examined the sequences for the occurrence of 11 types of TFBSs: 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$FREAC2_01), HD (I$AB- DA_Q6, V$CDX5_Q5, V$IFP_03, V$PAX4_02), and Mef2 (V$AMEF2_Q6, V$HMEF2_Q6) and binding sites for Tcf, Mad, Ets, Twi, Tin [5]. Since the Position Weight Matrices (PWMs) for Tcf, Mad, Ets, Twi, and Tin were constructed from only a small number of sequences and we obtained few significant matches using MAST, we re-inspected the sequences manually, searching for the known binding sites of these TFs. Over-represented TFBSs were determined by comparing the occurrence of the motifs among query sequences and background genomic sequence, and applying Fisher’s exact test. We used a P- value threshold of 0.05. When indicated, we adjusted the P-values for multiple testing using the procedure suggested by Benjamini and Hochberg [120]. Classifier Training Each nucleotide sequence in the FC enhancer and control sets was represented by the number of putative TFBSs per base pair. Putative TFBSs were identified by searching the sequences for motifs derived from three different sources: (1) 892 TF binding specificities characterized in TRANSFAC, (2) 3 binding sequences for Tin, Twi, and Pnt from the literature [5], and (3) at most 50 motifs that PRIORITY [51] found to be over-represented in the training set of FC enhancers. In order to prevent the over-representation of motifs in D. melanogaster enhancer sequences with a large number of orthologs, de novo motifs were identified on a restricted set of 34 sequences, including at most two randomly selected orthologs for each D. melanogaster FC enhancer. Features relevant for distinguishing between enhancer sequences and controls were identified using linear support vector machine (SVM). We used a standard ten-fold cross- validation procedure to assess the accuracy of the classifier. In each fold of the cross-validation procedure, the de novo motifs were extracted using the training data, only, thereby ensuring that the test data were completely unseen before the predictions were made. The cross-validation procedure should help to prevent overfitting of the classifier. SVM Parameter Selection We used Support Vector Machines [121] with a linear kernel, which only requires setting the penalization coefficient C. The performance of the SVM was evaluated using the area under the receiver operating characteristic (ROC) curve, which yields values between 0.5 (for a completely random guess) and 1.0 (for a perfect classifier). In order to compensate for the data unbalance, FC enhancer sequences and controls were assigned different misclas- sification costs (SVM soft-margin constants), giving equal overall weight to each class. Motif Ranking Given a training set of instances x1, …, xl[Rn with associated labels y1, …, yl[f{1,1g, linear SVM solves the optimization problem min 1wT wzC P ei subject to yiðwT xizbÞ§1{ei and w,b,e 2 i ei§0 [122]. Thus, after obtaining a linear SVM model, the weight vector w can be used to decide the relevance of each feature [123]. The larger wj , the more important role of feature j in the decision function. We rank features—in our case, motifs— according to wj . For this purpose, we trained a classifier for 100 random partitions of the training data (containing two thirds of the total training data), computed the ranking for each feature, and finally ranked the features according to their median ranking. Machine Learning Transcriptional Regulators Genome Scan We scanned the whole-genome of D. melanogaster (BDGP Release 5 assembly) with a sliding window of length 1000 base pairs and overlaps of 500 base pairs. The length of the window corresponds approximately to the average length of the sequences in the training data set (1280 base pairs). We scored 137,364 sequences after excluding sequences which overlap annotated coding regions by at least 50%. The cut-off for the genome scan was defined to obtain a false positive rate (FPR) of at most 5% by training and testing 100 classifiers on random partitions of the training data (containing two thirds of the total training data). Association between TFs and Sequence Motifs TF annotation for PWMs was obtained from TRANSFAC and the Broad Institute MsigDB database [124]. Classifier Performance with Orthologs of D. melanogaster Mesodermal Enhancers To understand the effects of a sample size on the classifier performances, we first extracted a dataset of Drosophila melanogaster 62 enhancers active in mesoderm from the REDfly database [32]. REDfly contains 176 partially overlapping enhancers active in mesoderm. To eliminate this redundancy, we clustered together overlapping sequences, and subsequently selected the shortest enhancer sequence from each cluster; sequences longer than 2 kb as well as sequences overlapping our dataset of enhancers active in muscle founder were excluded from the final dataset. Orthologs were selected randomly among sequences from 15 insect species [125] with nucleotide identity ranging from 50 to 80%, so that at most two orthologs were selected for each Drosophila melanogaster enhancer in REDfly. As controls, we randomly sampled for each enhancer (and orthologs) 10 non-coding sequences from the Drosophila melanogaster genome with similar length, GC- and repeat-content. Each nucleotide sequence in the enhancer and control sets was represented by the number of putative TFBSs per base pair. Putative TFBSs were identified by searching the sequences with MAST [118] for motifs derived from two sources: (1) 892 TF binding specificities characterized in TRANSFAC Release 2009.2 [119], and (2) 3 binding sequences for Tin, Twi, and Pnt from the literature [5]. Features relevant for distinguishing between enhancer sequences and controls were identified using linear SVM. Supporting Information Figure S1 Empirical validation of predicted FC enhancers conforming to a previously described regulatory model. Fluores- cent in situ hybridization analysis of stage 11 embryos containing RhoGEF3-lacZ (A), FBX011-lacZ (B), cib-lacZ (C) or fra-lacZ (D) transgenes using probes for endogenous RhoGEF3 (A), FBX011 (B), cib (C), and fra (D) transcripts. Panels A9 to D9 show the corresponding signals for lacZ transcripts, and panels A0 to D0 show the merged channels. All enhancers were selected from previously identified candidates [5]. (TIF) Figure S2 Variation of the classification performance with increasing sample size. Samples were randomly selected from a dataset of 62 D. melanogaster enhancers active in various mesodermal cell types. The sample size was varied from 10 to 60 by an increment of 5. Each sample was used to train a Support Vector Machine (SVM) classifier. For each sample size, we compared the performance of the classifier trained exclusively with D. melanogaster (‘‘dm3 only’’) enhancers with that of a classifier that, in addition, was trained with up to two orthologs for each D. PLoS Genetics | www.plosgenetics.org 17 March 2012 | Volume 8 | Issue 3 | e1002531