Project description:The binding patterns of some transcription factors have been shown to diverge substantially between closely related species. Here, we show that the binding pattern of the developmental transcription factor Twist is highly conserved across six Drosophila species, revealing strong functional constraints at developmental enhancers. Conserved binding correlates with sequence motifs for Twist and its partners, permitting the de novo discovery of their cooperative binding. It also includes over 10,000 low-occupancy sites near the detection limit, which tend to mark enhancers of later developmental stages. We predict that conservation, dynamic occupancy, and combinatorial regulation will be generally true for developmental enhancers.

2010-12-07 | E-MTAB-376 | biostudies-arrayexpress

Drosophila melanogaster

Project description:Effects of purine de novo synthesis knockdown on gene expression in Drosophila melanogaster S2 tissue culture cells

| PRJNA130063 | ENA

Drosophila melanogaster

Project description:De novo piRNA cluster formation in the Drosophila germline triggered by transgenes containing a transcribed transposon fragment

| PRJNA178167 | ENA

HP1 drives de novo 3D genome reorganization in early Drosophila embryos

Project description:HP1 drives de novo 3D genome reorganization in early Drosophila embryos

| PRJNA590149 | ENA

Drosophila melanogaster strain comparison

Project description:Comparison of gene expression in wild type, Oregon[R], Antennapedia[73b], Apterous[56f] Drosophila melanogaster strains. Keywords: other

2003-12-02 | GSE757 | GEO

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Drosophila oligo UMI-STARR-seq]

Project description:Enhancer sequences control gene expression and comprise binding sites (motifs) for different transcription factors (TFs). Despite extensive genetic and computational studies, the relationship between DNA sequence and regulatory activity is poorly understood and enhancer de novo design is considered impossible. Here we built a deep learning model, DeepSTARR, to quantitatively predict the activities of thousands of developmental and housekeeping enhancers directly from DNA sequence in Drosophila melanogaster S2 cells. The model learned relevant TF motifs and higher-order syntax rules, including functionally non-equivalent instances of the same TF motif that are determined by motif-flanking sequence and inter-motif distances. We validated these rules experimentally and demonstrated their conservation in human by testing more than 40,000 wildtype and mutant Drosophila and human enhancers. Finally, we designed and functionally validated synthetic enhancers with desired activities de novo.

2022-02-24 | GSE183937 | GEO

DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers [Drosophila genome-wide UMI-STARR-seq]

2022-02-24 | GSE183936 | GEO

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