Evidence map›Paper›PMID 27587669›Full record

ArticleBioinformatics (Oxford, England)2016

LuxGLM: a probabilistic covariate model for quantification of DNA methylation modifications with complex experimental designs.

Tarmo Äijö, Xiaojing Yue, Anjana Rao, Harri Lähdesmäki

Open access · bronzeAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
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

6 citing papers in PubMed, 24 citations in OpenAlex.

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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 at 4 institutions in 2 countries.

Tarmo ÄijöCenter for Computational Biology, Simons Foundation, New York, NY 10010, USA Department of Computer Science, Aalto University School of Science, Aalto FI-00076, Finland.
Xiaojing YueLa Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.
Anjana RaoLa Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA Department of Pharmacology and Moores Cancer Center, University of California, La Jolla, CA 92037, USA Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA.
Harri LähdesmäkiDepartment of Computer Science, Aalto University School of Science, Aalto FI-00076, Finland.
Aalto University · FILa Jolla Institute for Immunology · USMoores Cancer CenterSimons Foundation · US

Funding

REGULATED ACCESSIBILITY OF A CYTOKINE GENE LOCUSR01AI044432 · NIAID · LA JOLLA INSTITUTE FOR IMMUNOLOGY · PI RAO, ANJANA · 1999 to 2014
$7.7M
Role of TET proteins in myeloid malignanciesR01CA151535 · NCI · LA JOLLA INSTITUTE FOR IMMUNOLOGY · PI RAO, ANJANA · 2012 to 2016
$1.8M
NCI NIH HHS R01 CA151535NIAID NIH HHS R01 AI044432
6 · The paper itself

Abstract

motivation5-methylcytosine (5mC) is a widely studied epigenetic modification of DNA. The ten-eleven translocation (TET) dioxygenases oxidize 5mC into oxidized methylcytosines (oxi-mCs): 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC). DNA methylation modifications have multiple functions. For example, 5mC is shown to be associated with diseases and oxi-mC species are reported to have a role in active DNA demethylation through 5mC oxidation and DNA repair, among others, but the detailed mechanisms are poorly understood. Bisulphite sequencing and its various derivatives can be used to gain information about all methylation modifications at single nucleotide resolution. Analysis of bisulphite based sequencing data is complicated due to the convoluted read-outs and experiment-specific variation in biochemistry. Moreover, statistical analysis is often complicated by various confounding effects. How to analyse 5mC and oxi-mC data sets with arbitrary and complex experimental designs is an open and important problem.

resultsWe propose the first method to quantify oxi-mC species with arbitrary covariate structures from bisulphite based sequencing data. Our probabilistic modeling framework combines a previously proposed hierarchical generative model for oxi-mC-seq data and a general linear model component to account for confounding effects. We show that our method provides accurate methylation level estimates and accurate detection of differential methylation when compared with existing methods. Analysis of novel and published data gave insights into to the demethylation of the forkhead box P3 (Foxp3) locus during the induced T regulatory cell differentiation. We also demonstrate how our covariate model accurately predicts methylation levels of the Foxp3 locus. Collectively, LuxGLM method improves the analysis of DNA methylation modifications, particularly for oxi-mC species. AVAILABILITY AND IMPLEMENTATION: An implementation of the proposed method is available under MIT license at https://github.org/tare/LuxGLM/ CONTACT: taijo@simonsfoundation.org or harri.lahdesmaki@aalto.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

DNA Methylation5-MethylcytosineCytosineDNADNA-Binding ProteinsDNA RepairForkhead Transcription FactorsHumansOxidation-ReductionResearch DesignSequence Analysis, DNA5-MethylcytosineCytosineDNADNA-Binding ProteinsForkhead Transcription FactorsFOXP3 protein, human

Identifiers

PMID27587669
PMCPMC5013920
OpenAlexW2515130064

What OpenQuestion holds

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Registered trials

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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.