Evidence map›Paper›PMID 28222791›Full record

ArticleGenome biology2017

epiG: statistical inference and profiling of DNA methylation from whole-genome bisulfite sequencing data.

Martin Vincent, Kamilla Mundbjerg, Jakob Skou Pedersen, Gangning Liang, Peter A Jones, Torben Falck Ørntoft, Karina Dalsgaard Sørensen, Carsten Wiuf

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Epialleles and epiallelic heterogeneity in hematological malignancies.Medical oncology (Northwood, London, England) · 2022
    Review
  3. Article
  4. Article
  5. 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

8 authors.

Martin VincentDepartment of Mathematical Sciences, University of Copenhagen, Copenhagen, 2100, Denmark.
Kamilla MundbjergUSC Norris Comprehensive Cancer Center, Keck School of Medicine, Los Angeles, 90089-9176, CA, USA.
Jakob Skou PedersenDepartment of Molecular Medicine, Aarhus University Hospital, Aarhus, 8200, Denmark.
Gangning LiangDepartment of Urology, Keck School of Medicine, University of Southern California, Los Angeles, 90089, CA, USA.
Peter A JonesVan Andel Research Institute, Grand Rapids, 49503, MI, USA.
Torben Falck ØrntoftDepartment of Molecular Medicine, Aarhus University Hospital, Aarhus, 8200, Denmark.
Karina Dalsgaard SørensenDepartment of Molecular Medicine, Aarhus University Hospital, Aarhus, 8200, Denmark.
Carsten WiufDepartment of Mathematical Sciences, University of Copenhagen, Copenhagen, 2100, Denmark. wiuf@math.ku.dk.ORCID 0000-0002-1302-4445

Funding

Targeting DNA Methylation and the Cancer EpigenomeR35CA209859 · NCI · VAN ANDEL RESEARCH INSTITUTE · PI PETER A JONES · 2017 to 2026
$11.9M
NCI NIH HHS R35 CA209859
6 · The paper itself

Abstract

The study of epigenetic heterogeneity at the level of individual cells and in whole populations is the key to understanding cellular differentiation, organismal development, and the evolution of cancer. We develop a statistical method, epiG, to infer and differentiate between different epi-allelic haplotypes, annotated with CpG methylation status and DNA polymorphisms, from whole-genome bisulfite sequencing data, and nucleosome occupancy from NOMe-seq data. We demonstrate the capabilities of the method by inferring allele-specific methylation and nucleosome occupancy in cell lines, and colon and tumor samples, and by benchmarking the method against independent experimental data.

Indexed as

DNA MethylationHigh-Throughput Nucleotide SequencingSequence Analysis, DNASoftwareAllelesCpG IslandsEpigenomicsGene Expression ProfilingGenotypeNucleosomesPolymorphism, Single NucleotideProtein BindingReproducibility of ResultsNucleosomesCpG methylationEpi-alleleEpi-allelic haplotypeEpigenetic stateGpC methylationNOMe-seq

Identifiers

PMID28222791
PMCPMC5320668

What OpenQuestion holds

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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.