Journal Article

Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy

Biggin, Mark D., Fisher, William W., Bickel, Peter J., Celniker, Susan E., Henriquez, Clara, Booth, Benjamin W., Brown, James B., Keranen, Soile V., Solari, Omid Shams, Arbel, Hamutal, Basu, Sumanta, Hammonds, Ann S., Park, Soo, Weiszmann, Richard, Wan, Kenneth H.

2019

Overview

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.

• Publisher: National Academy of Sciences,National Academy of Sciences, Washington, DC (United States)
• Subject: Accuracy / Alternation learning / Animals / Artificial intelligence / BASIC BIOLOGICAL SCIENCES / Bayesian analysis / Biological Sciences / Deoxyribonucleic acid / Developmental Biology / DNA / Drosophila / Drosophila melanogaster / Drosophila Proteins - genetics / Drosophila Proteins - metabolism / embryo development / Embryo, Nonmammalian - embryology / Embryogenesis / Embryonic Development - physiology / Embryonic growth stage / Enhancer Elements, Genetic - physiology / Enhancers / ENVIRONMENTAL SCIENCES / Epigenetics / Fruit flies / Gene expression / Genome-Wide Association Study / Genomes / Heterogeneity / Image segmentation / machine learning / Pattern formation / PNAS Plus / Predictions / random forest / Regression analysis / Sequence Analysis, DNA / Transcription factors / Transcription Factors - genetics / Transcription Factors - metabolism