ArticleGenome biology2012
Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors.
Article in Genome biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 163 papers, 1 of them a synthesis that pooled it.
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Who cites it
163 citing papers in PubMed, 1 synthesis or guideline pooled it, 267 citations in OpenAlex.
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- Characterization of human transcription factor function and patterns of gene regulation in HepG2 cells.Genome research · 2023Article
- Identification of transcription factor high accumulation DNA zones.BMC bioinformatics · 2023Article
- Clustered and diverse transcription factor binding underlies cell type specificity of enhancers for housekeeping genes.Genome research · 2023Article
- A Novel Repetition Frequency-Based DNA Encoding Scheme to Predict Human and Mouse DNA Enhancers with Deep Learning.Biomimetics (Basel, Switzerland) · 2023Article
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- A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants.International journal of molecular sciences · 2023Article
- DNA methylation differences in noncoding regions in ER negative breast tumors between Black and White women.Frontiers in oncology · 2023Article
- Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques.BMC bioinformatics · 2022Article
- Context-dependent CpG methylation directs cell-specific binding of transcription factor ZBTB38.Epigenetics · 2022Article
- Regulation associated modules reflect 3D genome modularity associated with chromatin activity.Nature communications · 2022Article
- Epigenetics of neural differentiation: Spotlight on enhancers.Frontiers in cell and developmental biology · 2022Review
- Noncoding loci without epigenomic signals can be essential for maintaining global chromatin organization and cell viability.Science advances · 2021Article
- Accurate prediction ofNAR genomics and bioinformatics · 2021Article
103 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundTranscription factors function by binding different classes of regulatory elements. The Encyclopedia of DNA Elements (ENCODE) project has recently produced binding data for more than 100 transcription factors from about 500 ChIP-seq experiments in multiple cell types. While this large amount of data creates a valuable resource, it is nonetheless overwhelmingly complex and simultaneously incomplete since it covers only a small fraction of all human transcription factors.
resultsAs part of the consortium effort in providing a concise abstraction of the data for facilitating various types of downstream analyses, we constructed statistical models that capture the genomic features of three paired types of regions by machine-learning methods: firstly, regions with active or inactive binding; secondly, those with extremely high or low degrees of co-binding, termed HOT and LOT regions; and finally, regulatory modules proximal or distal to genes. From the distal regulatory modules, we developed computational pipelines to identify potential enhancers, many of which were validated experimentally. We further associated the predicted enhancers with potential target transcripts and the transcription factors involved. For HOT regions, we found a significant fraction of transcription factor binding without clear sequence motifs and showed that this observation could be related to strong DNA accessibility of these regions.
conclusionsOverall, the three pairs of regions exhibit intricate differences in chromosomal locations, chromatin features, factors that bind them, and cell-type specificity. Our machine learning approach enables us to identify features potentially general to all transcription factors, including those not included in the data.
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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.