Evidence map›Paper›PMID 26869583›Full record

ArticleNucleic acids research2016

Discovering hotspots in functional genomic data superposed on 3D chromatin configuration reconstructions.

Daniel Capurso, Henrik Bengtsson, Mark R Segal

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

15 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Chromatin Hubs: A biological and computational outlook.Computational and structural biotechnology journal · 2022
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Network analysis identifies chromosome intermingling regions as regulatory hotspots for transcription.Proceedings of the National Academy of Sciences of the United States of America · 2017
    Article
  13. Review
  14. Review
  15. 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

3 authors at 1 institution in 1 country.

Daniel CapursoDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA.
Henrik BengtssonDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.
Mark R SegalDepartment of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA Mark.Segal@ucsf.edu.
University of California, San Francisco · US

Funding

PHARMACEUTICAL SCIENCES AND PHARMACOGENOMICST32GM007175 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI AHITUV, NADAV, KROETZ, DEANNA L · 1985 to 2021
$7.1M
Reconstruction of 3D Genome Architecture from Chromatin Conformation Capture DataR01GM109457 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SEGAL, MARK R · 2013 to 2020
$2.8M
NIGMS NIH HHS R01 GM109457NIGMS NIH HHS R01GM109457NIGMS NIH HHS T32 GM007175
6 · The paper itself

Abstract

The spatial organization of the genome influences cellular function, notably gene regulation. Recent studies have assessed the three-dimensional (3D) co-localization of functional annotations (e.g. centromeres, long terminal repeats) using 3D genome reconstructions from Hi-C (genome-wide chromosome conformation capture) data; however, corresponding assessments for continuous functional genomic data (e.g. chromatin immunoprecipitation-sequencing (ChIP-seq) peak height) are lacking. Here, we demonstrate that applying bump hunting via the patient rule induction method (PRIM) to ChIP-seq data superposed on a Saccharomyces cerevisiae 3D genome reconstruction can discover 'functional 3D hotspots', regions in 3-space for which the mean ChIP-seq peak height is significantly elevated. For the transcription factor Swi6, the top hotspot by P-value contains MSB2 and ERG11 - known Swi6 target genes on different chromosomes. We verify this finding in a number of ways. First, this top hotspot is relatively stable under PRIM across parameter settings. Second, this hotspot is among the top hotspots by mean outcome identified by an alternative algorithm, k-Nearest Neighbor (k-NN) regression. Third, the distance between MSB2 and ERG11 is smaller than expected (by resampling) in two other 3D reconstructions generated via different normalization and reconstruction algorithms. This analytic approach can discover functional 3D hotspots and potentially reveal novel regulatory interactions.

Indexed as

AlgorithmsGene Expression Regulation, FungalGenome, FungalChromatinChromatin ImmunoprecipitationChromosome MappingCytochrome P-450 Enzyme SystemHigh-Throughput Nucleotide SequencingIntracellular Signaling Peptides and ProteinsModels, MolecularMolecular ConformationSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsTranscription FactorsChromatinCytochrome P-450 Enzyme SystemErg11 protein, S cerevisiaeIntracellular Signaling Peptides and ProteinsMSB2 protein, S cerevisiaeSaccharomyces cerevisiae ProteinsSWI6 protein, S cerevisiaeTranscription Factors

Identifiers

PMID26869583
PMCPMC4797302
OpenAlexW2272455530

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.