Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy

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

doi:10.1073/pnas.1808833115

Title: Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy

Journal Article · 05 November 2018 · Proceedings of the National Academy of Sciences of the United States of America

DOI: https://doi.org/10.1073/pnas.1808833115 · OSTI ID:1559177

Arbel, Hamutal ^[1]; Basu, Sumanta ^[2]; Fisher, William W. ^[3]; Hammonds, Ann S. ^[3]; Wan, Kenneth H. ^[3]; Park, Soo ^[3]; Weiszmann, Richard ^[3]; Booth, Benjamin W. ^[3]; Keranen, Soile V. ^[3]; Henriquez, Clara ^[3];

^[4]; Bickel, Peter J. ^[4]; Biggin, Mark D. ^[3]; Celniker, Susan E. ^[4]; Brown, James B. ^[5]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States); Cornell Univ., Ithaca, NY (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Univ. of California, Berkeley, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States); Univ. of Birmingham (United Kingdom)

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1559177

Journal Information:: Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Issue: 3 Vol. 116; ISSN 0027-8424

Publisher:: National Academy of Sciences, Washington, DC (United States)Copyright Statement

Country of Publication:: United States

Language:: English

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Additional file 1 of A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome Ni, Pengyu; Wilson, David; Su, Zhengchang figshare https://doi.org/10.6084/m9.figshare.21365999	dataset	January 2022
How to study enhancers in non-traditional insect models Tomoyasu, Yoshinori; Halfon, Marc S. The Journal of Experimental Biology, Vol. 223, Issue Suppl 1 https://doi.org/10.1242/jeb.212241	journal	February 2020

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