Evidence map›Paper›PMID 26553003›Full record

ArticleBMC bioinformatics2015

Reconstruction of 3D genome architecture via a two-stage algorithm.

Mark R Segal, Henrik L Bengtsson

Abstract read
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. 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
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

2 authors.

Mark R SegalDivision of Bioinformatics, Department of Epidemiology and Biostatistics, University of California, 550 16th Street, San Francisco, 94158, CA, USA. mark@biostat.ucsf.edu.
Henrik L BengtssonDivision of Bioinformatics, Department of Epidemiology and Biostatistics, University of California, 550 16th Street, San Francisco, 94158, CA, USA. henrik.bengtsson@ucsf.edu.

Funding

Translational InformaticsP30CA082103 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Jennifer Leigh Clarke · 1999 to 2026
$209.7M
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
NCI NIH HHS P30 CA082103NIGMS NIH HHS R01 GM109457
6 · The paper itself

Abstract

backgroundThe three-dimensional (3D) configuration of chromosomes within the eukaryote nucleus is an important factor for several cellular functions, including gene expression regulation, and has also been linked with cancer-causing translocation events. While visualization of such architecture remains limited to low resolutions, the ability to infer structures at increasing resolutions has been enabled by recently-devised chromosome conformation capture techniques. In particular, when coupled with next generation sequencing, such methods yield an inventory of genome-wide chromatin contacts or interactions. Various algorithms have been advanced to operate on such contact data to produce reconstructed 3D configurations. Studies have shown that these reconstructions can provide added value over raw interaction data with respect to downstream biological insights. However, only limited, low-resolution reconstructions have been realized for mammals due to computational bottlenecks.

resultsHere we propose a two-stage algorithm to partially overcome these computational barriers. The central idea is to initially utilize existing reconstruction techniques on an individual chromosome basis, using intra-chromosomal contacts, and then to relatively position these chromosome-level reconstructions using inter-chromosomal contacts. This two-stage strategy represents a natural approach in view of the within- versus between- chromosome distribution of contacts. It can increase resolution ≈ 20 fold for mouse and human. After describing the algorithm we present 3D architectures for mouse embryonic stem cells and human lymphoblastoid cells. We evaluate the impact of several factors on reconstruction reproducibility and explore a variety of sampling schemes. We further analyze replicate data at differing resolutions obtained from recently devised in situ Hi-C assays. In all instances we demonstrate insensitivity of the whole-genome 3D reconstruction obtained by the two-stage algorithm to the sampling strategy used.

conclusionsOur two-stage algorithm has the potential to significantly increase the resolution of 3D genome reconstructions. The improvements are such that we can progress from 1 Mb resolution to 100 kb resolution, notable since this latter value has been identified as critical to inferring topological domains in analyses performed on the contact (rather than 3D) level.

Indexed as

AlgorithmsGenomeMolecular ConformationAnimalsChromatinChromosomesEmbryonic Stem CellsHigh-Throughput Nucleotide SequencingHumansLymphocytesMiceModels, MolecularReproducibility of ResultsChromatin

Identifiers

PMID26553003
PMCPMC4638111

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.