scholarly journals SNP-guided identification of monoallelic DNA-methylation events from enrichment-based sequencing data

2014 ◽  
Author(s):  
Sandra Steyaert ◽  
Wim Van Criekinge ◽  
Ayla De Paepe ◽  
Simon Denil ◽  
Klaas Mensaert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Genetic Disorders ◽  
Data Sets ◽  
Sequencing Data ◽  
Promoter Regions ◽  
Sequencing Technologies ◽  
Genome Bisulfite Sequencing ◽  
Significant Enrichment ◽  
Bisulfite Sequencing Data

Monoallelic gene expression is typically initiated early in the development of an organism. Dysregulation of monoallelic gene expression has already been linked to several non-Mendelian inherited genetic disorders. In humans, DNA-methylation is deemed to be an important regulator of monoallelic gene expression, but only few examples are known. One important reason is that current, cost-affordable truly genome-wide methods to assess DNA-methylation are based on sequencing post enrichment. Here, we present a new methodology that combines methylomic data from MethylCap-seq with associated SNP profiles to identify monoallelically methylated loci. Using the Hardy-Weinberg theorem for each SNP locus, it could be established whether the observed frequency of samples featured by biallelic methylation was lower than randomly expected. Applied on 334 MethylCap-seq samples of very diverse origin, this resulted in the identification of 80 genomic regions featured by monoallelic DNA-methylation. Of these 80 loci, 49 are located in genic regions of which 25 have already been linked to imprinting. Further analysis revealed statistically significant enrichment of these loci in promoter regions, further establishing the relevance and usefulness of the method. Additional validation of the found loci was done using 14 whole-genome bisulfite sequencing data sets. Importantly, the developed approach can be easily applied to other enrichment-based sequencing technologies, such as the ChIP-seq-based identification of monoallelic histone modifications.

scholarly journals A Bayesian Approach for Analysis of Whole-Genome Bisulfite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation

Genetics ◽  
2017 ◽  
Vol 205 (4) ◽  
pp. 1443-1458 ◽  
Author(s):  
Owen J. L. Rackham ◽  
Sarah R. Langley ◽  
Thomas Oates ◽  
Eleni Vradi ◽  
Nathan Harmston ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bayesian Approach ◽  
Bisulfite Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Whole Genome Bisulfite Sequencing ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data

scholarly journals Focal disruption of DNA methylation dynamics at enhancers in IDH-mutant AML cells

Leukemia ◽  
2021 ◽  
Author(s):  
Elisabeth R. Wilson ◽  
Nichole M. Helton ◽  
Sharon E. Heath ◽  
Robert S. Fulton ◽  
Jacqueline E. Payton ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Myeloid Leukemia ◽  
Bisulfite Sequencing ◽  
Sequencing Data ◽  
Genome Wide ◽  
Cd34 Cells ◽  
Recurrent Mutations ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Acute Myeloid

AbstractRecurrent mutations in IDH1 or IDH2 in acute myeloid leukemia (AML) are associated with increased DNA methylation, but the genome-wide patterns of this hypermethylation phenotype have not been comprehensively studied in AML samples. We analyzed whole-genome bisulfite sequencing data from 15 primary AML samples with IDH1 or IDH2 mutations, which identified ~4000 focal regions that were uniquely hypermethylated in IDHmut samples vs. normal CD34+ cells and other AMLs. These regions had modest hypermethylation in AMLs with biallelic TET2 mutations, and levels of 5-hydroxymethylation that were diminished in IDH and TET-mutant samples, indicating that this hypermethylation results from inhibition of TET-mediated demethylation. Focal hypermethylation in IDHmut AMLs occurred at regions with low methylation in CD34+ cells, implying that DNA methylation and demethylation are active at these loci. AML samples containing IDH and DNMT3AR882 mutations were significantly less hypermethylated, suggesting that IDHmut-associated hypermethylation is mediated by DNMT3A. IDHmut-specific hypermethylation was highly enriched for enhancers that form direct interactions with genes involved in normal hematopoiesis and AML, including MYC and ETV6. These results suggest that focal hypermethylation in IDH-mutant AML occurs by altering the balance between DNA methylation and demethylation, and that disruption of these pathways at enhancers may contribute to AML pathogenesis.

scholarly journals Widespread natural variation of DNA methylation within angiosperms

2016 ◽  
Author(s):  
Chad E. Niederhuth ◽  
Adam J. Bewick ◽  
Lexiang Ji ◽  
Magdy S. Alabady ◽  
Kyung Do Kim ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Life Histories ◽  
Natural Variation ◽  
Gene Body ◽  
Sequencing Data ◽  
Gene Body Methylation ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Angiosperm Species ◽  
Methylation Patterns

AbstractTo understand the variation in genomic patterning of DNA methylation we compared methylomes of 34 diverse angiosperm species. By analyzing whole-genome bisulfite sequencing data in a phylogenetic context it becomes clear that there is extensive variation throughout angiosperms in gene body DNA methylation, euchromatic silencing of transposons and repeats, as well as silencing of heterochromatic transposons. The Brassicaceae have reduced CHG methylation levels and also reduced or loss of CG gene body methylation. The Poaceae are characterized by a lack or reduction of heterochromatic CHH methylation and enrichment of CHH methylation in genic regions. Reduced CHH methylation levels are found in clonally propagated species, suggesting that these methods of propagation may alter the epigenomic landscape over time. These results show that DNA methylation patterns are broadly a reflection of the evolutionary and life histories of plant species.

Development of a method for identifying and functionally analyzing allele-specific DNA methylation based on BS-seq data

Epigenomics ◽  
2019 ◽  
Vol 11 (15) ◽  
pp. 1679-1692
Author(s):  
Jiang Zhu ◽  
Mu Su ◽  
Yue Gu ◽  
Xingda Zhang ◽  
Wenhua Lv ◽  
...  
Keyword(s):  
Dna Methylation ◽  
High Throughput ◽  
Bisulfite Sequencing ◽  
The Cancer Genome Atlas ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Allele Specific ◽  
New Perspective

Aim: To comprehensively identify allele-specific DNA methylation (ASM) at the genome-wide level. Methods: Here, we propose a new method, called GeneASM, to identify ASM using high-throughput bisulfite sequencing data in the absence of haplotype information. Results: A total of 2194 allele-specific DNA methylated genes were identified in the GM12878 lymphocyte lineage using GeneASM. These genes are mainly enriched in cell cytoplasm function, subcellular component movement or cellular linkages. GM12878 methylated DNA immunoprecipitation sequencing, and methylation sensitive restriction enzyme sequencing data were used to evaluate ASM. The relationship between ASM and disease was further analyzed using the The Cancer Genome Atlas (TCGA) data of lung adenocarcinoma (LUAD), and whole genome bisulfite sequencing data. Conclusion: GeneASM, which recognizes ASM by high-throughput bisulfite sequencing and heterozygous single-nucleotide polymorphisms, provides new perspective for studying genomic imprinting.

scholarly journals Maximizing ecological and evolutionary insight from bisulfite sequencing data sets

2016 ◽  
Author(s):  
Amanda J. Lea ◽  
Tauras P. Vilgalys ◽  
Paul A.P. Durst ◽  
Jenny Tung
Keyword(s):  
Dna Methylation ◽  
Population Structure ◽  
Effect Size ◽  
Evolutionary Biology ◽  
Bisulfite Sequencing ◽  
Published Data ◽  
Data Sets ◽  
Cell Type ◽  
Sequencing Data ◽  
Bisulfite Sequencing Data

AbstractThe role of DNA methylation in development, divergence, and the response to environmental stimuli is of substantial interest in ecology and evolutionary biology. Measuring genome-wide DNA methylation is increasingly feasible using sodium bisulfite sequencing. Here, we analyze simulated and published data sets to demonstrate how effect size, kinship/population structure, taxonomic differences, and cell type heterogeneity influence the power to detect differential methylation in bisulfite sequencing data sets. Our results reveal that the effect sizes typical of evolutionary and ecological studies are modest, and will thus require data sets larger than those currently in common use. Additionally, our findings emphasize that statistical approaches that ignore the properties of bisulfite sequencing data (e.g., its count-based nature) or key sources of variance in natural populations (e.g., population structure or cell type heterogeneity) often produce false negatives or false positives, thus leading to incorrect biological conclusions. Finally, we provide recommendations for handling common issues that arise in bisulfite sequencing analyses and a freely available R Shiny application for simulating and performing power analyses on bisulfite sequencing data. This app, available at www.tung-lab.org/protocols-and-software.html, allows users to explore the effects of sequencing depth, sample size, population structure, and expected effect size, tailored to their own system.

AKSmooth: Enhancing low-coverage bisulfite sequencing data via kernel-based smoothing

2014 ◽  
Vol 12 (06) ◽  
pp. 1442005
Author(s):  
Junfang Chen ◽  
Pavlo Lutsik ◽  
Ruslan Akulenko ◽  
Jörn Walter ◽  
Volkhard Helms
Keyword(s):  
Dna Methylation ◽  
Bisulfite Sequencing ◽  
Human Colon ◽  
Sequencing Data ◽  
Human Colon Cancer ◽  
High Coverage ◽  
Comprehensive Picture ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Low Coverage

Whole-genome bisulfite sequencing (WGBS) is an approach of growing importance. It is the only approach that provides a comprehensive picture of the genome-wide DNA methylation profile. However, obtaining a sufficient amount of genome and read coverage typically requires high sequencing costs. Bioinformatics tools can reduce this cost burden by improving the quality of sequencing data. We have developed a statistical method Ajusted Local Kernel Smoother (AKSmooth) that can accurately and efficiently reconstruct the single CpG methylation estimate across the entire methylome using low-coverage bisulfite sequencing (Bi-Seq) data. We demonstrate the AKSmooth performance on the low-coverage (~ 4×) DNA methylation profiles of three human colon cancer samples and matched controls. Under the best set of parameters, AKSmooth-curated data showed high concordance with the gold standard high-coverage sample (Pearson 0.90), outperforming the popular analogous method. In addition, AKSmooth showed computational efficiency with runtime benchmark over 4.5 times better than the reference tool. To summarize, AKSmooth is a simple and efficient tool that can provide an accurate human colon methylome estimation profile from low-coverage WGBS data. The proposed method is implemented in R and is available at https://github.com/Junfang/AKSmooth .

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

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Martin Vincent ◽  
Kamilla Mundbjerg ◽  
Jakob Skou Pedersen ◽  
Gangning Liang ◽  
Peter A. Jones ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Statistical Inference ◽  
Bisulfite Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Whole Genome Bisulfite Sequencing ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data

scholarly journals Identification of diagnostic biomarkers in patients with gestational diabetes mellitus based on transcriptome gene expression and methylation correlation analysis

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Enchun Li ◽  
Tengfei Luo ◽  
Yingjun Wang
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Dna Methylation ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Enrichment Analysis ◽  
Data Sets ◽  
Hub Genes ◽  
Promoter Regions ◽  
The Relationship

Abstract Background Gestational diabetes mellitus (GDM) has a high prevalence in the period of pregnancy. However, the lack of gold standards in current screening and diagnostic methods posed the biggest limitation. Regulation of gene expression caused by DNA methylation plays an important role in metabolic diseases. In this study, we aimed to screen GDM diagnostic markers, and establish a diagnostic model for predicting GDM. Methods First, we acquired data of DNA methylation and gene expression in GDM samples (N = 41) and normal samples (N = 41) from the Gene Expression Omnibus (GEO) database. After pre-processing the data, linear models were used to identify differentially expressed genes (DEGs). Then we performed pathway enrichment analysis to extract relationships among genes from pathways, construct pathway networks, and further analyzed the relationship between gene expression and methylation of promoter regions. We screened for genes which are significantly negatively correlated with methylation and established mRNA-mRNA-CpGs network. The network topology was further analyzed to screen hub genes which were recognized as robust GDM biomarkers. Finally, the samples were randomly divided into training set (N = 28) and internal verification set (N = 27), and the support vector machine (SVM) ten-fold cross-validation method was used to establish a diagnostic classifier, which verified on internal and external data sets. Results In this study, we identified 465 significant DEGs. Functional enrichment analysis revealed that these genes were associated with Type I diabetes mellitus and immunization. And we constructed an interactional network including 1091 genes by using the regulatory relationships of all 30 enriched pathways. 184 epigenetics regulated genes were screened by analyzing the relationship between gene expression and promoter regions’ methylation in the network. Moreover, the accuracy rate in the training data set was increased up to 96.3, and 82.1% in the internal validation set, and 97.3% in external validation data sets after establishing diagnostic classifiers which were performed by analyzing the gene expression profiles of obtained 10 hub genes from this network, combined with SVM. Conclusions This study provided new features for the diagnosis of GDM and may contribute to the diagnosis and personalized treatment of GDM.

scholarly journals Analyzing whole genome bisulfite sequencing data from highly divergent genotypes

2019 ◽  
Vol 47 (19) ◽  
pp. e117-e117 ◽  
Author(s):  
Phillip Wulfridge ◽  
Ben Langmead ◽  
Andrew P Feinberg ◽  
Kasper D Hansen
Keyword(s):  
Dna Methylation ◽  
Genetic Variation ◽  
Bisulfite Sequencing ◽  
Joint Analysis ◽  
Whole Genome ◽  
Sequencing Data ◽  
Whole Genome Bisulfite Sequencing ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Personal Genomes

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.

scholarly journals Analyzing whole genome bisulfite sequencing data from highly divergent genotypes

2016 ◽  
Author(s):  
Phillip Wulfridge ◽  
Ben Langmead ◽  
Andrew P. Feinberg ◽  
Kasper D. Hansen
Keyword(s):  
Dna Methylation ◽  
Genetic Variation ◽  
Bisulfite Sequencing ◽  
Inbred Mouse ◽  
Mouse Strains ◽  
Whole Genome ◽  
Sequencing Data ◽  
Whole Genome Bisulfite Sequencing ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data

AbstractIn 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 inbred mouse strains to study this problem. We find that while the large number of strain-specific CpGs necessitates considerations regarding the reference genomes used during alignment, properties such as CpG density are surprisingly conserved across the genome. We introduce a method for including strain-specific CpGs in differential analysis, and show that accounting for strain-specific CpGs increases the power to find differentially methylated regions between the strains. Our method uses smoothing to impute methylation levels at strain-specific sites, thereby allowing strain-specific CpGs to contribute to the analysis, and also allowing us to account for differences in the spatial occurrences of CpGs. Our results have implications for analysis of genetic variation and DNA methylation using bisulfite-converted DNA.

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