ArticleGenome research2013
Integrating and mining the chromatin landscape of cell-type specificity using self-organizing maps.
Article in Genome research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed.
- Chromatin Accessibility in Cancer: Biological Functions, Mechanisms, Therapeutic Potential, and Future Directions.MedComm · 2026Review
- The functional small RNA interactome reveals targets for the vancomycin-responsive sRNA RsaOI in vancomycin-tolerantmSystems · 2024Article
- Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography.Journal of clinical medicine · 2023Article
- Uncovering the mesendoderm gene regulatory network through multi-omic data integration.Cell reports · 2022Article
- Universal annotation of the human genome through integration of over a thousand epigenomic datasets.Genome biology · 2022Article
- Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data.BMC genomics · 2021Article
- The intersectional genetics landscape for humans.GigaScience · 2020Article
- Article
- Systematic Analysis of Differential H3K27me3 and H3K4me3 Deposition in Callus and Seedling Reveals the Epigenetic Regulatory Mechanisms Involved in Callus Formation in Rice.Frontiers in genetics · 2020Article
- Widespread activation of developmental gene expression characterized by PRC1-dependent chromatin looping.Science advances · 2020Article
- Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps.PLoS computational biology · 2019Article
- FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.Methods (San Diego, Calif.) · 2019Article
- Chromatin State-Based Analysis of Epigenetic H3K4me3 Marks ofFrontiers in genetics · 2019Article
- Integrating ChIP-seq with other functional genomics data.Briefings in functional genomics · 2018Review
- Conserved and species-specific transcription factor co-binding patterns drive divergent gene regulation in human and mouse.Nucleic acids research · 2018Article
- PCSD: a plant chromatin state database.Nucleic acids research · 2018Article
- Chromatin-state discovery and genome annotation with ChromHMM.Nature protocols · 2017Review
- A genome-wide interactome of DNA-associated proteins in the human liver.Genome research · 2017Article
- Bcl11b and combinatorial resolution of cell fate in the T-cell gene regulatory network.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
We tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq data sets from six biologically diverse cell lines studied by The ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional data sets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell-type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type-specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity.
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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.