Enhancers of heart expression are easier to identify than enhancers active in other tissues at E11.5.

<p>(A) In Step 2 of our prediction pipeline, we trained EnhancerFinder using the same features as in Step 1 (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003677#pcbi-1003677-g001" target="_blank">Figure 1</a>), but using VISTA enhancers active in a given tissue as positives and tested regions that did not show activity in the tissue as negatives. Heart enhancers were dramatically easier to distinguish from other enhancers than enhancers of expression in other tissues. The heart enhancers have significantly higher GC content than other enhancers and the genomic background. This and several other unique attributes may explain the ease of identifying them (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003677#pcbi.1003677.s007" target="_blank">Figures S7</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003677#pcbi.1003677.s008" target="_blank">S8</a>). In general, functional genomics data are the most informative data type for predicting enhancer tissue specificity (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003677#pcbi-1003677-t001" target="_blank">Table 1</a>).</p

Four novel developmental enhancers near <i>FOXC2</i>.

<p>This UCSC Genome Browser (<a href="http://genome.ucsc.edu" target="_blank">http://genome.ucsc.edu</a>) snapshot shows the genomic context of four candidate human enhancers tested in transgenic zebrafish. For each enhancer, we show a zebrafish image that is representative of the reproducible expression patterns. <i>FOXC2</i> Enhancer Candidate 1 (F2EC-1) drives expression at 48 hpf in the eye and epidermis (arrows). F2EC-2 shows expression at 24 hpf in the forebrain, midbrain, and nerve. F2EC-3 drives expression at 48 hpf in the epidermis and heart. F2EC-4 shows expression at 48 hpf in the notochord, spinal cord, and heart. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003677#pcbi.1003677.s017" target="_blank">Table S6</a> for full list of expressed tissues seen in each candidate enhancer and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003677#pcbi.1003677.s010" target="_blank">Figure S10</a> for results on candidate enhancers near <i>FOXC1</i>.</p

Performance (ROC AUC) of classifiers on each tissue-specific enhancer prediction task (Step 2).

<p>Performance (ROC AUC) of classifiers on each tissue-specific enhancer prediction task (Step 2).</p

Integrating diverse functional genomics data improves enhancer prediction.

<p>(A) Considering functional genomics features from contexts and assays not directly associated with developmental enhancer activity (<b>All Functional Genomics</b> and <b>Relevant Functional Genomics</b>) improves the identification of developmental enhancers (p = 9.2E-9 and p = 2.7E-6, respectively, compared to <b>Embryonic Functional Genomics</b> only). (B) Combining available H3K4me1, p300, and H3K27ac data, which are commonly used in isolation to identify enhancers, in a linear SVM (<b>Basic Functional Genomics</b>) is better able to distinguish known developmental enhancers from the genomic background than considering each type of data alone (p<2E-7, for each). However, combining these marks still performs significantly worse than EnhancerFinder (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003677#pcbi-1003677-g002" target="_blank">Figure 2A</a>; AUC = 0.96) and considering additional data as in (A).</p

A novel cranial nerve enhancer in the <i>ZEB2</i> locus.

<p>This UCSC Genome Browser snapshot shows a dense region of predicted enhancers in a 1.5<i>ZEB2</i> and part of the adjacent gene desert. Tracks give the locations of four human accelerated regions (HARs), two validated VISTA enhancers (hs407 and hs1802), and the E1 region recently shown to have postnatal enhancer activity <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003677#pcbi.1003677-ElKasti1" target="_blank">[87]</a>. The inset shows a zoomed in view of <i>ZEB2</i> (hg19.chr2:145,100,000–145,425,000) along with summaries of several ENCODE functional genomics datasets and evolutionary conservation across placental mammals. We tested the predicted enhancer overlapping 2xHAR.240 for enhancer activity at E11.5 in transgenic mice. Both the human and chimp versions of this sequence drive consistent expression in the cranial nerve (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003677#pcbi.1003677.s011" target="_blank">Figure S11</a>).</p

Predicted tissue-specific enhancers exhibit tissue-specific characteristics.

<p>EnhancerFinder identifies thousands of novel high-confidence (FPR<0.05) heart, brain, and limb enhancers. These enhancers are enriched for tissue-specific GO Biological Processes. The five most enriched GO Biological Processes among genes near each enhancer set (as calculated using GREAT) are listed in the colored boxes. Nearly 90% of EnhancerFinder predicted heart, brain, and limb enhancers are unique to a single tissue. The larger number of high-confidence heart enhancers relative to brain and limb enhancers is the result of the superior performance of the heart classifier.</p

EnhancerFinder's two-step approach captures tissue-specific attributes of enhancers.

<p>(A) The true overlap of human enhancers of brain, heart, and limb in the VISTA database. The vast majority of characterized enhancers are unique to one of these tissues at this stage. For example, of the 84 validated heart enhancers, 71 are unique to heart, five are shared with brain, four with limb, and four with both. (B) The predicted overlap of VISTA enhancers based on predictions made with a single training step using MKL with only enhancers of that tissue considered positives and the genomic background as negatives. This approach overestimates the number of enhancers active in multiple tissues. Each classifier mainly learns general attributes of enhancers, rather than tissue-specific attributes. (C) The predicted overlap based on EnhancerFinder's two-step approach. These predictions are much more tissue-specific and exhibit overlaps between tissues similar to the true values (A). Predicted tissue distributions are similar when the methods are applied to other genomic regions, as illustrated in our genome-wide predictions, but only predictions on VISTA enhancers are shown here to enable comparisons to the distribution for validated enhancers (A).</p