Journal
NUCLEIC ACIDS RESEARCH
Volume 43, Issue 3, Pages 1726-1739Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkv011
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Funding
- National Heart Lung and Blood Institute Division of Intramural Research
- Intramural Research Program of the National Institutes of Health, National Library of Medicine
- National Institutes of Health
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Here we used discriminative training methods to uncover the chromatin, transcription factor (TF) binding and sequence features of enhancers underlying gene expression in individual cardiac cells. We used machine learning with TF motifs and ChIP data for a core set of cardiogenic TFs and histone modifications to classify Drosophila cell-type-specific cardiac enhancer activity. We show that the classifier models can be used to predict cardiac cell subtype cis-regulatory activities. Associating the predicted enhancers with an expression atlas of cardiac genes further uncovered clusters of genes with transcription and function limited to individual cardiac cell subtypes. Further, the cell-specific enhancer models revealed chromatin, TF binding and sequence features that distinguish enhancer activities in distinct subsets of heart cells. Collectively, our results show that computational modeling combined with empirical testing provides a powerful platform to uncover the enhancers, TF motifs and gene expression profiles which characterize individual cardiac cell fates.
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