scholarly journals Correcting Methylation Calls in Clinically Relevant Low-Mappability Regions

2021 ◽  
Author(s):  
Caiden M Kumar ◽  
Devon P. Ryan ◽  
Bradley W Langhorst
Keyword(s):  
Dna Methylation ◽  
Methylation Status ◽  
Biological Functions ◽  
Improved Method ◽  
Sequencing Data ◽  
Data Set ◽  
Detection And Diagnosis ◽  
New Feature ◽  
Effective Use ◽  
Genomic Methylation

DNA methylation is an important component in vital biological functions such as embryonic development, carcinogenesis, and heritable regulation. Accurate methods to assess genomic methylation status are crucial to its effective use in many scenarios, especially in the detection and diagnosis of disease. Methylation aligners, such as Bismark and bwa-meth, frequently assign significantly higher MapQ values than can be supported by the uniqueness of the region reads are mapped to. These incorrectly high MapQs result in inappropriate methylation calling in repetitive regions. We observe reads that should map to separate locations (possibly having different methylation states) actually end up mapping to the same locus, causing apparent mixed methylation at such loci. Methylation calling can be improved by using Bismap mappability data to filter out insufficiently unique reads. However, simply filtering out Cs in insufficiently unique regions is not adequate as it is prone to over-filtering Cs in small mappability dips. These Cs can in fact often be called using reads anchored in a nearby mappable region. We have created a new feature for the MethylDackel methylation caller to perform read-based filtering. This new methylation calling method resolves some of the apparent mixed methylation to either 0% or 100% methylation and removes many unsupportable methylation calls. We examined methylation calls with and without read-based filtering in or near the 7830 genes containing ClinVar variants in a methylation sequencing data set from the NA12878 cell line. Use of this improved method corrected 41,143 mixed methylation Cs to 0% methylation, and 22,345 to 100% methylation throughout the genome.

A smoothed EM-algorithm for DNA methylation profiles from sequencing-based methods in cell lines or for a single cell type

2017 ◽  
Vol 16 (5-6) ◽  
Author(s):  
Lajmi Lakhal-Chaieb ◽  
Celia M.T. Greenwood ◽  
Mohamed Ouhourane ◽  
Kaiqiong Zhao ◽  
Belkacem Abdous ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Em Algorithm ◽  
Single Cell ◽  
Cell Lines ◽  
Methylation Status ◽  
Embryonic Stem ◽  
Cell Type ◽  
Data Set ◽  
Cpg Sites ◽  
Single Cell Type

AbstractWe consider the assessment of DNA methylation profiles for sequencing-derived data from a single cell type or from cell lines. We derive a kernel smoothed EM-algorithm, capable of analyzing an entire chromosome at once, and to simultaneously correct for experimental errors arising from either the pre-treatment steps or from the sequencing stage and to take into account spatial correlations between DNA methylation profiles at neighbouring CpG sites. The outcomes of our algorithm are then used to (i) call the true methylation status at each CpG site, (ii) provide accurate smoothed estimates of DNA methylation levels, and (iii) detect differentially methylated regions. Simulations show that the proposed methodology outperforms existing analysis methods that either ignore the correlation between DNA methylation profiles at neighbouring CpG sites or do not correct for errors. The use of the proposed inference procedure is illustrated through the analysis of a publicly available data set from a cell line of induced pluripotent H9 human embryonic stem cells and also a data set where methylation measures were obtained for a small genomic region in three different immune cell types separated from whole blood.

scholarly journals Editing DNA Methylation in Mammalian Embryos

2020 ◽  
Vol 21 (2) ◽  
pp. 637 ◽  
Author(s):  
Taiga Yamazaki ◽  
Yu Hatano ◽  
Ryoya Taniguchi ◽  
Noritada Kobayashi ◽  
Kazuo Yamagata
Keyword(s):  
Dna Methylation ◽  
Transcriptional Regulation ◽  
Gene Knockout ◽  
Methylation Status ◽  
Dna Methyltransferases ◽  
Biological Functions ◽  
Epigenome Editing ◽  
Early Embryos ◽  
Mammalian Embryos ◽  
Genomic Regions

DNA methylation in mammals is essential for numerous biological functions, such as ensuring chromosomal stability, genomic imprinting, and X-chromosome inactivation through transcriptional regulation. Gene knockout of DNA methyltransferases and demethylation enzymes has made significant contributions to analyzing the functions of DNA methylation in development. By applying epigenome editing, it is now possible to manipulate DNA methylation in specific genomic regions and to understand the functions of these modifications. In this review, we first describe recent DNA methylation editing technology. We then focused on changes in DNA methylation status during mammalian gametogenesis and preimplantation development, and have discussed the implications of applying this technology to early embryos.

scholarly journals A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data

2015 ◽  
Author(s):  
Amanda J Lea ◽  
Jenny Tung ◽  
Xiang Zhou
Keyword(s):  
Dna Methylation ◽  
Count Data ◽  
Mixed Model ◽  
Bisulfite Sequencing ◽  
Structured Populations ◽  
P Value ◽  
Sequencing Data ◽  
Data Set ◽  
Age Related ◽  
Bisulfite Sequencing Data

Identifying sources of variation in DNA methylation levels is important for understanding gene regulation. Recently, bisulfite sequencing has become a popular tool for investigating DNA methylation levels. However, modeling bisulfite sequencing data is complicated by dramatic variation in coverage across sites and individual samples, and because of the computational challenges of controlling for genetic covariance in count data. To address these challenges, we present a binomial mixed model and an efficient, sampling-based algorithm (MACAU: Mixed model association for count data via data augmentation) for approximate parameter estimation and p-value computation. This framework allows us to simultaneously account for both the over-dispersed, count-based nature of bisulfite sequencing data, as well as genetic relatedness among individuals. Using simulations and two real data sets (whole genome bisulfite sequencing (WGBS) data from Arabidopsis thaliana and reduced representation bisulfite sequencing (RRBS) data from baboons), we show that our method provides well-calibrated test statistics in the presence of population structure. Further, it improves power to detect differentially methylated sites: in the RRBS data set, MACAU detected 1.6-fold more age-associated CpG sites than a beta-binomial model (the next best approach). Changes in these sites are consistent with known age-related shifts in DNA methylation levels, and are enriched near genes that are differentially expressed with age in the same population. Taken together, our results indicate that MACAU is an efficient, effective tool for analyzing bisulfite sequencing data, with particular salience to analyses of structured populations. MACAU is freely available at www.xzlab.org/software.html.

scholarly journals DNA methylation analysis in plants: review of computational tools and future perspectives

2019 ◽  
Vol 21 (3) ◽  
pp. 906-918 ◽  
Author(s):  
Jimmy Omony ◽  
Thomas Nussbaumer ◽  
Ruben Gutzat
Keyword(s):  
Dna Methylation ◽  
Methylation Status ◽  
Computational Time ◽  
Methylation Data ◽  
Sequencing Data ◽  
Model Species ◽  
Computational Tools ◽  
Bisulfite Sequencing Data ◽  
New Varieties ◽  
Regulatory Decisions

Abstract Genome-wide DNA methylation studies have quickly expanded due to advances in next-generation sequencing techniques along with a wealth of computational tools to analyze the data. Most of our knowledge about DNA methylation profiles, epigenetic heritability and the function of DNA methylation in plants derives from the model species Arabidopsis thaliana. There are increasingly many studies on DNA methylation in plants—uncovering methylation profiles and explaining variations in different plant tissues. Additionally, DNA methylation comparisons of different plant tissue types and dynamics during development processes are only slowly emerging but are crucial for understanding developmental and regulatory decisions. Translating this knowledge from plant model species to commercial crops could allow the establishment of new varieties with increased stress resilience and improved yield. In this review, we provide an overview of the most commonly applied bioinformatics tools for the analysis of DNA methylation data (particularly bisulfite sequencing data). The performances of a selection of the tools are analyzed for computational time and agreement in predicted methylated sites for A. thaliana, which has a smaller genome compared to the hexaploid bread wheat. The performance of the tools was benchmarked on five plant genomes. We give examples of applications of DNA methylation data analysis in crops (with a focus on cereals) and an outlook for future developments for DNA methylation status manipulations and data integration.

scholarly journals Erasure of DNA methylation, genomic imprints, and epimutations in a primordial germ-cell model derived from mouse pluripotent stem cells

2016 ◽  
Vol 113 (34) ◽  
pp. 9545-9550 ◽  
Author(s):  
Norikatsu Miyoshi ◽  
Jente M. Stel ◽  
Keiko Shioda ◽  
Na Qu ◽  
Junko Odajima ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Stem Cells ◽  
Pluripotent Stem Cells ◽  
Cell Model ◽  
Methylation Status ◽  
Primordial Germ Cell ◽  
Dna Demethylation ◽  
Imprinted Genes ◽  
Sequencing Data ◽  
Genome Wide

The genome-wide depletion of 5-methylcytosines (5meCs) caused by passive dilution through DNA synthesis without daughter strand methylation and active enzymatic processes resulting in replacement of 5meCs with unmethylated cytosines is a hallmark of primordial germ cells (PGCs). Although recent studies have shown that in vitro differentiation of pluripotent stem cells (PSCs) to PGC-like cells (PGCLCs) mimics the in vivo differentiation of epiblast cells to PGCs, how DNA methylation status of PGCLCs resembles the dynamics of 5meC erasure in embryonic PGCs remains controversial. Here, by differential detection of genome-wide 5meC and 5-hydroxymethylcytosine (5hmeC) distributions by deep sequencing, we show that PGCLCs derived from mouse PSCs recapitulated the process of genome-wide DNA demethylation in embryonic PGCs, including significant demethylation of imprint control regions (ICRs) associated with increased mRNA expression of the corresponding imprinted genes. Although 5hmeCs were also significantly diminished in PGCLCs, they retained greater amounts of 5hmeCs than intragonadal PGCs. The genomes of both PGCLCs and PGCs selectively retained both 5meCs and 5hmeCs at a small number of repeat sequences such as GSAT_MM, of which the significant retention of bisulfite-resistant cytosines was corroborated by reanalysis of previously published whole-genome bisulfite sequencing data for intragonadal PGCs. PSCs harboring abnormal hypermethylation at ICRs of the Dlk1-Gtl2-Dio3 imprinting cluster diminished these 5meCs upon differentiation to PGCLCs, resulting in transcriptional reactivation of the Gtl2 gene. These observations support the usefulness of PGCLCs in studying the germline epigenetic erasure including imprinted genes, epimutations, and erasure-resistant loci, which may be involved in transgenerational epigenetic inheritance.

DNA Methylation Status of Mash2 in Lungs of Somatic Cell Cloning Bovines*

2010 ◽  
Vol 37 (9) ◽  
pp. 960-966 ◽  
Author(s):  
Jie CHEN ◽  
Dong-Jie LI ◽  
Cui ZHANG ◽  
Ning LI ◽  
Shi-Jie LI
Keyword(s):  
Dna Methylation ◽  
Somatic Cell ◽  
Methylation Status ◽  
Cell Cloning

scholarly journals Epigenetic Changes During Mechanically Induced Osteogenic Lineage Commitment

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Julia C. Chen ◽  
Mardonn Chua ◽  
Raymond B. Bellon ◽  
Christopher R. Jacobs
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Shear Stress ◽  
Fluid Shear Stress ◽  
Methylation Status ◽  
Lineage Commitment ◽  
Fluid Shear ◽  
Osteogenic Lineage ◽  
Osteogenic Markers ◽  
Heterochromatin Structure

Osteogenic lineage commitment is often evaluated by analyzing gene expression. However, many genes are transiently expressed during differentiation. The availability of genes for expression is influenced by epigenetic state, which affects the heterochromatin structure. DNA methylation, a form of epigenetic regulation, is stable and heritable. Therefore, analyzing methylation status may be less temporally dependent and more informative for evaluating lineage commitment. Here we analyzed the effect of mechanical stimulation on osteogenic differentiation by applying fluid shear stress for 24 hr to osteocytes and then applying the osteocyte-conditioned medium (CM) to progenitor cells. We analyzed gene expression and changes in DNA methylation after 24 hr of exposure to the CM using quantitative real-time polymerase chain reaction and bisulfite sequencing. With fluid shear stress stimulation, methylation decreased for both adipogenic and osteogenic markers, which typically increases availability of genes for expression. After only 24 hr of exposure to CM, we also observed increases in expression of later osteogenic markers that are typically observed to increase after seven days or more with biochemical induction. However, we observed a decrease or no change in early osteogenic markers and decreases in adipogenic gene expression. Treatment of a demethylating agent produced an increase in all genes. The results indicate that fluid shear stress stimulation rapidly promotes the availability of genes for expression, but also specifically increases gene expression of later osteogenic markers.

scholarly journals DNA methylation differs extensively between strains of the same geographical origin and changes with age in Daphnia magna

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jack Hearn ◽  
Fiona Plenderleith ◽  
Tom J. Little
Keyword(s):  
Dna Methylation ◽  
Caloric Restriction ◽  
Daphnia Magna ◽  
Single Gene ◽  
Methylation Status ◽  
Strain Differences ◽  
Life History Trait ◽  
Food Level ◽  
Epigenetic Clock ◽  
Age Related

Abstract Background Patterns of methylation influence lifespan, but methylation and lifespan may also depend on diet, or differ between genotypes. Prior to this study, interactions between diet and genotype have not been explored together to determine their influence on methylation. The invertebrate Daphnia magna is an excellent choice for testing the epigenetic response to the environment: parthenogenetic offspring are identical to their siblings (making for powerful genetic comparisons), they are relatively short lived and have well-characterised inter-strain life-history trait differences. We performed a survival analysis in response to caloric restriction and then undertook a 47-replicate experiment testing the DNA methylation response to ageing and caloric restriction of two strains of D. magna. Results Methylated cytosines (CpGs) were most prevalent in exons two to five of gene bodies. One strain exhibited a significantly increased lifespan in response to caloric restriction, but there was no effect of food-level CpG methylation status. Inter-strain differences dominated the methylation experiment with over 15,000 differently methylated CpGs. One gene, Me31b, was hypermethylated extensively in one strain and is a key regulator of embryonic expression. Sixty-one CpGs were differentially methylated between young and old individuals, including multiple CpGs within the histone H3 gene, which were hypermethylated in old individuals. Across all age-related CpGs, we identified a set that are highly correlated with chronological age. Conclusions Methylated cytosines are concentrated in early exons of gene sequences indicative of a directed, non-random, process despite the low overall DNA methylation percentage in this species. We identify no effect of caloric restriction on DNA methylation, contrary to our previous results, and established impacts of caloric restriction on phenotype and gene expression. We propose our approach here is more robust in invertebrates given genome-wide CpG distributions. For both strain and ageing, a single gene emerges as differentially methylated that for each factor could have widespread phenotypic effects. Our data showed the potential for an epigenetic clock at a subset of age positions, which is exciting but requires confirmation.

scholarly journals DNA methylome signatures of prenatal exposure to synthetic glucocorticoids in hippocampus and peripheral whole blood of female guinea pigs in early life

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aya Sasaki ◽  
Margaret E. Eng ◽  
Abigail H. Lee ◽  
Alisa Kostaki ◽  
Stephen G. Matthews
Keyword(s):  
Dna Methylation ◽  
Whole Blood ◽  
Guinea Pigs ◽  
Critical Role ◽  
Methylation Status ◽  
Peripheral Tissues ◽  
Epigenetic Biomarkers ◽  
Differentially Methylated Genes ◽  
Increased Risk ◽  
Synthetic Glucocorticoids

AbstractSynthetic glucocorticoids (sGC) are administered to women at risk of preterm delivery, approximately 10% of all pregnancies. In animal models, offspring exposed to elevated glucocorticoids, either by administration of sGC or endogenous glucocorticoids as a result of maternal stress, show increased risk of developing behavioral, endocrine, and metabolic dysregulation. DNA methylation may play a critical role in long-lasting programming of gene regulation underlying these phenotypes. However, peripheral tissues such as blood are often the only accessible source of DNA for epigenetic analyses in humans. Here, we examined the hypothesis that prenatal sGC administration alters DNA methylation signatures in guinea pig offspring hippocampus and whole blood. We compared these signatures across the two tissue types to assess epigenetic biomarkers of common molecular pathways affected by sGC exposure. Guinea pigs were treated with sGC or saline in late gestation. Genome-wide modifications of DNA methylation were analyzed at single nucleotide resolution using reduced representation bisulfite sequencing in juvenile female offspring. Results indicate that there are tissue-specific as well as common methylation signatures of prenatal sGC exposure. Over 90% of the common methylation signatures associated with sGC exposure showed the same directionality of change in methylation. Among differentially methylated genes, 134 were modified in both hippocampus and blood, of which 61 showed methylation changes at identical CpG sites. Gene pathway analyses indicated that prenatal sGC exposure alters the methylation status of gene clusters involved in brain development. These data indicate concordance across tissues of epigenetic programming in response to alterations in glucocorticoid signaling.

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