A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis Brian W. Busser1., Leila Taher2., Yongsok Kim1, Terese Tansey1, Molly J. Bloom1, Ivan Ovcharenko2*, 1 Alan M. Michelson * 1 Laboratory of Developmental Systems Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America, 2 Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America Abstract Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns. Citation: Busser BW, Taher L, Kim Y, Tansey T, Bloom MJ, et al. (2012) A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis. PLoS Genet 8(3): e1002531. doi:10.1371/journal.pgen.1002531 Editor: James W. Posakony, University of California San Diego, United States of America Received March 30, 2011; Accepted December 23, 2011; Published March 8, 2012 This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Funding: This work was funded by the NHLBI Division of Intramural Research (AMM) and the Intramural Research Program of the NIH, National Library of Medicine (IO). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: ovcharen@nih.gov (IO); michelsonam@nhlbi.nih.gov (AMM) . These authors contributed equally to this work. Introduction Complex spatio-temporal gene expression programs guide the progressive determination of pluripotent cells allowing cell fates to become sequentially restricted during embryonic development. These transitions in cell fate are encoded in the genome by cis regulatory DNA sequences such as transcriptional enhancers. Enhancers respond to the combinatorial input of tissue-specific, cell-specific, ubiquitously-expressed and signal-activated transcrip- tion factors (TFs) that collectively control gene expression in the appropriate spatial and temporal patterns [1,2]. In recent years, we and others have shown that computational approaches can be used to predict enhancers of a given type with reasonable accuracy when prior knowledge exists of the TFs and their binding sites that contribute to the activity of this enhancer class [3–5]. However, this approach is limited when the identities and the binding site sequences of co-regulatory TFs are not known. To circumvent this problem, several groups have identified enhancers based on the presence of shared sequence features without the necessity of knowing the co-regulating TFs or their binding motifs [6–12]. These enhancer modeling approaches generally take advantage of two data sources: (1) the non-coding sequences surrounding the members of a gene set of interest, or a set of previously validated enhancers associated with such genes; and (2) previously described sequence motifs from transcription factor binding site (TFBS) libraries and/or de novo motif discovery. In this way, previously described or candidate motifs and/or word profiles can be used to ascertain a training set of enhancers, with the resulting model being used in a genome-wide scan to predict similar enhancers. The enhancer model is validated by testing the activity of these predictions in transgenic reporter assays [7,13]. A particular transcriptional regulatory model can also be validated by assaying the functionality of the motifs that are found to be relevant for making predictions, and subsequently by identifying the DNA binding proteins that target these sequences. The majority of the studies showing the utility of enhancer modeling have focused on regulatory sequences involved in segmentation of the Drosophila blastoderm embryo [11,13–15]. PLoS Genetics | www.plosgenetics.org 1 March 2012 | Volume 8 | Issue 3 | e1002531