Evidence map›Paper›PMID 27153657›Full record

ArticleBioinformatics (Oxford, England)2016

Spectral identification of topological domains.

Jie Chen, Alfred O Hero, Indika Rajapakse

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

42 citing papers in PubMed.

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  19. SBTD: A Novel Method for Detecting Topological Associated Domains from Hi-C Data.Interdisciplinary sciences, computational life sciences · 2021
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  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

3 authors.

Jie ChenCIAIC, School of Marine Science and Technology, Northwestern Polytechnical University, China Department of Electrical Engineering and Computer Science Department of Computational Medicine & Bioinformatics, Medical School.
Alfred O HeroDepartment of Electrical Engineering and Computer Science Department of Biomedical Engineering Department of Statistics.
Indika RajapakseDepartment of Computational Medicine & Bioinformatics, Medical School Department of Mathematics, College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI, USA.

Funding

Quantitative models and methods for genetic organizationK25DK082791 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI RAJAPAKSE, INDIKA · 2009 to 2013
$662k
NIDDK NIH HHS K25 DK082791
6 · The paper itself

Abstract

motivationTopological domains have been proposed as the backbone of interphase chromosome structure. They are regions of high local contact frequency separated by sharp boundaries. Genes within a domain often have correlated transcription. In this paper, we present a computational efficient spectral algorithm to identify topological domains from chromosome conformation data (Hi-C data). We consider the genome as a weighted graph with vertices defined by loci on a chromosome and the edge weights given by interaction frequency between two loci. Laplacian-based graph segmentation is then applied iteratively to obtain the domains at the given compactness level. Comparison with algorithms in the literature shows the advantage of the proposed strategy.

resultsAn efficient algorithm is presented to identify topological domains from the Hi-C matrix. AVAILABILITY AND IMPLEMENTATION: The Matlab source code and illustrative examples are available at http://bionetworks.ccmb.med.umich.edu/ CONTACT: : indikar@med.umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

AlgorithmsTranscription, GeneticChromosomesModels, TheoreticalProgramming Languages

Identifiers

PMID27153657
PMCPMC4937202

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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.