Evidence map›Paper›PMID 31392989›Full record

ArticleNucleic acids research2019

Analyzing whole genome bisulfite sequencing data from highly divergent genotypes.

Phillip Wulfridge, Ben Langmead, Andrew P Feinberg, Kasper D Hansen

Abstract read
In one paragraph

Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. BenchmarkingInternational journal of molecular sciences · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
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  13. Article
  14. The Chromatin Remodeling FactorPlants (Basel, Switzerland) · 2023
    Article
  15. Article
  16. Article
  17. Review
  18. Genome-wide detection of cytosine methylation by single molecule real-time sequencing.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  19. 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

4 authors.

Phillip WulfridgeCenter for Epigenetics, Johns Hopkins School of Medicine, 855 N. Wolfe St, Baltimore, MD 21205, USA.
Ben LangmeadDepartment of Computer Science, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 21218, USA.
Andrew P FeinbergCenter for Epigenetics, Johns Hopkins School of Medicine, 855 N. Wolfe St, Baltimore, MD 21205, USA.
Kasper D HansenCenter for Epigenetics, Johns Hopkins School of Medicine, 855 N. Wolfe St, Baltimore, MD 21205, USA.

Funding

MOLECULAR PATHOLOGY OF WILMS TUMOR RELATED GENES 11PR01CA054358 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ANDREW P. FEINBERG · 1991 to 2026
$5.5M
Integration of Genomics and the EnvironmentRM1HG008529 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI FEINBERG, ANDREW P., THREADGILL, DAVID W. · 2016 to 2020
$5.3M
Epigenetic Drivers of Intrinsic Phenotypic Variability in Metabolic DiseaseDP1DK119129 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI FEINBERG, ANDREW P. · 2018 to 2023
$3.9M
Analysis of genomics datasets at a massive scaleR01GM121459 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI HANSEN, KASPER DANIEL · 2017 to 2021
$2.3M
Hardening and Scaling Core Genomics SoftwareR01GM118568 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI LANGMEAD, BENJAMIN THOMAS · 2016 to 2020
$2.0M
NCI NIH HHS R01 CA054358NHGRI NIH HHS RM1 HG008529NIDDK NIH HHS DP1 DK119129NIGMS NIH HHS R01 GM118568NIGMS NIH HHS R01 GM121459
6 · The paper itself

Abstract

In the study of DNA methylation, genetic variation between species, strains or individuals can result in CpG sites that are exclusive to a subset of samples, and insertions and deletions can rearrange the spatial distribution of CpGs. How to account for this variation in an analysis of the interplay between sequence variation and DNA methylation is not well understood, especially when the number of CpG differences between samples is large. Here, we use whole-genome bisulfite sequencing data on two highly divergent mouse strains to study this problem. We show that alignment to personal genomes is necessary for valid methylation quantification. We introduce a method for including strain-specific CpGs in differential analysis, and show that this increases power. We apply our method to a human normal-cancer dataset, and show this improves accuracy and power, illustrating the broad applicability of our approach. Our method uses smoothing to impute methylation levels at strain-specific sites, thereby allowing strain-specific CpGs to contribute to the analysis, while accounting for differences in the spatial occurrences of CpGs. Our results have implications for joint analysis of genetic variation and DNA methylation using bisulfite-converted DNA, and unlocks the use of personal genomes for addressing this question.

Indexed as

AnimalsCpG IslandsDNA MethylationEpigenesis, GeneticGenetic VariationGenome, HumanGenotypeHigh-Throughput Nucleotide SequencingHumansMiceSequence Analysis, DNAWhole Genome Sequencing

Identifiers

PMID31392989
PMCPMC6821270

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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.