Machine Learning Transcriptional Regulators array of computational and experimental tools. This study is composed of 4 main components: (1) compiling a training set of FC enhancers from multiple sources including the literature, testing of additional computational predictions from a previous study [5], increasing the size of the dataset through phylogenetic profiling, including the empirical validation of a subset of those predictions; (2) machine learning on the FC enhancer training set; (3) experimental validation of classifier predictions using transgenic reporter assays and whole embryo in situ hybridization with gene- specific probes; and (4) functional examination of sequence features associated with the computational classification to define novel motifs and TFs regulating myogenesis. An overview of the approach utilized in this study is presented in Figure 1. In addition, we used the information derived from the abovementioned studies to examine the distribution of TFBSs across the entire set of known FC enhancers to ascertain the extent to which TF combinatorics contributes to the diversity of FC enhancer activities. Building a Training Set of Enhancers That Are Active in Muscle FCs Previous studies have characterized enhancers for individual FC genes that integrate many of the TFs downstream of the Wnt, Ras/MAPK and BMP signaling pathways, as well as input from the instrinsic TFs Twi and Tin [28–31]. However, relatively sparse information is available from these examples to understand the full complexity of the myogenic regulatory network. To begin unraveling the detailed architecture of this network, we previously used expression profiling of various mutants which perturb FC gene expression in a predictable manner to identify hundreds of candidate genes with FC expression patterns [22]. In situ hybridization of these candidates led to the validation of 180 FC genes (Table S1). To understand how these FC genes are coordinately regulated, we evaluated potential regulatory codes which were based on combinations of TFBSs found within two previously characterized FC enhancers [28,29]. These studies revealed that three TFs—Twi, Tin, and Pointed (Pnt), an Ets- Figure 1. Schematic of enhancer classification beginning with a small training set. A small set of known enhancers active in similar cells is increased by incorporating orthologous sequences. De novo and known motifs are mapped onto this training set and a set of control sequences. Feature vectors are used to build an enhancer model based upon the learned motif weighting. This model can be used to scan the genome for similar enhancers as the training set. These predictions can be tested using transgenic reporter assays or analysis of the expression of the associated gene. The motif weighting can likewise be used to identify novel classes of transcriptional regulators. The role of the motifs can be tested in cis and the identification of co-regulating TFs can be subsequently tested in trans. doi:10.1371/journal.pgen.1002531.g001 PLoS Genetics | www.plosgenetics.org 3 March 2012 | Volume 8 | Issue 3 | e1002531