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

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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>
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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>
</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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<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>
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<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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<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">
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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 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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<li>Prototypic analyses:</li>
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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><strong>Reduced representation bisulfite sequencing (RRBS) </strong> <span>enables </span><span>genome-s</span><span>cale </span>DNA methylation<span> analysis</span> at the single nucleotide level <span>in any vertebrate species. </span><span>The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of</span> whole genome sequencing. By cutting the genome using the restriction MspI enzyme (CCGG target sites) followed by size selection, DNA is enriched to represent<span> biologically relevant target</span> CpG-rich regions including <span>promoters and </span>CpG islands.<span> Our RRBS service makes this technology widely available and provides high coverage (up to&nbsp;7 million CpGs</span><span> detected </span><span>in human samples).</span></p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
<li>Robust and cost-effective solution with&nbsp;reliable results</li>
<li>Capability to use gDNA inputs down to 25 ng</li>
<li>Use of UDIs enhance sequencing results</li>
<li>UMI technology eliminates PCR duplicates</li>
<li>High coverage - up to&nbsp;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>
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<h2>Complete end-to-end service</h2>
<ul class="square">
<li><span><span class="ui-provider ee cfi bsv cfj cfk cfl cfm cfn cfo cfp cfq cfr cfs cft cfu cfv cfw cfx cfy cfz cga cgb cgc cgd cge cgf cgg cgh cgi cgj cgk cgl cgm cgn cgo" dir="ltr">From DNA QC to sequencing raw data</span></span></li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a>&nbsp;widely adopted</li>
<li>As little as 25 ng&nbsp;for&nbsp;any vertebrate species</li>
<li>Differentially methylated site analysis&nbsp;<span>using&nbsp;<a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></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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<h4>RRBS Service includes:</h4>
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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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<tr>
<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>
</ul>
</td>
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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>
</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>
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<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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			'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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			'pmid' => 'http://www.pubmed.gov/31276697',
			'doi' => '10.1016/j.bbamcr.2019.06.019',
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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',
			'pmid' => 'http://www.pubmed.gov/30846751',
			'doi' => '10.1038/s41598-019-40262-w',
			'modified' => '2019-05-09 17:33:09',
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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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    <div class="row collapse">
        <h2>Service Information</h2>
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    <div class="small-12 large-12 columns">
        <h4>Which services are you interested in?</h4>
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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>
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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>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="RNA-seq" id="QuoteEpigenomicsServiceRNASeq" /><label for="QuoteEpigenomicsServiceRNASeq">RNA-seq</label></div>
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    <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>
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<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>
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    <div class="small-9 large-10 columns">
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<div class="row collapse">
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        <span class="prefix">Last name <sup style="font-size:16px;color:red;">*</sup></span>
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        <input name="data[Quote][last_name]" placeholder="doe" maxlength="255" type="text" id="QuoteLastName" required="required"/>    </div>
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<div class="row collapse">
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        <span class="prefix">Company <sup style="font-size:16px;color:red;">*</sup></span>
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        <input name="data[Quote][company]" placeholder="Organisation / Institute" maxlength="255" type="text" id="QuoteCompany" required="required"/>    </div>
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        <span class="prefix">Phone number</span>
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    <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>
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<div class="row collapse">
    <div class="small-3 large-2 columns">
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    <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>
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<option value="BV">Bouvet Island</option>
<option value="BR">Brazil</option>
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<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>
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<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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<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>
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<option value="ET">Ethiopia</option>
<option value="FK">Falkland Islands (Malvinas)</option>
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<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>
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<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>
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<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>
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<option value="KR">Korea, Republic of</option>
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<option value="KG">Kyrgyzstan</option>
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<option value="LB">Lebanon</option>
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<option value="MT">Malta</option>
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<option value="MU">Mauritius</option>
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            <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>
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<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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<p>This analysis provides information on each single CpG with its methylation percentage.</p>
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<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>
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<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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<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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	'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',
	'pmid' => 'http://www.pubmed.gov/30846751',
	'doi' => '10.1038/s41598-019-40262-w',
	'modified' => '2019-05-09 17:33:09',
	'created' => '2019-03-21 14:12:08',
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		'product_id' => '2989',
		'publication_id' => '3569'
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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
Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 193
Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167
[main] - APP/webroot/index.php, line 118

Notice (8): Undefined variable: header [APP/View/Products/view.ctp, line 755]
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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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<ul class="square">
<li>Very powerful solution for genome-wide biomarker discovery compatible with low input and highly fragmented cfDNA and FFPE samples</li>
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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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<td style="width: 766px; height: 129px;">
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<td style="width: 766px; height: 129px; background-color: white;">
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<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>
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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="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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<li>Prototypic analyses:</li>
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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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<div class="extra-spaced">
<p><strong>Reduced representation bisulfite sequencing (RRBS) </strong> <span>enables </span><span>genome-s</span><span>cale </span>DNA methylation<span> analysis</span> at the single nucleotide level <span>in any vertebrate species. </span><span>The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of</span> whole genome sequencing. By cutting the genome using the restriction MspI enzyme (CCGG target sites) followed by size selection, DNA is enriched to represent<span> biologically relevant target</span> CpG-rich regions including <span>promoters and </span>CpG islands.<span> Our RRBS service makes this technology widely available and provides high coverage (up to&nbsp;7 million CpGs</span><span> detected </span><span>in human samples).</span></p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
<li>Robust and cost-effective solution with&nbsp;reliable results</li>
<li>Capability to use gDNA inputs down to 25 ng</li>
<li>Use of UDIs enhance sequencing results</li>
<li>UMI technology eliminates PCR duplicates</li>
<li>High coverage - up to&nbsp;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>
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<h2>Complete end-to-end service</h2>
<ul class="square">
<li><span><span class="ui-provider ee cfi bsv cfj cfk cfl cfm cfn cfo cfp cfq cfr cfs cft cfu cfv cfw cfx cfy cfz cga cgb cgc cgd cge cgf cgg cgh cgi cgj cgk cgl cgm cgn cgo" dir="ltr">From DNA QC to sequencing raw data</span></span></li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a>&nbsp;widely adopted</li>
<li>As little as 25 ng&nbsp;for&nbsp;any vertebrate species</li>
<li>Differentially methylated site analysis&nbsp;<span>using&nbsp;<a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></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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<h4>RRBS Service includes:</h4>
<table style="width: 905px;">
<tbody>
<tr>
<td style="width: 264px;"><strong>QC of the genomic DNA</strong></td>
<td style="width: 636px;">
<ul style="list-style-type: circle;">
<li>Measurement of DNA concentration&nbsp;</li>
<li>Assessment of DNA quality</li>
</ul>
</td>
</tr>
<tr>
<td style="width: 264px;"><strong>Preparation of RRBS libraries</strong></td>
<td style="width: 636px;">
<ul style="list-style-type: circle;">
<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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</td>
</tr>
<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>
</ul>
</td>
</tr>
</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>
</thead>
<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>
</ul>
</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>
</div>
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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>
</ul>
</div>
<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>
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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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			'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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			'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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            <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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<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="ChIP-seq" id="QuoteEpigenomicsServiceChIPSeq" /><label for="QuoteEpigenomicsServiceChIPSeq">ChIP-seq</label></div>
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            <span class="prefix">Total number of samples (including replicates)</span>
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<option value="">-- select a country --</option>
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<option value="AL">Albania</option>
<option value="DZ">Algeria</option>
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<option value="AO">Angola</option>
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<option value="BI">Burundi</option>
<option value="KH">Cambodia</option>
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<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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<option value="DJ">Djibouti</option>
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<option value="EG">Egypt</option>
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<option value="FI">Finland</option>
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<option value="GF">French Guiana</option>
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<option value="GD">Grenada</option>
<option value="GP">Guadeloupe</option>
<option value="GU">Guam</option>
<option value="GT">Guatemala</option>
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<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>
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<option value="HK">Hong Kong</option>
<option value="HU">Hungary</option>
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            </div>

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            <h6 style="height:60px">Methylation Data Analysis</h6>
        </div>

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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>
</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>
<div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/cytosine-schema.png" /></center></div>',
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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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include - APP/View/Products/view.ctp, line 755
View::_evaluate() - CORE/Cake/View/View.php, line 971
View::_render() - CORE/Cake/View/View.php, line 933
View::render() - CORE/Cake/View/View.php, line 473
Controller::render() - CORE/Cake/Controller/Controller.php, line 963
ProductsController::slug() - APP/Controller/ProductsController.php, line 1052
ReflectionMethod::invokeArgs() - [internal], line ??
Controller::invokeAction() - CORE/Cake/Controller/Controller.php, line 491
Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 193
Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167
[main] - APP/webroot/index.php, line 118

Notice (8): Undefined variable: message [APP/View/Products/view.ctp, line 755]
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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;">Theoretical Coverage &gt;30X for human, mouse and rat samples</li>
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<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>
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<li>Enrichment analysis on gene associated with DMCs and DMRs</li>
<li>Get functional insights</li>
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<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>
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<li><span>Biomarker discovery</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="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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<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 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><strong>Reduced representation bisulfite sequencing (RRBS) </strong> <span>enables </span><span>genome-s</span><span>cale </span>DNA methylation<span> analysis</span> at the single nucleotide level <span>in any vertebrate species. </span><span>The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of</span> whole genome sequencing. By cutting the genome using the restriction MspI enzyme (CCGG target sites) followed by size selection, DNA is enriched to represent<span> biologically relevant target</span> CpG-rich regions including <span>promoters and </span>CpG islands.<span> Our RRBS service makes this technology widely available and provides high coverage (up to&nbsp;7 million CpGs</span><span> detected </span><span>in human samples).</span></p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
<li>Robust and cost-effective solution with&nbsp;reliable results</li>
<li>Capability to use gDNA inputs down to 25 ng</li>
<li>Use of UDIs enhance sequencing results</li>
<li>UMI technology eliminates PCR duplicates</li>
<li>High coverage - up to&nbsp;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>
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<h2>Complete end-to-end service</h2>
<ul class="square">
<li><span><span class="ui-provider ee cfi bsv cfj cfk cfl cfm cfn cfo cfp cfq cfr cfs cft cfu cfv cfw cfx cfy cfz cga cgb cgc cgd cge cgf cgg cgh cgi cgj cgk cgl cgm cgn cgo" dir="ltr">From DNA QC to sequencing raw data</span></span></li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a>&nbsp;widely adopted</li>
<li>As little as 25 ng&nbsp;for&nbsp;any vertebrate species</li>
<li>Differentially methylated site analysis&nbsp;<span>using&nbsp;<a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></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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<h4>RRBS Service includes:</h4>
<table style="width: 905px;">
<tbody>
<tr>
<td style="width: 264px;"><strong>QC of the genomic DNA</strong></td>
<td style="width: 636px;">
<ul style="list-style-type: circle;">
<li>Measurement of DNA concentration&nbsp;</li>
<li>Assessment of DNA quality</li>
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</tr>
<tr>
<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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</td>
</tr>
<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>
</ul>
</td>
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<th style="width: 262px;">
<h4><strong>Analysis</strong></h4>
</th>
<th style="width: 624px;">
<h4><strong>Features</strong></h4>
</th>
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<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>
<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>
</div>
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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>
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</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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			'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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			'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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            <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="ChIP-seq" id="QuoteEpigenomicsServiceChIPSeq" /><label for="QuoteEpigenomicsServiceChIPSeq">ChIP-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>
<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>
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    <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>
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            <span class="prefix">Total number of samples (including replicates)</span>
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            <input name="data[Quote][number_samples]" maxlength="255" type="text" id="QuoteNumberSamples"/>        </div>
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<div class="row collapse">
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        <span class="prefix">First name <sup style="font-size:16px;color:red;">*</sup></span>
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        <span class="prefix">Company <sup style="font-size:16px;color:red;">*</sup></span>
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        <span class="prefix">Phone number</span>
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<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>
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<option value="AT">Austria</option>
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<option value="BD">Bangladesh</option>
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<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>
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            </div>

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

    </div>
</li>
'
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	'id' => '3061',
	'antibody_id' => null,
	'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>
</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>
</ul>
</div>
<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>
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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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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 ??
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Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167
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Notice (8): Undefined variable: campaign_id [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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			'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>
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<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>
</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><strong>Reduced representation bisulfite sequencing (RRBS) </strong> <span>enables </span><span>genome-s</span><span>cale </span>DNA methylation<span> analysis</span> at the single nucleotide level <span>in any vertebrate species. </span><span>The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of</span> whole genome sequencing. By cutting the genome using the restriction MspI enzyme (CCGG target sites) followed by size selection, DNA is enriched to represent<span> biologically relevant target</span> CpG-rich regions including <span>promoters and </span>CpG islands.<span> Our RRBS service makes this technology widely available and provides high coverage (up to&nbsp;7 million CpGs</span><span> detected </span><span>in human samples).</span></p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
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<li>Capability to use gDNA inputs down to 25 ng</li>
<li>Use of UDIs enhance sequencing results</li>
<li>UMI technology eliminates PCR duplicates</li>
<li>High coverage - up to&nbsp;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>
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<h2>Complete end-to-end service</h2>
<ul class="square">
<li><span><span class="ui-provider ee cfi bsv cfj cfk cfl cfm cfn cfo cfp cfq cfr cfs cft cfu cfv cfw cfx cfy cfz cga cgb cgc cgd cge cgf cgg cgh cgi cgj cgk cgl cgm cgn cgo" dir="ltr">From DNA QC to sequencing raw data</span></span></li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a>&nbsp;widely adopted</li>
<li>As little as 25 ng&nbsp;for&nbsp;any vertebrate species</li>
<li>Differentially methylated site analysis&nbsp;<span>using&nbsp;<a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
</ul>
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<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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<td style="width: 264px;"><strong>Preparation of RRBS libraries</strong></td>
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<li>Library preparation (ends preparation, adaptor ligation)</li>
<li>Size selection</li>
<li>Sample pooling</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>
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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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<h4><strong>Analysis</strong></h4>
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<th style="width: 624px;">
<h4><strong>Features</strong></h4>
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<td style="width: 262px;"><strong>Standard</strong></td>
<td style="width: 624px;">
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<li>FASTQ raw data</li>
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<li>Summary statistics</li>
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<td style="width: 262px;"><strong>Differential&nbsp;methylation analysis<br /></strong></td>
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<li>Differentially Methylated Regions (DMRs) analysis</li>
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<li>Clustering analysis</li>
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<li>Get functional insights</li>
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<p><strong>Pathway analysis</strong></p>
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<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>
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</td>
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			'meta_keywords' => 'RRBS,Reduced representation bisulfite sequencing,DNA methylation',
			'meta_description' => 'RRBS enables genome-scale DNA methylation analysis at the single nucleotide level in any vertebrate species. Robust and cost-effective solution with high coverage (up to 5 million CpGs in human samples).',
			'modified' => '2024-01-10 17:48:39',
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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>
</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>
</ul>
</div>
<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>
<div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/cytosine-schema.png" /></center></div>',
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			'meta_description' => 'Diagenode offers bioinformatics analysis service to explore  any DNA methylation data, from enriched based methods to single based resolution using NGS.',
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			'name' => 'Epigenomics Profiling Services',
			'description' => '<ul>
<li>Chromatin analysis</li>
<li>DNA methylation services</li>
<li>RNA-seq analysis</li>
</ul>',
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			'name' => 'Development and epigenetic plasticity of murine Müller glia.',
			'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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			'date' => '2019-07-02',
			'pmid' => 'http://www.pubmed.gov/31276697',
			'doi' => '10.1016/j.bbamcr.2019.06.019',
			'modified' => '2019-08-13 10:50:24',
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			'id' => '3569',
			'name' => 'The epigenetic basis for the impaired ability of adult murine retinal pigment epithelium cells to regenerate retinal tissue.',
			'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',
			'pmid' => 'http://www.pubmed.gov/30846751',
			'doi' => '10.1038/s41598-019-40262-w',
			'modified' => '2019-05-09 17:33:09',
			'created' => '2019-03-21 14:12:08',
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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="Targeted DNA methylation analysis" id="QuoteEpigenomicsServiceTargetedDNAMethylationAnalysis" /><label for="QuoteEpigenomicsServiceTargetedDNAMethylationAnalysis">Targeted DNA methylation analysis</label></div>
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<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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            <span class="prefix">Total number of samples (including replicates)</span>
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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>
<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>
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            </div>

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

    </div>
</li>
'
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	'id' => '3061',
	'antibody_id' => null,
	'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>
</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>
</ul>
</div>
<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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