ArticleNucleic acids research2015
Integrating motif, DNA accessibility and gene expression data to build regulatory maps in an organism.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 40 citations in OpenAlex.
- scGREAT: Transformer-based deep-language model for gene regulatory network inference from single-cell transcriptomics.iScience · 2024Article
- SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes.bioRxiv : the preprint server for biology · 2023Article
- Nonparametric single-cell multiomic characterization of trio relationships between transcription factors, target genes, and cis-regulatory regions.Cell systems · 2022Article
- Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.Nucleic acids research · 2021Article
- Automated generation of context-specific gene regulatory networks with a weighted approach inInterface focus · 2021Article
- An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes.Genome biology · 2021Article
- Filtering of Data-Driven Gene Regulatory Networks UsingFrontiers in genetics · 2021Article
- Harmonization of quality metrics and power calculation in multi-omic studies.Nature communications · 2020Article
- Predicting gene expression in the human malaria parasite Plasmodium falciparum using histone modification, nucleosome positioning, and 3D localization features.PLoS computational biology · 2019Article
- Article
- The Role of Chromatin Accessibility in cis-Regulatory Evolution.Genome biology and evolution · 2019Article
- Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells.Genome research · 2019Article
- Spatially varying cis-regulatory divergence in Drosophila embryos elucidates cis-regulatory logic.PLoS genetics · 2018Article
- An information theoretic treatment of sequence-to-expression modeling.PLoS computational biology · 2018Article
- coTRaCTE predicts co-occurring transcription factors within cell-type specific enhancers.PLoS computational biology · 2018Article
- Genome-scale identification of transcription factors that mediate an inflammatory network during breast cellular transformation.Nature communications · 2018Article
- Complex Relationships between Chromatin Accessibility, Sequence Divergence, and Gene Expression in Arabidopsis thaliana.Molecular biology and evolution · 2018Article
- Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes.Nucleic acids research · 2018Article
- McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.Genome biology · 2017Article
- Modeling gene regulation from paired expression and chromatin accessibility data.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Characterization of cell type specific regulatory networks and elements is a major challenge in genomics, and emerging strategies frequently employ high-throughput genome-wide assays of transcription factor (TF) to DNA binding, histone modifications or chromatin state. However, these experiments remain too difficult/expensive for many laboratories to apply comprehensively to their system of interest. Here, we explore the potential of elucidating regulatory systems in varied cell types using computational techniques that rely on only data of gene expression, low-resolution chromatin accessibility, and TF-DNA binding specificities ('motifs'). We show that static computational motif scans overlaid with chromatin accessibility data reasonably approximate experimentally measured TF-DNA binding. We demonstrate that predicted binding profiles and expression patterns of hundreds of TFs are sufficient to identify major regulators of ∼200 spatiotemporal expression domains in the Drosophila embryo. We are then able to learn reliable statistical models of enhancer activity for over 70 expression domains and apply those models to annotate domain specific enhancers genome-wide. Throughout this work, we apply our motif and accessibility based approach to comprehensively characterize the regulatory network of fruitfly embryonic development and show that the accuracy of our computational method compares favorably to approaches that rely on data from many experimental assays.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.