WGBS (Whole Genome Bisulfite Sequencing) and EM-seq (Enzymatic Methylation) Service

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			'name' => 'WGBS (Whole Genome Bisulfite Sequencing) and EM-seq (Enzymatic Methylation) Service',
			'description' => '<p><strong>Whole-genome bisulfite sequencing (WGBS)</strong> and <strong>Enzymatic Methylation (EM-seq)</strong> are the methods of choice for obtaining a comprehensive DNA methylation profiling, evaluating the methylation patterns of nearly every CpG site of the entire genome. By comparing the proportion of unconverted and converted cytosines at the same location, the methylation levels are determined with accuracy. Genome-wide methylation sequencing (WGBS and EM-seq) provides deep insights into gene regulation, allowing identification of novel epigenetic markers and targets for disease.</p>
<h2><span style="font-weight: 400;">In-depth&nbsp;DNA methylation&nbsp;analysis</span></h2>
<ul class="square">
<li>Very powerful solution for genome-wide biomarker discovery compatible with low input and highly fragmented cfDNA and FFPE samples</li>
<li>Single-nucleotide resolution</li>
<li><span>Evaluation of the methylation status of nearly every CpG sites of the entire genome</span></li>
</ul>
<ul class="square">
<li>Detection of global methylation patterns including outside of CpG islands and in low-CpG-density regions</li>
<li>Identification of regions or even loci with differential methylation between groups using bioinformatics tools</li>
<li>Identification of methylation pattern/signature predictive and discriminating different groups with Data Mining approach</li>
<li>Providing a better understanding of development and disease</li>
</ul>
<h2>Comprehensive end-to-end service</h2>
<ul class="square">
<li>From DNA QC to sequencing raw data</li>
<li>A dedicated scientist driving your project with high touch communication</li>
<li><span>High-quality <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatic support</a> (on request) to assist you with data analysis</span></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>',
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<table style="width: 942px;">
<tbody>
<tr style="text-align: left; height: 81px;">
<td style="width: 469px; height: 81px;" colspan="2">
<p><strong>QC of the genomic DNA</strong></p>
<p><strong></strong></p>
</td>
<td style="width: 766px; height: 81px;">
<ul>
<li style="font-weight: 400;"><span style="font-weight: 400;">Measurement of DNA concentration</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Assessment of DNA quality</span></li>
</ul>
</td>
</tr>
<tr style="height: 129px;">
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<p style="text-align: left;"><strong>Library Preparation</strong></p>
<p style="text-align: left;"><strong></strong></p>
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<td style="width: 94px; height: 129px;">
<p style="text-align: center;"><strong>WGBS</strong></p>
</td>
<td style="width: 766px; height: 129px;">
<ul>
<li style="font-weight: 400;"><span style="font-weight: 400;">gDNA shearing on Bioruptor Pico (not necessary for cfDNA or FFPE)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Bisulfite conversion</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Library preparation </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">QC of the WGBS libraries (DNA concentration, analysis of the profile)</span></li>
</ul>
</td>
</tr>
<tr style="height: 107px;">
<td style="width: 94px; text-align: center; height: 107px; background-color: #f9f9f9;"><strong>EM-seq</strong></td>
<td style="width: 766px; height: 107px; background-color: #f9f9f9;">
<ul>
<li style="font-weight: 400;"><span style="font-weight: 400;">gDNA shearing on Bioruptor Pico (not necessary for cfDNA or FFPE)</span></li>
<li style="font-weight: 400;">Library preparation with Enzymatic conversion</li>
<li style="font-weight: 400;">QC of EM-seq libraries (DNA concentration, analysis of the profile)</li>
</ul>
<span style="font-weight: 400;"></span></td>
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<tr style="height: 129px;">
<td style="width: 469px; height: 129px; background-color: white;" colspan="2">
<p><strong>Deep sequencing</strong></p>
<p><strong></strong></p>
</td>
<td style="width: 766px; height: 129px; background-color: white;">
<ul>
<li style="font-weight: 400;"><span style="font-weight: 400;"><span>Samples are sequenced on Illumina platform, paired-end reads of 150bp length (PE150)&nbsp;</span>&nbsp;</span></li>
<li style="font-weight: 400;">400M raw reads on average per samples (when pooling 6 samples/lane)</li>
<li style="font-weight: 400;">Theoretical Coverage &gt;30X for human, mouse and rat samples</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Detection of &gt;50 million CpGs with 6-9X average CpG coverage&nbsp;for human samples&nbsp;</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
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<h4><strong>Analysis</strong></h4>
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<th style="width: 624px; height: 69px;">
<h4><strong>Features</strong></h4>
</th>
</tr>
</thead>
<tbody>
<tr style="height: 123px;">
<td style="width: 262px; height: 123px;"><strong>Standard</strong></td>
<td style="width: 624px; height: 123px;">
<ul>
<li>FASTQ raw data</li>
<li>FASTQC quality control insights</li>
<li>Alignment of bisulfite sequencing data against reference genome</li>
<li>Methylation calling and extraction</li>
<li>Summary statistics</li>
</ul>
</td>
</tr>
<tr style="height: 146px;">
<td style="width: 262px; height: 146px;"><strong>Differential&nbsp;methylation analysis</strong></td>
<td style="width: 624px; height: 146px;">
<ul>
<li>Methylation level analysis</li>
<li>Differentially methylated CpGs (DMCs) analysis</li>
<li>Differentially methylated regions (DMRs) analysis</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, &hellip;) and for CpG island locations (islands, shores, shelves, ...)</li>
<li>Clustering analysis</li>
</ul>
</td>
</tr>
<tr style="height: 82px;">
<td style="width: 262px; height: 82px;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 624px; height: 82px;">
<ul>
<li>Enrichment analysis on gene associated with DMCs and DMRs</li>
<li>Get functional insights</li>
</ul>
</td>
</tr>
<tr style="height: 86px;">
<td style="width: 262px; height: 86px;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 624px; height: 86px;">
<ul>
<li>Identification of biological pathways in which genes associated with DMCs and DMRs may be over-represented (or under-represented)</li>
<li>Get mechanistic insights</li>
</ul>
</td>
</tr>
<tr style="height: 86px;">
<td style="width: 262px; height: 86px;">
<p><strong>Data mining</strong></p>
</td>
<td style="width: 624px; height: 86px;">
<ul>
<li><span>Biomarker discovery</span></li>
<li><span>Determination of methylation pattern/signature that can be predictive and discriminate between different groups/conditions</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
<h2></h2>
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		'description' => '<p><strong>Whole-genome bisulfite sequencing (WGBS)</strong> and <strong>Enzymatic Methylation (EM-seq)</strong> are the methods of choice for obtaining a comprehensive DNA methylation profiling, evaluating the methylation patterns of nearly every CpG site of the entire genome. By comparing the proportion of unconverted and converted cytosines at the same location, the methylation levels are determined with accuracy. Genome-wide methylation sequencing (WGBS and EM-seq) provides deep insights into gene regulation, allowing identification of novel epigenetic markers and targets for disease.</p>
<h2><span style="font-weight: 400;">In-depth&nbsp;DNA methylation&nbsp;analysis</span></h2>
<ul class="square">
<li>Very powerful solution for genome-wide biomarker discovery compatible with low input and highly fragmented cfDNA and FFPE samples</li>
<li>Single-nucleotide resolution</li>
<li><span>Evaluation of the methylation status of nearly every CpG sites of the entire genome</span></li>
</ul>
<ul class="square">
<li>Detection of global methylation patterns including outside of CpG islands and in low-CpG-density regions</li>
<li>Identification of regions or even loci with differential methylation between groups using bioinformatics tools</li>
<li>Identification of methylation pattern/signature predictive and discriminating different groups with Data Mining approach</li>
<li>Providing a better understanding of development and disease</li>
</ul>
<h2>Comprehensive end-to-end service</h2>
<ul class="square">
<li>From DNA QC to sequencing raw data</li>
<li>A dedicated scientist driving your project with high touch communication</li>
<li><span>High-quality <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatic support</a> (on request) to assist you with data analysis</span></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>',
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<table style="width: 942px;">
<tbody>
<tr style="text-align: left; height: 81px;">
<td style="width: 469px; height: 81px;" colspan="2">
<p><strong>QC of the genomic DNA</strong></p>
<p><strong></strong></p>
</td>
<td style="width: 766px; height: 81px;">
<ul>
<li style="font-weight: 400;"><span style="font-weight: 400;">Measurement of DNA concentration</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Assessment of DNA quality</span></li>
</ul>
</td>
</tr>
<tr style="height: 129px;">
<td style="width: 375px; height: 236px;" rowspan="2">
<p style="text-align: left;"><strong>Library Preparation</strong></p>
<p style="text-align: left;"><strong></strong></p>
</td>
<td style="width: 94px; height: 129px;">
<p style="text-align: center;"><strong>WGBS</strong></p>
</td>
<td style="width: 766px; height: 129px;">
<ul>
<li style="font-weight: 400;"><span style="font-weight: 400;">gDNA shearing on Bioruptor Pico (not necessary for cfDNA or FFPE)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Bisulfite conversion</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Library preparation </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">QC of the WGBS libraries (DNA concentration, analysis of the profile)</span></li>
</ul>
</td>
</tr>
<tr style="height: 107px;">
<td style="width: 94px; text-align: center; height: 107px; background-color: #f9f9f9;"><strong>EM-seq</strong></td>
<td style="width: 766px; height: 107px; background-color: #f9f9f9;">
<ul>
<li style="font-weight: 400;"><span style="font-weight: 400;">gDNA shearing on Bioruptor Pico (not necessary for cfDNA or FFPE)</span></li>
<li style="font-weight: 400;">Library preparation with Enzymatic conversion</li>
<li style="font-weight: 400;">QC of EM-seq libraries (DNA concentration, analysis of the profile)</li>
</ul>
<span style="font-weight: 400;"></span></td>
</tr>
<tr style="height: 129px;">
<td style="width: 469px; height: 129px; background-color: white;" colspan="2">
<p><strong>Deep sequencing</strong></p>
<p><strong></strong></p>
</td>
<td style="width: 766px; height: 129px; background-color: white;">
<ul>
<li style="font-weight: 400;"><span style="font-weight: 400;"><span>Samples are sequenced on Illumina platform, paired-end reads of 150bp length (PE150)&nbsp;</span>&nbsp;</span></li>
<li style="font-weight: 400;">400M raw reads on average per samples (when pooling 6 samples/lane)</li>
<li style="font-weight: 400;">Theoretical Coverage &gt;30X for human, mouse and rat samples</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Detection of &gt;50 million CpGs with 6-9X average CpG coverage&nbsp;for human samples&nbsp;</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
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<tr style="height: 69px;">
<th style="width: 262px; height: 69px;">
<h4><strong>Analysis</strong></h4>
</th>
<th style="width: 624px; height: 69px;">
<h4><strong>Features</strong></h4>
</th>
</tr>
</thead>
<tbody>
<tr style="height: 123px;">
<td style="width: 262px; height: 123px;"><strong>Standard</strong></td>
<td style="width: 624px; height: 123px;">
<ul>
<li>FASTQ raw data</li>
<li>FASTQC quality control insights</li>
<li>Alignment of bisulfite sequencing data against reference genome</li>
<li>Methylation calling and extraction</li>
<li>Summary statistics</li>
</ul>
</td>
</tr>
<tr style="height: 146px;">
<td style="width: 262px; height: 146px;"><strong>Differential&nbsp;methylation analysis</strong></td>
<td style="width: 624px; height: 146px;">
<ul>
<li>Methylation level analysis</li>
<li>Differentially methylated CpGs (DMCs) analysis</li>
<li>Differentially methylated regions (DMRs) analysis</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, &hellip;) and for CpG island locations (islands, shores, shelves, ...)</li>
<li>Clustering analysis</li>
</ul>
</td>
</tr>
<tr style="height: 82px;">
<td style="width: 262px; height: 82px;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 624px; height: 82px;">
<ul>
<li>Enrichment analysis on gene associated with DMCs and DMRs</li>
<li>Get functional insights</li>
</ul>
</td>
</tr>
<tr style="height: 86px;">
<td style="width: 262px; height: 86px;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 624px; height: 86px;">
<ul>
<li>Identification of biological pathways in which genes associated with DMCs and DMRs may be over-represented (or under-represented)</li>
<li>Get mechanistic insights</li>
</ul>
</td>
</tr>
<tr style="height: 86px;">
<td style="width: 262px; height: 86px;">
<p><strong>Data mining</strong></p>
</td>
<td style="width: 624px; height: 86px;">
<ul>
<li><span>Biomarker discovery</span></li>
<li><span>Determination of methylation pattern/signature that can be predictive and discriminate between different groups/conditions</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
<h2></h2>
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			'description' => '<h2 class="text-center"><span class="diacol">New!</span> <br />Data mining using machine learning (AI) for unique epigenetic data insights</h2>
<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
</div>
<center><iframe width="560" height="315" src="https://www.youtube.com/embed/KXjnSHz3Jk8" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen="allowfullscreen"></iframe></center>
<p></p>
<div class="row">
<div class="small-12 medium-12 large-12 columns">
<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
<div id="portal" class="main-portal">
<div class="portal-inner"><nav class="portal-nav" style="text-align: left;">
<ul data-tab="" class="tips-menu">
<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
<li><a href="#panel2" class="tips portal button">Breast cancer</a></li>
</ul>
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</div>
<div class="tabs-content">
<div class="content active" id="panel1">
<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
<div class="content" id="panel2">
<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
</div>
</blockquote>
</div>
</div>
<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially &ldquo;finding a needle in a haystack.&rdquo;</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
<table class="extra-spaced">
<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
</tr>
<tr>
<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
</td>
<td>
<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
</td>
</tr>
</tbody>
</table>
<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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<p>メチル化アレイとWGBS技術の長所を有するReduced representation bisulfite sequencing (RRBS) 技術は、あらゆる脊椎動物種で柔軟に使用できるため、大規模コホートの研究においても正確で費用対効果の高いアッセイを行えます。制限MspI酵素（CCGGターゲットサイト）を使用してゲノムを切断し、次にサイズを選択することで、濃縮されたDNAはプロモーターやCpGアイランドを含む関連するターゲットCpGリッチ領域を表します。またRRBS技術はゲノムの一部分を濃縮してメチル化解析を行うことによってシーケンス量を減らす手法でカバレッジが非常に高く、CpGメチル化解析に適しています。（ヒトサンプルで最大700万CpGが検出）。</p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
<li>Robust and cost-effective solution with&nbsp;reliable results</li>
<li>High coverage - up to 7 million CpGs detected in human samples</li>
<li>Single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Suitable for&nbsp;any vertebrate species</li>
</ul>
<h2>Complete end-to-end service</h2>
<ul class="square">
<li>From&nbsp;DNA QC to standard bioinformatics&nbsp;</li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2&nbsp;</a>widely adopted</li>
<li>As little as 25 ng&nbsp;for&nbsp;any vertebrate species</li>
<li>Differentially methylated site analysis&nbsp;</li>
</ul>
</div>
<div class="extra-spaced">
<h2></h2>
</div>
<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>
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<td style="width: 264px;"><strong>QC of the genomic DNA</strong></td>
<td style="width: 636px;">
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<li>Measurement of DNA concentration&nbsp;</li>
<li>Assessment of DNA quality</li>
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<li>Size selection</li>
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<li>Library amplification and clean-up</li>
<li>QC of the RRBS library pool (DNA concentration, analysis of the pool profile)</li>
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<td style="width: 264px;"><strong>Deep sequencing</strong></td>
<td style="width: 636px;">
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<li>Samples are sequenced on an Illumina platform, paired-end reads, read length 50 bp (PE50)</li>
<li>40 million raw reads (on average) per sample when pooling 10 samples/lane</li>
<li>7&nbsp;million CpGs (on average) for human samples</li>
<li>7-11x CpG coverage&nbsp;(on average) for human samples</li>
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</td>
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<th style="width: 262px;">
<h4><strong>Analysis</strong></h4>
</th>
<th style="width: 624px;">
<h4><strong>Features</strong></h4>
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</tr>
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<tbody>
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<td style="width: 262px;"><strong>Standard</strong></td>
<td style="width: 624px;">
<ul>
<li>FASTQ raw data</li>
<li>FASTQC quality control insights</li>
<li>Alignment of bisulfite sequencing data against reference genome</li>
<li>Methylation calling and extraction</li>
<li>Summary statistics</li>
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</td>
</tr>
<tr>
<td style="width: 262px;"><strong>Differential&nbsp;methylation analysis<br /></strong></td>
<td style="width: 624px;">
<ul>
<li>Methylation level analysis</li>
<li>Differentially Methylated CpGs (DMCs) analysis</li>
<li>Differentially Methylated Regions (DMRs) analysis</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, &hellip;)</li>
<li>Clustering analysis</li>
</ul>
</td>
</tr>
<tr>
<td style="width: 262px;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 624px;">
<ul>
<li>Enrichment analysis on gene associated with DMCs and DMRs</li>
<li>Get functional insights</li>
</ul>
</td>
</tr>
<tr>
<td style="width: 262px;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 624px;">
<ul>
<li>Identification of biological pathways in which genes associated with DMCs and DMRs may be over-represented (or under-represented)</li>
<li>Get mechanistic insights</li>
</ul>
</td>
</tr>
</tbody>
</table>
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			'description' => '<div class="extra-spaced">
<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
</ul>
</div>
<div class="extra-spaced">
<p>Based on the cytosine resolution, the analysis can be made at:</p>
<ul class="square">
<li><strong>Single base scale</strong> (for each cytosine in a CpG context &ndash; WGBS, RRBS, BSAS, EPIC, etc)</li>
<li><strong>Enrichment based method</strong> (MeDIP-Seq)</li>
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<div class="extra-spaced">
<h2>What do we provide with the analysis?</h2>
<ul class="accordion" data-accordion="" id="analysis">
<li class="accordion-navigation"><a href="#first"> <i class="fa fa-square-o"></i> Single-base resolution Analysis (WGBS, RRBS, BSAS, EPIC)</a>
<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
<ul>
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul>
<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
</ul>
</div>
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<div class="small-12 medium-4 large-4 columns">
<div class="panel"><center><img src="http://www.diagenode.com/img/categories/services/dna-workflow.png" alt="DNA-methylation -Diagenode" /></center>
<p>The pattern of DNA methylation and histone modification(s) plays an essential role in maintaining cellular function. Abnormal DNA methylation &ndash; hypermethylation and hypomethylation - can result in adverse outcomes such as cancer or other disease. The quantification of 5-mC through genome-wide DNA methylation analysis can provide information for detection and prognosis of disease. Our DNA methylation analysis services include numerous bisulfite sequencing options for targeted or whole genome DNA methylation analysis across any species.</p>
</div>
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<p>Our Epigenomics Profiling Services helps you study DNA methylation (5mC) and methylation variants (5hmC). Our experienced scientists will guide you to the best solution according to your needs, depending on whether you want to analyze DNA methylation at genome-wide or gene specific scales. They will&nbsp;process the arrays or generate meaningful libraries for DNA sequencing and deliver high quality data. Our bioinformatic experts will closely work with you to provide standard and customized analysis with comprehensive publication-ready figures.</p>
<h3><a href="https://www.diagenode.com/en/p/infinium-methylation-epic-array-v2-service">Infinium MethylationEPIC Array Service V2</a></h3>
<ul>
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>Quantitative interrogation of CpG, non-CpG, and CHH sites</li>
<li>Differential methylation analysis&nbsp;<span>using our&nbsp;<a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
</ul>
<!--<h3><a href="https://www.diagenode.com/en/p/infinium-mouse-methylation-array-service">Infinium Mouse Methylation Array Service</a></h3>
<ul>
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 285,000&nbsp;markers detected in&nbsp;mouse samples at single nucleotide resolution</li>
<li>Suitable for common laboratory mouse strains</li>
<li><span>Differential methylation analysis&nbsp;using our<span>&nbsp;</span><a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
</ul>-->
<h3><a href="https://www.diagenode.com/en/p/rrbs-service">Reduced Representation Bisulfite Sequencing (RRBS) Service</a></h3>
<ul>
<li>Robust and cost-effective solution with&nbsp;reliable results</li>
<li>Unique technology enables low DNA inputs down to 25ng</li>
<li>Accurate analyses with UDIs and UMIs</li>
<li>Up to 7&nbsp;million CpGs detected in human samples at single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG-rich regions across the genome including promoters and CpG islands</li>
<li><span>Differential methylation analysis&nbsp;<span>using our</span> <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
<li>Suitable for epigenetic biomarker discovery</li>
</ul>
<h3><a href="https://www.diagenode.com/en/p/wgbs-service">Whole Genome Bisulfite Sequencing (WGBS) or Enzymatic Methylation(EM-seq) Service</a></h3>
<ul>
<li>Very powerful solution for genome-wide biomarker discovery&nbsp;using bisulfite or enzymatic conversion</li>
<li><span>Evaluation of methylation status of nearly every CpG sites of the entire genome&nbsp;</span>at single&nbsp;nucleotide resolution</li>
<li>Detection of global methylation patterns&nbsp;including in low CpG-density regions and outside of CpG islands</li>
<li>Identification of regions or even loci with differential methylation&nbsp;levels between groups using our&nbsp;<a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></li>
</ul>
<h3><a href="https://www.diagenode.com/en/p/targeted-dna-methylation-service">Targeted&nbsp;Bisulfite Sequencing Service</a></h3>
<ul>
<li>Focus on D<span>NA methylation patterning of selective regions with single nucleotide resolution</span></li>
<li><span>Choose between two methodologies depending on the size of the region of interest: PCR enrichment or hybridization capture</span></li>
<li><span>Enhanced accuracy, sensitivity and specificity while reducing overall cost</span></li>
<li><span>Allowing deep sequencing at high coverage levels for rare variant identification</span></li>
</ul>
<!--<h3><a href="../p/medip-seq-service">Methylated DNA IP Sequencing</a></h3>
<ul>
<li>Immunoprecipitation assay (antibody-based approach) to study 5-mC genome-wide</li>
<li>Resolution of 100-500 bp</li>
</ul>-->
<h3><a href="../p/human-methylome-service">Human Methylome</a></h3>
<h3>Watch our webinar:</h3>
<p>Cost-Effective Genome Wide DNA Methylation Analysis using Twist Hybrid-Capture Methylome Panel</p>
<a class="popup-youtube" href="#webinar"><img src="https://www.diagenode.com/img/webinar/eshg-miniature.png" class="webinar" alt="Webinar series: Genome Wide DNA Methylation using Twist Hybrid-Capture Methylome Panel" /></a>
<p></p>
<ul>
<li>NGS service assay for comprehensive DNA methylation profiling in human samples from solid or liquid biopsy</li>
<li>Optimal biomarker discovery tool for cancer, neurodegenerative, cardiovascular and metabolic diseases</li>
<li>High coverage for more than 3.89 million methylation sites (~9 million CpGs at single strand level)</li>
</ul>
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			'authors' => 'Dvoriantchikova G, Seemungal RJ, Ivanov D',
			'description' => '<p>The ability to regenerate the entire retina and restore lost sight after injury is found in some species and relies mostly on the epigenetic plasticity of M&uuml;ller glia. To understand the role of mammalian M&uuml;ller glia as a source of progenitors for retinal regeneration, we investigated changes in gene expression during differentiation of retinal progenitor cells (RPCs) into M&uuml;ller glia. We also analyzed the global epigenetic profile of adult M&uuml;ller glia. We observed significant changes in gene expression during differentiation of RPCs into M&uuml;ller glia in only a small group of genes. We found a high similarity between RPCs and M&uuml;ller glia on the transcriptomic and epigenomic levels. Our findings also indicate that M&uuml;ller glia are epigenetically very close to late-born retinal neurons, but not early-born retinal neurons. Importantly, we found that key genes required for phototransduction were highly methylated. Thus, our data suggest that M&uuml;ller glia are epigenetically very similar to late RPCs. Meanwhile, obstacles for regeneration of the entire mammalian retina from M&uuml;ller glia may consist of repressive chromatin and highly methylated DNA in the promoter regions of many genes required for the development of early-born retinal neurons. In addition, DNA demethylation may be required for proper reprogramming and differentiation of M&uuml;ller glia into rod photoreceptors.</p>
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			'authors' => 'Dvoriantchikova G, Seemungal RJ, Ivanov D',
			'description' => '<p>The epigenetic plasticity of amphibian retinal pigment epithelium (RPE) allows them to regenerate the entire retina, a trait known to be absent in mammals. In this study, we investigated the epigenetic plasticity of adult murine RPE to identify possible mechanisms that prevent mammalian RPE from regenerating retinal tissue. RPE were analyzed using microarray, ChIP-seq, and whole-genome bisulfite sequencing approaches. We found that the majority of key genes required for progenitor phenotypes were in a permissive chromatin state and unmethylated in RPE. We observed that the majority of non-photoreceptor genes had promoters in a repressive chromatin state, but these promoters were in unmethylated or low-methylated regions. Meanwhile, the majority of promoters for photoreceptor genes were found in a permissive chromatin state, but were highly-methylated. Methylome states of photoreceptor-related genes in adult RPE and embryonic retina (which mostly contain progenitors) were very similar. However, promoters of these genes were demethylated and activated during retinal development. Our data suggest that, epigenetically, adult murine RPE cells are a progenitor-like cell type. Most likely two mechanisms prevent adult RPE from reprogramming and differentiating into retinal neurons: 1) repressive chromatin in the promoter regions of non-photoreceptor retinal neuron genes; 2) highly-methylated promoters of photoreceptor-related genes.</p>',
			'date' => '2019-03-07',
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            <h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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        <h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>our epigenomics services</strong>. Fill out the form below and we will be in touch with you very soon.</p><p><small>All <span style="font-size:16px;color:red;">*</span> fields are mandatory</small></p>

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        <h2>Service Information</h2>
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        <h4>Which services are you interested in?</h4>
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<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="ATAC-seq" id="QuoteEpigenomicsServiceATACSeq" /><label for="QuoteEpigenomicsServiceATACSeq">ATAC-seq</label></div>
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<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Targeted DNA methylation analysis" id="QuoteEpigenomicsServiceTargetedDNAMethylationAnalysis" /><label for="QuoteEpigenomicsServiceTargetedDNAMethylationAnalysis">Targeted DNA methylation analysis</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Infinium MethylationEPIC Array v2" id="QuoteEpigenomicsServiceInfiniumMethylationEPICArrayV2" /><label for="QuoteEpigenomicsServiceInfiniumMethylationEPICArrayV2">Infinium MethylationEPIC Array v2</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Infinium Mouse Methylation Array" id="QuoteEpigenomicsServiceInfiniumMouseMethylationArray" /><label for="QuoteEpigenomicsServiceInfiniumMouseMethylationArray">Infinium Mouse Methylation Array</label></div>
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<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Bioinformatics" id="QuoteEpigenomicsServiceBioinformatics" /><label for="QuoteEpigenomicsServiceBioinformatics">Bioinformatics</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Data mining" id="QuoteEpigenomicsServiceDataMining" /><label for="QuoteEpigenomicsServiceDataMining">Data mining</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Human Methylome" id="QuoteEpigenomicsServiceHumanMethylome" /><label for="QuoteEpigenomicsServiceHumanMethylome">Human Methylome</label></div>
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    <div class="row collapse">
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            <span class="prefix">Sample species</span>
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<option value="AL">Albania</option>
<option value="DZ">Algeria</option>
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<option value="AD">Andorra</option>
<option value="AO">Angola</option>
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<option value="BV">Bouvet Island</option>
<option value="BR">Brazil</option>
<option value="IO">British Indian Ocean Territory</option>
<option value="BN">Brunei Darussalam</option>
<option value="BG">Bulgaria</option>
<option value="BF">Burkina Faso</option>
<option value="BI">Burundi</option>
<option value="KH">Cambodia</option>
<option value="CM">Cameroon</option>
<option value="CA">Canada</option>
<option value="CV">Cape Verde</option>
<option value="KY">Cayman Islands</option>
<option value="CF">Central African Republic</option>
<option value="TD">Chad</option>
<option value="CL">Chile</option>
<option value="CN">China</option>
<option value="CX">Christmas Island</option>
<option value="CC">Cocos (Keeling) Islands</option>
<option value="CO">Colombia</option>
<option value="KM">Comoros</option>
<option value="CG">Congo</option>
<option value="CD">Congo, The Democratic Republic of the</option>
<option value="CK">Cook Islands</option>
<option value="CR">Costa Rica</option>
<option value="CI">Côte d&#039;Ivoire</option>
<option value="HR">Croatia</option>
<option value="CU">Cuba</option>
<option value="CW">Curaçao</option>
<option value="CY">Cyprus</option>
<option value="CZ">Czech Republic</option>
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            </div>

        </div>
        <div class="small-12 columns" >
            <h6 style="height:60px">Methylation Data Analysis</h6>
        </div>

    </div>
</li>
'
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	'name' => 'Methylation Data Analysis',
	'description' => '<div class="extra-spaced">
<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
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<div class="extra-spaced">
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<ul class="accordion" data-accordion="" id="analysis">
<li class="accordion-navigation"><a href="#first"> <i class="fa fa-square-o"></i> Single-base resolution Analysis (WGBS, RRBS, BSAS, EPIC)</a>
<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
<ul>
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
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<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
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	'description' => '<p>The epigenetic plasticity of amphibian retinal pigment epithelium (RPE) allows them to regenerate the entire retina, a trait known to be absent in mammals. In this study, we investigated the epigenetic plasticity of adult murine RPE to identify possible mechanisms that prevent mammalian RPE from regenerating retinal tissue. RPE were analyzed using microarray, ChIP-seq, and whole-genome bisulfite sequencing approaches. We found that the majority of key genes required for progenitor phenotypes were in a permissive chromatin state and unmethylated in RPE. We observed that the majority of non-photoreceptor genes had promoters in a repressive chromatin state, but these promoters were in unmethylated or low-methylated regions. Meanwhile, the majority of promoters for photoreceptor genes were found in a permissive chromatin state, but were highly-methylated. Methylome states of photoreceptor-related genes in adult RPE and embryonic retina (which mostly contain progenitors) were very similar. However, promoters of these genes were demethylated and activated during retinal development. Our data suggest that, epigenetically, adult murine RPE cells are a progenitor-like cell type. Most likely two mechanisms prevent adult RPE from reprogramming and differentiating into retinal neurons: 1) repressive chromatin in the promoter regions of non-photoreceptor retinal neuron genes; 2) highly-methylated promoters of photoreceptor-related genes.</p>',
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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
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Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167
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Notice (8): Undefined variable: header [APP/View/Products/view.ctp, line 755]
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<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>',
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<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>',
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<li style="font-weight: 400;"><span style="font-weight: 400;">Library preparation </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">QC of the WGBS libraries (DNA concentration, analysis of the profile)</span></li>
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<td style="width: 94px; text-align: center; height: 107px; background-color: #f9f9f9;"><strong>EM-seq</strong></td>
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<li style="font-weight: 400;">Library preparation with Enzymatic conversion</li>
<li style="font-weight: 400;">QC of EM-seq libraries (DNA concentration, analysis of the profile)</li>
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<span style="font-weight: 400;"></span></td>
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<p><strong>Deep sequencing</strong></p>
<p><strong></strong></p>
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<li style="font-weight: 400;"><span style="font-weight: 400;"><span>Samples are sequenced on Illumina platform, paired-end reads of 150bp length (PE150)&nbsp;</span>&nbsp;</span></li>
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<li style="font-weight: 400;">Theoretical Coverage &gt;30X for human, mouse and rat samples</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Detection of &gt;50 million CpGs with 6-9X average CpG coverage&nbsp;for human samples&nbsp;</span></li>
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			'description' => '<h2 class="text-center"><span class="diacol">New!</span> <br />Data mining using machine learning (AI) for unique epigenetic data insights</h2>
<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
</div>
<center><iframe width="560" height="315" src="https://www.youtube.com/embed/KXjnSHz3Jk8" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen="allowfullscreen"></iframe></center>
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<div class="row">
<div class="small-12 medium-12 large-12 columns">
<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
<div id="portal" class="main-portal">
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<ul data-tab="" class="tips-menu">
<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
<li><a href="#panel2" class="tips portal button">Breast cancer</a></li>
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</div>
<div class="tabs-content">
<div class="content active" id="panel1">
<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
<div class="content" id="panel2">
<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
</div>
</blockquote>
</div>
</div>
<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially &ldquo;finding a needle in a haystack.&rdquo;</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
<table class="extra-spaced">
<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
</tr>
<tr>
<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
</td>
<td>
<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
</td>
</tr>
</tbody>
</table>
<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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			'label1' => 'Data mining modules ',
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<li>Assessment of data characteristics and <span></span>applicability of different machine learning (ML)</li>
<li>Prototypic analyses:</li>
<li>Initial feasibility report</li>
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<p></p>
<ol start="2">
<li><strong>Data Mining</strong></li>
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<li>Machine learning on data</li>
<li>Data evaluation and validation</li>
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<li><strong>Data Interpretation</strong></li>
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			'info2' => '<p>A clear advantage of <strong>Diagenode&rsquo;s data mining services</strong> is the close connection with<span>&nbsp; </span>other service offerings like <strong>wet lab analysis services</strong> and <strong>bioinformatics services</strong>. You can retrieve a full service package from a single source.</p>
<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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<div class="extra-spaced">
<p>メチル化アレイとWGBS技術の長所を有するReduced representation bisulfite sequencing (RRBS) 技術は、あらゆる脊椎動物種で柔軟に使用できるため、大規模コホートの研究においても正確で費用対効果の高いアッセイを行えます。制限MspI酵素（CCGGターゲットサイト）を使用してゲノムを切断し、次にサイズを選択することで、濃縮されたDNAはプロモーターやCpGアイランドを含む関連するターゲットCpGリッチ領域を表します。またRRBS技術はゲノムの一部分を濃縮してメチル化解析を行うことによってシーケンス量を減らす手法でカバレッジが非常に高く、CpGメチル化解析に適しています。（ヒトサンプルで最大700万CpGが検出）。</p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
<li>Robust and cost-effective solution with&nbsp;reliable results</li>
<li>High coverage - up to 7 million CpGs detected in human samples</li>
<li>Single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Suitable for&nbsp;any vertebrate species</li>
</ul>
<h2>Complete end-to-end service</h2>
<ul class="square">
<li>From&nbsp;DNA QC to standard bioinformatics&nbsp;</li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2&nbsp;</a>widely adopted</li>
<li>As little as 25 ng&nbsp;for&nbsp;any vertebrate species</li>
<li>Differentially methylated site analysis&nbsp;</li>
</ul>
</div>
<div class="extra-spaced">
<h2></h2>
</div>
<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>
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<tr>
<td style="width: 264px;"><strong>QC of the genomic DNA</strong></td>
<td style="width: 636px;">
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<li>Measurement of DNA concentration&nbsp;</li>
<li>Assessment of DNA quality</li>
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<td style="width: 264px;"><strong>Preparation of RRBS libraries</strong></td>
<td style="width: 636px;">
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<li>MspI digestion</li>
<li>Library preparation (ends preparation, adaptor ligation)</li>
<li>Size selection</li>
<li>Sample pooling</li>
<li>Bisulfite conversion</li>
<li>Library amplification and clean-up</li>
<li>QC of the RRBS library pool (DNA concentration, analysis of the pool profile)</li>
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<tr>
<td style="width: 264px;"><strong>Deep sequencing</strong></td>
<td style="width: 636px;">
<ul style="list-style-type: circle;">
<li>Samples are sequenced on an Illumina platform, paired-end reads, read length 50 bp (PE50)</li>
<li>40 million raw reads (on average) per sample when pooling 10 samples/lane</li>
<li>7&nbsp;million CpGs (on average) for human samples</li>
<li>7-11x CpG coverage&nbsp;(on average) for human samples</li>
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</td>
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</tbody>
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<th style="width: 262px;">
<h4><strong>Analysis</strong></h4>
</th>
<th style="width: 624px;">
<h4><strong>Features</strong></h4>
</th>
</tr>
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<tbody>
<tr>
<td style="width: 262px;"><strong>Standard</strong></td>
<td style="width: 624px;">
<ul>
<li>FASTQ raw data</li>
<li>FASTQC quality control insights</li>
<li>Alignment of bisulfite sequencing data against reference genome</li>
<li>Methylation calling and extraction</li>
<li>Summary statistics</li>
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</td>
</tr>
<tr>
<td style="width: 262px;"><strong>Differential&nbsp;methylation analysis<br /></strong></td>
<td style="width: 624px;">
<ul>
<li>Methylation level analysis</li>
<li>Differentially Methylated CpGs (DMCs) analysis</li>
<li>Differentially Methylated Regions (DMRs) analysis</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, &hellip;)</li>
<li>Clustering analysis</li>
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</td>
</tr>
<tr>
<td style="width: 262px;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 624px;">
<ul>
<li>Enrichment analysis on gene associated with DMCs and DMRs</li>
<li>Get functional insights</li>
</ul>
</td>
</tr>
<tr>
<td style="width: 262px;">
<p><strong>Pathway analysis</strong></p>
</td>
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<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
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<p>Based on the cytosine resolution, the analysis can be made at:</p>
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<li><strong>Enrichment based method</strong> (MeDIP-Seq)</li>
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<h2>What do we provide with the analysis?</h2>
<ul class="accordion" data-accordion="" id="analysis">
<li class="accordion-navigation"><a href="#first"> <i class="fa fa-square-o"></i> Single-base resolution Analysis (WGBS, RRBS, BSAS, EPIC)</a>
<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
<ul>
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
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<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul>
<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
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<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
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<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
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<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
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<div class="small-12 medium-4 large-4 columns">
<div class="panel"><center><img src="http://www.diagenode.com/img/categories/services/dna-workflow.png" alt="DNA-methylation -Diagenode" /></center>
<p>The pattern of DNA methylation and histone modification(s) plays an essential role in maintaining cellular function. Abnormal DNA methylation &ndash; hypermethylation and hypomethylation - can result in adverse outcomes such as cancer or other disease. The quantification of 5-mC through genome-wide DNA methylation analysis can provide information for detection and prognosis of disease. Our DNA methylation analysis services include numerous bisulfite sequencing options for targeted or whole genome DNA methylation analysis across any species.</p>
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<center><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></center></div>
<div class="small-12 medium-8 large-8 columns">
<p>Our Epigenomics Profiling Services helps you study DNA methylation (5mC) and methylation variants (5hmC). Our experienced scientists will guide you to the best solution according to your needs, depending on whether you want to analyze DNA methylation at genome-wide or gene specific scales. They will&nbsp;process the arrays or generate meaningful libraries for DNA sequencing and deliver high quality data. Our bioinformatic experts will closely work with you to provide standard and customized analysis with comprehensive publication-ready figures.</p>
<h3><a href="https://www.diagenode.com/en/p/infinium-methylation-epic-array-v2-service">Infinium MethylationEPIC Array Service V2</a></h3>
<ul>
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>Quantitative interrogation of CpG, non-CpG, and CHH sites</li>
<li>Differential methylation analysis&nbsp;<span>using our&nbsp;<a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
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<!--<h3><a href="https://www.diagenode.com/en/p/infinium-mouse-methylation-array-service">Infinium Mouse Methylation Array Service</a></h3>
<ul>
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 285,000&nbsp;markers detected in&nbsp;mouse samples at single nucleotide resolution</li>
<li>Suitable for common laboratory mouse strains</li>
<li><span>Differential methylation analysis&nbsp;using our<span>&nbsp;</span><a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
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<h3><a href="https://www.diagenode.com/en/p/rrbs-service">Reduced Representation Bisulfite Sequencing (RRBS) Service</a></h3>
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<li>Unique technology enables low DNA inputs down to 25ng</li>
<li>Accurate analyses with UDIs and UMIs</li>
<li>Up to 7&nbsp;million CpGs detected in human samples at single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG-rich regions across the genome including promoters and CpG islands</li>
<li><span>Differential methylation analysis&nbsp;<span>using our</span> <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
<li>Suitable for epigenetic biomarker discovery</li>
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<h3><a href="https://www.diagenode.com/en/p/wgbs-service">Whole Genome Bisulfite Sequencing (WGBS) or Enzymatic Methylation(EM-seq) Service</a></h3>
<ul>
<li>Very powerful solution for genome-wide biomarker discovery&nbsp;using bisulfite or enzymatic conversion</li>
<li><span>Evaluation of methylation status of nearly every CpG sites of the entire genome&nbsp;</span>at single&nbsp;nucleotide resolution</li>
<li>Detection of global methylation patterns&nbsp;including in low CpG-density regions and outside of CpG islands</li>
<li>Identification of regions or even loci with differential methylation&nbsp;levels between groups using our&nbsp;<a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></li>
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<h3><a href="https://www.diagenode.com/en/p/targeted-dna-methylation-service">Targeted&nbsp;Bisulfite Sequencing Service</a></h3>
<ul>
<li>Focus on D<span>NA methylation patterning of selective regions with single nucleotide resolution</span></li>
<li><span>Choose between two methodologies depending on the size of the region of interest: PCR enrichment or hybridization capture</span></li>
<li><span>Enhanced accuracy, sensitivity and specificity while reducing overall cost</span></li>
<li><span>Allowing deep sequencing at high coverage levels for rare variant identification</span></li>
</ul>
<!--<h3><a href="../p/medip-seq-service">Methylated DNA IP Sequencing</a></h3>
<ul>
<li>Immunoprecipitation assay (antibody-based approach) to study 5-mC genome-wide</li>
<li>Resolution of 100-500 bp</li>
</ul>-->
<h3><a href="../p/human-methylome-service">Human Methylome</a></h3>
<h3>Watch our webinar:</h3>
<p>Cost-Effective Genome Wide DNA Methylation Analysis using Twist Hybrid-Capture Methylome Panel</p>
<a class="popup-youtube" href="#webinar"><img src="https://www.diagenode.com/img/webinar/eshg-miniature.png" class="webinar" alt="Webinar series: Genome Wide DNA Methylation using Twist Hybrid-Capture Methylome Panel" /></a>
<p></p>
<ul>
<li>NGS service assay for comprehensive DNA methylation profiling in human samples from solid or liquid biopsy</li>
<li>Optimal biomarker discovery tool for cancer, neurodegenerative, cardiovascular and metabolic diseases</li>
<li>High coverage for more than 3.89 million methylation sites (~9 million CpGs at single strand level)</li>
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			'description' => '<p>The ability to regenerate the entire retina and restore lost sight after injury is found in some species and relies mostly on the epigenetic plasticity of M&uuml;ller glia. To understand the role of mammalian M&uuml;ller glia as a source of progenitors for retinal regeneration, we investigated changes in gene expression during differentiation of retinal progenitor cells (RPCs) into M&uuml;ller glia. We also analyzed the global epigenetic profile of adult M&uuml;ller glia. We observed significant changes in gene expression during differentiation of RPCs into M&uuml;ller glia in only a small group of genes. We found a high similarity between RPCs and M&uuml;ller glia on the transcriptomic and epigenomic levels. Our findings also indicate that M&uuml;ller glia are epigenetically very close to late-born retinal neurons, but not early-born retinal neurons. Importantly, we found that key genes required for phototransduction were highly methylated. Thus, our data suggest that M&uuml;ller glia are epigenetically very similar to late RPCs. Meanwhile, obstacles for regeneration of the entire mammalian retina from M&uuml;ller glia may consist of repressive chromatin and highly methylated DNA in the promoter regions of many genes required for the development of early-born retinal neurons. In addition, DNA demethylation may be required for proper reprogramming and differentiation of M&uuml;ller glia into rod photoreceptors.</p>
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			'authors' => 'Dvoriantchikova G, Seemungal RJ, Ivanov D',
			'description' => '<p>The epigenetic plasticity of amphibian retinal pigment epithelium (RPE) allows them to regenerate the entire retina, a trait known to be absent in mammals. In this study, we investigated the epigenetic plasticity of adult murine RPE to identify possible mechanisms that prevent mammalian RPE from regenerating retinal tissue. RPE were analyzed using microarray, ChIP-seq, and whole-genome bisulfite sequencing approaches. We found that the majority of key genes required for progenitor phenotypes were in a permissive chromatin state and unmethylated in RPE. We observed that the majority of non-photoreceptor genes had promoters in a repressive chromatin state, but these promoters were in unmethylated or low-methylated regions. Meanwhile, the majority of promoters for photoreceptor genes were found in a permissive chromatin state, but were highly-methylated. Methylome states of photoreceptor-related genes in adult RPE and embryonic retina (which mostly contain progenitors) were very similar. However, promoters of these genes were demethylated and activated during retinal development. Our data suggest that, epigenetically, adult murine RPE cells are a progenitor-like cell type. Most likely two mechanisms prevent adult RPE from reprogramming and differentiating into retinal neurons: 1) repressive chromatin in the promoter regions of non-photoreceptor retinal neuron genes; 2) highly-methylated promoters of photoreceptor-related genes.</p>',
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        <div class="small-12 columns" >
            <h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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    <div class="row">
        <div class="small-12 columns">
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        <h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>our epigenomics services</strong>. Fill out the form below and we will be in touch with you very soon.</p><p><small>All <span style="font-size:16px;color:red;">*</span> fields are mandatory</small></p>

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    <div class="small-12 large-12 columns">
        <input type="hidden" name="data[Quote][epigenomics_service]" value="" id="QuoteEpigenomicsService"/>

<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="ChIP-seq" id="QuoteEpigenomicsServiceChIPSeq" /><label for="QuoteEpigenomicsServiceChIPSeq">ChIP-seq</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="ATAC-seq" id="QuoteEpigenomicsServiceATACSeq" /><label for="QuoteEpigenomicsServiceATACSeq">ATAC-seq</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="RRBS" id="QuoteEpigenomicsServiceRRBS" /><label for="QuoteEpigenomicsServiceRRBS">RRBS</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="WGBS" id="QuoteEpigenomicsServiceWGBS" /><label for="QuoteEpigenomicsServiceWGBS">WGBS</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="MeDIP-seq" id="QuoteEpigenomicsServiceMeDIPSeq" /><label for="QuoteEpigenomicsServiceMeDIPSeq">MeDIP-seq</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Targeted DNA methylation analysis" id="QuoteEpigenomicsServiceTargetedDNAMethylationAnalysis" /><label for="QuoteEpigenomicsServiceTargetedDNAMethylationAnalysis">Targeted DNA methylation analysis</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Infinium MethylationEPIC Array v2" id="QuoteEpigenomicsServiceInfiniumMethylationEPICArrayV2" /><label for="QuoteEpigenomicsServiceInfiniumMethylationEPICArrayV2">Infinium MethylationEPIC Array v2</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Infinium Mouse Methylation Array" id="QuoteEpigenomicsServiceInfiniumMouseMethylationArray" /><label for="QuoteEpigenomicsServiceInfiniumMouseMethylationArray">Infinium Mouse Methylation Array</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="RNA-seq" id="QuoteEpigenomicsServiceRNASeq" /><label for="QuoteEpigenomicsServiceRNASeq">RNA-seq</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Bioinformatics" id="QuoteEpigenomicsServiceBioinformatics" /><label for="QuoteEpigenomicsServiceBioinformatics">Bioinformatics</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Data mining" id="QuoteEpigenomicsServiceDataMining" /><label for="QuoteEpigenomicsServiceDataMining">Data mining</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Human Methylome" id="QuoteEpigenomicsServiceHumanMethylome" /><label for="QuoteEpigenomicsServiceHumanMethylome">Human Methylome</label></div>
    </div>


    <div class="row collapse">
        <div class="small-12 medium-12 large-3 columns">
            <span class="prefix">Sample species</span>
        </div>
        <div class="small-12 medium-12 large-9 columns">
            <input name="data[Quote][sample_species]" maxlength="510" type="text" id="QuoteSampleSpecies"/>        </div>
    </div>

    <div class="row collapse">
        <div class="small-12 medium-12 large-6 columns">
            <span class="prefix">Total number of samples (including replicates)</span>
        </div>
        <div class="small-12 medium-12 large-6 columns">
            <input name="data[Quote][number_samples]" maxlength="255" type="text" id="QuoteNumberSamples"/>        </div>
    </div>


<div class="row collapse">
    <h2>Contact Information</h2>
    <div class="small-3 large-2 columns">
        <span class="prefix">First name <sup style="font-size:16px;color:red;">*</sup></span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][first_name]" placeholder="john" maxlength="255" type="text" id="QuoteFirstName" required="required"/>    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Last name <sup style="font-size:16px;color:red;">*</sup></span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][last_name]" placeholder="doe" maxlength="255" type="text" id="QuoteLastName" required="required"/>    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Company <sup style="font-size:16px;color:red;">*</sup></span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][company]" placeholder="Organisation / Institute" maxlength="255" type="text" id="QuoteCompany" required="required"/>    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Phone number</span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][phone_number]" placeholder="+1 862 209-4680" maxlength="255" type="text" id="QuotePhoneNumber"/>    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">City</span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][city]" placeholder="Denville" maxlength="255" type="text" id="QuoteCity"/>    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Country <sup style="font-size:16px;color:red;">*</sup></span>
    </div>
    <div class="small-9 large-10 columns">
        <select name="data[Quote][country]" required="required" class="triggers" id="country_selector_quote-2836">
<option value="">-- select a country --</option>
<option value="AF">Afghanistan</option>
<option value="AX">Åland Islands</option>
<option value="AL">Albania</option>
<option value="DZ">Algeria</option>
<option value="AS">American Samoa</option>
<option value="AD">Andorra</option>
<option value="AO">Angola</option>
<option value="AI">Anguilla</option>
<option value="AQ">Antarctica</option>
<option value="AG">Antigua and Barbuda</option>
<option value="AR">Argentina</option>
<option value="AM">Armenia</option>
<option value="AW">Aruba</option>
<option value="AU">Australia</option>
<option value="AT">Austria</option>
<option value="AZ">Azerbaijan</option>
<option value="BS">Bahamas</option>
<option value="BH">Bahrain</option>
<option value="BD">Bangladesh</option>
<option value="BB">Barbados</option>
<option value="BY">Belarus</option>
<option value="BE">Belgium</option>
<option value="BZ">Belize</option>
<option value="BJ">Benin</option>
<option value="BM">Bermuda</option>
<option value="BT">Bhutan</option>
<option value="BO">Bolivia</option>
<option value="BQ">Bonaire, Sint Eustatius and Saba</option>
<option value="BA">Bosnia and Herzegovina</option>
<option value="BW">Botswana</option>
<option value="BV">Bouvet Island</option>
<option value="BR">Brazil</option>
<option value="IO">British Indian Ocean Territory</option>
<option value="BN">Brunei Darussalam</option>
<option value="BG">Bulgaria</option>
<option value="BF">Burkina Faso</option>
<option value="BI">Burundi</option>
<option value="KH">Cambodia</option>
<option value="CM">Cameroon</option>
<option value="CA">Canada</option>
<option value="CV">Cape Verde</option>
<option value="KY">Cayman Islands</option>
<option value="CF">Central African Republic</option>
<option value="TD">Chad</option>
<option value="CL">Chile</option>
<option value="CN">China</option>
<option value="CX">Christmas Island</option>
<option value="CC">Cocos (Keeling) Islands</option>
<option value="CO">Colombia</option>
<option value="KM">Comoros</option>
<option value="CG">Congo</option>
<option value="CD">Congo, The Democratic Republic of the</option>
<option value="CK">Cook Islands</option>
<option value="CR">Costa Rica</option>
<option value="CI">Côte d&#039;Ivoire</option>
<option value="HR">Croatia</option>
<option value="CU">Cuba</option>
<option value="CW">Curaçao</option>
<option value="CY">Cyprus</option>
<option value="CZ">Czech Republic</option>
<option value="DK">Denmark</option>
<option value="DJ">Djibouti</option>
<option value="DM">Dominica</option>
<option value="DO">Dominican Republic</option>
<option value="EC">Ecuador</option>
<option value="EG">Egypt</option>
<option value="SV">El Salvador</option>
<option value="GQ">Equatorial Guinea</option>
<option value="ER">Eritrea</option>
<option value="EE">Estonia</option>
<option value="ET">Ethiopia</option>
<option value="FK">Falkland Islands (Malvinas)</option>
<option value="FO">Faroe Islands</option>
<option value="FJ">Fiji</option>
<option value="FI">Finland</option>
<option value="FR">France</option>
<option value="GF">French Guiana</option>
<option value="PF">French Polynesia</option>
<option value="TF">French Southern Territories</option>
<option value="GA">Gabon</option>
<option value="GM">Gambia</option>
<option value="GE">Georgia</option>
<option value="DE">Germany</option>
<option value="GH">Ghana</option>
<option value="GI">Gibraltar</option>
<option value="GR">Greece</option>
<option value="GL">Greenland</option>
<option value="GD">Grenada</option>
<option value="GP">Guadeloupe</option>
<option value="GU">Guam</option>
<option value="GT">Guatemala</option>
<option value="GG">Guernsey</option>
<option value="GN">Guinea</option>
<option value="GW">Guinea-Bissau</option>
<option value="GY">Guyana</option>
<option value="HT">Haiti</option>
<option value="HM">Heard Island and McDonald Islands</option>
<option value="VA">Holy See (Vatican City State)</option>
<option value="HN">Honduras</option>
<option value="HK">Hong Kong</option>
<option value="HU">Hungary</option>
<option value="IS">Iceland</option>
<option value="IN">India</option>
<option value="ID">Indonesia</option>
<option value="IR">Iran, Islamic Republic of</option>
<option value="IQ">Iraq</option>
<option value="IE">Ireland</option>
<option value="IM">Isle of Man</option>
<option value="IL">Israel</option>
<option value="IT">Italy</option>
<option value="JM">Jamaica</option>
<option value="JP">Japan</option>
<option value="JE">Jersey</option>
<option value="JO">Jordan</option>
<option value="KZ">Kazakhstan</option>
<option value="KE">Kenya</option>
<option value="KI">Kiribati</option>
<option value="KP">Korea, Democratic People&#039;s Republic of</option>
<option value="KR">Korea, Republic of</option>
<option value="KW">Kuwait</option>
<option value="KG">Kyrgyzstan</option>
<option value="LA">Lao People&#039;s Democratic Republic</option>
<option value="LV">Latvia</option>
<option value="LB">Lebanon</option>
<option value="LS">Lesotho</option>
<option value="LR">Liberia</option>
<option value="LY">Libya</option>
<option value="LI">Liechtenstein</option>
<option value="LT">Lithuania</option>
<option value="LU">Luxembourg</option>
<option value="MO">Macao</option>
<option value="MK">Macedonia, Republic of</option>
<option value="MG">Madagascar</option>
<option value="MW">Malawi</option>
<option value="MY">Malaysia</option>
<option value="MV">Maldives</option>
<option value="ML">Mali</option>
<option value="MT">Malta</option>
<option value="MH">Marshall Islands</option>
<option value="MQ">Martinique</option>
<option value="MR">Mauritania</option>
<option value="MU">Mauritius</option>
<option value="YT">Mayotte</option>
<option value="MX">Mexico</option>
<option value="FM">Micronesia, Federated States of</option>
<option value="MD">Moldova</option>
<option value="MC">Monaco</option>
<option value="MN">Mongolia</option>
<option value="ME">Montenegro</option>
<option value="MS">Montserrat</option>
<option value="MA">Morocco</option>
<option value="MZ">Mozambique</option>
<option value="MM">Myanmar</option>
<option value="NA">Namibia</option>
<option value="NR">Nauru</option>
<option value="NP">Nepal</option>
<option value="NL">Netherlands</option>
<option value="NC">New Caledonia</option>
<option value="NZ">New Zealand</option>
<option value="NI">Nicaragua</option>
<option value="NE">Niger</option>
<option value="NG">Nigeria</option>
<option value="NU">Niue</option>
<option value="NF">Norfolk Island</option>
<option value="MP">Northern Mariana Islands</option>
<option value="NO">Norway</option>
<option value="OM">Oman</option>
<option value="PK">Pakistan</option>
<option value="PW">Palau</option>
<option value="PS">Palestine, State of</option>
<option value="PA">Panama</option>
<option value="PG">Papua New Guinea</option>
<option value="PY">Paraguay</option>
<option value="PE">Peru</option>
<option value="PH">Philippines</option>
<option value="PN">Pitcairn</option>
<option value="PL">Poland</option>
<option value="PT">Portugal</option>
<option value="PR">Puerto Rico</option>
<option value="QA">Qatar</option>
<option value="RE">Réunion</option>
<option value="RO">Romania</option>
<option value="RU">Russian Federation</option>
<option value="RW">Rwanda</option>
<option value="BL">Saint Barthélemy</option>
<option value="SH">Saint Helena, Ascension and Tristan da Cunha</option>
<option value="KN">Saint Kitts and Nevis</option>
<option value="LC">Saint Lucia</option>
<option value="MF">Saint Martin (French part)</option>
<option value="PM">Saint Pierre and Miquelon</option>
<option value="VC">Saint Vincent and the Grenadines</option>
<option value="WS">Samoa</option>
<option value="SM">San Marino</option>
<option value="ST">Sao Tome and Principe</option>
<option value="SA">Saudi Arabia</option>
<option value="SN">Senegal</option>
<option value="RS">Serbia</option>
<option value="SC">Seychelles</option>
<option value="SL">Sierra Leone</option>
<option value="SG">Singapore</option>
<option value="SX">Sint Maarten (Dutch part)</option>
<option value="SK">Slovakia</option>
<option value="SI">Slovenia</option>
<option value="SB">Solomon Islands</option>
<option value="SO">Somalia</option>
<option value="ZA">South Africa</option>
<option value="GS">South Georgia and the South Sandwich Islands</option>
<option value="ES">Spain</option>
<option value="LK">Sri Lanka</option>
<option value="SD">Sudan</option>
<option value="SR">Suriname</option>
<option value="SS">South Sudan</option>
<option value="SJ">Svalbard and Jan Mayen</option>
<option value="SZ">Swaziland</option>
<option value="SE">Sweden</option>
<option value="CH">Switzerland</option>
<option value="SY">Syrian Arab Republic</option>
<option value="TW">Taiwan</option>
<option value="TJ">Tajikistan</option>
<option value="TZ">Tanzania</option>
<option value="TH">Thailand</option>
<option value="TL">Timor-Leste</option>
<option value="TG">Togo</option>
<option value="TK">Tokelau</option>
<option value="TO">Tonga</option>
<option value="TT">Trinidad and Tobago</option>
<option value="TN">Tunisia</option>
<option value="TR">Turkey</option>
<option value="TM">Turkmenistan</option>
<option value="TC">Turks and Caicos Islands</option>
<option value="TV">Tuvalu</option>
<option value="UG">Uganda</option>
<option value="UA">Ukraine</option>
<option value="AE">United Arab Emirates</option>
<option value="GB">United Kingdom</option>
<option value="US" selected="selected">United States</option>
<option value="UM">United States Minor Outlying Islands</option>
<option value="UY">Uruguay</option>
<option value="UZ">Uzbekistan</option>
<option value="VU">Vanuatu</option>
<option value="VE">Venezuela</option>
<option value="VN">Viet Nam</option>
<option value="VG">Virgin Islands, British</option>
<option value="VI">Virgin Islands, U.S.</option>
<option value="WF">Wallis and Futuna</option>
<option value="EH">Western Sahara</option>
<option value="YE">Yemen</option>
<option value="ZM">Zambia</option>
<option value="ZW">Zimbabwe</option>
</select><script>
    $('#country_selector_quote-2836').selectize();
</script><br />
    </div>
</div>
<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">State</span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][state]" id="state-2836" maxlength="3" type="text"/><br />
    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Email <sup style="font-size:16px;color:red;">*</sup></span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][email]" placeholder="email@address.com" maxlength="255" type="email" id="QuoteEmail" required="required"/>    </div>
</div>
<div class="row collapse" id="email_v">
    <div class="small-3 large-2 columns">
        <span class="prefix">Email verification<sup style="font-size:16px;color:red;">*</sup></span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][email_v]" autocomplete="nope" type="text" id="QuoteEmailV"/>    </div>
</div>


<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Project</span>
    </div>
    <div class="small-9 large-10 columns">
        <textarea name="data[Quote][comment]" placeholder="Describe your project" cols="30" rows="6" id="QuoteComment"></textarea>    </div>
</div>

<!------------SERVICES PARTICULAR FORM START---------------->


<!------------DATA TO POPULATE REGARDING SPECIFIC SERVICES----->


<div class="row collapse">
    <div class="small-3 large-2 columns">
    </div>
    <div class="small-9 large-10 columns">
        <div class="recaptcha"><div id="recaptcha656a95d7892ab"></div></div>    </div>
</div>
<br />

<div class="row collapse">
    <div class="small-3 large-2 columns">
    </div>
    <div class="small-9 large-10 columns">
        <button id="submit_btn-2836" class="alert button expand" form="Quote-2836" type="submit">Contact me</button>    </div>
</div>
</form><script>
    var pardotFormHandlerURL = 'https://go.diagenode.com/l/928883/2022-10-10/36b1c';

    function postToPardot(formAction, id) {
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            $('#Quote-' + id).attr('action', formAction);
            $('#Quote-' + id).submit();
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        $('#pardot-form-handler').attr('src', pardotFormHandlerURL + '?' + $('#Quote-' + id).serialize());
        
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    $(document).ready(function() {
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    $("#Quote-2836 #submit_btn-2836").click(function (e) {
            
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            //disable the submit button
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            $("#Quote-2836 #submit_btn-2836").html("Processing...");

            //submit the form
            $("#Quote-2836").submit();
        } 
    })
</script>
<script>
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        var val = $("#state-2836").val();
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        <div class="small-12 columns" >
            <h6 style="height:60px">Methylation Data Analysis</h6>
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<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
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<p>Based on the cytosine resolution, the analysis can be made at:</p>
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<li><strong>Single base scale</strong> (for each cytosine in a CpG context &ndash; WGBS, RRBS, BSAS, EPIC, etc)</li>
<li><strong>Enrichment based method</strong> (MeDIP-Seq)</li>
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<ul class="accordion" data-accordion="" id="analysis">
<li class="accordion-navigation"><a href="#first"> <i class="fa fa-square-o"></i> Single-base resolution Analysis (WGBS, RRBS, BSAS, EPIC)</a>
<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
<ul>
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul>
<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
</ul>
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	'description' => '<p>The epigenetic plasticity of amphibian retinal pigment epithelium (RPE) allows them to regenerate the entire retina, a trait known to be absent in mammals. In this study, we investigated the epigenetic plasticity of adult murine RPE to identify possible mechanisms that prevent mammalian RPE from regenerating retinal tissue. RPE were analyzed using microarray, ChIP-seq, and whole-genome bisulfite sequencing approaches. We found that the majority of key genes required for progenitor phenotypes were in a permissive chromatin state and unmethylated in RPE. We observed that the majority of non-photoreceptor genes had promoters in a repressive chromatin state, but these promoters were in unmethylated or low-methylated regions. Meanwhile, the majority of promoters for photoreceptor genes were found in a permissive chromatin state, but were highly-methylated. Methylome states of photoreceptor-related genes in adult RPE and embryonic retina (which mostly contain progenitors) were very similar. However, promoters of these genes were demethylated and activated during retinal development. Our data suggest that, epigenetically, adult murine RPE cells are a progenitor-like cell type. Most likely two mechanisms prevent adult RPE from reprogramming and differentiating into retinal neurons: 1) repressive chromatin in the promoter regions of non-photoreceptor retinal neuron genes; 2) highly-methylated promoters of photoreceptor-related genes.</p>',
	'date' => '2019-03-07',
	'pmid' => 'http://www.pubmed.gov/30846751',
	'doi' => '10.1038/s41598-019-40262-w',
	'modified' => '2019-05-09 17:33:09',
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			'name' => 'WGBS (Whole Genome Bisulfite Sequencing) and EM-seq (Enzymatic Methylation) Service',
			'description' => '<p><strong>Whole-genome bisulfite sequencing (WGBS)</strong> and <strong>Enzymatic Methylation (EM-seq)</strong> are the methods of choice for obtaining a comprehensive DNA methylation profiling, evaluating the methylation patterns of nearly every CpG site of the entire genome. By comparing the proportion of unconverted and converted cytosines at the same location, the methylation levels are determined with accuracy. Genome-wide methylation sequencing (WGBS and EM-seq) provides deep insights into gene regulation, allowing identification of novel epigenetic markers and targets for disease.</p>
<h2><span style="font-weight: 400;">In-depth&nbsp;DNA methylation&nbsp;analysis</span></h2>
<ul class="square">
<li>Very powerful solution for genome-wide biomarker discovery compatible with low input and highly fragmented cfDNA and FFPE samples</li>
<li>Single-nucleotide resolution</li>
<li><span>Evaluation of the methylation status of nearly every CpG sites of the entire genome</span></li>
</ul>
<ul class="square">
<li>Detection of global methylation patterns including outside of CpG islands and in low-CpG-density regions</li>
<li>Identification of regions or even loci with differential methylation between groups using bioinformatics tools</li>
<li>Identification of methylation pattern/signature predictive and discriminating different groups with Data Mining approach</li>
<li>Providing a better understanding of development and disease</li>
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<h2>Comprehensive end-to-end service</h2>
<ul class="square">
<li>From DNA QC to sequencing raw data</li>
<li>A dedicated scientist driving your project with high touch communication</li>
<li><span>High-quality <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatic support</a> (on request) to assist you with data analysis</span></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>',
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<p><strong>QC of the genomic DNA</strong></p>
<p><strong></strong></p>
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<li style="font-weight: 400;"><span style="font-weight: 400;">Measurement of DNA concentration</span></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">gDNA shearing on Bioruptor Pico (not necessary for cfDNA or FFPE)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Bisulfite conversion</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Library preparation </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">QC of the WGBS libraries (DNA concentration, analysis of the profile)</span></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">gDNA shearing on Bioruptor Pico (not necessary for cfDNA or FFPE)</span></li>
<li style="font-weight: 400;">Library preparation with Enzymatic conversion</li>
<li style="font-weight: 400;">QC of EM-seq libraries (DNA concentration, analysis of the profile)</li>
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<span style="font-weight: 400;"></span></td>
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<p><strong></strong></p>
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<li style="font-weight: 400;"><span style="font-weight: 400;"><span>Samples are sequenced on Illumina platform, paired-end reads of 150bp length (PE150)&nbsp;</span>&nbsp;</span></li>
<li style="font-weight: 400;">400M raw reads on average per samples (when pooling 6 samples/lane)</li>
<li style="font-weight: 400;">Theoretical Coverage &gt;30X for human, mouse and rat samples</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Detection of &gt;50 million CpGs with 6-9X average CpG coverage&nbsp;for human samples&nbsp;</span></li>
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<tr style="height: 69px;">
<th style="width: 262px; height: 69px;">
<h4><strong>Analysis</strong></h4>
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<h4><strong>Features</strong></h4>
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<td style="width: 262px; height: 123px;"><strong>Standard</strong></td>
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<ul>
<li>FASTQ raw data</li>
<li>FASTQC quality control insights</li>
<li>Alignment of bisulfite sequencing data against reference genome</li>
<li>Methylation calling and extraction</li>
<li>Summary statistics</li>