DNA sequence patterns shared by a set of regulatory elements with the same or similar biological activity remains a highly effective method for the de novo discovery of tissue-specific enhancers, and the simultaneous elucidation of cell type-specific regulatory codes. The method presented in this study further extends the usefulness of computational sequence analysis by exploring phylogenetic information that can be used to improve the classification accuracy, a strategy that promises to be advantageous in the large number of cases where comparative genomics data are available. Computational approaches for predicting cis-regulatory mod- ules are commonly based on machine learning of arrangements of TFBSs in enhancers that have common functions [7,10,11,13,15,76]. These methods rely heavily on a training set of related enhancers to detect over-represented TFBS combina- tions. Unfortunately, in the vast majority of cases—including the present study of Drosophila muscle FC enhancers—the size of the training set is limited by the lack of experimentally validated tissue- and cell type-specific enhancers, which results in overfitting of computational models and poor accuracy of predictions. To overcome this problem, and to provide a generalizable approach for increasing the size of the training set, we developed a phylogenetic profiling strategy based on a search for diverged orthologous counterparts of available enhancers from distantly related species. Twenty-four Drosophila orthologs were identified using this approach, which more than doubled the size of the training set. We assessed the ability to accurately distinguish FC enhancers in a cross-validation framework using the extended training set, and determined that the classifier accuracy is 89% as assessed by the AUC approach. We then applied this classifier to scan the entire genome of D. melanogaster for novel FC enhancers, retrieving 5,500 high-scoring predictions at a FPR of 5%. These predictions were significantly associated with genes expressed in FCs, demonstrating that the model was able to capture essential features of FC gene co-regulation. A similar machine learning approach could be applied to a diverse array of datasets, including experimentally-verified regulatory elements from co-expressed targets at either a germ layer, organ, tissue or cellular level from invertebrate and vertebrate databases [32,33,77,78]. Alternatively, a similar approach could be coupled to a training set of predicted regulatory elements derived from genome-wide analyses of chromatin marks or DNAse hypersensitive sites in active enhancers associated with a co-expressed gene set [79,80]. Increasing a Small Training Set with Orthologous Sequences Evolutionary constraint of functional sequences is routinely employed as an effective filter to improve the prediction of regulatory elements [13]. Furthermore, cross-species comparisons have been successfully exploited to obtain evidence for functional TFBSs. For example, Rouault et al. [76] used twelve Drosophila species to identify over-represented motifs in the regulatory elements of genes expressed in neural progenitor cells, with sequence orthologs used to enrich the training set and to give prominence to conserved motifs. However, our method extends this approach by including suitably diverged orthologous enhanc- ers from other Drosophila species in the dataset used to train the classifier. Our purpose in designing this strategy was two-fold. First, we wanted to enrich for relevant sequence motifs in the training data, allowing for a level of variation that would improve the generalization of the model. Second, we wanted to provide a potentially wider variety of TFBS arrangements that characterize the architecture of authentic FC enhancers. In essence, the addition of orthologous sequences boosts the statistical power of Machine Learning Transcriptional Regulators the significance tests, revealing patterns of TFBSs that otherwise could have been neglected. Of note, when 5 of these orthologous sequences were tested in transgenic reporter assays in D. melanogaster, the overall expression pattern generated was similar to the D. melanogaster counterpart despite extensive evolutionary shuffling of known TFBSs. Similar binding site reorganization has been documented for the enhancers that regulate both the segmentation and mesodermal patterns of eve expression [40]. Numerous other studies have shown that the order and spacing of TFBSs is critical for enhancer function [23–27,50]. These results suggest that regulatory elements can direct similar expression patterns provided that the overall composition and order of collaborating TFs is maintained [40]. Our finding that enhancer function is preserved in the orthologous sequences examined here establishes the validity of the sequence conservation thresholds chosen for the present studies, and suggests that the incorporation of orthologous sequences to increase a training set without over-fitting the data will be a generally applicable approach. In Vivo Functions of Predicted Enhancers To assess the accuracy of our method, we selected 12 predicted FC enhancers and tested their in vivo functions. Seventy-five percent of the putative enhancers were experimentally validated as having transcriptional activity, demonstrating the effectiveness of our approach to identify regulatory sequences. However, of the sequences showing regulatory functions, only 4 of 9 were active in the mesoderm—including 2 in FCs—and 3 of 9 had nervous system activity. These data suggest that our model has been able to reliably recognize general properties of tissue-specific enhancers without specifically distinguishing an overall muscle FC code, even though numerous individual FC-specific motifs were identified (see below). The former finding is similar to the results of Sinha and colleagues [13] who found that the majority of their classifier predictions were active enhancers, but only a minority were expressed in the predicted pattern. A number of confounding factors can explain this outcome. First, most members of the enhancer training set are active in both FCs and other cell types, including additional mesodermal cells such as the cardiac and visceral mesoderm, as well as some cells of the nervous system. For example, the enhancer responsible for the FC activity of the hunchback gene is also active in the longitudinal visceral mesoderm, and enhancers directing the FC expression of the vestigial, big brain and king-tubby genes are also active in the peripheral nervous system (Table S3). These results suggest that the regulatory networks specifying the somatic and visceral mesoderm share common features, which is consistent with both the available genetic and genomic evidence for the diverse developmental functions of key mesodermal transcription factors [81,82]. Second, different members of a given TF family bind to similar motifs but have distinct tissue-specific expression patterns and developmental activities. Thus, combinations of motifs involved in the specification of muscle FCs and the nervous system may overlap. For example, this situation occurs with E-box and NK-homeodomain motifs [5,58,76,83,84]. Third, some TFs are expressed and functional in the derivatives of more than one germ layer [54,85]. Fourth, the sequence features characteristic of cell type-specific enhancers, such as those active in muscle FCs, are expected to be under-represented in available training sets owing to the diversity of combinatorial TF models required to specify such a heterogeneous cell type [18,20]. Identification of many examples of a particular cell-specific signature is a major challenge since each of the approximately 30 FCs in each Drosophila hemisegement expresses a unique combination of cell-specific PLoS Genetics | www.plosgenetics.org 14 March 2012 | Volume 8 | Issue 3 | e1002531