me (and my diabolic suspicion) wrong.

A couple of other comments: (i) I think the title of this paper is too broad and non-specific; (ii) on page 4 (last sentence) authors state that "instead of using all (available?) features to make a classifier we apply recursive feature elimination to select those that are most relevant ". It would be helpful to explain in the next sentence what those most relevant features are...

Other than that, this is certainly a strong computational study worth publishing and hopefully yeast biologists will make use of information presented here.

Reviewer's report 2

Todd Mockler, Center for Genome Research and Biocomputing, Oregon State University

Nominated by Valerian Dolja, Department of Botany and Plant Pathology and Center for Genome Research and Biocomputing, Oregon State University

Reviewer comments:

I think this is interesting and important new work in the area of supervised learning approaches to identifying transcription factor targets. Overall, the manuscript is very well written, and the principle findings are well supported by the data presented. The methods are appropriate and sufficiently described to allow replication and/or application to other datasets or species. Additionally, the data are presented in a clear manner. I have concern about one particular figure, which should not affect publication because it doesn't affect the major conclusions relating to the figure. Figure 3 is presented as further evidence of the regulatory network connection between Swi6 and the newly predicted target Rfx1. Figure 3 is supposed to depict the association of these two genes with the cell cycle due to an apparent correlation in their expression patterns. The expression profiles of Swi6 and Rfx1 across the 18 time points have a correlation coefficient of 0.6, and a selected subset of 8 time points has a correlation coefficient of 0.73. However, unlike the G1 and S phase reference genes shown, the expression profiles of Swi6 and Rfx1 show no obvious pattern that could be associated with the cell cycle in this experiment. Moreover, the amplitudes of their changes across this time course are minimal, and could be easily mistaken for slightly correlated noise. I find this figure unimpressive, and possibly unnecessary because the major conclusions drawn from this figure are well supported by other data and cited studies as described in the manuscript.

Reviewer's report 3

Sandor Pongor, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy

Reviewer comments:

The paper is about the classification/prediction of transcription targets. It summarizes new developments to the work summarized in two preceeding publications on the same subject (Machine Learning methods for data integration, IBM J. Res. Dev., 50, pp 631–643, 2006 and "Machine learning for regulatory analysis and transcription target prediction in yeast, Systh. Synth. biol., 1: 25–46, 2007). The added value of the present manuscript is the application of recursive feature elimination for selecting relevant classifier descriptors, a technique described by Vapnik and associates (Machine Learning, 46: 389–422, 2002), a heuristic selection of attributes, and several, sometimes not entirely specified improvements to the methods, applied to a greater dataset.

While the findings are potentially interesting for a wide audience, I find the writing very technical, at times repetitive and quite difficult to follow – even for readers interested in support vector machines, string kernels and feature selection. The abstract lists four principal findings, and I am not entirely sure if and where these points are dealt with in the Results section.

For instance, the first finding reads as: "Application of the method yields an amplification of information about yeast regulators." Is "amplification of information" defined later in the text? It is then stated: "The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4)." Apart form the general list in supplementary materials, I did not find a table in the text that would substantiate these points. The same holds for the second principal finding "Many predicted targets for TFs match well with the known biology of their regulators.". This finding should be underpinned with numerical data.

Principal finding 4 reads as follows "An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter." I found only one network figure which refers to a local subnetwork, but I did not find a figure or a table that would underpin this finding in a numerical way.

The authors made a large study, with many methodological improvementa that are difficult to coherently present for any audience. A better writing (removal of redundancies, a conceptual separation from previous work, and a bettter focus on added values, a clear comparison of previous and present predictions, comparison with other methods etc.) could in my opinon substantially improve this manuscript, because the work is novel and interesting. Perhaps the authors should make it clearer whether the goal is to describe methodological and Web-server details, or rather to present biological findings. It would be useful to quote examples of how a new methodology affects the efficiency of the prediction.