Infinium Methylation EPIC Array Service V2

Notice (8): Undefined variable: solution_of_interest [APP/View/Products/view.ctp, line 755]
Code Context
<!-- BEGIN: REQUEST_FORM MODAL -->
<div id="request_formModal" class="reveal-modal medium" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog">
    <?= $this->element('Forms/simple_form', array('solution_of_interest' => $solution_of_interest, 'header' => $header, 'message' => $message, 'campaign_id' => $campaign_id)) ?>
$viewFile = '/home/website-server/www/app/View/Products/view.ctp'
$dataForView = array(
	'language' => 'en',
	'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
	'meta_description' => '',
	'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
	'product' => array(
		'Product' => array(
			'id' => '3231',
			'antibody_id' => null,
			'name' => 'Infinium Methylation EPIC Array Service V2',
			'description' => '<p>The&nbsp;<strong>Infinium MethylationEPIC</strong><strong> v2.0</strong><strong> Array</strong>&nbsp;Services (improved version of the EPIC v1.0) is the new cutting-edge genome-wide DNA methylation analysis technique from Illumina<sup>&reg;</sup>&nbsp;based on bisulfite conversion. It allows to quantitatively detect the total&nbsp;methylation level of over 930,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;It offers comprehensive coverage of CpG islands, enhancer regions, open chromatin sites and&nbsp;other&nbsp;important regions of the methylome with extended content compared to the EPIC v1.0.</p>
<p></p>
<h2><span style="font-weight: 400;">Comprehensive and improved Genome-Wide Coverage</span></h2>
<ul class="square">
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>High percentage of probe overlap with previous version and 150,000 newly covered CpGs</li>
<li>More than 99% correlation in methylation values with EPIC v1.0</li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, array scan and data quality control</li>
<li>Bioinformatic analysis offering: <a href="https://www.diagenode.com/en/p/methylation-data-analysis">comparative&nbsp;analysis</a>, <a href="https://www.diagenode.com/en/p/data-mining-service">data mining</a></li>
</ul>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
			'label1' => 'Services Workflow',
			'info1' => '<div class="row">
<div class="small-12 medium-3 large-3 columns"><img alt="EPIC Array Service" src="https://www.diagenode.com/img/services/EPIC-ARRAY.png" caption="false" width="208" height="406" /></div>
<div class="small-12 medium-9 large-9 columns">
<table style="width: 680px;">
<tbody>
<tr style="height: 194px;">
<td style="height: 194px; width: 168px;">
<p><strong>End-to-end array </strong></p>
</td>
<td style="height: 194px; width: 504px;">
<ul>
<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array scanner&nbsp;</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
</div>',
			'label2' => 'Bioinformatics Analysis',
			'info2' => '<table>
<tbody>
<tr>
<td>
<h4><strong>Analysis</strong></h4>
</td>
<td>
<h4><strong>Features</strong></h4>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
</td>
</tr>
</tbody>
</table>',
			'label3' => '',
			'info3' => '',
			'format' => '',
			'catalog_number' => 'G0209006',
			'old_catalog_number' => '',
			'sf_code' => '',
			'type' => 'ACC',
			'search_order' => '',
			'price_EUR' => '/',
			'price_USD' => '/',
			'price_GBP' => '/',
			'price_JPY' => '/',
			'price_CNY' => '/',
			'price_AUD' => '/',
			'country' => 'ALL',
			'except_countries' => 'None',
			'quote' => true,
			'in_stock' => false,
			'featured' => true,
			'no_promo' => true,
			'online' => true,
			'master' => true,
			'last_datasheet_update' => '',
			'slug' => 'infinium-methylation-epic-array-v2-service',
			'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
			'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
			'meta_description' => '',
			'modified' => '2024-11-20 11:11:33',
			'created' => '2023-05-11 11:15:25',
			'locale' => 'eng'
		),
		'Antibody' => array(
			'host' => '*****',
			'id' => null,
			'name' => null,
			'description' => null,
			'clonality' => null,
			'isotype' => null,
			'lot' => null,
			'concentration' => null,
			'reactivity' => null,
			'type' => null,
			'purity' => null,
			'classification' => null,
			'application_table' => null,
			'storage_conditions' => null,
			'storage_buffer' => null,
			'precautions' => null,
			'uniprot_acc' => null,
			'slug' => null,
			'meta_keywords' => null,
			'meta_description' => null,
			'modified' => null,
			'created' => null,
			'select_label' => null
		),
		'Slave' => array(),
		'Group' => array(),
		'Related' => array(),
		'Application' => array(),
		'Category' => array(),
		'Document' => array(),
		'Feature' => array(),
		'Image' => array(),
		'Promotion' => array(),
		'Protocol' => array(),
		'Publication' => array(
			(int) 0 => array(
				[maximum depth reached]
			),
			(int) 1 => array(
				[maximum depth reached]
			),
			(int) 2 => array(
				[maximum depth reached]
			),
			(int) 3 => array(
				[maximum depth reached]
			),
			(int) 4 => array(
				[maximum depth reached]
			),
			(int) 5 => array(
				[maximum depth reached]
			),
			(int) 6 => array(
				[maximum depth reached]
			),
			(int) 7 => array(
				[maximum depth reached]
			),
			(int) 8 => array(
				[maximum depth reached]
			),
			(int) 9 => array(
				[maximum depth reached]
			)
		),
		'Testimonial' => array(),
		'Area' => array(),
		'SafetySheet' => array()
	)
)
$language = 'en'
$meta_keywords = 'Infinium MethylationEPIC Array Service v2'
$meta_description = ''
$meta_title = 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode'
$product = array(
	'Product' => array(
		'id' => '3231',
		'antibody_id' => null,
		'name' => 'Infinium Methylation EPIC Array Service V2',
		'description' => '<p>The&nbsp;<strong>Infinium MethylationEPIC</strong><strong> v2.0</strong><strong> Array</strong>&nbsp;Services (improved version of the EPIC v1.0) is the new cutting-edge genome-wide DNA methylation analysis technique from Illumina<sup>&reg;</sup>&nbsp;based on bisulfite conversion. It allows to quantitatively detect the total&nbsp;methylation level of over 930,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;It offers comprehensive coverage of CpG islands, enhancer regions, open chromatin sites and&nbsp;other&nbsp;important regions of the methylome with extended content compared to the EPIC v1.0.</p>
<p></p>
<h2><span style="font-weight: 400;">Comprehensive and improved Genome-Wide Coverage</span></h2>
<ul class="square">
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>High percentage of probe overlap with previous version and 150,000 newly covered CpGs</li>
<li>More than 99% correlation in methylation values with EPIC v1.0</li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, array scan and data quality control</li>
<li>Bioinformatic analysis offering: <a href="https://www.diagenode.com/en/p/methylation-data-analysis">comparative&nbsp;analysis</a>, <a href="https://www.diagenode.com/en/p/data-mining-service">data mining</a></li>
</ul>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
		'label1' => 'Services Workflow',
		'info1' => '<div class="row">
<div class="small-12 medium-3 large-3 columns"><img alt="EPIC Array Service" src="https://www.diagenode.com/img/services/EPIC-ARRAY.png" caption="false" width="208" height="406" /></div>
<div class="small-12 medium-9 large-9 columns">
<table style="width: 680px;">
<tbody>
<tr style="height: 194px;">
<td style="height: 194px; width: 168px;">
<p><strong>End-to-end array </strong></p>
</td>
<td style="height: 194px; width: 504px;">
<ul>
<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array scanner&nbsp;</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
</div>',
		'label2' => 'Bioinformatics Analysis',
		'info2' => '<table>
<tbody>
<tr>
<td>
<h4><strong>Analysis</strong></h4>
</td>
<td>
<h4><strong>Features</strong></h4>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
</td>
</tr>
</tbody>
</table>',
		'label3' => '',
		'info3' => '',
		'format' => '',
		'catalog_number' => 'G0209006',
		'old_catalog_number' => '',
		'sf_code' => '',
		'type' => 'ACC',
		'search_order' => '',
		'price_EUR' => '/',
		'price_USD' => '/',
		'price_GBP' => '/',
		'price_JPY' => '/',
		'price_CNY' => '/',
		'price_AUD' => '/',
		'country' => 'ALL',
		'except_countries' => 'None',
		'quote' => true,
		'in_stock' => false,
		'featured' => true,
		'no_promo' => true,
		'online' => true,
		'master' => true,
		'last_datasheet_update' => '',
		'slug' => 'infinium-methylation-epic-array-v2-service',
		'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
		'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
		'meta_description' => '',
		'modified' => '2024-11-20 11:11:33',
		'created' => '2023-05-11 11:15:25',
		'locale' => 'eng'
	),
	'Antibody' => array(
		'host' => '*****',
		'id' => null,
		'name' => null,
		'description' => null,
		'clonality' => null,
		'isotype' => null,
		'lot' => null,
		'concentration' => null,
		'reactivity' => null,
		'type' => null,
		'purity' => null,
		'classification' => null,
		'application_table' => null,
		'storage_conditions' => null,
		'storage_buffer' => null,
		'precautions' => null,
		'uniprot_acc' => null,
		'slug' => null,
		'meta_keywords' => null,
		'meta_description' => null,
		'modified' => null,
		'created' => null,
		'select_label' => null
	),
	'Slave' => array(),
	'Group' => array(),
	'Related' => array(),
	'Application' => array(),
	'Category' => array(),
	'Document' => array(),
	'Feature' => array(),
	'Image' => array(),
	'Promotion' => array(),
	'Protocol' => array(),
	'Publication' => array(
		(int) 0 => array(
			'id' => '4990',
			'name' => 'Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD',
			'authors' => 'Lillian Dipnall et al.',
			'description' => '<p><span>Research indicates that the underlying neurobiology of Attention Deficit/Hyperactivity Disorder (ADHD) may stem from a combination of genetic and environmental contributions. Genetic and epigenetic research have highlighted the potential role of the sialtransferase gene&nbsp;</span><em>ST3GAL3</em><span><span>&nbsp;</span>in this process. Adopting a pathways approach, this study sought to examine the role that<span>&nbsp;</span></span><em>ST3GAL3</em><span><span>&nbsp;</span>and other sialic acid metabolism (SAM) genes play in ADHD. Peripheral measures of DNA methylation (Illumina 850k EPIC; saliva samples) and clinical data were collected as part of a community-based pediatric cohort consisting of 90 children with ADHD [</span><em>m</em><sub>age</sub><span>= 10.40 (0.49); 66% male] and 50 non-ADHD controls [</span><em>m</em><sub>age</sub><span>= 10.40 (0.45); 48% male]. Using Reactome, 33 SAM genes were defined and resulted in a total of 1419 probes which included associated promotor/enhancer regions. Linear regression analysis was undertaken to explore differences in SAM probe DNA methylation between children with and without ADHD. The relationship with ADHD symptom severity was also examined. Analysis found 38 probes in the group-regression, and 64 probes in the symptom severity regression reached significance at an uncorrected level (a&thinsp;=&thinsp;0.05). No probes survived correction for multiple comparisons. Enrichment analysis revealed an overall pattern of hypermethylation across the SAM pathway for the ADHD group, with 84% of nominally significant probes being annotated to sialyltransferase genes. These results suggest that<span>&nbsp;</span></span><em>ST3GAL3</em><span><span>&nbsp;</span>and the broader SAM pathway could contribute to subtly disrupted epigenetic regulation in ADHD. However, extensive longitudinal research, across broad developmental age ranges, is necessary to further explore these findings.</span></p>',
			'date' => '2024-10-10',
			'pmid' => 'https://www.researchsquare.com/article/rs-4519315/v1',
			'doi' => 'https://doi.org/10.21203/rs.3.rs-4519315/v1',
			'modified' => '2024-10-18 11:52:30',
			'created' => '2024-10-18 11:52:30',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 1 => array(
			'id' => '4894',
			'name' => 'Matched analysis of detailed peripheral blood and tumor immune microenvironment profiles in bladder cancer',
			'authors' => 'Chen JQ et al.',
			'description' => '<p><b>Background:</b><span><span>&nbsp;</span>Bladder cancer and therapy responses hinge on immune profiles in the tumor microenvironment (TME) and blood, yet studies linking tumor-infiltrating immune cells to peripheral immune profiles are limited.<span>&nbsp;</span></span><b>Methods:</b><span><span>&nbsp;</span>DNA methylation cytometry quantified TME and matched peripheral blood immune cell proportions. With tumor immune profile data as the input, subjects were grouped by immune infiltration status and consensus clustering.<span>&nbsp;</span></span><b>Results:</b><span><span>&nbsp;</span>Immune hot and cold groups had different immune compositions in the TME but not in circulating blood. Two clusters of patients identified with consensus clustering had different immune compositions not only in the TME but also in blood.<span>&nbsp;</span></span><b>Conclusion:</b><span><span>&nbsp;</span>Detailed immune profiling via methylation cytometry reveals the significance of understanding tumor and systemic immune relationships in cancer patients.</span></p>',
			'date' => '2024-01-15',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/38221889/',
			'doi' => '10.2217/epi-2023-0358',
			'modified' => '2024-01-18 10:21:47',
			'created' => '2024-01-18 10:21:47',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 2 => array(
			'id' => '4767',
			'name' => 'Altered DNA methylation and gene expression predict disease severity inpatients with Aicardi-Goutières syndrome.',
			'authors' => 'Garau  J. et al.',
			'description' => '<p>Aicardi-Gouti&egrave;res Syndrome (AGS) is a rare neuro-inflammatory disease characterized by increased expression of interferon-stimulated genes (ISGs). Disease-causing mutations are present in genes associated with innate antiviral responses. Disease presentation and severity vary, even between patients with identical mutations from the same family. This study investigated DNA methylation signatures in PBMCs to understand phenotypic heterogeneity in AGS patients with mutations in RNASEH2B. AGS patients presented hypomethylation of ISGs and differential methylation patterns (DMPs) in genes involved in "neutrophil and platelet activation". Patients with "mild" phenotypes exhibited DMPs in genes involved in "DNA damage and repair", whereas patients with "severe" phenotypes had DMPs in "cell fate commitment" and "organ development" associated genes. DMPs in two ISGs (IFI44L, RSAD2) associated with increased gene expression in patients with "severe" when compared to "mild" phenotypes. In conclusion, altered DNA methylation and ISG expression as biomarkers and potential future treatment targets in AGS.</p>',
			'date' => '2023-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36963449',
			'doi' => '10.1016/j.clim.2023.109299',
			'modified' => '2023-04-17 13:07:38',
			'created' => '2023-04-14 13:41:22',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 3 => array(
			'id' => '4774',
			'name' => 'DNA methylation aberrancy is a reliable prognostic tool in uveal melanoma',
			'authors' => 'Soltysova  A. et al.',
			'description' => '<p>Despite outstanding advances in understanding the genetic background of uveal melanoma (UM) development and prognosis, the role of DNA methylation reprogramming remains elusive. This study aims to clarify the extent of DNA methylation deregulation in the context of gene expression changes and its utility as a reliable prognostic biomarker. Methods: Transcriptomic and DNA methylation landscapes in 25 high- and low-risk UMs were interrogated by Agilent SurePrint G3 Human Gene Expression 8x60K v2 Microarray and Human Infinium Methylation EPIC Bead Chip array, respectively. DNA methylation and gene expression of the nine top discriminatory genes, selected by the integrative analysis, were validated by pyrosequencing and qPCR in 58 tissues. Results: Among 2,262 differentially expressed genes discovered in UM samples differing in metastatic risk, 60 were epigenetic regulators, mostly histone modifiers and chromatin remodelers. A total of 44,398 CpGs were differentially methylated, 27,810 hypomethylated, and 16,588 hypermethylated in high-risk tumors, with delta beta values ranging between -0.78 and 0.79. By integrative analysis, 944 differentially expressed DNA methylation-regulated genes were revealed, 635 hypomethylated/upregulated, and 309 hypermethylated/downregulated. Aberrant DNA methylation in high-risk tumors was associated with the deregulation of key oncogenic pathways such as EGFR tyrosine kinase inhibitor resistance, focal adhesion, proteoglycans in cancer, PI3K-Akt signaling, or ECM-receptor interaction. Notably, the DNA methylation values of nine genes, HTR2B , AHNAK2, CALHM2, SLC25A38, EDNRB, TLR1, RNF43, IL12RB2 , and MEGF10, validated by pyrosequencing, demonstrated excellent risk group prediction accuracies (AUCs ranging between 0.870 and 0.956). Moreover, CALHM2 hypomethylation and MEGF10, TLR1 hypermethylation, as well as two three-gene methylation signatures, Signature 1 combining A HNAK2, CALHM2, and IL12RB and Signature 2 A HNAK2, CALHM2, and SLC25A38 genes, correlated with shorter overall survival (HR = 4.38, 95\% CI 1.30-16.41, HR = 5.59, 95\% CI 1.30-16.41; HR = 3.43, 95\% CI 1.30-16.41, HR = 4.61, 95\% CI 1.30-16.41 and HR = 4.95, 95\% CI 1.39-17.58, respectively). Conclusions: Our results demonstrate a significant role of DNA methylation aberrancy in UM progression. The advantages of DNA as a biological material and the excellent prediction accuracies of methylation markers open the perspective for their more extensive clinical use.</p>',
			'date' => '2023-02-01',
			'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-2502537%2Fv2',
			'doi' => '10.21203/rs.3.rs-2502537/v2',
			'modified' => '2023-04-17 13:12:52',
			'created' => '2023-04-14 13:41:22',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 4 => array(
			'id' => '4565',
			'name' => 'Decitabine increases neoantigen and cancer testis antigen expression toenhance T cell-mediated toxicity against glioblastoma.',
			'authors' => 'Ma  Ruichong et al.',
			'description' => '<p>BACKGROUND: Glioblastoma (GBM) is the most common and malignant primary brain tumour in adults. Despite maximal treatment, median survival remains dismal at 14-24 months. Immunotherapies, such as checkpoint inhibition, have revolutionised management of some cancers but have little benefit for GBM patients. This is, in part, due to the low mutational and neoantigen burden in this immunogenically 'cold' tumour. METHODS: U87MG and patient derived cell lines were treated with 5-aza-2'-deoxycytidine (DAC) and underwent whole exome and transcriptome sequencing. Cell lines were then subjected to cellular assays with neoantigen and cancer testis antigen (CTA) specific T cells. RESULTS: We demonstrate that DAC increases neoantigen and CTA mRNA expression through DNA hypomethylation. This results in increased neoantigen presentation by MHC class I in tumour cells, leading to increased neoantigen- and CTA-specific T cell activation and killing of DAC-treated cancer cells. In addition, we show that patients have endogenous cancer-specific T cells in both tumour and blood, which show increased tumour-specific activation in the presence of DAC-treated cells. CONCLUSIONS: Our work shows that DAC increases GBM immunogenicity and consequent susceptibility to T cell responses in-vitro. Our results support a potential use of DAC as a sensitizing agent to immunotherapy.</p>',
			'date' => '2022-04-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35468205',
			'doi' => '10.1093/neuonc/noac107',
			'modified' => '2022-11-24 09:12:45',
			'created' => '2022-11-24 08:49:52',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 5 => array(
			'id' => '4107',
			'name' => 'Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling',
			'authors' => 'Lucas A Salas, Ze Zhang, Devin C Koestler, Rondi A Butler, Helen M Hansen, Annette M Molinaro, John K Wiencke, Karl T Kelsey, Brock C Christensen',
			'description' => '<p><span>DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ na&iuml;ve and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures, and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of the immune system in human health and disease.</span></p>',
			'date' => '2021-04-12',
			'pmid' => 'https://doi.org/10.1101/2021.04.11.439377',
			'doi' => '10.1101/2021.04.11.439377',
			'modified' => '2021-06-29 14:17:36',
			'created' => '2021-06-29 14:17:36',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 6 => array(
			'id' => '4105',
			'name' => 'Interplay between Histone and DNA Methylation Seen through Comparative Methylomes in Rare Mendelian Disorders',
			'authors' => ' Guillaume Velasco, Damien Ulveling,Sophie Rondeau,Pauline Marzin,Motoko Unoki,Valérie Cormier-Daire, Claire Francastel',
			'description' => '<p><span>DNA methylation (DNAme) profiling is used to establish specific biomarkers to improve the diagnosis of patients with inherited neurodevelopmental disorders and to guide mutation screening. In the specific case of mendelian disorders of the epigenetic machinery, it also provides the basis to infer mechanistic aspects with regard to DNAme determinants and interplay between histone and DNAme that apply to humans. Here, we present comparative methylomes from patients with mutations in the de novo DNA methyltransferases DNMT3A and DNMT3B, in their catalytic domain or their N-terminal parts involved in reading histone methylation, or in histone H3 lysine (K) methylases NSD1 or SETD2 (H3 K36) or KMT2D/MLL2 (H3 K4). We provide disease-specific DNAme signatures and document the distinct consequences of mutations in enzymes with very similar or intertwined functions, including at repeated sequences and imprinted loci. We found that KMT2D and SETD2 germline mutations have little impact on DNAme profiles. In contrast, the overlapping DNAme alterations downstream of NSD1 or DNMT3 mutations underlines functional links, more specifically between NSD1 and DNMT3B at heterochromatin regions or DNMT3A at regulatory elements. Together, these data indicate certain discrepancy with the mechanisms described in animal models or the existence of redundant or complementary functions unforeseen in humans.</span></p>',
			'date' => '2021-04-03',
			'pmid' => 'https://doi.org/10.3390/ijms22073735',
			'doi' => '10.3390/ijms22073735',
			'modified' => '2021-06-29 14:12:51',
			'created' => '2021-06-29 14:12:51',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 7 => array(
			'id' => '4106',
			'name' => 'Genome-wide DNA methylation and transcriptome integration reveal distinct sex differences in skeletal muscle',
			'authors' => 'Shanie Landen, Macsue Jacques , Danielle Hiam , Javier Alvarez, Nicholas R Harvey, Larisa M. Haupt, Lyn R, Griffiths, Kevin J Ashton, Séverine Lamon, Sarah Voisin, Nir Eynon',
			'description' => '<p><span>Nearly all human complex traits and diseases exhibit some degree of sex differences, and epigenetics contributes to these differences as DNA methylation shows sex differences in various tissues. However, skeletal muscle epigenetic sex differences remain largely unexplored, yet skeletal muscle displays distinct sex differences at the transcriptome level. We conducted a large-scale meta-analysis of autosomal DNA methylation sex differences in human skeletal muscle in three separate cohorts (Gene SMART, FUSION, and GSE38291), totalling n = 369 human muscle samples (n = 222 males, n = 147 females). We found 10,240 differentially methylated regions (DMRs) at FDR &lt; 0.005, 94% of which were hypomethylated in males, and gene set enrichment analysis revealed that differentially methylated genes were involved in muscle contraction and metabolism. We then integrated our epigenetic results with transcriptomic data from the GTEx database and the FUSION cohort. Altogether, we identified 326 autosomal genes that display sex differences at both the DNA methylation, and transcriptome levels. Importantly, sex-biased genes at the transcriptional level were overrepresented among the sex-biased genes at the epigenetic level (p-value = 4.6e-13), which suggests differential DNA methylation and gene expression between males and females in muscle are functionally linked. Finally, we validated expression of three genes with large effect sizes (FOXO3A, ALDH1A1, and GGT7) in the Gene SMART cohort with qPCR. GGT7, involved in muscle metabolism, displays male-biased expression in skeletal muscle across the three cohorts, as well as lower methylation in males. In conclusion, we uncovered thousands of genes that exhibit DNA methylation differences between the males and females in human skeletal muscle that may modulate mechanisms controlling muscle metabolism and health.</span></p>',
			'date' => '2021-03-17',
			'pmid' => 'https://doi.org/10.1101/2021.03.16.435733',
			'doi' => '10.1101/2021.03.16.435733',
			'modified' => '2021-06-29 14:15:50',
			'created' => '2021-06-29 14:15:50',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 8 => array(
			'id' => '4358',
			'name' => 'ZNF718, HOXA4, and ZFP57 are differentially methylated inperiodontitis in comparison with periodontal health: Epigenome-wide DNAmethylation pilot study.',
			'authors' => 'Hernández H.G. et al. ',
			'description' => '<p>OBJECTIVE: To investigate the differences in the epigenomic patterns of DNA methylation in peripheral leukocytes between patients with periodontitis and gingivally healthy controls evaluating its functional meaning by functional enrichment analysis. BACKGROUND: The DNA methylation profiling of peripheral leukocytes as immune-related tissue potentially relevant as a source of biomarkers between periodontitis patients and gingivally healthy subjects has not been investigated. METHODS: A DNA methylation epigenome-wide study of peripheral leukocytes was conducted using the Illumina MethylationEPIC platform in sixteen subjects, eight diagnosed with periodontitis patients and eight age-matched and sex-matched periodontally healthy controls. A trained periodontist performed the clinical evaluation. Global DNA methylation was estimated using methylation-sensitive high-resolution melting in LINE1. Routine cell count cytometry and metabolic laboratory tests were also performed. The analysis of differentially methylated positions (DMPs) and differentially methylated regions (DMRs) was made using R/Bioconductor environment considering leukocyte populations assessed in both routine cell counts and using the FlowSorted.Blood.EPIC package. Finally, a DMP and DMR intersection analysis was performed. Functional enrichment analysis was carried out with the differentially methylated genes found in DMP. RESULTS: DMP analysis identified 81 differentially hypermethylated genes and 21 differentially hypomethylated genes. Importantly, the intersection analysis showed that zinc finger protein 718 (ZNF718) and homeobox A4 (HOXA4) were differentially hypermethylated and zinc finger protein 57 (ZFP57) was differentially hypomethylated in periodontitis. The functional enrichment analysis found clearly immune-related ontologies such as "detection of bacterium" and "antigen processing and presentation." CONCLUSION: The results of this study propose three new periodontitis-related genes: ZNF718, HOXA4, and ZFP57 but also evidence the suitability and relevance of studying leukocytes' DNA methylome for biological interpretation of systemic immune-related epigenetic patterns in periodontitis.</p>',
			'date' => '2021-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33660869',
			'doi' => '10.1111/jre.12868',
			'modified' => '2022-08-03 16:48:52',
			'created' => '2022-05-19 10:41:50',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 9 => array(
			'id' => '4590',
			'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
			'authors' => 'Tiane  A. et al.',
			'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
			'date' => '0000-00-00',
			'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
			'doi' => '10.1101/2023.01.12.523740',
			'modified' => '2023-04-11 10:06:33',
			'created' => '2023-02-21 09:59:46',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		)
	),
	'Testimonial' => array(),
	'Area' => array(),
	'SafetySheet' => array()
)
$country = 'US'
$countries_allowed = array(
	(int) 0 => 'CA',
	(int) 1 => 'US',
	(int) 2 => 'IE',
	(int) 3 => 'GB',
	(int) 4 => 'DK',
	(int) 5 => 'NO',
	(int) 6 => 'SE',
	(int) 7 => 'FI',
	(int) 8 => 'NL',
	(int) 9 => 'BE',
	(int) 10 => 'LU',
	(int) 11 => 'FR',
	(int) 12 => 'DE',
	(int) 13 => 'CH',
	(int) 14 => 'AT',
	(int) 15 => 'ES',
	(int) 16 => 'IT',
	(int) 17 => 'PT'
)
$outsource = false
$other_formats = array()
$edit = ''
$testimonials = ''
$featured_testimonials = ''
$related_products = ''
$rrbs_service = array(
	(int) 0 => (int) 1894,
	(int) 1 => (int) 1895
)
$chipseq_service = array(
	(int) 0 => (int) 2683,
	(int) 1 => (int) 1835,
	(int) 2 => (int) 1836,
	(int) 3 => (int) 2684,
	(int) 4 => (int) 1838,
	(int) 5 => (int) 1839,
	(int) 6 => (int) 1856
)
$labelize = object(Closure) {
	
}
$old_catalog_number = ''
$label = '<img src="/img/banners/banner-customizer-back.png" alt=""/>'
$publication = array(
	'id' => '4590',
	'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
	'authors' => 'Tiane  A. et al.',
	'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
	'date' => '0000-00-00',
	'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
	'doi' => '10.1101/2023.01.12.523740',
	'modified' => '2023-04-11 10:06:33',
	'created' => '2023-02-21 09:59:46',
	'ProductsPublication' => array(
		'id' => '6858',
		'product_id' => '3231',
		'publication_id' => '4590'
	)
)
$externalLink = ' <a href="https://doi.org/10.1101%2F2023.01.12.523740" target="_blank"><i class="fa fa-external-link"></i></a>'
include - APP/View/Products/view.ctp, line 755
View::_evaluate() - CORE/Cake/View/View.php, line 971
View::_render() - CORE/Cake/View/View.php, line 933
View::render() - CORE/Cake/View/View.php, line 473
Controller::render() - CORE/Cake/Controller/Controller.php, line 963
ProductsController::slug() - APP/Controller/ProductsController.php, line 1052
ReflectionMethod::invokeArgs() - [internal], line ??
Controller::invokeAction() - CORE/Cake/Controller/Controller.php, line 491
Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 193
Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167
[main] - APP/webroot/index.php, line 118

Notice (8): Undefined variable: header [APP/View/Products/view.ctp, line 755]
Code Context
<!-- BEGIN: REQUEST_FORM MODAL -->
<div id="request_formModal" class="reveal-modal medium" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog">
    <?= $this->element('Forms/simple_form', array('solution_of_interest' => $solution_of_interest, 'header' => $header, 'message' => $message, 'campaign_id' => $campaign_id)) ?>
$viewFile = '/home/website-server/www/app/View/Products/view.ctp'
$dataForView = array(
	'language' => 'en',
	'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
	'meta_description' => '',
	'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
	'product' => array(
		'Product' => array(
			'id' => '3231',
			'antibody_id' => null,
			'name' => 'Infinium Methylation EPIC Array Service V2',
			'description' => '<p>The&nbsp;<strong>Infinium MethylationEPIC</strong><strong> v2.0</strong><strong> Array</strong>&nbsp;Services (improved version of the EPIC v1.0) is the new cutting-edge genome-wide DNA methylation analysis technique from Illumina<sup>&reg;</sup>&nbsp;based on bisulfite conversion. It allows to quantitatively detect the total&nbsp;methylation level of over 930,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;It offers comprehensive coverage of CpG islands, enhancer regions, open chromatin sites and&nbsp;other&nbsp;important regions of the methylome with extended content compared to the EPIC v1.0.</p>
<p></p>
<h2><span style="font-weight: 400;">Comprehensive and improved Genome-Wide Coverage</span></h2>
<ul class="square">
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>High percentage of probe overlap with previous version and 150,000 newly covered CpGs</li>
<li>More than 99% correlation in methylation values with EPIC v1.0</li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, array scan and data quality control</li>
<li>Bioinformatic analysis offering: <a href="https://www.diagenode.com/en/p/methylation-data-analysis">comparative&nbsp;analysis</a>, <a href="https://www.diagenode.com/en/p/data-mining-service">data mining</a></li>
</ul>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
			'label1' => 'Services Workflow',
			'info1' => '<div class="row">
<div class="small-12 medium-3 large-3 columns"><img alt="EPIC Array Service" src="https://www.diagenode.com/img/services/EPIC-ARRAY.png" caption="false" width="208" height="406" /></div>
<div class="small-12 medium-9 large-9 columns">
<table style="width: 680px;">
<tbody>
<tr style="height: 194px;">
<td style="height: 194px; width: 168px;">
<p><strong>End-to-end array </strong></p>
</td>
<td style="height: 194px; width: 504px;">
<ul>
<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array scanner&nbsp;</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
</div>',
			'label2' => 'Bioinformatics Analysis',
			'info2' => '<table>
<tbody>
<tr>
<td>
<h4><strong>Analysis</strong></h4>
</td>
<td>
<h4><strong>Features</strong></h4>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
</td>
</tr>
</tbody>
</table>',
			'label3' => '',
			'info3' => '',
			'format' => '',
			'catalog_number' => 'G0209006',
			'old_catalog_number' => '',
			'sf_code' => '',
			'type' => 'ACC',
			'search_order' => '',
			'price_EUR' => '/',
			'price_USD' => '/',
			'price_GBP' => '/',
			'price_JPY' => '/',
			'price_CNY' => '/',
			'price_AUD' => '/',
			'country' => 'ALL',
			'except_countries' => 'None',
			'quote' => true,
			'in_stock' => false,
			'featured' => true,
			'no_promo' => true,
			'online' => true,
			'master' => true,
			'last_datasheet_update' => '',
			'slug' => 'infinium-methylation-epic-array-v2-service',
			'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
			'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
			'meta_description' => '',
			'modified' => '2024-11-20 11:11:33',
			'created' => '2023-05-11 11:15:25',
			'locale' => 'eng'
		),
		'Antibody' => array(
			'host' => '*****',
			'id' => null,
			'name' => null,
			'description' => null,
			'clonality' => null,
			'isotype' => null,
			'lot' => null,
			'concentration' => null,
			'reactivity' => null,
			'type' => null,
			'purity' => null,
			'classification' => null,
			'application_table' => null,
			'storage_conditions' => null,
			'storage_buffer' => null,
			'precautions' => null,
			'uniprot_acc' => null,
			'slug' => null,
			'meta_keywords' => null,
			'meta_description' => null,
			'modified' => null,
			'created' => null,
			'select_label' => null
		),
		'Slave' => array(),
		'Group' => array(),
		'Related' => array(),
		'Application' => array(),
		'Category' => array(),
		'Document' => array(),
		'Feature' => array(),
		'Image' => array(),
		'Promotion' => array(),
		'Protocol' => array(),
		'Publication' => array(
			(int) 0 => array(
				[maximum depth reached]
			),
			(int) 1 => array(
				[maximum depth reached]
			),
			(int) 2 => array(
				[maximum depth reached]
			),
			(int) 3 => array(
				[maximum depth reached]
			),
			(int) 4 => array(
				[maximum depth reached]
			),
			(int) 5 => array(
				[maximum depth reached]
			),
			(int) 6 => array(
				[maximum depth reached]
			),
			(int) 7 => array(
				[maximum depth reached]
			),
			(int) 8 => array(
				[maximum depth reached]
			),
			(int) 9 => array(
				[maximum depth reached]
			)
		),
		'Testimonial' => array(),
		'Area' => array(),
		'SafetySheet' => array()
	)
)
$language = 'en'
$meta_keywords = 'Infinium MethylationEPIC Array Service v2'
$meta_description = ''
$meta_title = 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode'
$product = array(
	'Product' => array(
		'id' => '3231',
		'antibody_id' => null,
		'name' => 'Infinium Methylation EPIC Array Service V2',
		'description' => '<p>The&nbsp;<strong>Infinium MethylationEPIC</strong><strong> v2.0</strong><strong> Array</strong>&nbsp;Services (improved version of the EPIC v1.0) is the new cutting-edge genome-wide DNA methylation analysis technique from Illumina<sup>&reg;</sup>&nbsp;based on bisulfite conversion. It allows to quantitatively detect the total&nbsp;methylation level of over 930,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;It offers comprehensive coverage of CpG islands, enhancer regions, open chromatin sites and&nbsp;other&nbsp;important regions of the methylome with extended content compared to the EPIC v1.0.</p>
<p></p>
<h2><span style="font-weight: 400;">Comprehensive and improved Genome-Wide Coverage</span></h2>
<ul class="square">
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>High percentage of probe overlap with previous version and 150,000 newly covered CpGs</li>
<li>More than 99% correlation in methylation values with EPIC v1.0</li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, array scan and data quality control</li>
<li>Bioinformatic analysis offering: <a href="https://www.diagenode.com/en/p/methylation-data-analysis">comparative&nbsp;analysis</a>, <a href="https://www.diagenode.com/en/p/data-mining-service">data mining</a></li>
</ul>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
		'label1' => 'Services Workflow',
		'info1' => '<div class="row">
<div class="small-12 medium-3 large-3 columns"><img alt="EPIC Array Service" src="https://www.diagenode.com/img/services/EPIC-ARRAY.png" caption="false" width="208" height="406" /></div>
<div class="small-12 medium-9 large-9 columns">
<table style="width: 680px;">
<tbody>
<tr style="height: 194px;">
<td style="height: 194px; width: 168px;">
<p><strong>End-to-end array </strong></p>
</td>
<td style="height: 194px; width: 504px;">
<ul>
<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array scanner&nbsp;</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
</div>',
		'label2' => 'Bioinformatics Analysis',
		'info2' => '<table>
<tbody>
<tr>
<td>
<h4><strong>Analysis</strong></h4>
</td>
<td>
<h4><strong>Features</strong></h4>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
</td>
</tr>
</tbody>
</table>',
		'label3' => '',
		'info3' => '',
		'format' => '',
		'catalog_number' => 'G0209006',
		'old_catalog_number' => '',
		'sf_code' => '',
		'type' => 'ACC',
		'search_order' => '',
		'price_EUR' => '/',
		'price_USD' => '/',
		'price_GBP' => '/',
		'price_JPY' => '/',
		'price_CNY' => '/',
		'price_AUD' => '/',
		'country' => 'ALL',
		'except_countries' => 'None',
		'quote' => true,
		'in_stock' => false,
		'featured' => true,
		'no_promo' => true,
		'online' => true,
		'master' => true,
		'last_datasheet_update' => '',
		'slug' => 'infinium-methylation-epic-array-v2-service',
		'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
		'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
		'meta_description' => '',
		'modified' => '2024-11-20 11:11:33',
		'created' => '2023-05-11 11:15:25',
		'locale' => 'eng'
	),
	'Antibody' => array(
		'host' => '*****',
		'id' => null,
		'name' => null,
		'description' => null,
		'clonality' => null,
		'isotype' => null,
		'lot' => null,
		'concentration' => null,
		'reactivity' => null,
		'type' => null,
		'purity' => null,
		'classification' => null,
		'application_table' => null,
		'storage_conditions' => null,
		'storage_buffer' => null,
		'precautions' => null,
		'uniprot_acc' => null,
		'slug' => null,
		'meta_keywords' => null,
		'meta_description' => null,
		'modified' => null,
		'created' => null,
		'select_label' => null
	),
	'Slave' => array(),
	'Group' => array(),
	'Related' => array(),
	'Application' => array(),
	'Category' => array(),
	'Document' => array(),
	'Feature' => array(),
	'Image' => array(),
	'Promotion' => array(),
	'Protocol' => array(),
	'Publication' => array(
		(int) 0 => array(
			'id' => '4990',
			'name' => 'Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD',
			'authors' => 'Lillian Dipnall et al.',
			'description' => '<p><span>Research indicates that the underlying neurobiology of Attention Deficit/Hyperactivity Disorder (ADHD) may stem from a combination of genetic and environmental contributions. Genetic and epigenetic research have highlighted the potential role of the sialtransferase gene&nbsp;</span><em>ST3GAL3</em><span><span>&nbsp;</span>in this process. Adopting a pathways approach, this study sought to examine the role that<span>&nbsp;</span></span><em>ST3GAL3</em><span><span>&nbsp;</span>and other sialic acid metabolism (SAM) genes play in ADHD. Peripheral measures of DNA methylation (Illumina 850k EPIC; saliva samples) and clinical data were collected as part of a community-based pediatric cohort consisting of 90 children with ADHD [</span><em>m</em><sub>age</sub><span>= 10.40 (0.49); 66% male] and 50 non-ADHD controls [</span><em>m</em><sub>age</sub><span>= 10.40 (0.45); 48% male]. Using Reactome, 33 SAM genes were defined and resulted in a total of 1419 probes which included associated promotor/enhancer regions. Linear regression analysis was undertaken to explore differences in SAM probe DNA methylation between children with and without ADHD. The relationship with ADHD symptom severity was also examined. Analysis found 38 probes in the group-regression, and 64 probes in the symptom severity regression reached significance at an uncorrected level (a&thinsp;=&thinsp;0.05). No probes survived correction for multiple comparisons. Enrichment analysis revealed an overall pattern of hypermethylation across the SAM pathway for the ADHD group, with 84% of nominally significant probes being annotated to sialyltransferase genes. These results suggest that<span>&nbsp;</span></span><em>ST3GAL3</em><span><span>&nbsp;</span>and the broader SAM pathway could contribute to subtly disrupted epigenetic regulation in ADHD. However, extensive longitudinal research, across broad developmental age ranges, is necessary to further explore these findings.</span></p>',
			'date' => '2024-10-10',
			'pmid' => 'https://www.researchsquare.com/article/rs-4519315/v1',
			'doi' => 'https://doi.org/10.21203/rs.3.rs-4519315/v1',
			'modified' => '2024-10-18 11:52:30',
			'created' => '2024-10-18 11:52:30',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 1 => array(
			'id' => '4894',
			'name' => 'Matched analysis of detailed peripheral blood and tumor immune microenvironment profiles in bladder cancer',
			'authors' => 'Chen JQ et al.',
			'description' => '<p><b>Background:</b><span><span>&nbsp;</span>Bladder cancer and therapy responses hinge on immune profiles in the tumor microenvironment (TME) and blood, yet studies linking tumor-infiltrating immune cells to peripheral immune profiles are limited.<span>&nbsp;</span></span><b>Methods:</b><span><span>&nbsp;</span>DNA methylation cytometry quantified TME and matched peripheral blood immune cell proportions. With tumor immune profile data as the input, subjects were grouped by immune infiltration status and consensus clustering.<span>&nbsp;</span></span><b>Results:</b><span><span>&nbsp;</span>Immune hot and cold groups had different immune compositions in the TME but not in circulating blood. Two clusters of patients identified with consensus clustering had different immune compositions not only in the TME but also in blood.<span>&nbsp;</span></span><b>Conclusion:</b><span><span>&nbsp;</span>Detailed immune profiling via methylation cytometry reveals the significance of understanding tumor and systemic immune relationships in cancer patients.</span></p>',
			'date' => '2024-01-15',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/38221889/',
			'doi' => '10.2217/epi-2023-0358',
			'modified' => '2024-01-18 10:21:47',
			'created' => '2024-01-18 10:21:47',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 2 => array(
			'id' => '4767',
			'name' => 'Altered DNA methylation and gene expression predict disease severity inpatients with Aicardi-Goutières syndrome.',
			'authors' => 'Garau  J. et al.',
			'description' => '<p>Aicardi-Gouti&egrave;res Syndrome (AGS) is a rare neuro-inflammatory disease characterized by increased expression of interferon-stimulated genes (ISGs). Disease-causing mutations are present in genes associated with innate antiviral responses. Disease presentation and severity vary, even between patients with identical mutations from the same family. This study investigated DNA methylation signatures in PBMCs to understand phenotypic heterogeneity in AGS patients with mutations in RNASEH2B. AGS patients presented hypomethylation of ISGs and differential methylation patterns (DMPs) in genes involved in "neutrophil and platelet activation". Patients with "mild" phenotypes exhibited DMPs in genes involved in "DNA damage and repair", whereas patients with "severe" phenotypes had DMPs in "cell fate commitment" and "organ development" associated genes. DMPs in two ISGs (IFI44L, RSAD2) associated with increased gene expression in patients with "severe" when compared to "mild" phenotypes. In conclusion, altered DNA methylation and ISG expression as biomarkers and potential future treatment targets in AGS.</p>',
			'date' => '2023-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36963449',
			'doi' => '10.1016/j.clim.2023.109299',
			'modified' => '2023-04-17 13:07:38',
			'created' => '2023-04-14 13:41:22',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 3 => array(
			'id' => '4774',
			'name' => 'DNA methylation aberrancy is a reliable prognostic tool in uveal melanoma',
			'authors' => 'Soltysova  A. et al.',
			'description' => '<p>Despite outstanding advances in understanding the genetic background of uveal melanoma (UM) development and prognosis, the role of DNA methylation reprogramming remains elusive. This study aims to clarify the extent of DNA methylation deregulation in the context of gene expression changes and its utility as a reliable prognostic biomarker. Methods: Transcriptomic and DNA methylation landscapes in 25 high- and low-risk UMs were interrogated by Agilent SurePrint G3 Human Gene Expression 8x60K v2 Microarray and Human Infinium Methylation EPIC Bead Chip array, respectively. DNA methylation and gene expression of the nine top discriminatory genes, selected by the integrative analysis, were validated by pyrosequencing and qPCR in 58 tissues. Results: Among 2,262 differentially expressed genes discovered in UM samples differing in metastatic risk, 60 were epigenetic regulators, mostly histone modifiers and chromatin remodelers. A total of 44,398 CpGs were differentially methylated, 27,810 hypomethylated, and 16,588 hypermethylated in high-risk tumors, with delta beta values ranging between -0.78 and 0.79. By integrative analysis, 944 differentially expressed DNA methylation-regulated genes were revealed, 635 hypomethylated/upregulated, and 309 hypermethylated/downregulated. Aberrant DNA methylation in high-risk tumors was associated with the deregulation of key oncogenic pathways such as EGFR tyrosine kinase inhibitor resistance, focal adhesion, proteoglycans in cancer, PI3K-Akt signaling, or ECM-receptor interaction. Notably, the DNA methylation values of nine genes, HTR2B , AHNAK2, CALHM2, SLC25A38, EDNRB, TLR1, RNF43, IL12RB2 , and MEGF10, validated by pyrosequencing, demonstrated excellent risk group prediction accuracies (AUCs ranging between 0.870 and 0.956). Moreover, CALHM2 hypomethylation and MEGF10, TLR1 hypermethylation, as well as two three-gene methylation signatures, Signature 1 combining A HNAK2, CALHM2, and IL12RB and Signature 2 A HNAK2, CALHM2, and SLC25A38 genes, correlated with shorter overall survival (HR = 4.38, 95\% CI 1.30-16.41, HR = 5.59, 95\% CI 1.30-16.41; HR = 3.43, 95\% CI 1.30-16.41, HR = 4.61, 95\% CI 1.30-16.41 and HR = 4.95, 95\% CI 1.39-17.58, respectively). Conclusions: Our results demonstrate a significant role of DNA methylation aberrancy in UM progression. The advantages of DNA as a biological material and the excellent prediction accuracies of methylation markers open the perspective for their more extensive clinical use.</p>',
			'date' => '2023-02-01',
			'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-2502537%2Fv2',
			'doi' => '10.21203/rs.3.rs-2502537/v2',
			'modified' => '2023-04-17 13:12:52',
			'created' => '2023-04-14 13:41:22',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 4 => array(
			'id' => '4565',
			'name' => 'Decitabine increases neoantigen and cancer testis antigen expression toenhance T cell-mediated toxicity against glioblastoma.',
			'authors' => 'Ma  Ruichong et al.',
			'description' => '<p>BACKGROUND: Glioblastoma (GBM) is the most common and malignant primary brain tumour in adults. Despite maximal treatment, median survival remains dismal at 14-24 months. Immunotherapies, such as checkpoint inhibition, have revolutionised management of some cancers but have little benefit for GBM patients. This is, in part, due to the low mutational and neoantigen burden in this immunogenically 'cold' tumour. METHODS: U87MG and patient derived cell lines were treated with 5-aza-2'-deoxycytidine (DAC) and underwent whole exome and transcriptome sequencing. Cell lines were then subjected to cellular assays with neoantigen and cancer testis antigen (CTA) specific T cells. RESULTS: We demonstrate that DAC increases neoantigen and CTA mRNA expression through DNA hypomethylation. This results in increased neoantigen presentation by MHC class I in tumour cells, leading to increased neoantigen- and CTA-specific T cell activation and killing of DAC-treated cancer cells. In addition, we show that patients have endogenous cancer-specific T cells in both tumour and blood, which show increased tumour-specific activation in the presence of DAC-treated cells. CONCLUSIONS: Our work shows that DAC increases GBM immunogenicity and consequent susceptibility to T cell responses in-vitro. Our results support a potential use of DAC as a sensitizing agent to immunotherapy.</p>',
			'date' => '2022-04-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35468205',
			'doi' => '10.1093/neuonc/noac107',
			'modified' => '2022-11-24 09:12:45',
			'created' => '2022-11-24 08:49:52',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 5 => array(
			'id' => '4107',
			'name' => 'Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling',
			'authors' => 'Lucas A Salas, Ze Zhang, Devin C Koestler, Rondi A Butler, Helen M Hansen, Annette M Molinaro, John K Wiencke, Karl T Kelsey, Brock C Christensen',
			'description' => '<p><span>DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ na&iuml;ve and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures, and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of the immune system in human health and disease.</span></p>',
			'date' => '2021-04-12',
			'pmid' => 'https://doi.org/10.1101/2021.04.11.439377',
			'doi' => '10.1101/2021.04.11.439377',
			'modified' => '2021-06-29 14:17:36',
			'created' => '2021-06-29 14:17:36',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 6 => array(
			'id' => '4105',
			'name' => 'Interplay between Histone and DNA Methylation Seen through Comparative Methylomes in Rare Mendelian Disorders',
			'authors' => ' Guillaume Velasco, Damien Ulveling,Sophie Rondeau,Pauline Marzin,Motoko Unoki,Valérie Cormier-Daire, Claire Francastel',
			'description' => '<p><span>DNA methylation (DNAme) profiling is used to establish specific biomarkers to improve the diagnosis of patients with inherited neurodevelopmental disorders and to guide mutation screening. In the specific case of mendelian disorders of the epigenetic machinery, it also provides the basis to infer mechanistic aspects with regard to DNAme determinants and interplay between histone and DNAme that apply to humans. Here, we present comparative methylomes from patients with mutations in the de novo DNA methyltransferases DNMT3A and DNMT3B, in their catalytic domain or their N-terminal parts involved in reading histone methylation, or in histone H3 lysine (K) methylases NSD1 or SETD2 (H3 K36) or KMT2D/MLL2 (H3 K4). We provide disease-specific DNAme signatures and document the distinct consequences of mutations in enzymes with very similar or intertwined functions, including at repeated sequences and imprinted loci. We found that KMT2D and SETD2 germline mutations have little impact on DNAme profiles. In contrast, the overlapping DNAme alterations downstream of NSD1 or DNMT3 mutations underlines functional links, more specifically between NSD1 and DNMT3B at heterochromatin regions or DNMT3A at regulatory elements. Together, these data indicate certain discrepancy with the mechanisms described in animal models or the existence of redundant or complementary functions unforeseen in humans.</span></p>',
			'date' => '2021-04-03',
			'pmid' => 'https://doi.org/10.3390/ijms22073735',
			'doi' => '10.3390/ijms22073735',
			'modified' => '2021-06-29 14:12:51',
			'created' => '2021-06-29 14:12:51',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 7 => array(
			'id' => '4106',
			'name' => 'Genome-wide DNA methylation and transcriptome integration reveal distinct sex differences in skeletal muscle',
			'authors' => 'Shanie Landen, Macsue Jacques , Danielle Hiam , Javier Alvarez, Nicholas R Harvey, Larisa M. Haupt, Lyn R, Griffiths, Kevin J Ashton, Séverine Lamon, Sarah Voisin, Nir Eynon',
			'description' => '<p><span>Nearly all human complex traits and diseases exhibit some degree of sex differences, and epigenetics contributes to these differences as DNA methylation shows sex differences in various tissues. However, skeletal muscle epigenetic sex differences remain largely unexplored, yet skeletal muscle displays distinct sex differences at the transcriptome level. We conducted a large-scale meta-analysis of autosomal DNA methylation sex differences in human skeletal muscle in three separate cohorts (Gene SMART, FUSION, and GSE38291), totalling n = 369 human muscle samples (n = 222 males, n = 147 females). We found 10,240 differentially methylated regions (DMRs) at FDR &lt; 0.005, 94% of which were hypomethylated in males, and gene set enrichment analysis revealed that differentially methylated genes were involved in muscle contraction and metabolism. We then integrated our epigenetic results with transcriptomic data from the GTEx database and the FUSION cohort. Altogether, we identified 326 autosomal genes that display sex differences at both the DNA methylation, and transcriptome levels. Importantly, sex-biased genes at the transcriptional level were overrepresented among the sex-biased genes at the epigenetic level (p-value = 4.6e-13), which suggests differential DNA methylation and gene expression between males and females in muscle are functionally linked. Finally, we validated expression of three genes with large effect sizes (FOXO3A, ALDH1A1, and GGT7) in the Gene SMART cohort with qPCR. GGT7, involved in muscle metabolism, displays male-biased expression in skeletal muscle across the three cohorts, as well as lower methylation in males. In conclusion, we uncovered thousands of genes that exhibit DNA methylation differences between the males and females in human skeletal muscle that may modulate mechanisms controlling muscle metabolism and health.</span></p>',
			'date' => '2021-03-17',
			'pmid' => 'https://doi.org/10.1101/2021.03.16.435733',
			'doi' => '10.1101/2021.03.16.435733',
			'modified' => '2021-06-29 14:15:50',
			'created' => '2021-06-29 14:15:50',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 8 => array(
			'id' => '4358',
			'name' => 'ZNF718, HOXA4, and ZFP57 are differentially methylated inperiodontitis in comparison with periodontal health: Epigenome-wide DNAmethylation pilot study.',
			'authors' => 'Hernández H.G. et al. ',
			'description' => '<p>OBJECTIVE: To investigate the differences in the epigenomic patterns of DNA methylation in peripheral leukocytes between patients with periodontitis and gingivally healthy controls evaluating its functional meaning by functional enrichment analysis. BACKGROUND: The DNA methylation profiling of peripheral leukocytes as immune-related tissue potentially relevant as a source of biomarkers between periodontitis patients and gingivally healthy subjects has not been investigated. METHODS: A DNA methylation epigenome-wide study of peripheral leukocytes was conducted using the Illumina MethylationEPIC platform in sixteen subjects, eight diagnosed with periodontitis patients and eight age-matched and sex-matched periodontally healthy controls. A trained periodontist performed the clinical evaluation. Global DNA methylation was estimated using methylation-sensitive high-resolution melting in LINE1. Routine cell count cytometry and metabolic laboratory tests were also performed. The analysis of differentially methylated positions (DMPs) and differentially methylated regions (DMRs) was made using R/Bioconductor environment considering leukocyte populations assessed in both routine cell counts and using the FlowSorted.Blood.EPIC package. Finally, a DMP and DMR intersection analysis was performed. Functional enrichment analysis was carried out with the differentially methylated genes found in DMP. RESULTS: DMP analysis identified 81 differentially hypermethylated genes and 21 differentially hypomethylated genes. Importantly, the intersection analysis showed that zinc finger protein 718 (ZNF718) and homeobox A4 (HOXA4) were differentially hypermethylated and zinc finger protein 57 (ZFP57) was differentially hypomethylated in periodontitis. The functional enrichment analysis found clearly immune-related ontologies such as "detection of bacterium" and "antigen processing and presentation." CONCLUSION: The results of this study propose three new periodontitis-related genes: ZNF718, HOXA4, and ZFP57 but also evidence the suitability and relevance of studying leukocytes' DNA methylome for biological interpretation of systemic immune-related epigenetic patterns in periodontitis.</p>',
			'date' => '2021-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33660869',
			'doi' => '10.1111/jre.12868',
			'modified' => '2022-08-03 16:48:52',
			'created' => '2022-05-19 10:41:50',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 9 => array(
			'id' => '4590',
			'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
			'authors' => 'Tiane  A. et al.',
			'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
			'date' => '0000-00-00',
			'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
			'doi' => '10.1101/2023.01.12.523740',
			'modified' => '2023-04-11 10:06:33',
			'created' => '2023-02-21 09:59:46',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		)
	),
	'Testimonial' => array(),
	'Area' => array(),
	'SafetySheet' => array()
)
$country = 'US'
$countries_allowed = array(
	(int) 0 => 'CA',
	(int) 1 => 'US',
	(int) 2 => 'IE',
	(int) 3 => 'GB',
	(int) 4 => 'DK',
	(int) 5 => 'NO',
	(int) 6 => 'SE',
	(int) 7 => 'FI',
	(int) 8 => 'NL',
	(int) 9 => 'BE',
	(int) 10 => 'LU',
	(int) 11 => 'FR',
	(int) 12 => 'DE',
	(int) 13 => 'CH',
	(int) 14 => 'AT',
	(int) 15 => 'ES',
	(int) 16 => 'IT',
	(int) 17 => 'PT'
)
$outsource = false
$other_formats = array()
$edit = ''
$testimonials = ''
$featured_testimonials = ''
$related_products = ''
$rrbs_service = array(
	(int) 0 => (int) 1894,
	(int) 1 => (int) 1895
)
$chipseq_service = array(
	(int) 0 => (int) 2683,
	(int) 1 => (int) 1835,
	(int) 2 => (int) 1836,
	(int) 3 => (int) 2684,
	(int) 4 => (int) 1838,
	(int) 5 => (int) 1839,
	(int) 6 => (int) 1856
)
$labelize = object(Closure) {
	
}
$old_catalog_number = ''
$label = '<img src="/img/banners/banner-customizer-back.png" alt=""/>'
$publication = array(
	'id' => '4590',
	'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
	'authors' => 'Tiane  A. et al.',
	'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
	'date' => '0000-00-00',
	'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
	'doi' => '10.1101/2023.01.12.523740',
	'modified' => '2023-04-11 10:06:33',
	'created' => '2023-02-21 09:59:46',
	'ProductsPublication' => array(
		'id' => '6858',
		'product_id' => '3231',
		'publication_id' => '4590'
	)
)
$externalLink = ' <a href="https://doi.org/10.1101%2F2023.01.12.523740" target="_blank"><i class="fa fa-external-link"></i></a>'
include - APP/View/Products/view.ctp, line 755
View::_evaluate() - CORE/Cake/View/View.php, line 971
View::_render() - CORE/Cake/View/View.php, line 933
View::render() - CORE/Cake/View/View.php, line 473
Controller::render() - CORE/Cake/Controller/Controller.php, line 963
ProductsController::slug() - APP/Controller/ProductsController.php, line 1052
ReflectionMethod::invokeArgs() - [internal], line ??
Controller::invokeAction() - CORE/Cake/Controller/Controller.php, line 491
Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 193
Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167
[main] - APP/webroot/index.php, line 118

Notice (8): Undefined variable: message [APP/View/Products/view.ctp, line 755]
Code Context
<!-- BEGIN: REQUEST_FORM MODAL -->
<div id="request_formModal" class="reveal-modal medium" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog">
    <?= $this->element('Forms/simple_form', array('solution_of_interest' => $solution_of_interest, 'header' => $header, 'message' => $message, 'campaign_id' => $campaign_id)) ?>
$viewFile = '/home/website-server/www/app/View/Products/view.ctp'
$dataForView = array(
	'language' => 'en',
	'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
	'meta_description' => '',
	'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
	'product' => array(
		'Product' => array(
			'id' => '3231',
			'antibody_id' => null,
			'name' => 'Infinium Methylation EPIC Array Service V2',
			'description' => '<p>The&nbsp;<strong>Infinium MethylationEPIC</strong><strong> v2.0</strong><strong> Array</strong>&nbsp;Services (improved version of the EPIC v1.0) is the new cutting-edge genome-wide DNA methylation analysis technique from Illumina<sup>&reg;</sup>&nbsp;based on bisulfite conversion. It allows to quantitatively detect the total&nbsp;methylation level of over 930,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;It offers comprehensive coverage of CpG islands, enhancer regions, open chromatin sites and&nbsp;other&nbsp;important regions of the methylome with extended content compared to the EPIC v1.0.</p>
<p></p>
<h2><span style="font-weight: 400;">Comprehensive and improved Genome-Wide Coverage</span></h2>
<ul class="square">
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>High percentage of probe overlap with previous version and 150,000 newly covered CpGs</li>
<li>More than 99% correlation in methylation values with EPIC v1.0</li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, array scan and data quality control</li>
<li>Bioinformatic analysis offering: <a href="https://www.diagenode.com/en/p/methylation-data-analysis">comparative&nbsp;analysis</a>, <a href="https://www.diagenode.com/en/p/data-mining-service">data mining</a></li>
</ul>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
			'label1' => 'Services Workflow',
			'info1' => '<div class="row">
<div class="small-12 medium-3 large-3 columns"><img alt="EPIC Array Service" src="https://www.diagenode.com/img/services/EPIC-ARRAY.png" caption="false" width="208" height="406" /></div>
<div class="small-12 medium-9 large-9 columns">
<table style="width: 680px;">
<tbody>
<tr style="height: 194px;">
<td style="height: 194px; width: 168px;">
<p><strong>End-to-end array </strong></p>
</td>
<td style="height: 194px; width: 504px;">
<ul>
<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array scanner&nbsp;</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
</div>',
			'label2' => 'Bioinformatics Analysis',
			'info2' => '<table>
<tbody>
<tr>
<td>
<h4><strong>Analysis</strong></h4>
</td>
<td>
<h4><strong>Features</strong></h4>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
</td>
</tr>
</tbody>
</table>',
			'label3' => '',
			'info3' => '',
			'format' => '',
			'catalog_number' => 'G0209006',
			'old_catalog_number' => '',
			'sf_code' => '',
			'type' => 'ACC',
			'search_order' => '',
			'price_EUR' => '/',
			'price_USD' => '/',
			'price_GBP' => '/',
			'price_JPY' => '/',
			'price_CNY' => '/',
			'price_AUD' => '/',
			'country' => 'ALL',
			'except_countries' => 'None',
			'quote' => true,
			'in_stock' => false,
			'featured' => true,
			'no_promo' => true,
			'online' => true,
			'master' => true,
			'last_datasheet_update' => '',
			'slug' => 'infinium-methylation-epic-array-v2-service',
			'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
			'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
			'meta_description' => '',
			'modified' => '2024-11-20 11:11:33',
			'created' => '2023-05-11 11:15:25',
			'locale' => 'eng'
		),
		'Antibody' => array(
			'host' => '*****',
			'id' => null,
			'name' => null,
			'description' => null,
			'clonality' => null,
			'isotype' => null,
			'lot' => null,
			'concentration' => null,
			'reactivity' => null,
			'type' => null,
			'purity' => null,
			'classification' => null,
			'application_table' => null,
			'storage_conditions' => null,
			'storage_buffer' => null,
			'precautions' => null,
			'uniprot_acc' => null,
			'slug' => null,
			'meta_keywords' => null,
			'meta_description' => null,
			'modified' => null,
			'created' => null,
			'select_label' => null
		),
		'Slave' => array(),
		'Group' => array(),
		'Related' => array(),
		'Application' => array(),
		'Category' => array(),
		'Document' => array(),
		'Feature' => array(),
		'Image' => array(),
		'Promotion' => array(),
		'Protocol' => array(),
		'Publication' => array(
			(int) 0 => array(
				[maximum depth reached]
			),
			(int) 1 => array(
				[maximum depth reached]
			),
			(int) 2 => array(
				[maximum depth reached]
			),
			(int) 3 => array(
				[maximum depth reached]
			),
			(int) 4 => array(
				[maximum depth reached]
			),
			(int) 5 => array(
				[maximum depth reached]
			),
			(int) 6 => array(
				[maximum depth reached]
			),
			(int) 7 => array(
				[maximum depth reached]
			),
			(int) 8 => array(
				[maximum depth reached]
			),
			(int) 9 => array(
				[maximum depth reached]
			)
		),
		'Testimonial' => array(),
		'Area' => array(),
		'SafetySheet' => array()
	)
)
$language = 'en'
$meta_keywords = 'Infinium MethylationEPIC Array Service v2'
$meta_description = ''
$meta_title = 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode'
$product = array(
	'Product' => array(
		'id' => '3231',
		'antibody_id' => null,
		'name' => 'Infinium Methylation EPIC Array Service V2',
		'description' => '<p>The&nbsp;<strong>Infinium MethylationEPIC</strong><strong> v2.0</strong><strong> Array</strong>&nbsp;Services (improved version of the EPIC v1.0) is the new cutting-edge genome-wide DNA methylation analysis technique from Illumina<sup>&reg;</sup>&nbsp;based on bisulfite conversion. It allows to quantitatively detect the total&nbsp;methylation level of over 930,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;It offers comprehensive coverage of CpG islands, enhancer regions, open chromatin sites and&nbsp;other&nbsp;important regions of the methylome with extended content compared to the EPIC v1.0.</p>
<p></p>
<h2><span style="font-weight: 400;">Comprehensive and improved Genome-Wide Coverage</span></h2>
<ul class="square">
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>High percentage of probe overlap with previous version and 150,000 newly covered CpGs</li>
<li>More than 99% correlation in methylation values with EPIC v1.0</li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, array scan and data quality control</li>
<li>Bioinformatic analysis offering: <a href="https://www.diagenode.com/en/p/methylation-data-analysis">comparative&nbsp;analysis</a>, <a href="https://www.diagenode.com/en/p/data-mining-service">data mining</a></li>
</ul>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
		'label1' => 'Services Workflow',
		'info1' => '<div class="row">
<div class="small-12 medium-3 large-3 columns"><img alt="EPIC Array Service" src="https://www.diagenode.com/img/services/EPIC-ARRAY.png" caption="false" width="208" height="406" /></div>
<div class="small-12 medium-9 large-9 columns">
<table style="width: 680px;">
<tbody>
<tr style="height: 194px;">
<td style="height: 194px; width: 168px;">
<p><strong>End-to-end array </strong></p>
</td>
<td style="height: 194px; width: 504px;">
<ul>
<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array scanner&nbsp;</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
</div>',
		'label2' => 'Bioinformatics Analysis',
		'info2' => '<table>
<tbody>
<tr>
<td>
<h4><strong>Analysis</strong></h4>
</td>
<td>
<h4><strong>Features</strong></h4>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
</td>
</tr>
</tbody>
</table>',
		'label3' => '',
		'info3' => '',
		'format' => '',
		'catalog_number' => 'G0209006',
		'old_catalog_number' => '',
		'sf_code' => '',
		'type' => 'ACC',
		'search_order' => '',
		'price_EUR' => '/',
		'price_USD' => '/',
		'price_GBP' => '/',
		'price_JPY' => '/',
		'price_CNY' => '/',
		'price_AUD' => '/',
		'country' => 'ALL',
		'except_countries' => 'None',
		'quote' => true,
		'in_stock' => false,
		'featured' => true,
		'no_promo' => true,
		'online' => true,
		'master' => true,
		'last_datasheet_update' => '',
		'slug' => 'infinium-methylation-epic-array-v2-service',
		'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
		'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
		'meta_description' => '',
		'modified' => '2024-11-20 11:11:33',
		'created' => '2023-05-11 11:15:25',
		'locale' => 'eng'
	),
	'Antibody' => array(
		'host' => '*****',
		'id' => null,
		'name' => null,
		'description' => null,
		'clonality' => null,
		'isotype' => null,
		'lot' => null,
		'concentration' => null,
		'reactivity' => null,
		'type' => null,
		'purity' => null,
		'classification' => null,
		'application_table' => null,
		'storage_conditions' => null,
		'storage_buffer' => null,
		'precautions' => null,
		'uniprot_acc' => null,
		'slug' => null,
		'meta_keywords' => null,
		'meta_description' => null,
		'modified' => null,
		'created' => null,
		'select_label' => null
	),
	'Slave' => array(),
	'Group' => array(),
	'Related' => array(),
	'Application' => array(),
	'Category' => array(),
	'Document' => array(),
	'Feature' => array(),
	'Image' => array(),
	'Promotion' => array(),
	'Protocol' => array(),
	'Publication' => array(
		(int) 0 => array(
			'id' => '4990',
			'name' => 'Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD',
			'authors' => 'Lillian Dipnall et al.',
			'description' => '<p><span>Research indicates that the underlying neurobiology of Attention Deficit/Hyperactivity Disorder (ADHD) may stem from a combination of genetic and environmental contributions. Genetic and epigenetic research have highlighted the potential role of the sialtransferase gene&nbsp;</span><em>ST3GAL3</em><span><span>&nbsp;</span>in this process. Adopting a pathways approach, this study sought to examine the role that<span>&nbsp;</span></span><em>ST3GAL3</em><span><span>&nbsp;</span>and other sialic acid metabolism (SAM) genes play in ADHD. Peripheral measures of DNA methylation (Illumina 850k EPIC; saliva samples) and clinical data were collected as part of a community-based pediatric cohort consisting of 90 children with ADHD [</span><em>m</em><sub>age</sub><span>= 10.40 (0.49); 66% male] and 50 non-ADHD controls [</span><em>m</em><sub>age</sub><span>= 10.40 (0.45); 48% male]. Using Reactome, 33 SAM genes were defined and resulted in a total of 1419 probes which included associated promotor/enhancer regions. Linear regression analysis was undertaken to explore differences in SAM probe DNA methylation between children with and without ADHD. The relationship with ADHD symptom severity was also examined. Analysis found 38 probes in the group-regression, and 64 probes in the symptom severity regression reached significance at an uncorrected level (a&thinsp;=&thinsp;0.05). No probes survived correction for multiple comparisons. Enrichment analysis revealed an overall pattern of hypermethylation across the SAM pathway for the ADHD group, with 84% of nominally significant probes being annotated to sialyltransferase genes. These results suggest that<span>&nbsp;</span></span><em>ST3GAL3</em><span><span>&nbsp;</span>and the broader SAM pathway could contribute to subtly disrupted epigenetic regulation in ADHD. However, extensive longitudinal research, across broad developmental age ranges, is necessary to further explore these findings.</span></p>',
			'date' => '2024-10-10',
			'pmid' => 'https://www.researchsquare.com/article/rs-4519315/v1',
			'doi' => 'https://doi.org/10.21203/rs.3.rs-4519315/v1',
			'modified' => '2024-10-18 11:52:30',
			'created' => '2024-10-18 11:52:30',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 1 => array(
			'id' => '4894',
			'name' => 'Matched analysis of detailed peripheral blood and tumor immune microenvironment profiles in bladder cancer',
			'authors' => 'Chen JQ et al.',
			'description' => '<p><b>Background:</b><span><span>&nbsp;</span>Bladder cancer and therapy responses hinge on immune profiles in the tumor microenvironment (TME) and blood, yet studies linking tumor-infiltrating immune cells to peripheral immune profiles are limited.<span>&nbsp;</span></span><b>Methods:</b><span><span>&nbsp;</span>DNA methylation cytometry quantified TME and matched peripheral blood immune cell proportions. With tumor immune profile data as the input, subjects were grouped by immune infiltration status and consensus clustering.<span>&nbsp;</span></span><b>Results:</b><span><span>&nbsp;</span>Immune hot and cold groups had different immune compositions in the TME but not in circulating blood. Two clusters of patients identified with consensus clustering had different immune compositions not only in the TME but also in blood.<span>&nbsp;</span></span><b>Conclusion:</b><span><span>&nbsp;</span>Detailed immune profiling via methylation cytometry reveals the significance of understanding tumor and systemic immune relationships in cancer patients.</span></p>',
			'date' => '2024-01-15',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/38221889/',
			'doi' => '10.2217/epi-2023-0358',
			'modified' => '2024-01-18 10:21:47',
			'created' => '2024-01-18 10:21:47',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 2 => array(
			'id' => '4767',
			'name' => 'Altered DNA methylation and gene expression predict disease severity inpatients with Aicardi-Goutières syndrome.',
			'authors' => 'Garau  J. et al.',
			'description' => '<p>Aicardi-Gouti&egrave;res Syndrome (AGS) is a rare neuro-inflammatory disease characterized by increased expression of interferon-stimulated genes (ISGs). Disease-causing mutations are present in genes associated with innate antiviral responses. Disease presentation and severity vary, even between patients with identical mutations from the same family. This study investigated DNA methylation signatures in PBMCs to understand phenotypic heterogeneity in AGS patients with mutations in RNASEH2B. AGS patients presented hypomethylation of ISGs and differential methylation patterns (DMPs) in genes involved in "neutrophil and platelet activation". Patients with "mild" phenotypes exhibited DMPs in genes involved in "DNA damage and repair", whereas patients with "severe" phenotypes had DMPs in "cell fate commitment" and "organ development" associated genes. DMPs in two ISGs (IFI44L, RSAD2) associated with increased gene expression in patients with "severe" when compared to "mild" phenotypes. In conclusion, altered DNA methylation and ISG expression as biomarkers and potential future treatment targets in AGS.</p>',
			'date' => '2023-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36963449',
			'doi' => '10.1016/j.clim.2023.109299',
			'modified' => '2023-04-17 13:07:38',
			'created' => '2023-04-14 13:41:22',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 3 => array(
			'id' => '4774',
			'name' => 'DNA methylation aberrancy is a reliable prognostic tool in uveal melanoma',
			'authors' => 'Soltysova  A. et al.',
			'description' => '<p>Despite outstanding advances in understanding the genetic background of uveal melanoma (UM) development and prognosis, the role of DNA methylation reprogramming remains elusive. This study aims to clarify the extent of DNA methylation deregulation in the context of gene expression changes and its utility as a reliable prognostic biomarker. Methods: Transcriptomic and DNA methylation landscapes in 25 high- and low-risk UMs were interrogated by Agilent SurePrint G3 Human Gene Expression 8x60K v2 Microarray and Human Infinium Methylation EPIC Bead Chip array, respectively. DNA methylation and gene expression of the nine top discriminatory genes, selected by the integrative analysis, were validated by pyrosequencing and qPCR in 58 tissues. Results: Among 2,262 differentially expressed genes discovered in UM samples differing in metastatic risk, 60 were epigenetic regulators, mostly histone modifiers and chromatin remodelers. A total of 44,398 CpGs were differentially methylated, 27,810 hypomethylated, and 16,588 hypermethylated in high-risk tumors, with delta beta values ranging between -0.78 and 0.79. By integrative analysis, 944 differentially expressed DNA methylation-regulated genes were revealed, 635 hypomethylated/upregulated, and 309 hypermethylated/downregulated. Aberrant DNA methylation in high-risk tumors was associated with the deregulation of key oncogenic pathways such as EGFR tyrosine kinase inhibitor resistance, focal adhesion, proteoglycans in cancer, PI3K-Akt signaling, or ECM-receptor interaction. Notably, the DNA methylation values of nine genes, HTR2B , AHNAK2, CALHM2, SLC25A38, EDNRB, TLR1, RNF43, IL12RB2 , and MEGF10, validated by pyrosequencing, demonstrated excellent risk group prediction accuracies (AUCs ranging between 0.870 and 0.956). Moreover, CALHM2 hypomethylation and MEGF10, TLR1 hypermethylation, as well as two three-gene methylation signatures, Signature 1 combining A HNAK2, CALHM2, and IL12RB and Signature 2 A HNAK2, CALHM2, and SLC25A38 genes, correlated with shorter overall survival (HR = 4.38, 95\% CI 1.30-16.41, HR = 5.59, 95\% CI 1.30-16.41; HR = 3.43, 95\% CI 1.30-16.41, HR = 4.61, 95\% CI 1.30-16.41 and HR = 4.95, 95\% CI 1.39-17.58, respectively). Conclusions: Our results demonstrate a significant role of DNA methylation aberrancy in UM progression. The advantages of DNA as a biological material and the excellent prediction accuracies of methylation markers open the perspective for their more extensive clinical use.</p>',
			'date' => '2023-02-01',
			'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-2502537%2Fv2',
			'doi' => '10.21203/rs.3.rs-2502537/v2',
			'modified' => '2023-04-17 13:12:52',
			'created' => '2023-04-14 13:41:22',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 4 => array(
			'id' => '4565',
			'name' => 'Decitabine increases neoantigen and cancer testis antigen expression toenhance T cell-mediated toxicity against glioblastoma.',
			'authors' => 'Ma  Ruichong et al.',
			'description' => '<p>BACKGROUND: Glioblastoma (GBM) is the most common and malignant primary brain tumour in adults. Despite maximal treatment, median survival remains dismal at 14-24 months. Immunotherapies, such as checkpoint inhibition, have revolutionised management of some cancers but have little benefit for GBM patients. This is, in part, due to the low mutational and neoantigen burden in this immunogenically 'cold' tumour. METHODS: U87MG and patient derived cell lines were treated with 5-aza-2'-deoxycytidine (DAC) and underwent whole exome and transcriptome sequencing. Cell lines were then subjected to cellular assays with neoantigen and cancer testis antigen (CTA) specific T cells. RESULTS: We demonstrate that DAC increases neoantigen and CTA mRNA expression through DNA hypomethylation. This results in increased neoantigen presentation by MHC class I in tumour cells, leading to increased neoantigen- and CTA-specific T cell activation and killing of DAC-treated cancer cells. In addition, we show that patients have endogenous cancer-specific T cells in both tumour and blood, which show increased tumour-specific activation in the presence of DAC-treated cells. CONCLUSIONS: Our work shows that DAC increases GBM immunogenicity and consequent susceptibility to T cell responses in-vitro. Our results support a potential use of DAC as a sensitizing agent to immunotherapy.</p>',
			'date' => '2022-04-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35468205',
			'doi' => '10.1093/neuonc/noac107',
			'modified' => '2022-11-24 09:12:45',
			'created' => '2022-11-24 08:49:52',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 5 => array(
			'id' => '4107',
			'name' => 'Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling',
			'authors' => 'Lucas A Salas, Ze Zhang, Devin C Koestler, Rondi A Butler, Helen M Hansen, Annette M Molinaro, John K Wiencke, Karl T Kelsey, Brock C Christensen',
			'description' => '<p><span>DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ na&iuml;ve and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures, and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of the immune system in human health and disease.</span></p>',
			'date' => '2021-04-12',
			'pmid' => 'https://doi.org/10.1101/2021.04.11.439377',
			'doi' => '10.1101/2021.04.11.439377',
			'modified' => '2021-06-29 14:17:36',
			'created' => '2021-06-29 14:17:36',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 6 => array(
			'id' => '4105',
			'name' => 'Interplay between Histone and DNA Methylation Seen through Comparative Methylomes in Rare Mendelian Disorders',
			'authors' => ' Guillaume Velasco, Damien Ulveling,Sophie Rondeau,Pauline Marzin,Motoko Unoki,Valérie Cormier-Daire, Claire Francastel',
			'description' => '<p><span>DNA methylation (DNAme) profiling is used to establish specific biomarkers to improve the diagnosis of patients with inherited neurodevelopmental disorders and to guide mutation screening. In the specific case of mendelian disorders of the epigenetic machinery, it also provides the basis to infer mechanistic aspects with regard to DNAme determinants and interplay between histone and DNAme that apply to humans. Here, we present comparative methylomes from patients with mutations in the de novo DNA methyltransferases DNMT3A and DNMT3B, in their catalytic domain or their N-terminal parts involved in reading histone methylation, or in histone H3 lysine (K) methylases NSD1 or SETD2 (H3 K36) or KMT2D/MLL2 (H3 K4). We provide disease-specific DNAme signatures and document the distinct consequences of mutations in enzymes with very similar or intertwined functions, including at repeated sequences and imprinted loci. We found that KMT2D and SETD2 germline mutations have little impact on DNAme profiles. In contrast, the overlapping DNAme alterations downstream of NSD1 or DNMT3 mutations underlines functional links, more specifically between NSD1 and DNMT3B at heterochromatin regions or DNMT3A at regulatory elements. Together, these data indicate certain discrepancy with the mechanisms described in animal models or the existence of redundant or complementary functions unforeseen in humans.</span></p>',
			'date' => '2021-04-03',
			'pmid' => 'https://doi.org/10.3390/ijms22073735',
			'doi' => '10.3390/ijms22073735',
			'modified' => '2021-06-29 14:12:51',
			'created' => '2021-06-29 14:12:51',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 7 => array(
			'id' => '4106',
			'name' => 'Genome-wide DNA methylation and transcriptome integration reveal distinct sex differences in skeletal muscle',
			'authors' => 'Shanie Landen, Macsue Jacques , Danielle Hiam , Javier Alvarez, Nicholas R Harvey, Larisa M. Haupt, Lyn R, Griffiths, Kevin J Ashton, Séverine Lamon, Sarah Voisin, Nir Eynon',
			'description' => '<p><span>Nearly all human complex traits and diseases exhibit some degree of sex differences, and epigenetics contributes to these differences as DNA methylation shows sex differences in various tissues. However, skeletal muscle epigenetic sex differences remain largely unexplored, yet skeletal muscle displays distinct sex differences at the transcriptome level. We conducted a large-scale meta-analysis of autosomal DNA methylation sex differences in human skeletal muscle in three separate cohorts (Gene SMART, FUSION, and GSE38291), totalling n = 369 human muscle samples (n = 222 males, n = 147 females). We found 10,240 differentially methylated regions (DMRs) at FDR &lt; 0.005, 94% of which were hypomethylated in males, and gene set enrichment analysis revealed that differentially methylated genes were involved in muscle contraction and metabolism. We then integrated our epigenetic results with transcriptomic data from the GTEx database and the FUSION cohort. Altogether, we identified 326 autosomal genes that display sex differences at both the DNA methylation, and transcriptome levels. Importantly, sex-biased genes at the transcriptional level were overrepresented among the sex-biased genes at the epigenetic level (p-value = 4.6e-13), which suggests differential DNA methylation and gene expression between males and females in muscle are functionally linked. Finally, we validated expression of three genes with large effect sizes (FOXO3A, ALDH1A1, and GGT7) in the Gene SMART cohort with qPCR. GGT7, involved in muscle metabolism, displays male-biased expression in skeletal muscle across the three cohorts, as well as lower methylation in males. In conclusion, we uncovered thousands of genes that exhibit DNA methylation differences between the males and females in human skeletal muscle that may modulate mechanisms controlling muscle metabolism and health.</span></p>',
			'date' => '2021-03-17',
			'pmid' => 'https://doi.org/10.1101/2021.03.16.435733',
			'doi' => '10.1101/2021.03.16.435733',
			'modified' => '2021-06-29 14:15:50',
			'created' => '2021-06-29 14:15:50',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 8 => array(
			'id' => '4358',
			'name' => 'ZNF718, HOXA4, and ZFP57 are differentially methylated inperiodontitis in comparison with periodontal health: Epigenome-wide DNAmethylation pilot study.',
			'authors' => 'Hernández H.G. et al. ',
			'description' => '<p>OBJECTIVE: To investigate the differences in the epigenomic patterns of DNA methylation in peripheral leukocytes between patients with periodontitis and gingivally healthy controls evaluating its functional meaning by functional enrichment analysis. BACKGROUND: The DNA methylation profiling of peripheral leukocytes as immune-related tissue potentially relevant as a source of biomarkers between periodontitis patients and gingivally healthy subjects has not been investigated. METHODS: A DNA methylation epigenome-wide study of peripheral leukocytes was conducted using the Illumina MethylationEPIC platform in sixteen subjects, eight diagnosed with periodontitis patients and eight age-matched and sex-matched periodontally healthy controls. A trained periodontist performed the clinical evaluation. Global DNA methylation was estimated using methylation-sensitive high-resolution melting in LINE1. Routine cell count cytometry and metabolic laboratory tests were also performed. The analysis of differentially methylated positions (DMPs) and differentially methylated regions (DMRs) was made using R/Bioconductor environment considering leukocyte populations assessed in both routine cell counts and using the FlowSorted.Blood.EPIC package. Finally, a DMP and DMR intersection analysis was performed. Functional enrichment analysis was carried out with the differentially methylated genes found in DMP. RESULTS: DMP analysis identified 81 differentially hypermethylated genes and 21 differentially hypomethylated genes. Importantly, the intersection analysis showed that zinc finger protein 718 (ZNF718) and homeobox A4 (HOXA4) were differentially hypermethylated and zinc finger protein 57 (ZFP57) was differentially hypomethylated in periodontitis. The functional enrichment analysis found clearly immune-related ontologies such as "detection of bacterium" and "antigen processing and presentation." CONCLUSION: The results of this study propose three new periodontitis-related genes: ZNF718, HOXA4, and ZFP57 but also evidence the suitability and relevance of studying leukocytes' DNA methylome for biological interpretation of systemic immune-related epigenetic patterns in periodontitis.</p>',
			'date' => '2021-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33660869',
			'doi' => '10.1111/jre.12868',
			'modified' => '2022-08-03 16:48:52',
			'created' => '2022-05-19 10:41:50',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 9 => array(
			'id' => '4590',
			'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
			'authors' => 'Tiane  A. et al.',
			'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
			'date' => '0000-00-00',
			'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
			'doi' => '10.1101/2023.01.12.523740',
			'modified' => '2023-04-11 10:06:33',
			'created' => '2023-02-21 09:59:46',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		)
	),
	'Testimonial' => array(),
	'Area' => array(),
	'SafetySheet' => array()
)
$country = 'US'
$countries_allowed = array(
	(int) 0 => 'CA',
	(int) 1 => 'US',
	(int) 2 => 'IE',
	(int) 3 => 'GB',
	(int) 4 => 'DK',
	(int) 5 => 'NO',
	(int) 6 => 'SE',
	(int) 7 => 'FI',
	(int) 8 => 'NL',
	(int) 9 => 'BE',
	(int) 10 => 'LU',
	(int) 11 => 'FR',
	(int) 12 => 'DE',
	(int) 13 => 'CH',
	(int) 14 => 'AT',
	(int) 15 => 'ES',
	(int) 16 => 'IT',
	(int) 17 => 'PT'
)
$outsource = false
$other_formats = array()
$edit = ''
$testimonials = ''
$featured_testimonials = ''
$related_products = ''
$rrbs_service = array(
	(int) 0 => (int) 1894,
	(int) 1 => (int) 1895
)
$chipseq_service = array(
	(int) 0 => (int) 2683,
	(int) 1 => (int) 1835,
	(int) 2 => (int) 1836,
	(int) 3 => (int) 2684,
	(int) 4 => (int) 1838,
	(int) 5 => (int) 1839,
	(int) 6 => (int) 1856
)
$labelize = object(Closure) {
	
}
$old_catalog_number = ''
$label = '<img src="/img/banners/banner-customizer-back.png" alt=""/>'
$publication = array(
	'id' => '4590',
	'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
	'authors' => 'Tiane  A. et al.',
	'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
	'date' => '0000-00-00',
	'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
	'doi' => '10.1101/2023.01.12.523740',
	'modified' => '2023-04-11 10:06:33',
	'created' => '2023-02-21 09:59:46',
	'ProductsPublication' => array(
		'id' => '6858',
		'product_id' => '3231',
		'publication_id' => '4590'
	)
)
$externalLink = ' <a href="https://doi.org/10.1101%2F2023.01.12.523740" target="_blank"><i class="fa fa-external-link"></i></a>'
include - APP/View/Products/view.ctp, line 755
View::_evaluate() - CORE/Cake/View/View.php, line 971
View::_render() - CORE/Cake/View/View.php, line 933
View::render() - CORE/Cake/View/View.php, line 473
Controller::render() - CORE/Cake/Controller/Controller.php, line 963
ProductsController::slug() - APP/Controller/ProductsController.php, line 1052
ReflectionMethod::invokeArgs() - [internal], line ??
Controller::invokeAction() - CORE/Cake/Controller/Controller.php, line 491
Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 193
Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167
[main] - APP/webroot/index.php, line 118

Notice (8): Undefined variable: campaign_id [APP/View/Products/view.ctp, line 755]
Code Context
<!-- BEGIN: REQUEST_FORM MODAL -->
<div id="request_formModal" class="reveal-modal medium" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog">
    <?= $this->element('Forms/simple_form', array('solution_of_interest' => $solution_of_interest, 'header' => $header, 'message' => $message, 'campaign_id' => $campaign_id)) ?>
$viewFile = '/home/website-server/www/app/View/Products/view.ctp'
$dataForView = array(
	'language' => 'en',
	'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
	'meta_description' => '',
	'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
	'product' => array(
		'Product' => array(
			'id' => '3231',
			'antibody_id' => null,
			'name' => 'Infinium Methylation EPIC Array Service V2',
			'description' => '<p>The&nbsp;<strong>Infinium MethylationEPIC</strong><strong> v2.0</strong><strong> Array</strong>&nbsp;Services (improved version of the EPIC v1.0) is the new cutting-edge genome-wide DNA methylation analysis technique from Illumina<sup>&reg;</sup>&nbsp;based on bisulfite conversion. It allows to quantitatively detect the total&nbsp;methylation level of over 930,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;It offers comprehensive coverage of CpG islands, enhancer regions, open chromatin sites and&nbsp;other&nbsp;important regions of the methylome with extended content compared to the EPIC v1.0.</p>
<p></p>
<h2><span style="font-weight: 400;">Comprehensive and improved Genome-Wide Coverage</span></h2>
<ul class="square">
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>High percentage of probe overlap with previous version and 150,000 newly covered CpGs</li>
<li>More than 99% correlation in methylation values with EPIC v1.0</li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, array scan and data quality control</li>
<li>Bioinformatic analysis offering: <a href="https://www.diagenode.com/en/p/methylation-data-analysis">comparative&nbsp;analysis</a>, <a href="https://www.diagenode.com/en/p/data-mining-service">data mining</a></li>
</ul>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
			'label1' => 'Services Workflow',
			'info1' => '<div class="row">
<div class="small-12 medium-3 large-3 columns"><img alt="EPIC Array Service" src="https://www.diagenode.com/img/services/EPIC-ARRAY.png" caption="false" width="208" height="406" /></div>
<div class="small-12 medium-9 large-9 columns">
<table style="width: 680px;">
<tbody>
<tr style="height: 194px;">
<td style="height: 194px; width: 168px;">
<p><strong>End-to-end array </strong></p>
</td>
<td style="height: 194px; width: 504px;">
<ul>
<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array scanner&nbsp;</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
</div>',
			'label2' => 'Bioinformatics Analysis',
			'info2' => '<table>
<tbody>
<tr>
<td>
<h4><strong>Analysis</strong></h4>
</td>
<td>
<h4><strong>Features</strong></h4>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
</td>
</tr>
</tbody>
</table>',
			'label3' => '',
			'info3' => '',
			'format' => '',
			'catalog_number' => 'G0209006',
			'old_catalog_number' => '',
			'sf_code' => '',
			'type' => 'ACC',
			'search_order' => '',
			'price_EUR' => '/',
			'price_USD' => '/',
			'price_GBP' => '/',
			'price_JPY' => '/',
			'price_CNY' => '/',
			'price_AUD' => '/',
			'country' => 'ALL',
			'except_countries' => 'None',
			'quote' => true,
			'in_stock' => false,
			'featured' => true,
			'no_promo' => true,
			'online' => true,
			'master' => true,
			'last_datasheet_update' => '',
			'slug' => 'infinium-methylation-epic-array-v2-service',
			'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
			'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
			'meta_description' => '',
			'modified' => '2024-11-20 11:11:33',
			'created' => '2023-05-11 11:15:25',
			'locale' => 'eng'
		),
		'Antibody' => array(
			'host' => '*****',
			'id' => null,
			'name' => null,
			'description' => null,
			'clonality' => null,
			'isotype' => null,
			'lot' => null,
			'concentration' => null,
			'reactivity' => null,
			'type' => null,
			'purity' => null,
			'classification' => null,
			'application_table' => null,
			'storage_conditions' => null,
			'storage_buffer' => null,
			'precautions' => null,
			'uniprot_acc' => null,
			'slug' => null,
			'meta_keywords' => null,
			'meta_description' => null,
			'modified' => null,
			'created' => null,
			'select_label' => null
		),
		'Slave' => array(),
		'Group' => array(),
		'Related' => array(),
		'Application' => array(),
		'Category' => array(),
		'Document' => array(),
		'Feature' => array(),
		'Image' => array(),
		'Promotion' => array(),
		'Protocol' => array(),
		'Publication' => array(
			(int) 0 => array(
				[maximum depth reached]
			),
			(int) 1 => array(
				[maximum depth reached]
			),
			(int) 2 => array(
				[maximum depth reached]
			),
			(int) 3 => array(
				[maximum depth reached]
			),
			(int) 4 => array(
				[maximum depth reached]
			),
			(int) 5 => array(
				[maximum depth reached]
			),
			(int) 6 => array(
				[maximum depth reached]
			),
			(int) 7 => array(
				[maximum depth reached]
			),
			(int) 8 => array(
				[maximum depth reached]
			),
			(int) 9 => array(
				[maximum depth reached]
			)
		),
		'Testimonial' => array(),
		'Area' => array(),
		'SafetySheet' => array()
	)
)
$language = 'en'
$meta_keywords = 'Infinium MethylationEPIC Array Service v2'
$meta_description = ''
$meta_title = 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode'
$product = array(
	'Product' => array(
		'id' => '3231',
		'antibody_id' => null,
		'name' => 'Infinium Methylation EPIC Array Service V2',
		'description' => '<p>The&nbsp;<strong>Infinium MethylationEPIC</strong><strong> v2.0</strong><strong> Array</strong>&nbsp;Services (improved version of the EPIC v1.0) is the new cutting-edge genome-wide DNA methylation analysis technique from Illumina<sup>&reg;</sup>&nbsp;based on bisulfite conversion. It allows to quantitatively detect the total&nbsp;methylation level of over 930,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;It offers comprehensive coverage of CpG islands, enhancer regions, open chromatin sites and&nbsp;other&nbsp;important regions of the methylome with extended content compared to the EPIC v1.0.</p>
<p></p>
<h2><span style="font-weight: 400;">Comprehensive and improved Genome-Wide Coverage</span></h2>
<ul class="square">
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>High percentage of probe overlap with previous version and 150,000 newly covered CpGs</li>
<li>More than 99% correlation in methylation values with EPIC v1.0</li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, array scan and data quality control</li>
<li>Bioinformatic analysis offering: <a href="https://www.diagenode.com/en/p/methylation-data-analysis">comparative&nbsp;analysis</a>, <a href="https://www.diagenode.com/en/p/data-mining-service">data mining</a></li>
</ul>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
		'label1' => 'Services Workflow',
		'info1' => '<div class="row">
<div class="small-12 medium-3 large-3 columns"><img alt="EPIC Array Service" src="https://www.diagenode.com/img/services/EPIC-ARRAY.png" caption="false" width="208" height="406" /></div>
<div class="small-12 medium-9 large-9 columns">
<table style="width: 680px;">
<tbody>
<tr style="height: 194px;">
<td style="height: 194px; width: 168px;">
<p><strong>End-to-end array </strong></p>
</td>
<td style="height: 194px; width: 504px;">
<ul>
<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array scanner&nbsp;</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
</div>',
		'label2' => 'Bioinformatics Analysis',
		'info2' => '<table>
<tbody>
<tr>
<td>
<h4><strong>Analysis</strong></h4>
</td>
<td>
<h4><strong>Features</strong></h4>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
</td>
</tr>
</tbody>
</table>',
		'label3' => '',
		'info3' => '',
		'format' => '',
		'catalog_number' => 'G0209006',
		'old_catalog_number' => '',
		'sf_code' => '',
		'type' => 'ACC',
		'search_order' => '',
		'price_EUR' => '/',
		'price_USD' => '/',
		'price_GBP' => '/',
		'price_JPY' => '/',
		'price_CNY' => '/',
		'price_AUD' => '/',
		'country' => 'ALL',
		'except_countries' => 'None',
		'quote' => true,
		'in_stock' => false,
		'featured' => true,
		'no_promo' => true,
		'online' => true,
		'master' => true,
		'last_datasheet_update' => '',
		'slug' => 'infinium-methylation-epic-array-v2-service',
		'meta_title' => 'Infinium MethylationEPIC Array v2.0 Service - Methylation Profiling Microarray | Diagenode',
		'meta_keywords' => 'Infinium MethylationEPIC Array Service v2',
		'meta_description' => '',
		'modified' => '2024-11-20 11:11:33',
		'created' => '2023-05-11 11:15:25',
		'locale' => 'eng'
	),
	'Antibody' => array(
		'host' => '*****',
		'id' => null,
		'name' => null,
		'description' => null,
		'clonality' => null,
		'isotype' => null,
		'lot' => null,
		'concentration' => null,
		'reactivity' => null,
		'type' => null,
		'purity' => null,
		'classification' => null,
		'application_table' => null,
		'storage_conditions' => null,
		'storage_buffer' => null,
		'precautions' => null,
		'uniprot_acc' => null,
		'slug' => null,
		'meta_keywords' => null,
		'meta_description' => null,
		'modified' => null,
		'created' => null,
		'select_label' => null
	),
	'Slave' => array(),
	'Group' => array(),
	'Related' => array(),
	'Application' => array(),
	'Category' => array(),
	'Document' => array(),
	'Feature' => array(),
	'Image' => array(),
	'Promotion' => array(),
	'Protocol' => array(),
	'Publication' => array(
		(int) 0 => array(
			'id' => '4990',
			'name' => 'Epigenetic Analysis of ST3GAL3 and other Sialic Acid Metabolism Genes in ADHD',
			'authors' => 'Lillian Dipnall et al.',
			'description' => '<p><span>Research indicates that the underlying neurobiology of Attention Deficit/Hyperactivity Disorder (ADHD) may stem from a combination of genetic and environmental contributions. Genetic and epigenetic research have highlighted the potential role of the sialtransferase gene&nbsp;</span><em>ST3GAL3</em><span><span>&nbsp;</span>in this process. Adopting a pathways approach, this study sought to examine the role that<span>&nbsp;</span></span><em>ST3GAL3</em><span><span>&nbsp;</span>and other sialic acid metabolism (SAM) genes play in ADHD. Peripheral measures of DNA methylation (Illumina 850k EPIC; saliva samples) and clinical data were collected as part of a community-based pediatric cohort consisting of 90 children with ADHD [</span><em>m</em><sub>age</sub><span>= 10.40 (0.49); 66% male] and 50 non-ADHD controls [</span><em>m</em><sub>age</sub><span>= 10.40 (0.45); 48% male]. Using Reactome, 33 SAM genes were defined and resulted in a total of 1419 probes which included associated promotor/enhancer regions. Linear regression analysis was undertaken to explore differences in SAM probe DNA methylation between children with and without ADHD. The relationship with ADHD symptom severity was also examined. Analysis found 38 probes in the group-regression, and 64 probes in the symptom severity regression reached significance at an uncorrected level (a&thinsp;=&thinsp;0.05). No probes survived correction for multiple comparisons. Enrichment analysis revealed an overall pattern of hypermethylation across the SAM pathway for the ADHD group, with 84% of nominally significant probes being annotated to sialyltransferase genes. These results suggest that<span>&nbsp;</span></span><em>ST3GAL3</em><span><span>&nbsp;</span>and the broader SAM pathway could contribute to subtly disrupted epigenetic regulation in ADHD. However, extensive longitudinal research, across broad developmental age ranges, is necessary to further explore these findings.</span></p>',
			'date' => '2024-10-10',
			'pmid' => 'https://www.researchsquare.com/article/rs-4519315/v1',
			'doi' => 'https://doi.org/10.21203/rs.3.rs-4519315/v1',
			'modified' => '2024-10-18 11:52:30',
			'created' => '2024-10-18 11:52:30',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 1 => array(
			'id' => '4894',
			'name' => 'Matched analysis of detailed peripheral blood and tumor immune microenvironment profiles in bladder cancer',
			'authors' => 'Chen JQ et al.',
			'description' => '<p><b>Background:</b><span><span>&nbsp;</span>Bladder cancer and therapy responses hinge on immune profiles in the tumor microenvironment (TME) and blood, yet studies linking tumor-infiltrating immune cells to peripheral immune profiles are limited.<span>&nbsp;</span></span><b>Methods:</b><span><span>&nbsp;</span>DNA methylation cytometry quantified TME and matched peripheral blood immune cell proportions. With tumor immune profile data as the input, subjects were grouped by immune infiltration status and consensus clustering.<span>&nbsp;</span></span><b>Results:</b><span><span>&nbsp;</span>Immune hot and cold groups had different immune compositions in the TME but not in circulating blood. Two clusters of patients identified with consensus clustering had different immune compositions not only in the TME but also in blood.<span>&nbsp;</span></span><b>Conclusion:</b><span><span>&nbsp;</span>Detailed immune profiling via methylation cytometry reveals the significance of understanding tumor and systemic immune relationships in cancer patients.</span></p>',
			'date' => '2024-01-15',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/38221889/',
			'doi' => '10.2217/epi-2023-0358',
			'modified' => '2024-01-18 10:21:47',
			'created' => '2024-01-18 10:21:47',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 2 => array(
			'id' => '4767',
			'name' => 'Altered DNA methylation and gene expression predict disease severity inpatients with Aicardi-Goutières syndrome.',
			'authors' => 'Garau  J. et al.',
			'description' => '<p>Aicardi-Gouti&egrave;res Syndrome (AGS) is a rare neuro-inflammatory disease characterized by increased expression of interferon-stimulated genes (ISGs). Disease-causing mutations are present in genes associated with innate antiviral responses. Disease presentation and severity vary, even between patients with identical mutations from the same family. This study investigated DNA methylation signatures in PBMCs to understand phenotypic heterogeneity in AGS patients with mutations in RNASEH2B. AGS patients presented hypomethylation of ISGs and differential methylation patterns (DMPs) in genes involved in "neutrophil and platelet activation". Patients with "mild" phenotypes exhibited DMPs in genes involved in "DNA damage and repair", whereas patients with "severe" phenotypes had DMPs in "cell fate commitment" and "organ development" associated genes. DMPs in two ISGs (IFI44L, RSAD2) associated with increased gene expression in patients with "severe" when compared to "mild" phenotypes. In conclusion, altered DNA methylation and ISG expression as biomarkers and potential future treatment targets in AGS.</p>',
			'date' => '2023-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36963449',
			'doi' => '10.1016/j.clim.2023.109299',
			'modified' => '2023-04-17 13:07:38',
			'created' => '2023-04-14 13:41:22',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 3 => array(
			'id' => '4774',
			'name' => 'DNA methylation aberrancy is a reliable prognostic tool in uveal melanoma',
			'authors' => 'Soltysova  A. et al.',
			'description' => '<p>Despite outstanding advances in understanding the genetic background of uveal melanoma (UM) development and prognosis, the role of DNA methylation reprogramming remains elusive. This study aims to clarify the extent of DNA methylation deregulation in the context of gene expression changes and its utility as a reliable prognostic biomarker. Methods: Transcriptomic and DNA methylation landscapes in 25 high- and low-risk UMs were interrogated by Agilent SurePrint G3 Human Gene Expression 8x60K v2 Microarray and Human Infinium Methylation EPIC Bead Chip array, respectively. DNA methylation and gene expression of the nine top discriminatory genes, selected by the integrative analysis, were validated by pyrosequencing and qPCR in 58 tissues. Results: Among 2,262 differentially expressed genes discovered in UM samples differing in metastatic risk, 60 were epigenetic regulators, mostly histone modifiers and chromatin remodelers. A total of 44,398 CpGs were differentially methylated, 27,810 hypomethylated, and 16,588 hypermethylated in high-risk tumors, with delta beta values ranging between -0.78 and 0.79. By integrative analysis, 944 differentially expressed DNA methylation-regulated genes were revealed, 635 hypomethylated/upregulated, and 309 hypermethylated/downregulated. Aberrant DNA methylation in high-risk tumors was associated with the deregulation of key oncogenic pathways such as EGFR tyrosine kinase inhibitor resistance, focal adhesion, proteoglycans in cancer, PI3K-Akt signaling, or ECM-receptor interaction. Notably, the DNA methylation values of nine genes, HTR2B , AHNAK2, CALHM2, SLC25A38, EDNRB, TLR1, RNF43, IL12RB2 , and MEGF10, validated by pyrosequencing, demonstrated excellent risk group prediction accuracies (AUCs ranging between 0.870 and 0.956). Moreover, CALHM2 hypomethylation and MEGF10, TLR1 hypermethylation, as well as two three-gene methylation signatures, Signature 1 combining A HNAK2, CALHM2, and IL12RB and Signature 2 A HNAK2, CALHM2, and SLC25A38 genes, correlated with shorter overall survival (HR = 4.38, 95\% CI 1.30-16.41, HR = 5.59, 95\% CI 1.30-16.41; HR = 3.43, 95\% CI 1.30-16.41, HR = 4.61, 95\% CI 1.30-16.41 and HR = 4.95, 95\% CI 1.39-17.58, respectively). Conclusions: Our results demonstrate a significant role of DNA methylation aberrancy in UM progression. The advantages of DNA as a biological material and the excellent prediction accuracies of methylation markers open the perspective for their more extensive clinical use.</p>',
			'date' => '2023-02-01',
			'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-2502537%2Fv2',
			'doi' => '10.21203/rs.3.rs-2502537/v2',
			'modified' => '2023-04-17 13:12:52',
			'created' => '2023-04-14 13:41:22',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 4 => array(
			'id' => '4565',
			'name' => 'Decitabine increases neoantigen and cancer testis antigen expression toenhance T cell-mediated toxicity against glioblastoma.',
			'authors' => 'Ma  Ruichong et al.',
			'description' => '<p>BACKGROUND: Glioblastoma (GBM) is the most common and malignant primary brain tumour in adults. Despite maximal treatment, median survival remains dismal at 14-24 months. Immunotherapies, such as checkpoint inhibition, have revolutionised management of some cancers but have little benefit for GBM patients. This is, in part, due to the low mutational and neoantigen burden in this immunogenically 'cold' tumour. METHODS: U87MG and patient derived cell lines were treated with 5-aza-2'-deoxycytidine (DAC) and underwent whole exome and transcriptome sequencing. Cell lines were then subjected to cellular assays with neoantigen and cancer testis antigen (CTA) specific T cells. RESULTS: We demonstrate that DAC increases neoantigen and CTA mRNA expression through DNA hypomethylation. This results in increased neoantigen presentation by MHC class I in tumour cells, leading to increased neoantigen- and CTA-specific T cell activation and killing of DAC-treated cancer cells. In addition, we show that patients have endogenous cancer-specific T cells in both tumour and blood, which show increased tumour-specific activation in the presence of DAC-treated cells. CONCLUSIONS: Our work shows that DAC increases GBM immunogenicity and consequent susceptibility to T cell responses in-vitro. Our results support a potential use of DAC as a sensitizing agent to immunotherapy.</p>',
			'date' => '2022-04-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35468205',
			'doi' => '10.1093/neuonc/noac107',
			'modified' => '2022-11-24 09:12:45',
			'created' => '2022-11-24 08:49:52',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 5 => array(
			'id' => '4107',
			'name' => 'Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling',
			'authors' => 'Lucas A Salas, Ze Zhang, Devin C Koestler, Rondi A Butler, Helen M Hansen, Annette M Molinaro, John K Wiencke, Karl T Kelsey, Brock C Christensen',
			'description' => '<p><span>DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ na&iuml;ve and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures, and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of the immune system in human health and disease.</span></p>',
			'date' => '2021-04-12',
			'pmid' => 'https://doi.org/10.1101/2021.04.11.439377',
			'doi' => '10.1101/2021.04.11.439377',
			'modified' => '2021-06-29 14:17:36',
			'created' => '2021-06-29 14:17:36',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 6 => array(
			'id' => '4105',
			'name' => 'Interplay between Histone and DNA Methylation Seen through Comparative Methylomes in Rare Mendelian Disorders',
			'authors' => ' Guillaume Velasco, Damien Ulveling,Sophie Rondeau,Pauline Marzin,Motoko Unoki,Valérie Cormier-Daire, Claire Francastel',
			'description' => '<p><span>DNA methylation (DNAme) profiling is used to establish specific biomarkers to improve the diagnosis of patients with inherited neurodevelopmental disorders and to guide mutation screening. In the specific case of mendelian disorders of the epigenetic machinery, it also provides the basis to infer mechanistic aspects with regard to DNAme determinants and interplay between histone and DNAme that apply to humans. Here, we present comparative methylomes from patients with mutations in the de novo DNA methyltransferases DNMT3A and DNMT3B, in their catalytic domain or their N-terminal parts involved in reading histone methylation, or in histone H3 lysine (K) methylases NSD1 or SETD2 (H3 K36) or KMT2D/MLL2 (H3 K4). We provide disease-specific DNAme signatures and document the distinct consequences of mutations in enzymes with very similar or intertwined functions, including at repeated sequences and imprinted loci. We found that KMT2D and SETD2 germline mutations have little impact on DNAme profiles. In contrast, the overlapping DNAme alterations downstream of NSD1 or DNMT3 mutations underlines functional links, more specifically between NSD1 and DNMT3B at heterochromatin regions or DNMT3A at regulatory elements. Together, these data indicate certain discrepancy with the mechanisms described in animal models or the existence of redundant or complementary functions unforeseen in humans.</span></p>',
			'date' => '2021-04-03',
			'pmid' => 'https://doi.org/10.3390/ijms22073735',
			'doi' => '10.3390/ijms22073735',
			'modified' => '2021-06-29 14:12:51',
			'created' => '2021-06-29 14:12:51',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 7 => array(
			'id' => '4106',
			'name' => 'Genome-wide DNA methylation and transcriptome integration reveal distinct sex differences in skeletal muscle',
			'authors' => 'Shanie Landen, Macsue Jacques , Danielle Hiam , Javier Alvarez, Nicholas R Harvey, Larisa M. Haupt, Lyn R, Griffiths, Kevin J Ashton, Séverine Lamon, Sarah Voisin, Nir Eynon',
			'description' => '<p><span>Nearly all human complex traits and diseases exhibit some degree of sex differences, and epigenetics contributes to these differences as DNA methylation shows sex differences in various tissues. However, skeletal muscle epigenetic sex differences remain largely unexplored, yet skeletal muscle displays distinct sex differences at the transcriptome level. We conducted a large-scale meta-analysis of autosomal DNA methylation sex differences in human skeletal muscle in three separate cohorts (Gene SMART, FUSION, and GSE38291), totalling n = 369 human muscle samples (n = 222 males, n = 147 females). We found 10,240 differentially methylated regions (DMRs) at FDR &lt; 0.005, 94% of which were hypomethylated in males, and gene set enrichment analysis revealed that differentially methylated genes were involved in muscle contraction and metabolism. We then integrated our epigenetic results with transcriptomic data from the GTEx database and the FUSION cohort. Altogether, we identified 326 autosomal genes that display sex differences at both the DNA methylation, and transcriptome levels. Importantly, sex-biased genes at the transcriptional level were overrepresented among the sex-biased genes at the epigenetic level (p-value = 4.6e-13), which suggests differential DNA methylation and gene expression between males and females in muscle are functionally linked. Finally, we validated expression of three genes with large effect sizes (FOXO3A, ALDH1A1, and GGT7) in the Gene SMART cohort with qPCR. GGT7, involved in muscle metabolism, displays male-biased expression in skeletal muscle across the three cohorts, as well as lower methylation in males. In conclusion, we uncovered thousands of genes that exhibit DNA methylation differences between the males and females in human skeletal muscle that may modulate mechanisms controlling muscle metabolism and health.</span></p>',
			'date' => '2021-03-17',
			'pmid' => 'https://doi.org/10.1101/2021.03.16.435733',
			'doi' => '10.1101/2021.03.16.435733',
			'modified' => '2021-06-29 14:15:50',
			'created' => '2021-06-29 14:15:50',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 8 => array(
			'id' => '4358',
			'name' => 'ZNF718, HOXA4, and ZFP57 are differentially methylated inperiodontitis in comparison with periodontal health: Epigenome-wide DNAmethylation pilot study.',
			'authors' => 'Hernández H.G. et al. ',
			'description' => '<p>OBJECTIVE: To investigate the differences in the epigenomic patterns of DNA methylation in peripheral leukocytes between patients with periodontitis and gingivally healthy controls evaluating its functional meaning by functional enrichment analysis. BACKGROUND: The DNA methylation profiling of peripheral leukocytes as immune-related tissue potentially relevant as a source of biomarkers between periodontitis patients and gingivally healthy subjects has not been investigated. METHODS: A DNA methylation epigenome-wide study of peripheral leukocytes was conducted using the Illumina MethylationEPIC platform in sixteen subjects, eight diagnosed with periodontitis patients and eight age-matched and sex-matched periodontally healthy controls. A trained periodontist performed the clinical evaluation. Global DNA methylation was estimated using methylation-sensitive high-resolution melting in LINE1. Routine cell count cytometry and metabolic laboratory tests were also performed. The analysis of differentially methylated positions (DMPs) and differentially methylated regions (DMRs) was made using R/Bioconductor environment considering leukocyte populations assessed in both routine cell counts and using the FlowSorted.Blood.EPIC package. Finally, a DMP and DMR intersection analysis was performed. Functional enrichment analysis was carried out with the differentially methylated genes found in DMP. RESULTS: DMP analysis identified 81 differentially hypermethylated genes and 21 differentially hypomethylated genes. Importantly, the intersection analysis showed that zinc finger protein 718 (ZNF718) and homeobox A4 (HOXA4) were differentially hypermethylated and zinc finger protein 57 (ZFP57) was differentially hypomethylated in periodontitis. The functional enrichment analysis found clearly immune-related ontologies such as "detection of bacterium" and "antigen processing and presentation." CONCLUSION: The results of this study propose three new periodontitis-related genes: ZNF718, HOXA4, and ZFP57 but also evidence the suitability and relevance of studying leukocytes' DNA methylome for biological interpretation of systemic immune-related epigenetic patterns in periodontitis.</p>',
			'date' => '2021-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33660869',
			'doi' => '10.1111/jre.12868',
			'modified' => '2022-08-03 16:48:52',
			'created' => '2022-05-19 10:41:50',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 9 => array(
			'id' => '4590',
			'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
			'authors' => 'Tiane  A. et al.',
			'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
			'date' => '0000-00-00',
			'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
			'doi' => '10.1101/2023.01.12.523740',
			'modified' => '2023-04-11 10:06:33',
			'created' => '2023-02-21 09:59:46',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		)
	),
	'Testimonial' => array(),
	'Area' => array(),
	'SafetySheet' => array()
)
$country = 'US'
$countries_allowed = array(
	(int) 0 => 'CA',
	(int) 1 => 'US',
	(int) 2 => 'IE',
	(int) 3 => 'GB',
	(int) 4 => 'DK',
	(int) 5 => 'NO',
	(int) 6 => 'SE',
	(int) 7 => 'FI',
	(int) 8 => 'NL',
	(int) 9 => 'BE',
	(int) 10 => 'LU',
	(int) 11 => 'FR',
	(int) 12 => 'DE',
	(int) 13 => 'CH',
	(int) 14 => 'AT',
	(int) 15 => 'ES',
	(int) 16 => 'IT',
	(int) 17 => 'PT'
)
$outsource = false
$other_formats = array()
$edit = ''
$testimonials = ''
$featured_testimonials = ''
$related_products = ''
$rrbs_service = array(
	(int) 0 => (int) 1894,
	(int) 1 => (int) 1895
)
$chipseq_service = array(
	(int) 0 => (int) 2683,
	(int) 1 => (int) 1835,
	(int) 2 => (int) 1836,
	(int) 3 => (int) 2684,
	(int) 4 => (int) 1838,
	(int) 5 => (int) 1839,
	(int) 6 => (int) 1856
)
$labelize = object(Closure) {
	
}
$old_catalog_number = ''
$label = '<img src="/img/banners/banner-customizer-back.png" alt=""/>'
$publication = array(
	'id' => '4590',
	'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
	'authors' => 'Tiane  A. et al.',
	'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
	'date' => '0000-00-00',
	'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
	'doi' => '10.1101/2023.01.12.523740',
	'modified' => '2023-04-11 10:06:33',
	'created' => '2023-02-21 09:59:46',
	'ProductsPublication' => array(
		'id' => '6858',
		'product_id' => '3231',
		'publication_id' => '4590'
	)
)
$externalLink = ' <a href="https://doi.org/10.1101%2F2023.01.12.523740" target="_blank"><i class="fa fa-external-link"></i></a>'
include - APP/View/Products/view.ctp, line 755
View::_evaluate() - CORE/Cake/View/View.php, line 971
View::_render() - CORE/Cake/View/View.php, line 933
View::render() - CORE/Cake/View/View.php, line 473
Controller::render() - CORE/Cake/Controller/Controller.php, line 963
ProductsController::slug() - APP/Controller/ProductsController.php, line 1052
ReflectionMethod::invokeArgs() - [internal], line ??
Controller::invokeAction() - CORE/Cake/Controller/Controller.php, line 491
Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 193
Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167
[main] - APP/webroot/index.php, line 118