BMC GenomicsGenomicsPeer-reviewed journal article
Identification and prediction of developmental enhancers in sea urchin embryos
César Arenas‐MenaiD, Sofija Miljovska, Edward J. Rice, Justin Gurges, Tanvi ShashikantiD, Zihe WangiD, Sevinç ErcaniD, Charles G. DankoiD
Abstract
BackgroundThe transcription of developmental regulatory genes is often controlled by multiple cis-regulatory elements. The identification and functional characterization of distal regulatory elements remains challenging, even in tractable model organisms like sea urchins. ResultsWe evaluate the use of chromatin accessibility, transcription and RNA Polymerase II for their ability to predict enhancer activity of genomic regions in sea urchin embryos. ATAC-seq, PRO-seq, and Pol II ChIP-seq from early and late blastula embryos are manually contrasted with experimental cis-regulatory analyses available in sea urchin embryos, with particular attention to common developmental regulatory elements known to have enhancer and silencer functions differentially deployed among embryonic territories. Using the three functional genomic data types, machine learning models are trained and tested to classify and quantitatively predict the enhancer activity of several hundred genomic regions previously validated with reporter constructs in vivo. ConclusionsOverall, chromatin accessibility and transcription have substantial power for predicting enhancer activity. For promoter-overlapping cis-regulatory elements in particular, the distribution of Pol II is the best predictor of enhancer activity in blastula embryos. Furthermore, ATAC- and PRO-seq predictive value is stage dependent for the promoter-overlapping subset. This suggests that the sequence of regulatory mechanisms leading to transcriptional activation have distinct relevance at different levels of the developmental gene regulatory hierarchy deployed during embryogenesis.
- assay: ATAC-seq
- assay: ChIP-seq
- assay: long-read sequencing
- research focus: gene regulatory network inference
- approach: machine learning (classical)
- Citations
- 10
- FWCI
- 0.55
- Field percentile
- top 41%
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