Evidence map›Paper›PMID 25237057›Full record

ArticleNucleic acids research2014

SNP-guided identification of monoallelic DNA-methylation events from enrichment-based sequencing data.

Sandra Steyaert, Wim Van Criekinge, Ayla De Paepe, Simon Denil, Klaas Mensaert, Katrien Vandepitte, Wim Vanden Berghe, Geert Trooskens, Tim De Meyer

Abstract readValidation Study
In one paragraph

Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

9 authors.

Sandra SteyaertDepartment of Mathematical Modelling, Statistics and Bioinformatics, University of Ghent, Ghent 9000, Belgium Sandra.Steyaert@Ugent.be.
Wim Van CriekingeDepartment of Mathematical Modelling, Statistics and Bioinformatics, University of Ghent, Ghent 9000, Belgium.
Ayla De PaepeDepartment of Mathematical Modelling, Statistics and Bioinformatics, University of Ghent, Ghent 9000, Belgium.
Simon DenilDepartment of Mathematical Modelling, Statistics and Bioinformatics, University of Ghent, Ghent 9000, Belgium.
Klaas MensaertDepartment of Mathematical Modelling, Statistics and Bioinformatics, University of Ghent, Ghent 9000, Belgium.
Katrien VandepitteDepartment of Biology, University of Leuven, Leuven 3000, Belgium.
Wim Vanden BerghePPES, Department of Biomedical Sciences, University of Antwerp, Wilrijk 2610, Belgium.
Geert TrooskensDepartment of Mathematical Modelling, Statistics and Bioinformatics, University of Ghent, Ghent 9000, Belgium.
Tim De MeyerDepartment of Mathematical Modelling, Statistics and Bioinformatics, University of Ghent, Ghent 9000, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monoallelic gene expression is typically initiated early in the development of an organism. Dysregulation of monoallelic gene expression has already been linked to several non-Mendelian inherited genetic disorders. In humans, DNA-methylation is deemed to be an important regulator of monoallelic gene expression, but only few examples are known. One important reason is that current, cost-affordable truly genome-wide methods to assess DNA-methylation are based on sequencing post-enrichment. Here, we present a new methodology based on classical population genetic theory, i.e. the Hardy-Weinberg theorem, that combines methylomic data from MethylCap-seq with associated SNP profiles to identify monoallelically methylated loci. Applied on 334 MethylCap-seq samples of very diverse origin, this resulted in the identification of 80 genomic regions featured by monoallelic DNA-methylation. Of these 80 loci, 49 are located in genic regions of which 25 have already been linked to imprinting. Further analysis revealed statistically significant enrichment of these loci in promoter regions, further establishing the relevance and usefulness of the method. Additional validation was done using both 14 whole-genome bisulfite sequencing data sets and 16 mRNA-seq data sets. Importantly, the developed approach can be easily applied to other enrichment-based sequencing technologies, like the ChIP-seq-based identification of monoallelic histone modifications.

Indexed as

AllelesDNA MethylationPolymorphism, Single NucleotideGenetic LociGenomicsHumansSequence Analysis, DNASequence Analysis, RNA

Identifiers

PMID25237057
PMCPMC4227762

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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.