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DOI . ORG {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Doi.org Make Money
  6. Keywords
  7. Topics
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We began analyzing https://www.nature.com/articles/ng.3539, but it redirected us to https://www.nature.com/articles/ng.3539. The analysis below is for the second page.

Title[redir]:
Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin | Nature Genetics
Description:
Sean Whalen and colleagues present a computational method, TargetFinder, for reconstructing three-dimensional regulatory landscapes using one-dimensional genomic features. TargetFinder identifies the minimal set of features necessary to predict individual interacting enhancer–promoter pairs and accurately distinguishes them from non-interacting pairs. Discriminating the gene target of a distal regulatory element from other nearby transcribed genes is a challenging problem with the potential to illuminate the causal underpinnings of complex diseases. We present TargetFinder, a computational method that reconstructs regulatory landscapes from diverse features along the genome. The resulting models accurately predict individual enhancer–promoter interactions across multiple cell lines with a false discovery rate up to 15 times smaller than that obtained using the closest gene. By evaluating the genomic features driving this accuracy, we uncover interactions between structural proteins, transcription factors, epigenetic modifications, and transcription that together distinguish interacting from non-interacting enhancer–promoter pairs. Most of this signature is not proximal to the enhancers and promoters but instead decorates the looping DNA. We conclude that complex but consistent combinations of marks on the one-dimensional genome encode the three-dimensional structure of fine-scale regulatory interactions.

Matching Content Categories {📚}

  • Education
  • Science
  • Business & Finance

Content Management System {📝}

What CMS is doi.org built with?

Custom-built

No common CMS systems were detected on Doi.org, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of doi.org audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month


Based on our best estimate, this website will receive around 5,000,019 visitors per month in the current month.
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How Does Doi.org Make Money? {💸}

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While profit motivates many websites, others exist to inspire, entertain, or provide valuable resources. Websites have a variety of goals. And this might be one of them. Doi.org could be secretly minting cash, but we can't detect the process.

Keywords {🔍}

pubmed, article, google, scholar, cas, central, nature, genome, human, biol, chromatin, cell, nat, gene, interactions, res, data, genet, regulatory, genes, transcription, access, genetics, enhancerpromoter, genomic, enhancer, usa, content, complex, analysis, mol, longrange, san, cookies, whalen, pollard, elements, methods, factor, francisco, california, privacy, information, looping, sean, katherine, features, promoters, functional, variants,

Topics {✒️}

nature portfolio permissions reprints privacy policy ccaat/enhancer-binding protein β author information authors advertising long-range interaction landscape lncrna–protein interaction prediction microarray gene-expression data long-range interacting elements leveraging enhancer-promoter interactions social media interacting enhancer–promoter pairs fine-scale regulatory interactions enhancer–promoter interactions cell-type-specific variants fast gapped-read alignment automating chromatin-state discovery high-resolution capture springerlink instant access high-resolution map nf-κb-dependent regulation genome-scale functional characterization distant enhancer function nature 478 nature 489 nature 459 nature 473 nature 512 nature 462 nature 518 nature 504 nature 469 nature 457 nature 431 nature 460 nature permissions 3d genome connecting temporal gene expression linking disease associations secure chromatin interactions model-based analysis ctcf insulator protein core promoter factor data analysis promoter-proximal pausing capturing chromosome conformation serum response factor super enhancer loci

Questions {❓}

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Schema {🗺️}

WebPage:
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         headline:Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin
         description:Sean Whalen and colleagues present a computational method, TargetFinder, for reconstructing three-dimensional regulatory landscapes using one-dimensional genomic features. TargetFinder identifies the minimal set of features necessary to predict individual interacting enhancer–promoter pairs and accurately distinguishes them from non-interacting pairs. Discriminating the gene target of a distal regulatory element from other nearby transcribed genes is a challenging problem with the potential to illuminate the causal underpinnings of complex diseases. We present TargetFinder, a computational method that reconstructs regulatory landscapes from diverse features along the genome. The resulting models accurately predict individual enhancer–promoter interactions across multiple cell lines with a false discovery rate up to 15 times smaller than that obtained using the closest gene. By evaluating the genomic features driving this accuracy, we uncover interactions between structural proteins, transcription factors, epigenetic modifications, and transcription that together distinguish interacting from non-interacting enhancer–promoter pairs. Most of this signature is not proximal to the enhancers and promoters but instead decorates the looping DNA. We conclude that complex but consistent combinations of marks on the one-dimensional genome encode the three-dimensional structure of fine-scale regulatory interactions.
         datePublished:2016-04-04T00:00:00Z
         dateModified:2016-04-04T00:00:00Z
         pageStart:488
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            Cancer Research
            Agriculture
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      headline:Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin
      description:Sean Whalen and colleagues present a computational method, TargetFinder, for reconstructing three-dimensional regulatory landscapes using one-dimensional genomic features. TargetFinder identifies the minimal set of features necessary to predict individual interacting enhancer–promoter pairs and accurately distinguishes them from non-interacting pairs. Discriminating the gene target of a distal regulatory element from other nearby transcribed genes is a challenging problem with the potential to illuminate the causal underpinnings of complex diseases. We present TargetFinder, a computational method that reconstructs regulatory landscapes from diverse features along the genome. The resulting models accurately predict individual enhancer–promoter interactions across multiple cell lines with a false discovery rate up to 15 times smaller than that obtained using the closest gene. By evaluating the genomic features driving this accuracy, we uncover interactions between structural proteins, transcription factors, epigenetic modifications, and transcription that together distinguish interacting from non-interacting enhancer–promoter pairs. Most of this signature is not proximal to the enhancers and promoters but instead decorates the looping DNA. We conclude that complex but consistent combinations of marks on the one-dimensional genome encode the three-dimensional structure of fine-scale regulatory interactions.
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      dateModified:2016-04-04T00:00:00Z
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         Data mining
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         Genomics
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         Cancer Research
         Agriculture
         Gene Function
         Animal Genetics and Genomics
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                     type:PostalAddress
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Social Networks {👍}(2)

External Links {🔗}(421)

Analytics and Tracking {📊}

  • Google Tag Manager

Libraries {📚}

  • Prism.js
  • Zoom.js

Emails and Hosting {✉️}

Mail Servers:

  • mx.zoho.eu
  • mx2.zoho.eu
  • mx3.zoho.eu

Name Servers:

  • josh.ns.cloudflare.com
  • zita.ns.cloudflare.com

CDN Services {📦}

  • Crossref

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