Evidence map›Paper›PMID 28981571›Full record

ArticleBriefings in bioinformatics2019

Detecting differential DNA methylation from sequencing of bisulfite converted DNA of diverse species.

Iksoo Huh, Xin Wu, Taesung Park, Soojin V Yi

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Uncovering alterations in cancer epigenetics via trans-dimensional Markov chain Monte Carlo and hidden Markov models.Journal of the Royal Statistical Society. Series C, Applied statistics · 2026
    Article
  2. Epigenetic Mechanisms inCurrent issues in molecular biology · 2025
    Review
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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

4 authors.

Iksoo HuhSchool of Biological Sciences, Georgia Institute of Technology.
Xin WuSchool of Biological Sciences, Georgia Institute of Technology.
Taesung ParkDepartment of Statistics, Seoul National University.
Soojin V YiSchool of Biological Sciences, Georgia Institute of Technology.

Funding

Human Specific Brain DNA Methylation and Neuropsychiatric DiseasesR01MH103517 · NIMH · GEORGIA INSTITUTE OF TECHNOLOGY · PI JORDAN, IRVING KING, PREUSS, TODD M · 2015 to 2019
$3.5M
Resource development for a new model of social threat responseR21MH102677 · NIMH · EMORY UNIVERSITY · PI MANEY, DONNA L, YI, SOOJIN · 2014 to 2015
$430k
NIMH NIH HHS R01 MH103517NIMH NIH HHS R21 MH102677
6 · The paper itself

Abstract

DNA methylation is one of the most extensively studied epigenetic modifications of genomic DNA. In recent years, sequencing of bisulfite-converted DNA, particularly via next-generation sequencing technologies, has become a widely popular method to study DNA methylation. This method can be readily applied to a variety of species, dramatically expanding the scope of DNA methylation studies beyond the traditionally studied human and mouse systems. In parallel to the increasing wealth of genomic methylation profiles, many statistical tools have been developed to detect differentially methylated loci (DMLs) or differentially methylated regions (DMRs) between biological conditions. We discuss and summarize several key properties of currently available tools to detect DMLs and DMRs from sequencing of bisulfite-converted DNA. However, the majority of the statistical tools developed for DML/DMR analyses have been validated using only mammalian data sets, and less priority has been placed on the analyses of invertebrate or plant DNA methylation data. We demonstrate that genomic methylation profiles of non-mammalian species are often highly distinct from those of mammalian species using examples of honey bees and humans. We then discuss how such differences in data properties may affect statistical analyses. Based on these differences, we provide three specific recommendations to improve the power and accuracy of DML and DMR analyses of invertebrate data when using currently available statistical tools. These considerations should facilitate systematic and robust analyses of DNA methylation from diverse species, thus advancing our understanding of DNA methylation.

Indexed as

DNA MethylationAnimalsBeesComputational BiologyComputer SimulationCpG IslandsGenome, HumanHigh-Throughput Nucleotide SequencingHumansModels, GeneticModels, StatisticalSequence Analysis, DNASpecies SpecificitySulfiteshydrogen sulfiteSulfites

Identifiers

PMID28981571
PMCPMC6357555

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.