Evidence map›Paper›PMID 30176808›Full record

ArticleBMC bioinformatics2018

HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures.

Hani Z Girgis, Alfredo Velasco, Zachary E Reyes

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 50% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Epigenetic memory in development and disease: Unraveling the mechanism.Biochimica et biophysica acta. Reviews on cancer · 2020
    Review
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.

Hani Z GirgisTandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, 74104-9700, OK, USA. hani-girgis@utulsa.edu.ORCID http://orcid.org/0000-0002-6080-0320
Alfredo VelascoTandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, 74104-9700, OK, USA.
Zachary E ReyesTandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, 74104-9700, OK, USA.
University of Tulsa · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHistone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distributions of tens of chromatin marks around thousands of regions.

resultsTo this end, we developed a software tool, HebbPlot, which utilizes a Hebbian neural network in learning a general chromatin signature from regions with a common function. Hebbian networks can learn the associations between tens of marks and thousands of regions. HebbPlot presents a signature as a digital image, which can be easily interpreted. Moreover, signatures produced by HebbPlot can be compared quantitatively. We validated HebbPlot in six case studies. The results of these case studies are novel or validating results already reported in the literature, indicating the accuracy of HebbPlot. Our results indicate that promoters have a directional chromatin signature; several marks tend to stretch downstream or upstream. H3K4me3 and H3K79me2 have clear directional distributions around active promoters. In addition, the signatures of high- and low-CpG promoters are different; H3K4me3, H3K9ac, and H3K27ac are the most different marks. When we studied the signatures of enhancers active in eight tissues, we observed that these signatures are similar, but not identical. Further, we identified some histone modifications - H3K36me3, H3K79me1, H3K79me2, and H4K8ac - that are associated with coding regions of active genes. Other marks - H4K12ac, H3K14ac, H3K27me3, and H2AK5ac - were found to be weakly associated with coding regions of inactive genes.

conclusionsThis study resulted in a novel software tool, HebbPlot, for learning and visualizing the chromatin signature of a genetic element. Using HebbPlot, we produced a visual catalog of the signatures of multiple genetic elements in 57 cell types available through the Roadmap Epigenomics Project. Furthermore, we made a progress toward a functional catalog consisting of 22 histone marks. In sum, HebbPlot is applicable to a wide array of studies, facilitating the deciphering of the histone code.

Indexed as

EpigenomicsGene Expression RegulationPromoter Regions, GeneticSoftwareChromatinHistonesHumansProtein Processing, Post-TranslationalRegulatory Sequences, Nucleic AcidChromatinHistonesArtificial neural networksAssociative learningChromatin modificationsEpigenetic signaturesHebbian learningHistone marksVisualization

Identifiers

PMID30176808
PMCPMC6122555
OpenAlexW2949326540

What Socratic 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 Socratic graph.