Evidence map›Paper›PMID 23771139›Full record

ArticleNucleic acids research2013

A probabilistic approach to learn chromatin architecture and accurate inference of the NF-κB/RelA regulatory network using ChIP-Seq.

Jun Yang, Abhishek Mitra, Norbert Dojer, Shuhua Fu, Maga Rowicka, Allan R Brasier

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 40 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Jun YangDepartment of Internal Medicine, The University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 77555-1060, USA, Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 77555-1060, USA, Institute for Translational Sciences, The University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 77555-1060, USA, Institute of Informatics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland and Sealy Center for Molecular Medicine, The University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 77555-1060, USA.
Abhishek Mitra
Norbert Dojer
Shuhua Fu
Maga Rowicka
Allan R Brasier
University of Warsaw · PLThe University of Texas Medical Branch at Galveston · US

Funding

Virus, Tissue Culture and ImmunoassayP01AI062885 · NIAID · UNIVERSITY OF TEXAS MEDICAL BR GALVESTON · PI CASOLA, ANTONELLA · 2005 to 2022
$23.9M
Synthetic Organic Chemistry CoreP30ES006676 · NIEHS · UNIVERSITY OF TEXAS MEDICAL BR GALVESTON · PI ELFERINK, CORNELIS JOHAN · 1995 to 2017
$21.7M
UTMB Clinical and Translational Science AwardUL1TR000071 · NCATS · UNIVERSITY OF TEXAS MED BR GALVESTON · PI BRASIER, ALLAN R. · 2012 to 2014
$14.4M
Collaborative Research : Stochastic Modeling and Estimation of Gene TranscriptionR01GM086885 · NIGMS · RICE UNIVERSITY · PI KIMMEL, MAREK · 2008 to 2011
$1.7M
NCATS NIH HHS UL1 TR000071NCATS NIH HHS UL1TR000071NHLBI NIH HHS HHSN268201000037CNIAID NIH HHS P01 AI062885NIEHS NIH HHS P30 ES006676NIGMS NIH HHS R01 GM086885NIGMS NIH HHS R01GM086885
6 · The paper itself

Abstract

Using nuclear factor-κB (NF-κB) ChIP-Seq data, we present a framework for iterative learning of regulatory networks. For every possible transcription factor-binding site (TFBS)-putatively regulated gene pair, the relative distance and orientation are calculated to learn which TFBSs are most likely to regulate a given gene. Weighted TFBS contributions to putative gene regulation are integrated to derive an NF-κB gene network. A de novo motif enrichment analysis uncovers secondary TFBSs (AP1, SP1) at characteristic distances from NF-κB/RelA TFBSs. Comparison with experimental ENCODE ChIP-Seq data indicates that experimental TFBSs highly correlate with predicted sites. We observe that RelA-SP1-enriched promoters have distinct expression profiles from that of RelA-AP1 and are enriched in introns, CpG islands and DNase accessible sites. Sixteen novel NF-κB/RelA-regulated genes and TFBSs were experimentally validated, including TANK, a negative feedback gene whose expression is NF-κB/RelA dependent and requires a functional interaction with the AP1 TFBSs. Our probabilistic method yields more accurate NF-κB/RelA-regulated networks than a traditional, distance-based approach, confirmed by both analysis of gene expression and increased informativity of Genome Ontology annotations. Our analysis provides new insights into how co-occurring TFBSs and local chromatin context orchestrate activation of NF-κB/RelA sub-pathways differing in biological function and temporal expression patterns.

Indexed as

Gene Regulatory NetworksGenome, HumanAlu ElementsBinding SitesCell Line, TumorChromatinChromatin Assembly and DisassemblyChromatin ImmunoprecipitationHumansModels, StatisticalMolecular Sequence AnnotationNF-kappa BNucleotide MotifsReproducibility of ResultsSensitivity and SpecificitySequence Analysis, RNAChromatinNF-kappa BRELA protein, humanTranscription Factor RelA

Identifiers

PMID23771139
PMCPMC3753626
OpenAlexW2170864127

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.