Relevant bibliographies by topics / Method for DNA methylation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Method for DNA methylation'

Author: Grafiati
Published: 11 March 2023

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Journal articles on the topic "Method for DNA methylation"

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Majchrzak-Celińska, A., M. Naskret-Barciszewska, M. Giel-Pietraszuk, W. Nowak, P. Śron, and A. Barciszewska. "P02.08.A The relations of focal and total DNA methylation in gliomas." Neuro-Oncology 24, Supplement_2 (September 1, 2022): ii31. http://dx.doi.org/10.1093/neuonc/noac174.101.

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Abstract Background The role of epigenetic events in gliomagenesis is undoubtful. However, the role of specific pathological events is not so clear. It was shown that loss in total DNA methylation correlates with higher tumor malignancy and oxidative DNA damage. But promoter methylation of many genes was reported to be significant for gliomas’ malignancy and predictive for the treatment outcome. In carcinogenesis in general global DNA hypomethylation and focal hypermethylation coexist. The aim of our project was to evaluate the correlation between total DNA methylation and promoter methylation of selected genes. Material and Methods We analysed glioma tissues from 60 patients. For total DNA methylation analysis we used the radiolabelling method with TLC separation of nucleotides and content estimation with phosphoimager. For promoter methylation analysis we have chosen: MGMT (O-6-Methylguanine-DNA Methyltransferase), MPG (DNA-3-methyladenine glycosylase), GJA1 (Gap junction alpha-1 protein / connexin 43). The promoter methylation level was evaluated with the methylation-sensitive high-resolution melting (MS-HRM) method. Results Total DNA methylation was reversely correlated with brain tumor grade, confirming that 5-methylcytosine loss is important step in gliomagenesis. From 3 genes only MPG promoter methylation showed clear low reverse correlation with tumor grade. Promoter methylation in GJA1 show low correlation with both, MGMT and MPG, but there was no link between MGMT and MPG. IDHwt presence was significantly correlated with higher tumor grade. Promoter methylations in MPG and GJA1 were better correlated with IDH status than in MGMT. Conclusion There is a clear correlation between total DNA methylation and tumor malignancy. Gene promoter methylation is not highly correlated with total DNA methylation and shows low significance in selected cases. Promoter methylation showed clear correlation with tumor grade only in MPG case. That suggests diverse mechanisms steering DNA methylation in general and local changes. It also shows that total DNA methylation is best predictor of tumor grade.
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Kim, Sook Ho, Hae Jun Jung, and Seok-Cheol Hong. "Z-DNA as a Tool for Nuclease-Free DNA Methyltransferase Assay." International Journal of Molecular Sciences 22, no. 21 (November 5, 2021): 11990. http://dx.doi.org/10.3390/ijms222111990.

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Methylcytosines in mammalian genomes are the main epigenetic molecular codes that switch off the repertoire of genes in cell-type and cell-stage dependent manners. DNA methyltransferases (DMT) are dedicated to managing the status of cytosine methylation. DNA methylation is not only critical in normal development, but it is also implicated in cancers, degeneration, and senescence. Thus, the chemicals to control DMT have been suggested as anticancer drugs by reprogramming the gene expression profile in malignant cells. Here, we report a new optical technique to characterize the activity of DMT and the effect of inhibitors, utilizing the methylation-sensitive B-Z transition of DNA without bisulfite conversion, methylation-sensing proteins, and polymerase chain reaction amplification. With the high sensitivity of single-molecule FRET, this method detects the event of DNA methylation in a single DNA molecule and circumvents the need for amplification steps, permitting direct interpretation. This method also responds to hemi-methylated DNA. Dispensing with methylation-sensitive nucleases, this method preserves the molecular integrity and methylation state of target molecules. Sparing methylation-sensing nucleases and antibodies helps to avoid errors introduced by the antibody’s incomplete specificity or variable activity of nucleases. With this new method, we demonstrated the inhibitory effect of several natural bio-active compounds on DMT. All taken together, our method offers quantitative assays for DMT and DMT-related anticancer drugs.
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Li, W., A. Van Soom, and L. Peelman. "Repeats as global DNA methylation marker in bovine preimplantation embryos." Czech Journal of Animal Science 62, No. 2 (February 6, 2017): 43–50. http://dx.doi.org/10.17221/29/2016-cjas.

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DNA methylation undergoes dynamic changes and is a crucial part of the epigenetic regulation during mammalian early development. To determine the DNA methylation levels in bovine embryos, we applied a bisulfite sequencing based method aimed at repetitive sequences including three retrotransposons (L1_BT, BovB, and ERV1-1-I_BT) and Satellite I. A more accurate estimate of the global DNA methylation level compared to previous methods using only one repeat sequence, like Alu, could be made by calculation of the weighted arithmetic mean of multiple repetitive sequences, considering the copy number of each repetitive sequence. Satellite I and L1_BT showed significant methylation reduction at the blastocyst stage, while BovB and ERV1-1-I_BT showed no difference. The mean methylation level of the repetitive sequences during preimplantation development was the lowest at the blastocyst stage. No methylation difference was found between embryos cultured in 5% and 20% O<sub>2</sub>. Because mutations of CpGs negatively influence the calculation accuracy, we checked the mutation rate of the sequenced CpG sites. Satellite I and L1_BT showed a relatively low mutation rate (1.92 and 3.72% respectively) while that of ERV1-1-I_BT and BovB was higher (11.95 and 24% respectively). Therefore we suggest using a combination of repeats with low mutation rate, taking into account the proportion of each sequence, as a relatively quick marker for the global DNA methylation status of preimplantation stages and possibly also for other cell types.
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Kurdyukov, Sergey, and Martyn Bullock. "DNA Methylation Analysis: Choosing the Right Method." Biology 5, no. 1 (January 6, 2016): 3. http://dx.doi.org/10.3390/biology5010003.

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IMAMURA, TAKUYA. "DNA methylation analysis with bisulfite sequencing method." Newsletter of Japan Society for Comparative Endocrinology, no. 113 (2004): 25–29. http://dx.doi.org/10.5983/nl2001jsce.2004.113_25.

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Hou, Peng, Meiju Ji, Song Li, Nongyue He, and Zuhong Lu. "High-throughput method for detecting DNA methylation." Journal of Biochemical and Biophysical Methods 60, no. 2 (August 2004): 139–50. http://dx.doi.org/10.1016/j.jbbm.2004.05.001.

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Choi, Woo Lee, Young Geun Mok, and Jin Hoe Huh. "Application of 5-Methylcytosine DNA Glycosylase to the Quantitative Analysis of DNA Methylation." International Journal of Molecular Sciences 22, no. 3 (January 22, 2021): 1072. http://dx.doi.org/10.3390/ijms22031072.

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In higher eukaryotes DNA methylation is a prominent epigenetic mark important for chromatin structure and gene expression. Thus, profiling DNA methylation is important for predicting gene expressions associated with specific traits or diseases. DNA methylation is achieved by DNA methyltransferases and can be actively removed by specific enzymes in a replication-independent manner. DEMETER (DME) is a bifunctional 5-methylcytosine (5mC) DNA glycosylase responsible for active DNA demethylation that excises 5mC from DNA and cleaves a sugar-phosphate bond generating a single strand break (SSB). In this study, DME was used to analyze DNA methylation levels at specific epialleles accompanied with gain or loss of DNA methylation. DME treatment on genomic DNA generates SSBs in a nonsequence-specific fashion proportional to 5mC density, and thus DNA methylation levels can be easily measured when combined with the quantitative PCR (qPCR) method. The DME-qPCR analysis was applied to measure DNA methylation levels at the FWA gene in late-flowering Arabidopsis mutants and the CNR gene during fruit ripening in tomato. Differentially methylated epialleles were successfully distinguished corresponding to their expression levels and phenotypes. DME-qPCR is proven a simple yet effective method for quantitative DNA methylation analysis, providing advantages over current techniques based on methylation-sensitive restriction digestion.
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Khodadadi, Ehsan, Leila Fahmideh, Ehsaneh Khodadadi, Sounkalo Dao, Mehdi Yousefi, Sepehr Taghizadeh, Mohammad Asgharzadeh, Bahman Yousefi, and Hossein Samadi Kafil. "Current Advances in DNA Methylation Analysis Methods." BioMed Research International 2021 (March 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/8827516.

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DNA methylation is one of the epigenetic changes, which plays a major role in regulating gene expression and, thus, many biological processes and diseases. There are several methods for determining the methylation of DNA samples. However, selecting the most appropriate method for answering biological questions appears to be a challenging task. The primary methods in DNA methylation focused on identifying the state of methylation of the examined genes and determining the total amount of 5-methyl cytosine. The study of DNA methylation at a large scale of genomic levels became possible following the use of microarray hybridization technology. The new generation of sequencing platforms now allows the preparation of genomic maps of DNA methylation at the single-open level. This review includes the majority of methods available to date, introducing the most widely used methods, the bisulfite treatment, biological identification, and chemical cutting along with their advantages and disadvantages. The techniques are then scrutinized according to their robustness, high throughput capabilities, and cost.
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Celik Uzuner, Selcen. "Mitochondrial DNA methylation misleads global DNA methylation detected by antibody-based methods." Analytical Biochemistry 601 (July 2020): 113789. http://dx.doi.org/10.1016/j.ab.2020.113789.

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Han, Pingping, and Sašo Ivanovski. "Effect of Saliva Collection Methods on the Detection of Periodontium-Related Genetic and Epigenetic Biomarkers—A Pilot Study." International Journal of Molecular Sciences 20, no. 19 (September 24, 2019): 4729. http://dx.doi.org/10.3390/ijms20194729.

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Different collection methods may influence the ability to detect and quantify biomarker levels in saliva, particularly in the expression of DNA/RNA methylation regulators of several inflammations and tissue turnover markers. This pilot study recruited five participants and unstimulated saliva were collected by either spitting or drooling, and the relative preference for each method was evaluated using a visual analogue scale. Subsequently, total RNA, gDNA and proteins were isolated using the Trizol method. Thereafter, a systematic evaluation was carried out on the potential effects of different saliva collection methods on periodontium-associated genes, DNA/RNA epigenetic factors and periodontium-related DNA methylation levels. The quantity and quality of DNA and RNA were comparable from different collection methods. Periodontium-related genes, DNA/RNA methylation epigenetic factors and periodontium-associated DNA methylation could be detected in the saliva sample, with a similar expression for both methods. The methylation of tumour necrosis factor-alpha gene promoter from drooling method showed a significant positive correlation (TNF α, r = 0.9) with clinical parameter (bleeding on probing-BOP). In conclusion, the method of saliva collection has a minimal impact on detecting periodontium-related genetic and epigenetic regulators in saliva. The pilot data shows that TNF α methylation may be correlated with clinical parameters.
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Dissertations / Theses on the topic "Method for DNA methylation"

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Meng, Wei. "DNA Mutation/Methylation Screening Method for Colon Cancer Screening." Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1290364705.

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Trimarchi, Michael Paul Trimarchi. "Identification of endometrial cancer methylation features using a combined methylation analysis method." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461302615.

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ZERILLI, FRANCESCO. "Development of an isothermal method for the detection of DNA hypermethylation." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2009. http://hdl.handle.net/10281/7481.

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This thesis relates to the development of an isothermal method for the detection of DNA hypermethylation. The function of DNA methylation is to silence specific areas of the human genome and its maintenance is necessary to regulate key functions of the cell and of the organism. Aberrant hypermethylation of the promoter regions of several tumor suppressor genes can lead to formation of tumors and its detection, even at very early stages, can be an useful and powerful instrument for the diagnosis of the cancer, the determination of the prognosis and also to gauge the therapy. In this work we developed a molecular assay (MS-LAMP), based on the Loop-mediated Isothermal Amplification, for the rapid and specific amplification and detection of the methylated DNA in three human gene promoters after a sodium bisulfite treatment. The assay, showed PCR-level performance in terms of specificity, sensitivity and selectivity both on synthetic genes and fully methylated and unmethylated genomic DNA. The method was also validated on DNA extracted from clinical samples in comparison with a reference technique. Finally the notable specificity of LAMP was also exploited for the development of a triplex reaction, tested successfully on control DNA and clinical samples, with two possible signal detection approaches.
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Hu, Ke. "METHODS AND ANALYSES IN THE STUDY OF HUMAN DNA METHYLATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522760441838452.

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Van, Heerden Chrisna. "Establishing a method for measuring the DNA methylation status of specific human genes / Chrisna van Heerden." Thesis, North-West University, 2006. http://hdl.handle.net/10394/1443.

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Antunes, Joana AP. "The Study of Tissue-Specific DNA Methylation as a Method for the Epigenetic Discrimination of Forensic Samples." FIU Digital Commons, 2017. https://digitalcommons.fiu.edu/etd/3676.

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In forensic sciences, the serological methods used to determine which body fluid was collected from the crime scene are merely presumptive or labor intensive since they rely on protein detection or on microscopic identification of cells. Given that certain forensic cases may need the precise identification of a body fluid to determine criminal contact, such is the example of a suspected sexual assault of a minor; certainty in the body fluid of origin may depict a precise picture of the events. The identification of loci that show differences in methylation according to the tissue of origin can aid forensic analysts in determining the origin of a DNA sample. The process of DNA methylation occurs naturally in the genome of living organisms and consists in the presence of a methyl group on the carbon 5 of a cytosine, which is typically followed by a guanine (CpG). Analyzing patterns of DNA methylation in body fluids collected from a crime scene is preferential to the analysis of proteins or mRNA since the same extracted DNA used for STR typing can be used for DNA methylation analysis. We have validated and identified loci able to discriminate blood, saliva, semen and vaginal epithelia. In the current study, we have also established the minimum amount of DNA able to provide reliable results using methodologies such as pyrosequencing and high-resolution melt (HRM) analysis for the different markers identified. Lastly, we performed an alternative bioinformatic analysis of data collected using an array that studied methylation in over 450,000 individual cytosines on the human genome. We were able to sort the locations that showed potentially higher methylation differences between body fluids and investigated over 100 of them using HRM analysis. The results of that study, allowed the identification of three new loci able to distinguish blood and two new loci able to distinguish saliva and vaginal epithelia, respectively. The use of DNA methylation patterns to aid forensic investigations started with a publication in 2010, therefore each small contribution such as this work may, similarly to what occured in the biochemistry field, result in the discovery of a method able to put the technology in the hands of forensic analysts.
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Capparuccini, Maria. "Inferential Methods for High-Throughput Methylation Data." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/156.

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The role of abnormal DNA methylation in the progression of disease is a growing area of research that relies upon the establishment of sound statistical methods. The common method for declaring there is differential methylation between two groups at a given CpG site, as summarized by the difference between proportions methylated db=b1-b2, has been through use of a Filtered Two Sample t-test, using the recommended filter of 0.17 (Bibikova et al., 2006b). In this dissertation, we performed a re-analysis of the data used in recommending the threshold by fitting a mixed-effects ANOVA model. It was determined that the 0.17 filter is not accurate and conjectured that application of a Filtered Two Sample t-test likely leads to loss of power. Further, the Two Sample t-test assumes that data arise from an underlying distribution encompassing the entire real number line, whereas b1 and b2 are constrained on the interval . Additionally, the imposition of a filter at a level signifying the minimum level of detectable difference to a Two Sample t-test likely reduces power for smaller but truly differentially methylated CpG sites. Therefore, we compared the Two Sample t-test and the Filtered Two Sample t-test, which are widely used but largely untested with respect to their performance, to three proposed methods. These three proposed methods are a Beta distribution test, a Likelihood ratio test, and a Bootstrap test, where each was designed to address distributional concerns present in the current testing methods. It was ultimately shown through simulations comparing Type I and Type II error rates that the (unfiltered) Two Sample t-test and the Beta distribution test performed comparatively well.
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Kretzmer, Helene. "Methods for DNA Methylation Sequencing Analysis and their Application on Cancer Data." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-203416.

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The fundamental subject of this thesis is the development of tools for the analysis of DNA methylation data as well as their application on bisulfite sequencing data comprising a large number of samples. DNA methylation is one of the major epigenetic modifications. It affects the cytosines of the DNA and is essential for the normal development of cells and tissues. Unusual alterations are associated with a variety of diseases and, specially, in cancergeneous tissues global changes in the DNA methylation level have been detected. To sequence DNA methylation on single nucleotide resolution, the sequences are treated with sodium bisulfite before sequencing, whereby unmethylated cytosines are represented as thymines. Thus, specialized techniques are required to process and analyze these kind of data. Here, the bisulfite analysis toolkit BAT is introduced, that is designed to facilitate an quick analysis of bisulfite treated DNA methylation sequencing data. It covers all steps of processing raw sequencing data up to calling of differential DNA methylation. At the begin of analysis, sodium bisulfite treated sequence data are aligned and DNA methylation rates for each covered cytosine in the reference genome are called. Subsequently, BAT integrates annotation data and performs basic analysis, i. e., methylation rate distribution plots and hierarchical clustering of the samples. In addition, calling of differentially methylated regions is performed and statistics of called regions are automatically created. Finally, DNA methylation and gene expression data integration is covered by the calculation of correlating regions. Secondly, a novel algorithm, metilene, for the calculation of differentially methylated regions (DMRs) between two groups of samples is introduced. Existing methods are limited in terms of detection sensitivity as well as time and memory consumption. Our approach is based on a circular binary segmentation, using a scoring function to detect sub-regions that show a stronger difference between the mean methylation levels of two groups than the surrounding background. These sub-regions are tested using a two-dimensional Kolmogorov Smirnov test (2D-KS test) [Fasano 1987] for significant differences taking all samples of each group into account. The use of the non-parametric 2D-KS test allows to avoid assumptions about a background distribution. Furthermore, the two dimensions of the problem, i. e., (i) the detection of a region, such that (ii) the methylation rates of the samples in the groups are significantly different, are taken into account in a single test. The algorithm calls DMRs in sufficiently short time on single sample comparisons as well as on about 50 samples per group. Furthermore, it works on whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) data and is able so estimate missing data points from the methylation rates of other samples in the group. Benchmarks on simulated and real data sets show that metilene outperforms other existing methods and is especially suitable for noisy datasets often found for example in cancer analysis. In the framework of this thesis, the previously introduced methods and algorithms are used to analyze a WGBS dataset of two different subtypes of germinal-center derived B-cell lymphomas and healthy controls. In both lymphoma subgroups genome-wide hypomethylation was found, with an exception for a specific type of promoter regions, i. e., poised promoters, that were frequently found to be hypermethylated. Using the previously presented algorithm, DMRs were called between the three entities. A strong enrichment of DMRs immediately downstream of the transcription start site was observed, indicating the regulatory relevance of this regions. The integration of gene expression data of the same samples, revealed that a considerable amount of the DMRs showed significant correlation between gene expression and DNA methylation. Finally, transcription factor binding sites and mutation data were combined with the methylation and expression data analysis. This identified strongly altered signaling pathways and cancer subtype specific genes. Furthermore, the data integration indicates that mutations and DNA methylation changes may act complementary to another. Finally, findings from the lymphoma study regarding the hypermethylation of poised promoters in cancer were extended to a huge data set comprising a variety of cancers. We could show that the relation of DNA methylation at a small set of frequently poised regions with respect to the background methylation level is sufficient to classify almost all samples based on DNA methylation data from 450k BeadChips into cancer or non-cancer probes. In addition, we found that the increase in methylation co-occurs with upregulated gene expression of several poised promoter regulated genes in almost all fresh cancer samples, implying a de-poising of poised regions. This upregulated gene expression is in contrast to the silencing of those genes in cancer cell lines, indicating that the upregulated gene expression might be a temporary status and possibly contributes to cancerogenesis.
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Han, Chenggong. "Statistical models and computational methods for studying DNA differential methylation and 3D genome structure." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595417277891892.

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Jackel, Jamie Nicole. "GEMINIVIRUSES AS MODELS TO STUDY THE ESTABLISHMENT AND MAINTENANCE OF DNA METHYLATION." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1367494030.

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Books on the topic "Method for DNA methylation"

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Jörg, Tost, ed. DNA methylation: Methods and protocols. 2nd ed. New York: Humana Press, 2009.

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Jörg, Tost, ed. DNA methylation: Methods and protocols. 2nd ed. New York: Humana Press, 2009.

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Kenkyūjo, Kokuritsu Kankyō. Gurōbaru na DNA mechiru-ka henka ni chakumokushita kankyō kagaku busshitsu no epijenetikusu sayō sukurīningu-hō no kaihatsu: Kankyōshō kankyō kenkyū gijutsu kaihatsu suishinhi shūryō kenkyū seika hōkokusho : Heisei 20-nendo--Heisei 21-nendo = Studies on the method for detecting epigenetics effects of environmental chemicals focusing on global changes of DNA methylation. [Tokyo]: Kankyōshō Sōgō Kankyō Seisakukyoku Kankyō Hokenbu Kankyō Anzenka Kankyō Risuku Hyōkashitsu, 2010.

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Wang, Sun-Chong. DNA methylation microarrays: Experimental design and statistical analysis. Edited by Petronis Art. Boca Raton: Taylor & Francis, 2008.

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Erturk, Ece. Photochemical and Enzymatic Method for DNA Methylation Profiling and Walking Approach for Increasing Read Length of DNA Sequencing by Synthesis. [New York, N.Y.?]: [publisher not identified], 2018.

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Jost, Jean-Pierre, and Hans-Peter Saluz, eds. DNA Methylation. Basel: Birkhäuser Basel, 1993. http://dx.doi.org/10.1007/978-3-0348-9118-9.

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Tost, Jörg, ed. DNA Methylation. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-522-0.

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V, Beck Stephen, and Olek A, eds. The epigenome: Molecular hide and seek. Weinheim: Wiley-VCH, 2003.

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Mills, Ken I., and Bernie H. Ramsahoye. DNA Methylation Protocols. New Jersey: Humana Press, 2002. http://dx.doi.org/10.1385/1592591825.

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Tost, Jörg, ed. DNA Methylation Protocols. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7481-8.

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Book chapters on the topic "Method for DNA methylation"

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El-Maarri, Osman. "Methods: DNA Methylation." In Advances in Experimental Medicine and Biology, 197–204. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9072-3_23.

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Feng, Lingfang, and Jianlin Lou. "DNA Methylation Analysis." In Methods in Molecular Biology, 181–227. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8916-4_12.

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Akbari, Vahid, and Steven J. M. Jones. "Phasing DNA Methylation." In Methods in Molecular Biology, 219–35. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2819-5_14.

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Rauch, Tibor A., and Gerd P. Pfeifer. "The MIRA Method for DNA Methylation Analysis." In Methods in Molecular Biology, 65–75. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-522-0_6.

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Lin, Shili, Denise Scholtens, and Sujay Datta. "Sequencing-Based DNA Methylation Data." In Bioinformatics Methods, 119–46. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315153728-6.

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Cheishvili, David, Sophie Petropoulos, Steffan Christiansen, and Moshe Szyf. "Targeted DNA Methylation Analysis Methods." In Methods in Pharmacology and Toxicology, 33–50. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6743-8_3.

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Sant, Karilyn E., Muna S. Nahar, and Dana C. Dolinoy. "DNA Methylation Screening and Analysis." In Methods in Molecular Biology, 385–406. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-867-2_24.

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Kuramochi-Miyagawa, Satomi, Kanako Kita-Kojima, Yusuke Shiromoto, Daisuke Ito, Hirotaka Koshima, and Toru Nakano. "DNA Methylation in Mouse Testes." In Methods in Molecular Biology, 97–109. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-694-8_8.

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Jiang, Yale, Erick Forno, and Wei Chen. "DNA Methylation and Atopic Diseases." In Methods in Molecular Biology, 85–99. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1994-0_7.

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Li, Gang, Guosheng Zhang, and Yun Li. "DNA Methylation Imputation Across Platforms." In Methods in Molecular Biology, 137–51. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1994-0_11.

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Conference papers on the topic "Method for DNA methylation"

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Sun, Xifang, Jiaqiang Zhu, and Shiquan Sun. "A Nonparametric Method for Detecting Differential DNA Methylation Regions." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9312983.

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Shiddiky, Muhammad J. A., Abu Ali Ibn Sina, Laura G. Carrascosa, Ramkumar Palanisamy, Sakandar Rauf, and Matt Trau. "Methylsorb: A simple method for quantifying DNA methylation using DNA-gold affinity interactions." In 2014 8th International Conference on Electrical and Computer Engineering (ICECE). IEEE, 2014. http://dx.doi.org/10.1109/icece.2014.7027002.

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Zheng, Kai, Hanzhe Liu, Simin Zhu, Huamei Li, and Xiaozhou Chen. "DNA Methylation Analysis Methods for Cancer Research." In 5th International Conference on Information Engineering for Mechanics and Materials. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/icimm-15.2015.188.

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Kalofonou, Melpomeni, and Chris Toumazou. "An ISFET based analogue ratiometric method for DNA methylation detection." In 2014 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2014. http://dx.doi.org/10.1109/iscas.2014.6865514.

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Yudianto, Ahmad, Masniari Novita, Muhammad Afiful Jauhani, and Deka Bagus Binarsa. "DNA Methylation on Bloodstain as a Forensic Age Estimation Method." In International Conference on Law, Economics and Health (ICLEH 2020). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/aebmr.k.200513.006.

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Sukumar, S. "DNA Methylation – Role in Gene Regulation and Detection Methods." In Abstracts: Thirty-Second Annual CTRC‐AACR San Antonio Breast Cancer Symposium‐‐ Dec 10‐13, 2009; San Antonio, TX. American Association for Cancer Research, 2009. http://dx.doi.org/10.1158/0008-5472.sabcs-09-es4-1.

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Zhang, Minmin, Changchun Pan, Haichun Liu, Qinting Zhang, and Haozhe Li. "An Attention-Based Deep Learning Method for Schizophrenia Patients Classification Using DNA Methylation Data*." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175934.

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Xia, Chao, Yawen Xiao, Jun Wu, Xiaodong Zhao, and Hua Li. "A Convolutional Neural Network Based Ensemble Method for Cancer Prediction Using DNA Methylation Data." In the 2019 11th International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3318299.3318372.

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Peng, Quan, Daniel Kim, Mohammad Nezami Ranjbar, Sascha Strauss, Scott Winter, and Yexun Wang. "Abstract 5335: A highly efficient targeted bisulfite sequencing method for accurate DNA methylation profiling." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-5335.

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Tang, Man-Hung Eric, Vinay Varadan, Sid Kamalakaran, Michael Q. Zhang, Nevenka Dimitrova, and James Hicks. "A method for finding novel associations between genome-wide copy number and dna methylation patterns." In 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2011. http://dx.doi.org/10.1109/gensips.2011.6169448.

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Reports on the topic "Method for DNA methylation"

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Cohen, Yuval, Christopher A. Cullis, and Uri Lavi. Molecular Analyses of Soma-clonal Variation in Date Palm and Banana for Early Identification and Control of Off-types Generation. United States Department of Agriculture, October 2010. http://dx.doi.org/10.32747/2010.7592124.bard.

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Abstract:
Date palm (Phoenix dactylifera L.) is the major fruit tree grown in arid areas in the Middle East and North Africa. In the last century, dates were introduced to new regions including the USA. Date palms are traditionally propagated through offshoots. Expansion of modern date palm groves led to the development of Tissue Culture propagation methods that generate a large number of homogenous plants, have no seasonal effect on plant source and provide tools to fight the expansion of date pests and diseases. The disadvantage of this procedure is the occurrence of off-type trees which differ from the original cultivar. In the present project we focused on two of the most common date palm off-types: (1) trees with reduced fruit setting, in which most of the flowers turn into three-carpel parthenocarpic fruits. In a severe form, multi-carpel flowers and fruitlets (with up to six or eight carpels instead of the normal three-carpel flowers) are also formed. (2) dwarf trees, having fewer and shorter leaves, very short trunk and are not bearing fruits at their expected age, compared to the normal trees. Similar off-types occur in other crop species propagated by tissue culture, like banana (mainly dwarf plants) or oil palm (with a common 'Mantled' phenotype with reduced fruit setting and occurrence of supernumerary carpels). Some off-types can only be detected several years after planting in the fields. Therefore, efficient methods for prevention of the generation of off-types, as well as methods for their detection and early removal, are required for date palms, as well as for other tissue culture propagated crops. This research is aimed at the understanding of the mechanisms by which off-types are generated, and developing markers for their early identification. Several molecular and genomic approaches were applied. Using Methylation Sensitive AFLP and bisulfite sequencing, we detected changes in DNA methylation patterns occurring in off-types. We isolated and compared the sequence and expression of candidate genes, genes related to vegetative growth and dwarfism and genes related to flower development. While no sequence variation were detected, changes in gene expression, associated with the severity of the "fruit set" phenotype were detected in two genes - PdDEF (Ortholog of rice SPW1, and AP3 B type MADS box gene), and PdDIF (a defensin gene, highly homologous to the oil palm gene EGAD). We applied transcriptomic analyses, using high throughput sequencing, to identify genes differentially expressed in the "palm heart" (the apical meristem and the region of embryonic leaves) of dwarf vs. normal trees. Among the differentially expressed genes we identified genes related to hormonal biosynthesis, perception and regulation, genes related to cell expansion, and genes related to DNA methylation. Using Representation Difference Analyses, we detected changes in the genomes of off-type trees, mainly chloroplast-derived sequences that were incorporated in the nuclear genome and sequences of transposable elements. Sequences previously identified as differing between normal and off-type trees of oil palms or banana, successfully identified variation among date palm off-types, suggesting that these represent highly labile regions of monocot genomes. The data indicate that the date palm genome, similarly to genomes of other monocot crops as oil palm and banana, is quite unstable when cells pass through a cycle of tissue culture and regeneration. Changes in DNA sequences, translocation of DNA fragments and alteration of methylation patterns occur. Consequently, patterns of gene expression are changed, resulting in abnormal phenotypes. The data can be useful for future development of tools for early identification of off-type as well as for better understanding the phenomenon of somaclonal variation during propagation in vitro.
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Head, Thomas J., and Susannah Gal. DNA Based Fluid Computing Using Methylation. Fort Belvoir, VA: Defense Technical Information Center, August 2005. http://dx.doi.org/10.21236/ada436701.

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Yamamoto, Fumiichiro. DNA Methylation Alterations in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada405531.

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Yamamoto, Fumiichiro. DNA Methylation Alterations in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada408999.

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Cullen, Kevin J. The Effect of DNA Methylation on IGF2 Expression. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada361324.

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Huang, Tim H. M. Epigenetic Changes in DNA methylation in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada384184.

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Singal, Rakesh. Aberrant Promoter Methylation in Serium DNA as a Biomarker for Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada452528.

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Rusiecki, Jennifer A., Louis French, Zygmunt Galdzicki, Celia Byrne, Ligong Chen, Liying Yan, and Matthew Polin. Epigenetic Patterns of TBI: DNA Methylation in Serum of OIF/OEF Servicemembers. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada585498.

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Rusiecki, Jennifer A. Epigenetic Patterns of TBI: DNA Methylation in Serum of OIF/OEF Service Members. Fort Belvoir, VA: Defense Technical Information Center, April 2010. http://dx.doi.org/10.21236/ada540727.

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Rusiecki, Jennifer A. Epigenetic Patterns of PTSD: DNA Methylation in Serum of OIF/OEF Service Members. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada506346.

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