Evidence map›Paper›PMID 39701600›Full record

ArticleBriefings in bioinformatics2024

Higher order interaction analysis quantifies coordination in the epigenome revealing novel biological relationships in Kabuki syndrome.

Sara Cuvertino, Terence Garner, Evgenii Martirosian, Bridgious Walusimbi, Susan J Kimber, Siddharth Banka, Adam Stevens

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

7 authors.

Sara CuvertinoDivision of Evolution and Genomic Sciences, Faculty of Biology, Medicine, and Health, School of Biological Sciences, The University of Manchester, Manchester, UK.ORCID 0000-0002-6750-5615
Terence GarnerDivision of Developmental Biology & Medicine, Faculty of Biology, Medicine, and Health, School of Biological Sciences, The University of Manchester, Manchester, UK.
Evgenii MartirosianDivision of Evolution and Genomic Sciences, Faculty of Biology, Medicine, and Health, School of Biological Sciences, The University of Manchester, Manchester, UK.ORCID 0000-0002-7984-9810
Bridgious WalusimbiDivision of Developmental Biology & Medicine, Faculty of Biology, Medicine, and Health, School of Biological Sciences, The University of Manchester, Manchester, UK.
Susan J KimberDivision of Cell Matrix Biology and Regenerative Medicine, Faculty of Biology, Medicine, and Health, School of Biological Sciences, The University of Manchester, Manchester, UK.
Siddharth BankaDivision of Evolution and Genomic Sciences, Faculty of Biology, Medicine, and Health, School of Biological Sciences, The University of Manchester, Manchester, UK.
Adam StevensDivision of Developmental Biology & Medicine, Faculty of Biology, Medicine, and Health, School of Biological Sciences, The University of Manchester, Manchester, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Complex direct and indirect relationships between multiple variables, termed higher order interactions (HOIs), are characteristics of all natural systems. Traditional differential and network analyses fail to account for the omic datasets richness and miss HOIs. We investigated peripheral blood DNA methylation data from Kabuki syndrome type 1 (KS1) and control individuals, identified 2,002 differentially methylated points (DMPs), and inferred 17 differentially methylated regions, which represent only 189 DMPs. We applied hypergraph models to measure HOIs on all the CpGs and revealed differences in the coordination of DMPs with lower entropy and higher coordination of the peripheral epigenome in KS1 implying reduced network complexity. Hypergraphs also capture epigenomic trans-relationships, and identify biologically relevant pathways that escape the standard analyses. These findings construct the basis of a suitable model for the analysis of organization in the epigenome in rare diseases, which can be applied to investigate mechanism in big data.

Indexed as

Abnormalities, MultipleDNA MethylationEpigenomeFaceHematologic DiseasesVestibular DiseasesCpG IslandsEpigenesis, GeneticEpigenomicsGene Regulatory NetworksHumansDNA methylationintegration analysisKabuki syndromeKMT2D

Identifiers

PMID39701600
PMCPMC11658816

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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.