Probe Lasso: a novel method to rope in differentially methylated regions with 450K DNA methylation data.
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ABSTRACT:
SUBMITTER: Butcher LM
PROVIDER: S-EPMC4304833 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationOne of the main goals of large scale methylation studies is to detect differentially methylated loci. One way is to approach this problem sitewise, i.e. to find differentially methylated positions (DMPs). However, it has been shown that methylation is regulated in longer genomic regions. So it is more desirable to identify differentially methylated regions (DMRs) instead of DMPs. The new high coverage arrays, like Illuminas 450k platform, make it possible at a reasonable cost. Few tools exist for DMR identification from this type of data, but there is no standard approach.ResultsWe propose a novel method for DMR identification that detects the region boundaries according to the minimum description length (MDL) principle, essentially solving the problem of model selection. The significance of the regions is established using linear mixed models. Using both simulated and large publicly available methylation datasets, we compare seqlm performance to alternative approaches. We demonstrate that it is both more sensitive and specific than competing methods. This is achieved with minimal parameter tuning and, surprisingly, quickest running time of all the tried methods. Finally, we show that the regional differential methylation patterns identified on sparse array data are confirmed by higher resolution sequencing approaches.Availability and implementationThe methods have been implemented in R package seqlm that is available through Github: https://github.com/raivokolde/seqlmContactrkolde@gmail.comSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5013909 | biostudies-literature
Project description:BACKGROUND:DNA methylation has been recognized as a key mechanism in cell differentiation. Various studies have compared tissues to characterize epigenetically regulated genomic regions, but due to differences in study design and focus there still is no consensus as to the annotation of genomic regions predominantly involved in tissue-specific methylation. We used a new algorithm to identify and annotate tissue-specific differentially methylated regions (tDMRs) from Illumina 450k chip data for four peripheral tissues (blood, saliva, buccal swabs and hair follicles) and six internal tissues (liver, muscle, pancreas, subcutaneous fat, omentum and spleen with matched blood samples). RESULTS:The majority of tDMRs, in both relative and absolute terms, occurred in CpG-poor regions. Further analysis revealed that these regions were associated with alternative transcription events (alternative first exons, mutually exclusive exons and cassette exons). Only a minority of tDMRs mapped to gene-body CpG islands (13%) or CpG islands shores (25%) suggesting a less prominent role for these regions than indicated previously. Implementation of ENCODE annotations showed enrichment of tDMRs in DNase hypersensitive sites and transcription factor binding sites. Despite the predominance of tissue differences, inter-individual differences in DNA methylation in internal tissues were correlated with those for blood for a subset of CpG sites in a locus- and tissue-specific manner. CONCLUSIONS:We conclude that tDMRs preferentially occur in CpG-poor regions and are associated with alternative transcription. Furthermore, our data suggest the utility of creating an atlas cataloguing variably methylated regions in internal tissues that correlate to DNA methylation measured in easy accessible peripheral tissues.
| S-EPMC3750594 | biostudies-literature
Project description:MotivationDNA methylation is an important epigenetic mechanism in gene regulation and the detection of differentially methylated regions (DMRs) is enthralling for many disease studies. There are several aspects that we can improve over existing DMR detection methods: (i) methylation statuses of nearby CpG sites are highly correlated, but this fact has seldom been modelled rigorously due to the uneven spacing; (ii) it is practically important to be able to handle both paired and unpaired samples; and (iii) the capability to detect DMRs from a single pair of samples is demanded.ResultsWe present DMRMark (DMR detection based on non-homogeneous hidden Markov model), a novel Bayesian framework for detecting DMRs from methylation array data. It combines the constrained Gaussian mixture model that incorporates the biological knowledge with the non-homogeneous hidden Markov model that models spatial correlation. Unlike existing methods, our DMR detection is achieved without predefined boundaries or decision windows. Furthermore, our method can detect DMRs from a single pair of samples and can also incorporate unpaired samples. Both simulation studies and real datasets from The Cancer Genome Atlas showed the significant improvement of DMRMark over other methods.Availability and implementationDMRMark is freely available as an R package at the CRAN R package repository.Contactxfan@cuhk.edu.hk.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6355111 | biostudies-literature
Project description:BACKGROUND:DNA methylation is an epigenetic modification that is studied at a single-base resolution with bisulfite treatment followed by high-throughput sequencing. After alignment of the sequence reads to a reference genome, methylation counts are analyzed to determine genomic regions that are differentially methylated between two or more biological conditions. Even though a variety of software packages is available for different aspects of the bioinformatics analysis, they often produce results that are biased or require excessive computational requirements. RESULTS:DMRfinder is a novel computational pipeline that identifies differentially methylated regions efficiently. Following alignment, DMRfinder extracts methylation counts and performs a modified single-linkage clustering of methylation sites into genomic regions. It then compares methylation levels using beta-binomial hierarchical modeling and Wald tests. Among its innovative attributes are the analyses of novel methylation sites and methylation linkage, as well as the simultaneous statistical analysis of multiple sample groups. To demonstrate its efficiency, DMRfinder is benchmarked against other computational approaches using a large published dataset. Contrasting two replicates of the same sample yielded minimal genomic regions with DMRfinder, whereas two alternative software packages reported a substantial number of false positives. Further analyses of biological samples revealed fundamental differences between DMRfinder and another software package, despite the fact that they utilize the same underlying statistical basis. For each step, DMRfinder completed the analysis in a fraction of the time required by other software. CONCLUSIONS:Among the computational approaches for identifying differentially methylated regions from high-throughput bisulfite sequencing datasets, DMRfinder is the first that integrates all the post-alignment steps in a single package. Compared to other software, DMRfinder is extremely efficient and unbiased in this process. DMRfinder is free and open-source software, available on GitHub ( github.com/jsh58/DMRfinder ); it is written in Python and R, and is supported on Linux.
| S-EPMC5817627 | biostudies-literature
Project description:Differentially methylated regions (DMRs) are genomic regions with methylation patterns across multiple CpG sites that are associated with a phenotype. In this study, we proposed a Principal Component (PC) based DMR analysis method for use with data generated using the Illumina Infinium MethylationEPIC BeadChip (EPIC) array. We obtained methylation residuals by regressing the M-values of CpGs within a region on covariates, extracted PCs of the residuals, and then combined association information across PCs to obtain regional significance. Simulation-based genome-wide false positive (GFP) rates and true positive rates were estimated under a variety of conditions before determining the final version of our method, which we have named DMRPC. Then, DMRPC and another DMR method, coMethDMR, were used to perform epigenome-wide analyses of several phenotypes known to have multiple associated methylation loci (age, sex, and smoking) in a discovery and a replication cohort. Among regions that were analysed by both methods, DMRPC identified 50% more genome-wide significant age-associated DMRs than coMethDMR. The replication rate for the loci that were identified by only DMRPC was higher than the rate for those that were identified by only coMethDMR (90% for DMRPC vs. 76% for coMethDMR). Furthermore, DMRPC identified replicable associations in regions of moderate between-CpG correlation which are typically not analysed by coMethDMR. For the analyses of sex and smoking, the advantage of DMRPC was less clear. In conclusion, DMRPC is a new powerful DMR discovery tool that retains power in genomic regions with moderate correlation across CpGs.
| S-EPMC10193914 | biostudies-literature
Project description:Tissue specific differentially methylated regions (TDMRs) were identified and localized in the mouse genome using second generation virtual RLGS (vRLGS). Sequenom MassARRAY quantitative methylation analysis was used to confirm and determine the fine structure of tissue specific differences in DNA methylation. TDMRs have a broad distribution of locations to intragenic and intergenic regions including both CpG islands, and non-CpG islands regions. Somewhat surprising, there is a strong bias for TDMR location in non-promoter intragenic regions. Although some TDMRs are within or close to repeat sequences, overall they are less frequently associated with repetitive elements than expected from a random distribution. Many TDMRs are methylated at early developmental stages, but unmethylated later, suggesting active or passive demethylation, or expansions of populations of cells with unmethylated TDMRs. This is notable during postnatal testis differentiation where many testis specific TDMRs become progressively "demethylated". These results suggest that methylation changes during development are dynamic, involve demethylation and methylation, and may occur at late stages of embryonic development or even postnatally.
| S-EPMC2658018 | biostudies-literature
Project description:BackgroundImprinted genes are regulated by DNA methylation at imprinting-associated differentially methylated regions (iDMRs). Abnormal expression of imprinted genes is implicated in imprinting disorders and tumors. In colorectal cancer (CRC), methylation and imprinting status of the IGF2/H19 domain have been studied. However, no comprehensive methylation analysis of iDMRs in CRC has been reported. Furthermore, the relationship between iDMR methylation status and other methylation-related issues, such as CpG island methylator phenotype (CIMP) and long interspersed element-1 (LINE-1) methylation, remains unclear.ResultsWe analyzed the methylation status of 38 iDMRs in 106 CRC patients. We also investigated CIMP, LINE-1 methylation, KRAS and BRAF gene mutations, and loss of imprinting (LOI) of IGF2. We further examined the relationship between these factors and clinicopathological factors. The overall trend in iDMR methylation was towards hypermethylation, and iDMRs could be grouped into three categories: susceptible, resistant, and intermediate-to-aberrant methylation. The susceptible and resistant iDMRs consisted of all types of iDMR (gametic and somatic, maternally and paternally methylated). Hypermethylation of multiple iDMRs (HyMiD)-positive status was statistically associated with CIMP-positive status, but not associated with mutations in the BRAF and KRAS genes. HyMiD-positive status was inversely associated with LINE-1 methylation. Among four iDMRs within the IGF2/H19 domain, IGF2-DMR0 hypomethylation occurred most frequently, but was not associated with IGF2 LOI. Finally, we statistically calculated predictive prognostic scores based on aberrant methylation status of three iDMRs.ConclusionIn CRC tissues, some iDMRs were susceptible to hypermethylation independent of the type of iDMR and genomic sequence. Although HyMiD-positive status was associated with CIMP-positive status, this was independent of the BRAF and KRAS pathways, which are responsible for CIMP. Since IGF2-DMR0 hypomethylation and aberrant methylation of other iDMRs within the IGF2/H19 domain were not associated with IGF2 LOI, dysfunction of any of the molecular components related to imprinting regulation may be involved in IGF2 LOI. The prognostic score calculated based on aberrant methylation of three iDMRs has potential clinical applications as a prognostic predictor in patients. Further study is required to understand the biological significance of, and mechanisms behind, aberrant methylation of iDMRs and IGF2 LOI in CRCs.
| S-EPMC6278095 | biostudies-literature
Project description:Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called "supervised" methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative "unsupervised" approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.
| S-EPMC4018258 | biostudies-literature
Project description:DNA methylation plays critical roles in transcriptional regulation and chromatin remodeling. Differentially methylated regions (DMRs) have important implications for development, aging and diseases. Therefore, genome-wide mapping of DMRs across various temporal and spatial methylomes is important in revealing the impact of epigenetic modifications on heritable phenotypic variation. We present a quantitative approach, quantitative differentially methylated regions (QDMRs), to quantify methylation difference and identify DMRs from genome-wide methylation profiles by adapting Shannon entropy. QDMR was applied to synthetic methylation patterns and methylation profiles detected by methylated DNA immunoprecipitation microarray (MeDIP-chip) in human tissues/cells. This approach can give a reasonable quantitative measure of methylation difference across multiple samples. Then DMR threshold was determined from methylation probability model. Using this threshold, QDMR identified 10,651 tissue DMRs which are related to the genes enriched for cell differentiation, including 4740 DMRs not identified by the method developed by Rakyan et al. QDMR can also measure the sample specificity of each DMR. Finally, the application to methylation profiles detected by reduced representation bisulphite sequencing (RRBS) in mouse showed the platform-free and species-free nature of QDMR. This approach provides an effective tool for the high-throughput identification of potential functional regions involved in epigenetic regulation.
| S-EPMC3089487 | biostudies-literature
Project description:Atherosclerosis involves phenotypic modulation and transdifferentiation of vascular smooth muscle cells (SMCs). Data are given in tabular or figure format that illustrate genome-wide DNA methylation alterations in atherosclerotic vs. control aorta (athero DMRs). Data based upon publicly available chromatin state profiles are also shown for normal aorta, monocyte, and skeletal muscle tissue-specific DMRs and for aorta-specific chromatin features (enhancer chromatin, promoter chromatin, repressed chromatin, actively transcribed chromatin). Athero hypomethylated and hypermethylated DMRs as well as epigenetic and transcription profiles are described for the following genes: ACTA2, MYH10, MYH11 (SMC-associated genes); SMAD3 (a signaling gene for SMCs and other cell types); CD79B and SH3BP2 (leukocyte-associated genes); and TBX20 and genes in the HOXA, HOXB, HOXC, and HOXD clusters (T-box and homeobox developmental genes). The data reveal strong correlations between athero hypermethylated DMRs and regions of enhancer chromatin in aorta, which are discussed in the linked research article "Atherosclerosis-associated differentially methylated regions can reflect the disease phenotype and are often at enhancers" (M. Lacey et al., 2019).
| S-EPMC6660590 | biostudies-literature