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Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy.
- Source :
-
Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2019 Jan 15; Vol. 116 (3), pp. 900-908. Date of Electronic Publication: 2018 Dec 31. - Publication Year :
- 2019
-
Abstract
- Identifying functional enhancer elements in metazoan systems is a major challenge. Large-scale validation of enhancers predicted by ENCODE reveal false-positive rates of at least 70%. We used the pregrastrula-patterning network of Drosophila melanogaster to demonstrate that loss in accuracy in held-out data results from heterogeneity of functional signatures in enhancer elements. We show that at least two classes of enhancers are active during early Drosophila embryogenesis and that by focusing on a single, relatively homogeneous class of elements, greater than 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well-predicted elements is composed predominantly of enhancers driving multistage segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naïve Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome. An analysis of 32 SDEs using whole-mount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.<br />Competing Interests: The authors declare no conflict of interest.<br /> (Copyright © 2019 the Author(s). Published by PNAS.)
- Subjects :
- Animals
Drosophila melanogaster
Genome-Wide Association Study
Drosophila Proteins genetics
Drosophila Proteins metabolism
Embryo, Nonmammalian embryology
Embryonic Development physiology
Enhancer Elements, Genetic physiology
Sequence Analysis, DNA
Transcription Factors genetics
Transcription Factors metabolism
Subjects
Details
- Language :
- English
- ISSN :
- 1091-6490
- Volume :
- 116
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Proceedings of the National Academy of Sciences of the United States of America
- Publication Type :
- Academic Journal
- Accession number :
- 30598455
- Full Text :
- https://doi.org/10.1073/pnas.1808833115