domain TF acting downstream of Ras/MAPK signaling— combine to regulate a subset of FC genes (termed C1) that are particularly Ras- and Pnt-responsive [5]. Furthermore, we originally showed that 3 out of 4 genomic regions associated with C1 FC genes that contain clusters of binding sites for Pnt, Twi and Tin are functional FC enhancers when tested in transgenic reporter assays [5]. To extend that study, we have now tested 16 more predicted enhancers associated with C1 FC genes and found that 8 of these are bona fide FC regulatory elements (Figure S1 and Table S1). In contrast, only 2 out of 18 similarly selected candidate regions associated with non-C1 FC genes were validated as FC enhancers, although 4 of these predicted elements were active in other mesodermal tissues (Table S1). Similar to our previous work [5], these enhancers are active in differing subsets of the 30 individual FCs per hemisegment, with the only requirement being activity in one or more FCs. In total, these findings suggest that the transcriptional code governing C1 FC gene expression is missing one or more critical regulatory components, including cell type-specific factors. Interestingly, the activities of 14 of these 16 FC enhancers are not restricted to FCs but also include other mesodermal and non- mesodermal cell types (Table S1). It is generally believed that an individual enhancer controls a particular spatio-temporal aspect of a gene’s total expression pattern, with each enhancer composed of distinct clusters of binding sites for different combinations of TFs [2]. However, we have recently shown that the enhancer for the Drosophila Nidogen (Ndg) gene is active at different developmental stages and in multiple cell types (including FCs) due to the binding of multiple cell-specific TFs of the same family ([5] and X. Zhu, S. M. Ahmad, A. Aboukhalil, B. W. Busser, Y. Kim, T. R. Tansey, A. Haimovich, N. Jeffries, M. L. Bulyk, and A. M. Michelson, unpublished data). In this context, it is important to note that in several cases where attempts have been made to separate FC from other sites of mesodermal activity, it has not been possible to identify independent enhancers for the different cell types [5,28]. Furthermore, a survey of Drosophila enhancers shows that the majority are active in multiple cell types ([32] and see Table S2). A similar survey of vertebrate enhancers shows that this diversity of enhancer activities is not a reflection of the relatively compact Drosophila genome ([33,34] and data not shown). Thus, the regulation of some genes occurs through multiple enhancers, with each individual enhancer directing a specific spatio-temporal aspect of a particular gene’s expression. In contrast, other genes are regulated by a single enhancer which directs the entirety (or a large fraction) of the spatio-temporal expression pattern of the gene through the combinatorial activities of TFs that themselves have cell type restricted expression. The latter model appears to predominate for Drosophila FC enhancers. Despite the potential challenges of machine learning on a set of regulatory sequences having broad expression activities, our goal was to use existing information about FC gene regulation to identify both additional enhancers and novel TFBSs that convey individual FC specificity (Figure 1). Sequences Orthologous to Known FC Enhancers Have Similar Regulatory Functions Combining the aforementioned studies and previously pub- lished work, the training set contained a total of 16 FC enhancers [5,28,29,35,36]. Machine learning approaches require large and representative datasets to learn robust decision rules. Small training sets often lead to over-fitting of such decision rules and, consequently, do not satisfactorily generalize data that vary slightly in their statistical structure. In addition, limited datasets are likely to only partially represent the distribution of all instances of their Machine Learning Transcriptional Regulators class. Thus, to accurately learn the TFBSs that are responsible for FC gene regulation, and to reliably predict additional related enhancers, we investigated options to expand the set of training sequences. This goal was accomplished by a phylogenetic profiling approach which integrates orthologous sequences from the genomes of the 11 other fully sequenced Drosophila species, mosquito, honeybee and red flour beetle by searching for regions displaying at least 50% but less than 80% sequence identity between any two species [37]. These empirically determined sequence identity thresholds were chosen to avoid overly- conserved regions that would introduce redundancy and cause overfitting, as well as overly-divergent regions that would unlikely constitute functional FC enhancers [38,39]. Therefore, these identity cutoffs should ensure the representation of functional TFBSs in the training set that correspond to the regulatory function of interest, and thus provide sufficient information for training an accurate classifier [26,40]. This approach is also consistent with the flexible information display or billboard model of transcriptional enhancers, as proposed by Arnosti and Kulkarni [41]. Using these parameters, we identified 24 orthologous FC enhancer sequences from 6 of the 14 orthologous species based on compliance with our sequence identity constraints, bringing the total size of the training set to 40 elements (Table S1). To confirm the validity of the phylogenetic profiling approach, we assessed the performance of different classifiers trained on subsets of 62 Drosophila melanogaster enhancers having activities in various mesodermal cell types that was retrieved from the REDfly database [32] and 72 of their orthologs (see Materials and Methods for details). The large size of this dataset, and the functional similarity of its members to the activities of the elements that are the focus of this study, allowed an accurate evaluation of the impact of phylogenetic profiling on the prediction performance across training sets of gradually increasing sizes (including 10, 15, 20, …, and 60 randomly chosen mesodermal enhancers). As expected, increasing the size of the training set improves the classification performance until approaching its maximum and thereby rising to an asymptote (Figure S2A). The improvement in the classification performance, measured by the area under the curve (AUC) of receiver operating characteristic (ROC), stabilizes for classifiers trained on approximately 40 elements, suggesting this to be the minimum necessary number of enhancers to train a reliable classifier. We also found that the addition of orthologous sequences to the training set significantly improves the perfor- mance of the 91% of the classifiers independently of the size of the training set (all P-values,0.05, Figure S2A) and reduces the error in the estimation of the true accuracy of all classifiers (Figure S2B). Furthermore, phylogenetic profiling improves the concordance between predicted outcomes, and thus, classifiers including enhancer orthologs systematically recognize a larger proportion of enhancers as compared with the classifiers trained only on Drosophila melanogaster enhancers (Figure S2C and S2D). Therefore, supplementary orthologs not only increase the prediction accura- cy, but generate more stable classifiers, with more reproducible predictions. In addition, increasing the size of the training set by including presumably functional orthologous sequences that span different evolutionary distances increases our statistical power. For example, we identified over-represented binding sites of 14 TFs in the expanded set that included the orthologs and none in the original FC enhancer set (as compared with background sequence, correcting for multiple testing; Figure 2A). Among over-repre- sented TFBSs are motifs for FoxO1, Ets and the MyoD family of TFs, which are known to play a role in muscle differentiation [42]. Overall, these results are consistent with what would be expected PLoS Genetics | www.plosgenetics.org 4 March 2012 | Volume 8 | Issue 3 | e1002531