Author Summary The development of multicellular organisms requires the formation of a diversity of cell types. Each cell has a unique genetic program that is orchestrated by regulatory sequences called enhancers, comprising multiple short DNA sequences that bind distinct transcription factors. Understanding developmental regulatory networks re- quires knowledge of the sequence features of functionally related enhancers. We developed an integrated evolution- ary and computational approach for deciphering enhancer regulatory codes and applied this method to discover new components of the transcriptional network controlling muscle development in the fruit fly, Drosophila melanoga- ster. Our method involves assembling known muscle enhancers, expanding this set with evolutionarily con- served sequences, computationally classifying these en- hancers based on their shared sequence features, and scanning the entire Drosophila genome to predict additional related enhancers. Using this approach, we created a map of 5,500 putative muscle enhancers, identified candidate transcription factors to which they bind, observed a strong correlation between mapped enhancers and muscle gene expression, and uncovered extensive heterogeneity among combinations of transcrip- tion factor binding sites in validated muscle enhancers, a feature that may contribute to the individual cellular specificities of these regulatory elements. Our strategy can readily be generalized to study transcriptional networks in other organisms and developmental contexts. Furthermore, we have recently demonstrated that enhancer modeling can be used to reveal the enhancers and constituent sequence motifs involved in human heart development [7]. Surprisingly, recently predicted blastoderm segmentation enhanc- ers were often active in other tissues and developmental stages [13], whereas the validation rate for predicted human heart enhancers was much higher [7]. These differences in success rates could reflect methodology or might reflect the composition of the training set of sequences. In support of the latter possibility, there are sequence features unique to the blastoderm segmentation enhancers which might limit their amenability to this approach [16]. The development of the Drosophila larval somatic, visceral and heart muscles from mesodermal progenitors requires the coordi- nated input of multiple different regulators, including the intrinsic TFs Twist (Twi), Tinman (Tin) and Mef2 [17,18], and the intercellular signaling pathways mediated by the epidermal growth factor, fibroblast growth factor, Wnt, hedgehog and bone morphogenetic proteins (BMPs) [18–20]. These tissue-specific and downstream signal-activated TFs are highly conserved in sequence and function from Drosophila to vertebrates [21]. Although these factors function in various combinations to confer general and subtype properties on differentiating mesodermal cells, they also have pleiotropic effects in development such that additional factors are required to specify individual cellular identities. For example, the Drosophila larval somatic muscles are multinucleated myotubes each having unique properties that include their size, shape, orientation, epidermal attachments and innervation [18,19]. The formation of each myotube is initiated by a single muscle founder cell (FC) whose fate prefigures that of the corresponding muscle and is controlled by the combinatorial activities of muscle FC identity TFs [18,19]. FCs fuse with a more homogeneous population of neighboring muscle cells termed fusion-competent myoblasts (FCMs) to form muscle precursors Machine Learning Transcriptional Regulators [18,19]. The complexity of FC genetic programs [22] necessitates that a large number of identity TFs be involved in their specification, yet only a small number of such factors are known [20], few direct targets of these factors have been characterized, and little information is available about the combinatorial control of FC enhancers by TFs of different classes. Here we applied evolutionary and machine learning approaches to model Drosophila mesodermal enhancers having FC activities in order to uncover the motifs that orchestrate gene expression at the level of individual cells, to generate testable hypotheses about the nature of the corresponding FC identity TFs, and to gain insights into the combinations of TFs that contribute to individual FC enhancer specificities. The coordinated input of tissue-specific and signal-activated TFs, combined with the discrete identities of individual FCs, suggests that the regulatory network specifying distinct FC genetic programs is likely to share some common features while differing substantially with respect to others. Furthermore, a series of studies by Erives and colleagues has shown that a family of non-homologous enhancers is characterized by a discrete regulatory signature [23–25] in spite of the inherent complexity of isolated enhancers [26,27]. Taken together, this information suggests that the FC regulatory network should be amenable to an enhancer modeling approach. To address this problem, we first compiled a small set of enhancers with activity in FCs. To overcome issues associated with small sample sizes, and to increase the diversity of sequences with similar functions, we extended this set by adding orthologs derived from other Drosophila species. In vivo testing revealed that these orthologous sequences are functional FC enhancers in spite of having extensive reorganization of their DNA sequences. We show that increasing the training set through the addition of orthologous sequences improves the performance of our enhancer prediction model. By training on this extended set of enhancers, we were able to computationally predict functionally relevant TFBSs and enhancers for the FC gene set. When the resulting classifier was run genome-wide to search for new D. melanogaster FC enhancers, we identified 5,500 high-scoring predictions at a false-positive rate (FPR) of 5%. Moreover, these predicted enhancers were significantly enriched in the noncoding regions associated with known FC genes. While many of the TFBSs learned by the classifier are known to regulate the transcription of muscle FC genes, our classifier predicted additional motifs which have not previously been identified as contributing to FC enhancer activities. Site-directed mutagenesis of five newly discovered motifs in previously characterized FC enhancers demonstrated the critical role played by these TFBSs in supporting full enhancer activity. These validated motifs also suggest plausible candidate TFs acting in the myogenic regulatory network. In one such case—that of the T-box protein encoded by optomotor-blind-related- gene-1 (org-1)—we were able to use loss- and gain-of-function genetic perturbations to establish that this TF functions as a regulator of muscle identity. Furthermore, an analysis of the TFBS compositions of all known FC enhancers revealed an unanticipat- ed complexity in the combinations of TFs that contribute to the unique specificities of individual regulatory elements, a finding that provides a molecular explanation for the well-known diversity of muscle cell identities and their associated gene expression programs. Results Here we utilized phylogenetic profiling and machine learning to decipher the motifs and enhancers that underlie the gene expression patterns of individual muscle FCs, which required an PLoS Genetics | www.plosgenetics.org 2 March 2012 | Volume 8 | Issue 3 | e1002531