Machine Learning Transcriptional Regulators Figure 8. TFBS combinatorics within FC enhancers. (A) Distribution of Tcf, Mad, Pnt, Twi, Tin, POUHD, Tbx, Myb, Fkh, HD and Mef2 TFBSs in FC enhancers. Binding sites for Tcf, Mad, Pnt, Twi and Tin were previously published [5]. Motif matches for motifs most relevant to the classification for a given DNA binding domain class: POUHD (V$OCT_01, V$POU1F1_Q6, V$OCT4_02), Tbx (V$TBX5_01, I$BYN_Q6), Myb (V$MYB_Q6), Fkh (V$FOXO3_01, V$FOXO1_Q5, V$FREAC2_01), HD (I$ABDA_Q6, V$CDX5_Q5, V$IFP_03, V$PAX4_02), and Mef2 (V$AMEF2_Q6, V$HMEF2_Q6). These sites were mapped using MAST under default parameters [118]. (B) A generic FC enhancer receives differential input from signal-activated, ubiquitous, tissue-restricted and cell type-specific TFs. HD binding motifs are represented as both tissue-restricted and cell type-specific classes since these motifs receive input from both Hox TFs, which are widely expressed in the mesoderm [35,59,67], and muscle identity HD TFs—such as Slou, Msh and Ap—which are cell type-specific [54,68,69]. For this diagram, HD binding sites were not subdivided into the distinct binding profiles that have been identified for each individual HD TF ([83,126] and B. W. Busser, L. Shokri, S. A. Jaeger, S. S. Gisselbrecht, A. Singhania, M. F. Berger, B. Zhou, M. L. Bulyk and A. M. Michelson, unpublished data). doi:10.1371/journal.pgen.1002531.g008 Discussion Prediction of Enhancers There are three main approaches for the prediction of tissue- specific regulatory elements that are based on high-throughput sequencing coupled with chromatin immunoprecipitation (ChIP- Seq), DNA sequence pattern analysis, or hybrid methods that combine both of these strategies. ChIP-Seq for p300 using mouse embryonic tissue has proven to be an accurate means for identifying enhancers and their associated activities, with in vivo validation rates varying from 62% to 88% [53,71]. Computational analysis of whole-genome histone modification profiles using hidden Markov models [72,73] and machine learning techniques [74] has also been highly successful at linking chromatin signatures with regulatory elements. Finally, computational models that identify tissue-specific enhancers relying on sequence motifs and linear regression and support vector machines have been similarly effective, with in vivo validation rates of de novo predictions ranging from 62% for heart enhancers [75] to 91% for brain enhancers [Taher et al., unpublished data]. Although experimental tech- niques are often preferred for identifying enhancers on a genome- wide scale, ChIP-Seq has several limitations. For example, ChIP- Seq experiments are typically carried out in only one species and for individual cell types, and are currently not sufficiently precise for low-quality genome sequences. Thus, de novo prediction of regulatory elements based on ChIP-Seq data critically depends on the availability of relevant data for the species, cell type and genomic regions of interest. Currently, computational analysis of PLoS Genetics | www.plosgenetics.org 13 March 2012 | Volume 8 | Issue 3 | e1002531