Epigenomic mapping of enhancer-associated chromatin modifications facilitates the genome-wide discovery of tissue-specific enhancers in vivo. However, reliance on single chromatin marks leads to high rates of false-positive predictions. More sophisticated, integrative methods have been described, but commonly suffer from limited accessibility to the resulting predictions and reduced biological interpretability. Here we present the Limb-Enhancer Genie (LEG), a collection of highly accurate, genome-wide predictions of enhancers in the developing limb, available through a user-friendly online interface. We predict limb enhancers using a combination of >50 published limb-specific datasets and clusters of evolutionarily conserved transcription factor binding sites, taking advantage of the patterns observed at previously in vivo validated elements. By combining different statistical models, our approach outperforms current state-of-the-art methods and provides interpretable measures of feature importance. Our results indicate that including a previously unappreciated score that quantifies tissue-specific nuclease accessibility significantly improves prediction performance. We demonstrate the utility of our approach through in vivo validation of newly predicted elements. Moreover, we describe general features that can guide the type of datasets to include when predicting tissue-specific enhancers genome-wide, while providing an accessible resource to the general biological community and facilitating the functional interpretation of genetic studies of limb malformations.
Epigenomic mapping of enhancer - associated chromatin modifications facilitates the genome - wide discovery of tissue - specific enhancers in vivo . However , reliance on single chromatin marks leads to high rates of false - positive predictions . More sophisticated , integrative methods have been described , but commonly suffer from limited accessibility to the resulting predictions and reduced biological interpretability . Here we present the Limb - Enhancer Genie ( LEG ) , a collection of highly accurate , genome - wide predictions of enhancers in the developing limb , available through a user - friendly online interface . We predict limb enhancers using a combination of > 50 published limb - specific datasets and clusters of evolutionarily conserved transcription factor binding sites , taking advantage of the patterns observed at previously in vivo validated elements . By combining different statistical models , our approach outperforms current state - of - the - art methods and provides interpretable measures of feature importance . Our results indicate that including a previously unappreciated score that quantifies tissue - specific nuclease accessibility significantly improves prediction performance . We demonstrate the utility of our approach through in vivo validation of newly predicted elements . Moreover , we describe general features that can guide the type of datasets to include when predicting tissue - specific enhancers genome - wide , while providing an accessible resource to the general biological community and facilitating the functional interpretation of genetic studies of limb malformations .
Which resource contains accurate enhancer predictions in the developing limb?
Which resource contains accurate enhancer predictions in the developing limb ?
Limb-Enhancer Genie (LEG)
Limb-Enhancer Genie (LEG)