ArticlePLoS computational biology2014
Automated discovery of tissue-targeting enhancers and transcription factors from binding motif and gene function data.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Characterization of a cis-regulatory element upstream of matrix metalloproteinase-9.Scientific reports · 2025Article
- Core conserved transcriptional regulatory networks define the invasive trophoblast cell lineage.Development (Cambridge, England) · 2023Article
- CORE CONSERVED TRANSCRIPTIONAL REGULATORY NETWORKS DEFINE THE INVASIVE TROPHOBLAST CELL LINEAGE.bioRxiv : the preprint server for biology · 2023Article
- Transcription Factor PLAGL1 Is Associated with Angiogenic Gene Expression in the Placenta.International journal of molecular sciences · 2020Article
- Review
- Combined analysis of dissimilar promoter accessibility and gene expression profiles identifies tissue-specific genes and actively repressed networks.Epigenetics & chromatin · 2019Article
- Article
- Changes in the enhancer landscape during early placental development uncover a trophoblast invasion gene-enhancer network.Placenta · 2016Article
- A family of transposable elements co-opted into developmental enhancers in the mouse neocortex.Nature communications · 2015Article
- c-Myb Binding Sites in Haematopoietic Chromatin Landscapes.PloS one · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying enhancers regulating gene expression remains an important and challenging task. While recent sequencing-based methods provide epigenomic characteristics that correlate well with enhancer activity, it remains onerous to comprehensively identify all enhancers across development. Here we introduce a computational framework to identify tissue-specific enhancers evolving under purifying selection. First, we incorporate high-confidence binding site predictions with target gene functional enrichment analysis to identify transcription factors (TFs) likely functioning in a particular context. We then search the genome for clusters of binding sites for these TFs, overcoming previous constraints associated with biased manual curation of TFs or enhancers. Applying our method to the placenta, we find 33 known and implicate 17 novel TFs in placental function, and discover 2,216 putative placenta enhancers. Using luciferase reporter assays, 31/36 (86%) tested candidates drive activity in placental cells. Our predictions agree well with recent epigenomic data in human and mouse, yet over half our loci, including 7/8 (87%) tested regions, are novel. Finally, we establish that our method is generalizable by applying it to 5 additional tissues: heart, pancreas, blood vessel, bone marrow, and liver.
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