scholarly journals Tumor purity and differential methylation in cancer epigenomics

2016 ◽  
pp. elw016 ◽  
Author(s):  
Fayou Wang ◽  
Naiqian Zhang ◽  
Jun Wang ◽  
Hao Wu ◽  
Xiaoqi Zheng
Keyword(s):  
Differential Methylation ◽  
Tumor Purity ◽  
Cancer Epigenomics

Detection of differentially methylated CpG sites between tumor samples with uneven tumor purities

2019 ◽  
Vol 36 (7) ◽  
pp. 2017-2024
Author(s):  
Weiwei Zhang ◽  
Ziyi Li ◽  
Nana Wei ◽  
Hua-Jun Wu ◽  
Xiaoqi Zheng
Keyword(s):  
Real Data ◽  
R Package ◽  
Differential Methylation ◽  
Least Square ◽  
Epigenetic Mechanism ◽  
Supplementary Information ◽  
Cpg Sites ◽  
Tumor Purity ◽  
Different Sources ◽  
Normal Controls

Abstract Motivation Inference of differentially methylated (DM) CpG sites between two groups of tumor samples with different geno- or pheno-types is a critical step to uncover the epigenetic mechanism of tumorigenesis, and identify biomarkers for cancer subtyping. However, as a major source of confounding factor, uneven distributions of tumor purity between two groups of tumor samples will lead to biased discovery of DM sites if not properly accounted for. Results We here propose InfiniumDM, a generalized least square model to adjust tumor purity effect for differential methylation analysis. Our method is applicable to a variety of experimental designs including with or without normal controls, different sources of normal tissue contaminations. We compared our method with conventional methods including minfi, limma and limma corrected by tumor purity using simulated datasets. Our method shows significantly better performance at different levels of differential methylation thresholds, sample sizes, mean purity deviations and so on. We also applied the proposed method to breast cancer samples from TCGA database to further evaluate its performance. Overall, both simulation and real data analyses demonstrate favorable performance over existing methods serving similar purpose. Availability and implementation InfiniumDM is a part of R package InfiniumPurify, which is freely available from GitHub (https://github.com/Xiaoqizheng/InfiniumPurify). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MethylPurify: tumor purity deconvolution and differential methylation detection from single tumor DNA methylomes

2014 ◽  
Vol 15 (7) ◽  
Author(s):  
Xiaoqi Zheng ◽  
Qian Zhao ◽  
Hua-Jun Wu ◽  
Wei Li ◽  
Haiyun Wang ◽  
...  
Keyword(s):  
Differential Methylation ◽  
Tumor Purity ◽  
Tumor Dna ◽  
Single Tumor

Vestibular Schwannoma Clinical Phenotypes Are Associated with Differential Methylation Patterns

2020 ◽  
Author(s):  
Avital Perry ◽  
Christopher S. Graffeo ◽  
Lucas P. Carlstrom ◽  
Amanda Munoz Casabella ◽  
Matthew L. Carlson ◽  
...  
Keyword(s):  
Vestibular Schwannoma ◽  
Differential Methylation ◽  
Clinical Phenotypes ◽  
Methylation Patterns

scholarly journals Differential Methylation in the GSTT1 Regulatory Region in Sudden Unexplained Death and Sudden Unexpected Death in Epilepsy

2021 ◽  
Vol 22 (6) ◽  
pp. 2790
Author(s):  
Steffan Noe Christiansen ◽  
Stine Bøttcher Jacobsen ◽  
Jeppe Dyrberg Andersen ◽  
Marie-Louise Kampmann ◽  
Linea Christine Trudsø ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cardiac Tissue ◽  
Heart Diseases ◽  
Regulatory Region ◽  
Differential Methylation ◽  
Sudden Unexpected Death ◽  
Diagnostic Challenge ◽  
Unexpected Death ◽  
Sudden Unexplained Death ◽  
Unexplained Death

Sudden cardiac death (SCD) is a diagnostic challenge in forensic medicine. In a relatively large proportion of the SCDs, the deaths remain unexplained after autopsy. This challenge is likely caused by unknown disease mechanisms. Changes in DNA methylation have been associated with several heart diseases, but the role of DNA methylation in SCD is unknown. In this study, we investigated DNA methylation in two SCD subtypes, sudden unexplained death (SUD) and sudden unexpected death in epilepsy (SUDEP). We assessed DNA methylation of more than 850,000 positions in cardiac tissue from nine SUD and 14 SUDEP cases using the Illumina Infinium MethylationEPIC BeadChip. In total, six differently methylated regions (DMRs) between the SUD and SUDEP cases were identified. The DMRs were located in proximity to or overlapping genes encoding proteins that are a part of the glutathione S-transferase (GST) superfamily. Whole genome sequencing (WGS) showed that the DNA methylation alterations were not caused by genetic changes, while whole transcriptome sequencing (WTS) showed that DNA methylation was associated with expression levels of the GSTT1 gene. In conclusion, our results indicate that cardiac DNA methylation is similar in SUD and SUDEP, but with regional differential methylation in proximity to GST genes.

scholarly journals scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Ricard Argelaguet ◽  
Guido Sanguinetti ◽  
Catalina A. Vallejos
Keyword(s):  
Single Cell ◽  
Bayesian Modeling ◽  
Bayesian Model ◽  
Regulation Of Gene Expression ◽  
Differential Methylation ◽  
Hierarchical Bayesian Model ◽  
Genomic Features ◽  
Distinct Cell ◽  
Biological Heterogeneity

AbstractHigh-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.

scholarly journals Stable DNMT3L overexpression in SH-SY5Y neurons recreates a facet of the genome-wide Down syndrome DNA methylation signature

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Benjamin I. Laufer ◽  
J. Antonio Gomez ◽  
Julia M. Jianu ◽  
Janine M. LaSalle
Keyword(s):  
Dna Methylation ◽  
Down Syndrome ◽  
Neuronal Differentiation ◽  
Molecular Mechanisms ◽  
Cpg Island ◽  
Differential Methylation ◽  
Chromosome 21 ◽  
Pairwise Comparisons ◽  
Genome Wide ◽  
A Genome

Abstract Background Down syndrome (DS) is characterized by a genome-wide profile of differential DNA methylation that is skewed towards hypermethylation in most tissues, including brain, and includes pan-tissue differential methylation. The molecular mechanisms involve the overexpression of genes related to DNA methylation on chromosome 21. Here, we stably overexpressed the chromosome 21 gene DNA methyltransferase 3L (DNMT3L) in the human SH-SY5Y neuroblastoma cell line and assayed DNA methylation at over 26 million CpGs by whole genome bisulfite sequencing (WGBS) at three different developmental phases (undifferentiated, differentiating, and differentiated). Results DNMT3L overexpression resulted in global CpG and CpG island hypermethylation as well as thousands of differentially methylated regions (DMRs). The DNMT3L DMRs were skewed towards hypermethylation and mapped to genes involved in neurodevelopment, cellular signaling, and gene regulation. Consensus DNMT3L DMRs showed that cell lines clustered by genotype and then differentiation phase, demonstrating sets of common genes affected across neuronal differentiation. The hypermethylated DNMT3L DMRs from all pairwise comparisons were enriched for regions of bivalent chromatin marked by H3K4me3 as well as differentially methylated sites from previous DS studies of diverse tissues. In contrast, the hypomethylated DNMT3L DMRs from all pairwise comparisons displayed a tissue-specific profile enriched for regions of heterochromatin marked by H3K9me3 during embryonic development. Conclusions Taken together, these results support a mechanism whereby regions of bivalent chromatin that lose H3K4me3 during neuronal differentiation are targeted by excess DNMT3L and become hypermethylated. Overall, these findings demonstrate that DNMT3L overexpression during neurodevelopment recreates a facet of the genome-wide DS DNA methylation signature by targeting known genes and gene clusters that display pan-tissue differential methylation in DS.

Differential Methylation as a Cause of Allele Dropout at the ImprintedGNASLocus

2010 ◽  
Vol 14 (4) ◽  
pp. 455-460 ◽  
Author(s):  
Rute A. Tomaz ◽  
Branca M. Cavaco ◽  
Valeriano Leite
Keyword(s):  
Differential Methylation ◽  
Allele Dropout

Translating cancer epigenomics into the clinic: focus on lung cancer

2017 ◽  
Vol 189 ◽  
pp. 76-92 ◽  
Author(s):  
Josep Mari-Alexandre ◽  
Angel Diaz-Lagares ◽  
Maria Villalba ◽  
Oscar Juan ◽  
Ana B. Crujeiras ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Epigenomics

scholarly journals Differential Methylation as a Biomarker of Response to Etanercept in Patients With Rheumatoid Arthritis

2016 ◽  
Vol 68 (6) ◽  
pp. 1353-1360 ◽  
Author(s):  
Darren Plant ◽  
Amy Webster ◽  
Nisha Nair ◽  
James Oliver ◽  
Samantha L. Smith ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Differential Methylation
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