ArticleGenome research2011
Discriminative prediction of mammalian enhancers from DNA sequence.
Article in Genome research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 164 papers.
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Who cites it
164 citing papers in PubMed.
- Comparative characterization of chromatin-targeting mechanisms across seven H3K4 methyltransferases in Arabidopsis.Plant communications · 2026Article
- DcSE: an improved densenet with enhanced attention fusion for super-enhancer prediction.BMC genomics · 2026Article
- Motif-based models accurately predict cell type-specific distal regulatory elements.Nature communications · 2025Article
- QTFPred: robust high-performance quantum machine learning modeling that predicts main and cooperative transcription factor bindings with base resolution.Briefings in bioinformatics · 2025Article
- Natural variation in regulatory code revealed through Bayesian analysis of plant pan-genomes and pan-transcriptomes.bioRxiv : the preprint server for biology · 2025Article
- In silico prediction of variant effects: promises and limitations for precision plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Benchmarking transcription factor binding site prediction models: a comparative analysis on synthetic and biological data.Briefings in bioinformatics · 2025Article
- Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction.BMC biology · 2025Article
- Non-local modeling of enhancer-promoter interactions, a correspondence on "LOCO-EPI: Leave-one-chromosome-out (LOCO) as a benchmarking paradigm for deep learning based prediction of enhancer-promoter interactions".Applied intelligence (Dordrecht, Netherlands) · 2025Article
- The combinatorial binding syntax of transcription factors in forebrain-specific enhancers.Biology open · 2025Article
- Active learning of enhancers and silencers in the developing neural retina.Cell systems · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Beyond sequence: A physics-informed machine learning framework for predicting DNA mutations.Computational and structural biotechnology journal · 2025Article
- DeepRice6mA: A convolutional neural network approach for 6mA site prediction in the rice Genome.PloS one · 2025Article
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- Machine and Deep Learning Methods for Predicting 3D Genome Organization.Methods in molecular biology (Clifton, N.J.) · 2025Review
- LncSL: A Novel Stacked Ensemble Computing Tool for Subcellular Localization of lncRNA by Amino Acid-Enhanced Features and Two-Stage Automated Selection Strategy.International journal of molecular sciences · 2024Article
- HIV-1 M group subtype classification using deep learning approach.Computers in biology and medicine · 2024Article
- Predicting Promoters in Multiple Prokaryotes with Prompt.Interdisciplinary sciences, computational life sciences · 2024Article
- GATv2EPI: Predicting Enhancer-Promoter Interactions with a Dynamic Graph Attention Network.Genes · 2024Article
104 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Accurately predicting regulatory sequences and enhancers in entire genomes is an important but difficult problem, especially in large vertebrate genomes. With the advent of ChIP-seq technology, experimental detection of genome-wide EP300/CREBBP bound regions provides a powerful platform to develop predictive tools for regulatory sequences and to study their sequence properties. Here, we develop a support vector machine (SVM) framework which can accurately identify EP300-bound enhancers using only genomic sequence and an unbiased set of general sequence features. Moreover, we find that the predictive sequence features identified by the SVM classifier reveal biologically relevant sequence elements enriched in the enhancers, but we also identify other features that are significantly depleted in enhancers. The predictive sequence features are evolutionarily conserved and spatially clustered, providing further support of their functional significance. Although our SVM is trained on experimental data, we also predict novel enhancers and show that these putative enhancers are significantly enriched in both ChIP-seq signal and DNase I hypersensitivity signal in the mouse brain and are located near relevant genes. Finally, we present results of comparisons between other EP300/CREBBP data sets using our SVM and uncover sequence elements enriched and/or depleted in the different classes of enhancers. Many of these sequence features play a role in specifying tissue-specific or developmental-stage-specific enhancer activity, but our results indicate that some features operate in a general or tissue-independent manner. In addition to providing a high confidence list of enhancer targets for subsequent experimental investigation, these results contribute to our understanding of the general sequence structure of vertebrate enhancers.
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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.