GeneDMRs: An R Package for Gene-Based Differentially Methylated Regions Analysis.
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ABSTRACT:
SUBMITTER: Wang X
PROVIDER: S-EPMC7994424 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Alterations in DNA methylation have been implicated in mammalian development. Hence, the identification of tissue-specific differentially methylated regions (TDMs) is indispensable for understanding its role. Using restriction landmark genomic scanning of six mouse tissues, 150 putative TDMs were identified and 14 were further analyzed. The DNA sequences of the 14 mouse TDMs are analyzed in this study. Six of the human homologous regions show TDMs to both mouse and human and genes in five of these regions have conserved tissue-specific expression: preferential expression in testis. A TDM, DDX4, is further analyzed in nine testis tissues. An increase in methylation of the promoter region is significantly associated with a marked reduction of the gene expression and defects in spermatogenesis, suggesting that hypomethylation of the DDX4 promoter region regulates DDX4 gene expression in spermatogenic cells. Our results indicate that some genomic regions with tissue-specific methylation and expression are conserved between mouse and human and suggest that DNA methylation may have an important role in regulating differentiation and tissue-/cell-specific gene expression of some genes.
| S-EPMC1847344 | biostudies-other
Project description:BackgroundWe present here a computational shortcut to improve a powerful wavelet-based method by Shim and Stephens (Ann Appl Stat 9(2):665-686, 2015. https://doi.org/10.1214/14-AOAS776 ) called WaveQTL that was originally designed to identify DNase I hypersensitivity quantitative trait loci (dsQTL).ResultsWaveQTL relies on permutations to evaluate the significance of an association. We applied a recent method by Zhou and Guan (J Am Stat Assoc 113(523):1362-1371, 2017. https://doi.org/10.1080/01621459.2017.1328361 ) to boost computational speed, which involves calculating the distribution of Bayes factors and estimating the significance of an association by simulations rather than permutations. We called this simulation-based approach "fast functional wavelet" (FFW), and tested it on a publicly available DNA methylation (DNAm) dataset on colorectal cancer. The simulations confirmed a substantial gain in computational speed compared to the permutation-based approach in WaveQTL. Furthermore, we show that FFW controls the type I error satisfactorily and has good power for detecting differentially methylated regions.ConclusionsOur approach has broad utility and can be applied to detect associations between different types of functions and phenotypes. As more and more DNAm datasets are being made available through public repositories, an attractive application of FFW would be to re-analyze these data and identify associations that might have been missed by previous efforts. The full R package for FFW is freely available at GitHub https://github.com/william-denault/ffw .
| S-EPMC7876806 | biostudies-literature
Project description:BACKGROUND:We explored the association between gestational age and cord blood DNA methylation at birth and whether DNA methylation could be effective in predicting gestational age due to limitations with the presently used methods. We used data from the Norwegian Mother and Child Birth Cohort study (MoBa) with Illumina HumanMethylation450 data measured for 1753 newborns in two batches: MoBa 1, n?=?1068; and MoBa 2, n?=?685. Gestational age was computed using both ultrasound and the last menstrual period. We evaluated associations between DNA methylation and gestational age and developed a statistical model for predicting gestational age using MoBa 1 for training and MoBa 2 for predictions. The prediction model was additionally used to compare ultrasound and last menstrual period-based gestational age predictions. Furthermore, both CpGs and associated genes detected in the training models were compared to those detected in a published prediction model for chronological age. RESULTS:There were 5474 CpGs associated with ultrasound gestational age after adjustment for a set of covariates, including estimated cell type proportions, and Bonferroni-correction for multiple testing. Our model predicted ultrasound gestational age more accurately than it predicted last menstrual period gestational age. CONCLUSIONS:DNA methylation at birth appears to be a good predictor of gestational age. Ultrasound gestational age is more strongly associated with methylation than last menstrual period gestational age. The CpGs linked with our gestational age prediction model, and their associated genes, differed substantially from the corresponding CpGs and genes associated with a chronological age prediction model.
| S-EPMC5054559 | biostudies-literature
Project description:Analysis of bisulfite sequencing data usually requires two tasks: to call methylated cytosines (mCs) in a sample, and to detect differentially methylated regions (DMRs) between paired samples. Although numerous tools have been proposed for mC calling, methods for DMR detection have been largely limited. Here, we present Bisulfighter, a new software package for detecting mCs and DMRs from bisulfite sequencing data. Bisulfighter combines the LAST alignment tool for mC calling, and a novel framework for DMR detection based on hidden Markov models (HMMs). Unlike previous attempts that depend on empirical parameters, Bisulfighter can use the expectation-maximization algorithm for HMMs to adjust parameters for each data set. We conduct extensive experiments in which accuracy of mC calling and DMR detection is evaluated on simulated data with various mC contexts, read qualities, sequencing depths and DMR lengths, as well as on real data from a wide range of biological processes. We demonstrate that Bisulfighter consistently achieves better accuracy than other published tools, providing greater sensitivity for mCs with fewer false positives, more precise estimates of mC levels, more exact locations of DMRs and better agreement of DMRs with gene expression and DNase I hypersensitivity. The source code is available at http://epigenome.cbrc.jp/bisulfighter.
| S-EPMC3973284 | biostudies-other
Project description:Early studies proposed that DNA methylation could have a role in regulating gene expression during development [Riggs, A.D. (1975) Cytogenet. Cell Genet. 14, 9-25]. However, some studies of DNA methylation in known tissue-specific genes during development do not support a major role for DNA methylation. In the results presented here, tissue-specific differentially methylated regions (TDMs) were first identified, and then expression of genes associated with these regions correlated with methylation status. Restriction landmark genomic scanning (RLGS) was used in conjunction with virtual RLGS to identify 150 TDMs [Matsuyama, T., Kimura, M.T., Koike, K., Abe, T., Nakao, T., Asami, T., Ebisuzaki, T., Held, W.A., Yoshida, S. & Nagase, H. (2003) Nucleic Acids Res. 31, 4490-4496]. Analysis of 14 TDMs by methylation-specific PCR and by bisulfite genomic sequencing confirms that the regions identified by RLGS are differentially methylated in a tissue-specific manner. The results indicate that 5% or more of the CpG islands are TDMs, disputing the general notion that all CpG islands are unmethylated. Some of the TDMs are within 5' promoter CpG islands of genes, which exhibit a tissue-specific expression pattern that is consistent with methylation status and a role in tissue differentiation.
| S-EPMC552919 | biostudies-literature
Project description:DNA methylation is a well-established epigenetic mark, whose pattern throughout the genome, especially in the promoter or CpG islands, may be modified in a cell at a disease stage. Recently developed probabilistic approaches allow distributing methylation signals at nucleotide resolution from MethylCap-seq data. Standard statistical methods for detecting differential methylation suffer from 'curse of dimensionality' and sparsity in signals, resulting in high false-positive rates. Strong correlation of signals between CG sites also yields spurious results. In this article, we review applicability of high-dimensional mean vector tests for detection of differentially methylated regions (DMRs) and compare and contrast such tests with other methods for detecting DMRs. Comprehensive simulation studies are conducted to highlight the performance of these tests under different settings. Based on our observation, we make recommendations on the optimal test to use. We illustrate the superiority of mean vector tests in detecting cancer-related canonical gene pathways, which are significantly enriched for acute myeloid leukemia and ovarian cancer.
| S-EPMC5142008 | biostudies-other
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:The addition of a methyl group to, typically, a cytosine-guanine dinucleotide (CpG) creates distinct DNA methylation patterns across the genome that can regulate gene expression. Aberrant DNA methylation of CpG sites has been associated with many psychiatric disorders including bipolar disorder (BD) and suicide. Using the SureSelectXT system, Methyl-Seq, we investigated the DNA methylation status of CpG sites throughout the genome in 50 BD individuals (23 subjects who died by suicide and 27 subjects who died from other causes) and 31 nonpsychiatric controls. We identified differentially methylated regions (DMRs) from three analyses: (a) BD subjects compared to nonpsychiatric controls (BD-NC), (b) BD subjects who died by suicide compared to nonpsychiatric controls (BDS-NC), and (c) BDS subjects compared to BD subjects who died from other causes (BDS-BDNS). One DMR from the BDS-NC analysis, located in ARHGEF38, was significantly hypomethylated (23.4%) in BDS subjects. This finding remained significant after multiple testing (PBootstrapped = 9.0 × 10-3 ), was validated using pyrosequencing, and was more significant in males. A secondary analysis utilized Ingenuity Pathway Analysis to identify enrichment in nominally significant DMRs. This identified an association with several pathways including axonal guidance signaling, calcium signaling, β-adrenergic signaling, and opioid signaling. Our comprehensive study provides further support that DNA methylation alterations influence the risk for BD and suicide. However, further investigation is required to confirm these associations and identify their functional consequences.
| S-EPMC8375453 | biostudies-literature
Project description:Obesity with adiposity is a common disorder in modern days, influenced by environmental factors such as eating and lifestyle habits and affecting the epigenetics of adipose-based gene regulations and metabolic pathways in colorectal cancer (CRC). We compared epigenetic changes of differentially methylated regions (DMR) of genes in colon tissues of 225 colon cancer cases (154 non-obese and 71 obese) and 15 healthy non-obese controls by accessing The Cancer Genome Atlas (TCGA) data. We applied machine-learning-based analytics including generalized regression (GR) as a confirmatory validation model to identify the factors that could contribute to DMRs impacting colon cancer to enhance prediction accuracy. We found that age was a significant predictor in obese cancer patients, both alone (p = 0.003) and interacting with hypomethylated DMRs of ZBTB46, a tumor suppressor gene (p = 0.008). DMRs of three additional genes: HIST1H3I (p = 0.001), an oncogene with a hypomethylated DMR in the promoter region; SRGAP2C (p = 0.006), a tumor suppressor gene with a hypermethylated DMR in the promoter region; and NFATC4 (p = 0.006), an adipocyte differentiating oncogene with a hypermethylated DMR in an intron region, are also significant predictors of cancer in obese patients, independent of age. The genes affected by these DMR could be potential novel biomarkers of colon cancer in obese patients for cancer prevention and progression.
| S-EPMC9142939 | biostudies-literature
Project description:BackgroundArray based methylation profiling is a cost-effective solution to study the association between genome methylation and human disease & development. Available tools to analyze the Illumina Infinium HumanMethylation450 BeadChip focus on comparing methylation levels per locus. Other tools combine multiple probes into a range, identifying differential methylated regions (DMRs). These tools all require groups of samples to compare. However, comparison of unique, individual samples is essential in situations where larger sample sizes are not possible.ResultsDMRforPairs was designed to compare regional methylation status between unique samples. It identifies probe dense genomic regions and quantifies/tests their (difference in) methylation level between the samples. As a proof of concept, DMRforPairs is applied to public data from four human cell lines: two lymphoblastoid cell lines from healthy individuals and the cancer cell lines A431 and MCF7 (including 2 technical replicates each). DMRforPairs identified an increasing number of DMRs related to the sample phenotype when biological similarity of the samples decreased. DMRs identified by DMRforPairs were related to the biological origin of the cell lines.ConclusionTo our knowledge, DMRforPairs is the first tool to identify and visualize relevant and significant differentially methylated regions between unique samples.
| S-EPMC4046028 | biostudies-literature