DNA methylation, one of the most important epigenetic modifications, plays a crucial role in various biological processes. The level of DNA methylation can be measured using whole-genome bisulfite sequencing at single base resolution. However, until now, there is a paucity of publicly available software for carrying out integrated methylation data analysis. In this study, we implemented Methy-Pipe, which not only fulfills the core data analysis requirements (e.g. sequence alignment, differential methylation analysis, etc.) but also provides useful tools for methylation data annotation and visualization. Specifically, it uses Burrow-Wheeler Transform (BWT) algorithm to directly align bisulfite sequencing reads to a reference genome and implements a novel sliding window based approach with statistical methods for the identification of differentially methylated regions (DMRs). The capability of processing data parallelly allows it to outperform a number of other bisulfite alignment software packages. To demonstrate its utility and performance, we applied it to both real and simulated bisulfite sequencing datasets. The results indicate that Methy-Pipe can accurately estimate methylation densities, identify DMRs and provide a variety of utility programs for downstream methylation data analysis. In summary, Methy-Pipe is a useful pipeline that can process whole genome bisulfite sequencing data in an efficient, accurate, and user-friendly manner. Software and test dataset are available at http://sunlab.lihs.cuhk.edu.hk/methy-pipe/.

Epigenetic mechanisms may be important for a native species' response to rapid environmental change. Red Imported Fire Ants (Solenopsis invicta Santschi, 1916) were recently introduced to areas occupied by the Eastern Fence Lizard (Sceloporus undulatus Bosc & Daudin, 1801). Behavioral, morphological, and physiological phenotypes of the Eastern Fence Lizard have changed following invasion, creating a natural biological system to investigate environmentally induced epigenetic changes. We tested for variation in DNA methylation patterns in Eastern Fence Lizard populations associated with different histories of invasion by Red Imported Fire Ants. At MS-AFLP loci, we detected a higher diversity of methylation in Eastern Fence Lizard populations from Fire Ant uninvaded versus invaded sites, and uninvaded sites had higher methylation. Our results suggest that invasive species may alter methylation frequencies and the pattern of methylation among native individuals. While our data indicate a high level of intrinsic variability in DNA methylation, DNA methylation at some genomic loci may underlie observed phenotypic changes in Eastern Fence Lizard populations in response to invasion of Red Imported Fire Ants. This process may be important in facilitating adaptation of native species to novel pressures imposed by a rapidly changing environment.

Variation in the presence or absence of transposable elements (TEs) is a major source of genetic variation between individuals. Here, we identified 23,095 TE presence/absence variants between 216 Arabidopsis accessions. Most TE variants were rare, and we find these rare variants associated with local extremes of gene expression and DNA methylation levels within the population. Of the common alleles identified, two thirds were not in linkage disequilibrium with nearby SNPs, implicating these variants as a source of novel genetic diversity. Many common TE variants were associated with significantly altered expression of nearby genes, and a major fraction of inter-accession DNA methylation differences were associated with nearby TE insertions. Overall, this demonstrates that TE variants are a rich source of genetic diversity that likely plays an important role in facilitating epigenomic and transcriptional differences between individuals, and indicates a strong genetic basis for epigenetic variation.

Epigenetic changes can provide a pathway for organisms to respond to local environmental conditions by influencing gene expression. However, we still know little about the spatial distribution of epigenetic variation in natural systems, how it relates to the distribution of genetic variation and the environmental structure of the landscape, and the processes that generate and maintain it. Studies examining spatial patterns of genetic and epigenetic variation can provide valuable insights into how ecological and population processes contribute to epigenetic divergence across heterogeneous landscapes. Here, we perform a comparative analysis of spatial genetic and epigenetic variation based on 8,459 single nucleotide polymorphisms (SNPs) and 8,580 single methylation variants (SMVs) from eight populations of the Puerto Rican crested anole, Anolis cristatellus, an abundant lizard in the adaptive radiations of anoles on the Greater Antilles that occupies a diverse range of habitats. Using generalized dissimilarity modeling and multiple matrix regression, we found that genome-wide epigenetic differentiation is strongly correlated with environmental divergence, even after controlling for the underlying genetic structure. We also detected significant associations between key environmental variables and 96 SMVs, including 42 located in promoter regions or gene bodies. Our results suggest an environmental basis for population-level epigenetic differentiation in this system and contribute to better understanding how environmental gradients structure epigenetic variation in nature.

Functional Annotation of Variants - Online Resource (FAVOR, https://favor.genohub.org) is a comprehensive whole-genome variant annotation database and a variant browser, providing hundreds of functional annotation scores from a variety of aspects of variant biological function. This FAVOR Essential Database (aGDS Format) is comprised of a collection of essential annotation scores for all possible SNVs (8,812,917,339) and observed indels (79,997,898) in Build GRCh38/hg38, including variant info, chromosome, position, reference allele, alternative allele, aPC-Conservation, aPC-Epigenetics, aPC-Epigenetics-Active, aPC-Epigenetics-Repressed, aPC-Epigenetics-Transcription, aPC-Local-Nucleotide-Diversity, aPC-Mappability, aPC-Mutation-Density, aPC-Protein-Function, aPC-Proximity-To-TSSTES, aPC-Transcription-Factor, CAGE promoter, CAGE, MetaSVM, rsID, FATHMM-XF, Gencode Comprehensive Category, Gencode Comprehensive Info, Gencode Comprehensive Exonic Category, Gencode Comprehensive Exonic Info, GeneHancer, LINSIGHT, CADD, rDHS. These annotation scores are stored in annotated Genomic Data Structure (aGDS) file format (without genotype data) to support fast query and retrieval at variant-level. The aGDS file can then facilitate a wide range of functionally-informed downstream analyses.

AlbaladejoRG_PistaciaZip file contains each of the datasets that accompany: Albaladejo et al. 2019 (https://doi.org/10.1007/s11295-019-1325-x) Linking DNA methylation with performance in a woody plant species. See README for variable definitions.

Motivation Bisulfite sequencing data carry invaluable information about epigenetic states of a cell population beyond DNA methylation levels. Phased DNA methylation states (DNA methylation pattern; i.e., an array of DNA methylation states of CpGs simultaneously covered by a single read) can serve as a local barcode representing the epigenetic state of a single cell. Therefore we can compute approximate epigenetic diversity through measuring the diversity of DNA methylation patterns (inter-molecule / inter-cellular heterogeneity). On the other hand, DNA methylation patterns also inform us of the local disorder of DNA methylation states, which already have been shown to have prognostic potential (Landau et al., 2014). To facilitate studies on such concept of DNA methylation heterogeneity, we developed an efficient software named Metheor and here provide a comprehensive DNA methylation profiles of 928 cancer cell lines from cancer cell line encyclopedia (CCLE) computed by Metheor. Data processing Raw reduced representation bisulfite sequencing (RRBS) reads for 928 CCLE cell lines were downloaded under SRA study accession SRP186687, and preprocessed using Trim Galore! v0.6.7 with --rrbs option. Reads were then aligned to hg38 reference genome using Bismark v0.23.1. The resulting alignments are used to compute DNA methylation heterogeneity levels (see below) through Metheor v0.1.0. Seven measures for DNA methylation heterogeneity Profiles of seven DNA methylation heterogeneity measures are provided in this dataset.

Proportion of discordant reads (PDR) Local pairwise methylation disorder (LPMD) Methylation haplotype load (MHL) Epipolymorphism (PM) Methylation entropy (ME) Fraction of discordant read pairs (FDRP) Quantitative fraction of discordant pairs (qFDRP)

For a more detailed description of those measures, please refer to this GitHub repository. Data tables We provide 7 tables for DNA methylation heterogeneity profiles and an additional table that contains the average methylation level information.

ccle.pdr.csv: Table for average proportion of discordant reads (PDR) for various genomic contexts ccle.lpmd.csv:Table for average local pairwise methylation disorder (LPMD) for various genomic contexts ccle.mhl.csv: Table for average methylation haplotype load (MHL) for various genomic contexts ccle.pm.csv: Table for average epipolymorphism (PM) for various genomic contexts ccle.me.csv: Table for average methylation entropy (ME) for various genomic contexts. ccle.fdrp.csv: Table for average FDRP levels for various genomic contexts. ccle.qfdrp.csv: Table for average qFDRP levels for various genomic contexts. ccle.beta.csv: Table for average DNA methylation levels for various genomic contexts.

Schema for data tables All data tables are in comma-separated values (csv) format sharing the following columns:

cell_line_name: Identifier for the cell line. run_accession: SRA run accession of the corresponding RRBS data. tissue: Tissue collection site. disease: Full disease type (e.g., carcinoma (ductal carcinoma), carcinoma (squamous_cell_carcinoma), or lymphoid_noeplasm (Hodgkin_lymphoma)) disease_primary: General disease type (e.g., carcinoma or lymphoid_neoplasm). disease_secondary: Specific disease type (e.g., ductal carcinoma, squamous_cell_carcinoma or Hodgkin_lymphoma). disease_stage: Indicates whether tissue sample is from primary or metastatic site. age_at_sampling: Age of tissue donor at sampling if known. Otherwise, values are left empty. sex: Sex of tissue donor if known. Otherwise, values are left empty. ethnicity: Ethnicity of tissue donor if known. Otherwise, values are left empty. genomewide: Genomewide average DNA methylation heterogeneity levels. promoter: Average DNA methylation heterogeneity levels at promoters of protein-coding genes. cgi: Average DNA methylation heterogeneity levels at CpG islands. Annotations were downloaded from UCSC table browser. cpg_shore: Average DNA methylation heterogeneity levels at CpG shores. CpG shores are defined as 2kb regions flanking upstream or downstream of CpG islands. Regions overlapping CpG islands were excluded. cpg_shelf: Average DNA methylation heterogeneity levels at CpG shelves. CpG shelves are defined as 2kb regions flanking upstream or downstream of (CpG island + CpG shore) regions. Regions overlapping CpG islands or shores were excluded. methylation_canyon: Average DNA methylation heterogeneity levels at methylation canyons. DNA methylation canyons are defined as broad (> 3.5kb) under-methylated regions (Jeong et al., 2014), and their hg38 annotations were downloaded from (Su et al., 2018). exon: Average DNA methylation heterogeneity levels at exons of protein coding genes. intron: Average DNA methylation heterogeneity levels at introns of protein coding genes. gene_body: Average DNA methylation heterogeneity levels at gene bodies of protein coding genes. LINE: Average DNA methylation heterogeneity levels at LINEs. Annotations were downloaded from UCSC table browser (hg38, Repeats-RepeatMasker). SINE: Average DNA methylation heterogeneity levels at SINEs LTR: Average DNA methylation heterogeneity levels at LTR retrotransposons

Availability of Metheor The source code for Metheor can be found at https://github.com/dohlee/metheor You can install Metheor using conda at commandline: $ conda install -c dohlee metheor

Apis_mellifera_amplicon_bisulfite_sequencing_queen_2Amplicon bisulfite sequencing reads from Apis mellifera newly-emerged queen brains (5 pooled brains). Archive contains four Illumina Miseq libraries (paired end).Apis_mellifera_amplicon_bisulfite_sequencing_queen_3Amplicon bisulfite sequencing reads from Apis mellifera newly-emerged queen brains (5 pooled brains). Archive contains four Illumina Miseq libraries (paired end).queen3.tarApis_mellifera_amplicon_bisulfite_sequencing_worker_1Amplicon bisulfite sequencing reads from Apis mellifera newly-emerged worker brains (5 pooled brains). Archive contains four Illumina Miseq libraries (paired end).worker1.tarApis_mellifera_amplicon_bisulfite_sequencing_worker_2Amplicon bisulfite sequencing reads from Apis mellifera newly-emerged worker brains (5 pooled brains). Archive contains four Illumina Miseq libraries (paired end).worker2.tarApis_mellifera_amplicon_bisulfite_sequencing_worker_3Amplicon bisulfite sequencing reads from Apis mellifera ...

Museum genomics has transformed the field of collections-based research, opening up a range of new research directions for paleontological specimens as well as natural history specimens collected over the past few centuries. Recent work demonstrates that it is possible to characterize epigenetic markers such as DNA methylation in well preserved ancient tissues. This approach has not yet been tested in traditionally prepared natural history specimens such as dried bones and skins, the most common specimen types in vertebrate collections. In this study, we developed and tested methods to characterize cytosine methylation in dried skulls up to 76 years old. Using a combination of ddRAD and bisulphite treatment, we characterized patterns of cytosine methylation in two species of deer mouse (Peromyscus spp.) collected in the same region in Michigan in 1940, 2003, and 2013–2016. We successfully estimated methylation in specimens of all age groups, although older specimens yielded less data and showed greater interindividual variation in data yield than newer specimens. Global methylation estimates were reduced in the oldest specimens (76 years old) relative to the newest specimens (1–3 years old), which may reflect post-mortem hydrolytic deamination. Methylation was reduced in promoter regions relative to gene bodies and showed greater bimodality in autosomes relative to female X chromosomes, consistent with expectations for methylation in mammalian somatic cells. Our work demonstrates the utility of historic specimens for methylation analyses, as with genomic analyses; however, studies will need to accommodate the large variance in the quantity of data produced by older specimens.

Database that provides a resource to store DNA methylation data and to make these data readily available to the public. Future development of the database will focus on environmental effects on DNA methylation. No restriction applies on the type of data, i.e. as well as global estimations (e.g. HPLC) as data from high resolution analysis (i.e. sequencing) can be stored. As much background information as possible should be provided by the users. This includes the origin of the sample, phenotype, expression of the related gene, etc..

Epigenetic changes associated with early life conditions are known to play a significant role in shaping the adult phenotype. Studies of DNA methylation in wild animals are lacking, yet are important for understanding the fitness consequences of environmentally induced epigenetic change. In our study, we quantified variation in DNA methylation in wild, post-hatch zebra finches Taeniopygia guttata developing at seasonally variable temperatures in the Australian desert. We also compared variation in DNA methylation among captive zebra finch siblings raised in temperature controlled ‘hot’ or ‘cool’ rooms. We detected an increase in genome-wide levels of DNA methylation between day 3 and day 11 of post hatch development in wild zebra finches. In the wild, ambient temperatures were also found to affect genome-wide levels of DNA methylation and plasticity in the methylation state of individual loci. Family effects had a significant influence on DNA methylation throughout our study, and while we did not detect an effect of temperature on DNA methylation levels in non-related captive birds, our sibling pair analyses revealed that within families, elevated temperatures were associated with higher levels of DNA methylation. Our findings suggest a wide window in early development during which climatically induced variation in DNA methylation could occur. Further work is necessary to understand the potential for such variation to promote ecologically relevant variation in wild birds.

Methods See Methods section of paper for more details.

Blood samples were collected from zebra finch nestlings at day 3 and day 11 of development. DNA was then extracted and screened for variation in DNA methylation using MS-AFLP. We used peakscanner and rawgeno to analyse the data. We calculated percent DNA methylation and the extent of DNA-methylation state change across developemnt. We assessed variation in DNA methylation using R 3.6.1.

Human and animal studies have shown that exposure to the organochlorine pesticide dieldrin is associated with increased risk of Parkinson's disease (PD). Despite previous work showing a link between developmental dieldrin exposure and increased neuronal susceptibility to MPTP toxicity in male C57BL/6 mice, the mechanism mediating this effect has not been identified. Here, we tested the hypothesis that developmental exposure to dieldrin increases neuronal susceptibility via genome-wide changes in DNA methylation. Starting at 8 weeks of age and prior to mating, female C57BL/6 mice were exposed to 0.3 mg/kg dieldrin by feeding (every 3 days) throughout breeding, gestation, and lactation. At 12 weeks of age, pups were sacrificed and ventral mesencephalon, containing primarily substantia nigra, were microdissected. DNA was isolated and dieldrin-related changes in DNA methylation were assessed via reduced representation bisulfite sequencing (RRBS). We identified significant, sex-specific differentially methylated CpGs (DMCs) and regions (DMRs) by developmental dieldrin exposure (FDRNr4a2 and Lmx1b genes, which are involved in dopaminergic neuron development and maintenance. Developmental dieldrin exposure had distinct effects on the male and female epigenome. Together, our data suggest that developmental dieldrin exposure establishes sex-specific poised epigenetic states early in life. These poised epigenomes may mediate sensitivity to subsequent toxic stimuli and contribute to the development of late-life neurodegenerative disease, including PD.

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Epigenetics Market Size 2024-2028

The epigenetics market size is forecast to increase by USD 1.48 billion, at a CAGR of 14.75% between 2023 and 2028.

The market is experiencing significant growth due to the increasing applications of epigenetics in various non-oncology diseases. The advancements in medical technologies, such as cell and gene therapy, enzymes, and artificial intelligence (AI) and machine learning (ML), are catalysts driving market expansion. Precision medicine and healthcare services are also adopting epigenetics for the development of new treatments and diagnostics. However, the lack of clinical validation on direct-to-consumer genetic tests poses a challenge to market growth. In the realm of nutrition, epigenetics is being explored for its potential impact on insulin sensitivity and other health conditions. Epigenetic biomarkers, proteins, and solvents are being studied for their role in disease diagnosis and treatment. The use of AI and ML in epigenetics research is expected to accelerate the discovery of new epigenetic targets and therapies, further fueling market growth.

What will be the Size of the Epigenetics Market During the Forecast Period?

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The market encompasses a diverse range of assays and epigenetic products used to investigate the complex interplay between external stimuli and the control of genes. This market is driven by the growing recognition of epigenetic contributions to various ailments, including autoimmune diseases, cancers such as breast and cervical cancer, and chronic diseases caused by causative agents like cigarette smoke, heavy metals, and pesticides.
Epigenetic testing is increasingly utilized to identify epigenetic modifications associated with these conditions, providing valuable insights into disease mechanisms and potential therapeutic targets. DNA methylation, a prominent epigenetic pathway, is a particular area of focus due to its role in regulating gene expression and its association with various health issues. The market is expected to continue growing as research advances and the potential applications of epigenetic knowledge expand into areas such as cognitive function and reproductive problems.

How is this Epigenetics Industry segmented and which is the largest segment?

The epigenetics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Oncology
  Non-oncology


Product

  Reagents
  Kits
  Enzymes and others


Technology

  DNA Methylation
  Histone Methylation
  Histone Acetylation
  Large non-coding RNA
  MicroRNA modification
  Chromatin structures


End-Use

  Academic Research
  Clinical Research
  Hospitals & Clinics
  Pharmaceutical & Biotechnology Companies
  Other Users


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  Asia

    China
    India
    Japan
    South Korea
    Australia


  Rest of World (ROW)

By Application Insights

The oncology segment is estimated to witness significant growth during the forecast period.

Epigenetics, the study of heritable changes in gene expression without alteration of the DNA sequence, is gaining significant attention in the healthcare industry, particularly In the oncology segment. The rising prevalence of cancers, including breast, lung, liver, cervical, and oral cavity cancer, is driving the demand for epigenetic solutions. These diseases can be caused by various factors, such as genetics, lifestyle choices, and environmental toxins like cigarette smoke, heavy metals, pesticides, and radiation. Epigenetic processes, including DNA methylation, histone modification, and RNA regulation, play a crucial role In the development of these conditions. Epigenetic testing, using assays like methylation-specific PCR (MSP), helps identify abnormal methylation patterns in DNA, aiding In the diagnosis and treatment of cancer.

Epigenetic products, including kits and instruments, are essential for conducting epigenetic research and testing. The aging population, neurological disorders, and autoimmune diseases are other non-oncology conditions where epigenetics holds promise. Funding, approvals, affordability, and reproducibility are key considerations In the market. Advancements in technology, such as genome sequencing, artificial intelligence (AI), and machine learning (ML), are enhancing the capabilities of epigenetic research and diagnostics. However, challenges like cross-reactivity, inaccurate findings, inconsistent sensitivity, and batch-to-batch variations need to be addressed to ensure the reliability and accuracy of epigenetic markers.

Get a glance at the Epigenetics Industry report of share of various segments Request Free Sample

The oncology segment was

In addition to genetic differences between individuals due to nucleotide sequence variation, epigenetic changes experienced by genotypes due to DNA methylation may also contribute to population niche width, an intriguing possibility that remains essentially untested. Using the nectar-living yeast Metschnikowia reukaufii as study subject, we examine the hypothesis that changes in genome-wide DNA methylation patterns underly the ability of this fugitive species to exploit a broad resource range in its heterogeneous patchy environment. Data on floral nectar characteristics and use by M. reukaufii in the wild were combined with laboratory experiments and methylation sensitive amplified polymorphism (MSAP) analyses designed to detect epigenetic responses of single genotypes to variations in sugar environment that mimicked those occurring naturally in nectar. Metschnikowia reukaufii exploited a broad range of resources, occurring in nectar of 48% of species and 52% of families surveyed, and its host plants exhibited broad intra- and interspecific variation in sugar-related nectar features. Under experimental conditions, sugar composition, sugar concentration, and their interaction influenced significantly the mean probability of MSAP markers experiencing a transition from unmethylated to methylated state. The methylation inhibitor 5-Azacytidine had strong inhibitory effects on M. reukaufii proliferation in sugar-containing media, and a direct relationship existed across sugar x concentration experimental levels linking inhibitory effect of 5-Azacytidine and mean per-marker probability of genome-wide methylation. DNA methylation polymorphisms induced by variable sugar environments allowed genotypes to grow successfully in extreme sugar environments, and the broad population niche width of M. reukaufii was largely made possible by environmentally-induced epigenetic changes enabling genotype plasticity in resource use.

In mammalian societies, dominance hierarchies translate into inequalities in health, reproductive performance and survival. DNA methylation is thought to mediate the effects of social status on gene expression and phenotypic outcomes, yet, a study of social status-specific DNA methylation profiles in different age classes in a wild social mammal is missing. We tested for social status signatures in DNA methylation profiles in wild female spotted hyenas (Crocuta crocuta) in cubs and adults using non-invasively collected gut epithelium samples. In spotted hyena clans, female social status influences access to resources, foraging behaviour, health, reproductive performance and survival. We identified 149 differentially methylated regions between 42 high- and low-ranking spotted hyena females (cubs and adults). Differentially methylated genes were associated with energy conversion, immune function and glutamate receptor signalling. Our results provide evidence that socio-environmental inequ..., In the Serengeti National Park in Tanzania, we collected gut epithelium cells from faecal samples of 42 free-ranging female hyenas (24 cubs and 18 adults) of either low (15 cubs, 9 adults) or high (9 cubs, 9 adults) social status. In spotted hyenas, social status is determined by the animals’ interactions within their social group (clan). We extracted DNA and RNA from each sample. Epithelium cells contain mainly one cell-type, reducing the effect of cell-type specific methylation. We first performed whole genome bisulfite sequencing (WGBS), but mapping yielded in up to 99% bacterial data and parasitic DNA and the analysis with MACAU did not give coherent and in trustworthy results (Supplementary Table 7). Using methylation capture targeting CpG-methylation (with MBD2, MethylMiner, Invitrogen), we enriched methylated DNA fragments of hyenas. After Illumina sequencing (HiSeqX 150 bp PE, Macrogen), we mapped the quality-filtered reads to the novel spotted hyena reference sequence50 with th..., , # Data from: Epigenetic signatures of social status in free-ranging spotted hyenas (Crocuta crocuta)

The MBD-Seq data are uploaded to NCBI BioProject ID PRJNA1036526, including reference genome "crocuta.fasta". The statistical analysis is available as a R-package “Weyrich23†at https://github.com/vullioud/Weyrich23. Dryad data contain the novel annotation file and read count tables for our study DOI xxx.

Description of the data and file structure

Annotation file "crocuta_liftoff_Hhy_ASM300989v1_addPromoter.gtf": We performed the custom annotation file in which we incorporated relevant positions for epigenetic regulation (i.e. gene annotations, Transcription Start Sites (TSS), promoters). In a first step, we mapped the annotation of the recently published striped hyena (Hyena hyena) annotation (RefSeq Assembly ID: GCF_003009895.1) to the spotted hyena genome as closest relative using the Liftoff software (v1.6.1). This provided the ann...

This data set contains necessary data sets (simulated next-generation sequencing data, preprocessed public next-generation sequencing data, precomputed analysis results) used to evaluate performance of epialleleR (https://github.com/BBCG/epialleleR, http://www.bioconductor.org/packages/epialleleR/) - a computational framework for sensitive detection, quantification and visualisation of low-frequency, mosaic epimutations in methylation sequencing data. All the supplementary R scripts that were used for preparation, testing and analysis of data sets are also provided. For additional information please check epialleleR package README.md file, vignettes or reference citation. Please use TREE VIEW to browse files efficiently Abstract Constitutional epigenetic silencing of tumour suppressor genes has been detected in a small number of cancer patients. Recent finding have indicated that low-level mosaic methylation of the BRCA1 gene promoter occurs in 5-10% of healthy individuals and is associated with a significantly elevated risk of breast and ovarian cancer. This further suggests that similar mosaic constitutional methylation may occur in other tumour suppressor genes as well, potentially being a significant contributor to cancer burden. However, detection of low-level mosaic epigenetic events requires highly sensitive and robust methodology for methylation analysis. We here present epialleleR, a computational framework for sensitive detection, quantification and visualisation of low-frequency, mosaic epimutations in methylation sequencing data. We provide in-depth analysis of epialleleR performance using simulated and real data sets, as compared to the other three commonly applied tools for methylation assessment, and conclude that linkage to epihaplotype data allows very sensitive detection of low-frequency methylation events.

The environment experienced during early life is a crucial factor in the life of many organisms. This early life environment has been shown to have profound effects on morphology, physiology and fitness. However, the molecular mechanisms that mediate these effects are largely unknown, even though this is essential for our understanding of the processes that induce phenotypic variation in natural populations. DNA methylation is an epigenetic mechanism that has been suggested to explain such environmentally induced phenotypic changes early in life. To investigate whether DNA methylation changes are associated with experimentally induced early developmental effects, we cross-fostered great tit (Parus major) nestlings and manipulated their brood sizes in a natural study population. We assessed experimental brood size effects on pre-fledging biometry and behaviour. We linked this to genome-wide DNA methylation levels of CpG sites in erythrocyte DNA, using 122 individuals and an improved epiG...

This is the first release of the data to be made public and accessible with the manuscript.

Glioblastoma multiforme (GBM) is one of the most aggressive types of cancer and exhibits profound genetic and epigenetic heterogeneity, making the development of an effective treatment a major challenge. The recent incorporation of molecular features into the diagnosis of GBM patients has led to an improved categorisation into various tumour subtypes with different prognoses and disease management. In this work, we have exploited the benefits of genome-wide multi-omic approaches to identify potential molecular vulnerabilities existing in GBM patients. Integration of gene expression and DNA methylation data from both bulk GBM and patient-derived GBM stem cell lines has revealed the presence of major sources of GBM variability, pinpointing subtype-specific tumour vulnerabilities amenable to pharmacological interventions. In this sense, inhibition of the AP1, SMAD3 and RUNX1 / RUNX2 pathways, in combination or not with the chemotherapeutic agent temozolomide, led to the subtype-specific impairment of tumour growth, particularly in the context of the aggressive, mesenchymal-like subtype. These results emphasize the involvement of these molecular pathways in the development of GBM and have potential implications for the development of personalized therapeutic approaches.

BackgroundDNA methylation of promoter CpG islands is associated with gene suppression, and its unique genome-wide profiles have been linked to tumor progression. Coupled with high-throughput sequencing technologies, it can now efficiently determine genome-wide methylation profiles in cancer cells. Also, experimental and computational technologies make it possible to find the functional relationship between cancer-specific methylation patterns and their clinicopathological parameters.Methodology/Principal FindingsCancer methylome system (CMS) is a web-based database application designed for the visualization, comparison and statistical analysis of human cancer-specific DNA methylation. Methylation intensities were obtained from MBDCap-sequencing, pre-processed and stored in the database. 191 patient samples (169 tumor and 22 normal specimen) and 41 breast cancer cell-lines are deposited in the database, comprising about 6.6 billion uniquely mapped sequence reads. This provides comprehensive and genome-wide epigenetic portraits of human breast cancer and endometrial cancer to date. Two views are proposed for users to better understand methylation structure at the genomic level or systemic methylation alteration at the gene level. In addition, a variety of annotation tracks are provided to cover genomic information. CMS includes important analytic functions for interpretation of methylation data, such as the detection of differentially methylated regions, statistical calculation of global methylation intensities, multiple gene sets of biologically significant categories, interactivity with UCSC via custom-track data. We also present examples of discoveries utilizing the framework.Conclusions/SignificanceCMS provides visualization and analytic functions for cancer methylome datasets. A comprehensive collection of datasets, a variety of embedded analytic functions and extensive applications with biological and translational significance make this system powerful and unique in cancer methylation research. CMS is freely accessible at: http://cbbiweb.uthscsa.edu/KMethylomes/.