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G02500000

進行中または神経変性のガンや慢性疾患などの病理学的過程などで観察される表現型の変化をよりよく理解するために、複数のエピゲノム技術を組み合わせる必要性が数多くの研究で証明されています。

癌細胞で観察される表現型の変化は、DNAメチル化とクロマチン修飾パターンの両方の変化と強く関連していて、遺伝子発現は、DNAメチル化とクロマチンダイナミクスによって強く制御されています。一般的に特定のタンパク質が調節する遺伝子を発見するために、クロマチン修飾に関する結合部位とトランスクリプトームデータが統合されます。トランスクリプトミクスデータをオープンクロマチン領域と統合することで、遺伝子調節パターンの正確な評価を可能にします。メチル化プロファイルは、他のエピゲノミクスまたはトランスクリプトミクス測定と統合的に分析することがで、遺伝子の過剰発現または過少発現によるメチル化の役割を明らかにします。さまざまな「オミクス」データが利用可能であるからこそ、複数のエピゲノム技術を組み合わせる事が推奨されています。

どのような種類のデータを組み合わせることができますか？

ChIP-Seq with methylation
ATAC-Seq with methylation
mRNA-Seq with methylation data
sncRNA-Seq with ChIP-Seq or ATAC-Seq
mRNA-Seq with ChIP-Seq or ATAC-Seq
...and more

この解析は様々な線形数学モデルを調査し、2セットのオミクスデータ間の関係を推定します。

完全な解析

Summary statistics for the different technologies used
Trimmed and filtered reads in fastQ files after sequencing QC
BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)
Outcome of interest:

List of peaks with their enrichment level (ChIP-Seq, ATAC-Seq, MeDIP-Seq)
List of CpGs with their methylation percentage (WGBS, RRBS, EPIC, BSAS)
Matrix with expression abundance estimation (total, messenger and small non-coding RNA)

Comparative analysis aimed at finding differential responses between two groups of samples
Functional gene annotation on features with differential responses
Integration of two sets of omics data - linear regression
Gene ontology enrichment analysis of correlated features

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<p>進行中または神経変性のガンや慢性疾患などの病理学的過程などで観察される表現型の変化をよりよく理解するために、複数のエピゲノム技術を組み合わせる必要性が数多くの研究で証明されています。</p>
<p>癌細胞で観察される表現型の変化は、DNAメチル化とクロマチン修飾パターンの両方の変化と強く関連していて、遺伝子発現は、DNAメチル化とクロマチンダイナミクスによって強く制御されています。一般的に特定のタンパク質が調節する遺伝子を発見するために、 クロマチン修飾に関する結合部位とトランスクリプトームデータが統合されます。 トランスクリプトミクスデータをオープンクロマチン領域と統合することで、遺伝子調節パターンの正確な評価を可能にします。メチル化プロファイルは、他のエピゲノミクスまたはトランスクリプトミクス測定と統合的に分析することがで、遺伝子の過剰発現または過少発現によるメチル化の役割を明らかにします。さまざまな「オミクス」データが利用可能であるからこそ、複数のエピゲノム技術を組み合わせる事が推奨されています。</p>
</div>
<h2>どのような種類のデータを組み合わせることができますか？</h2>
<ul style="list-style-type: circle;">
<li>ChIP-Seq with methylation</li>
<li>ATAC-Seq with methylation</li>
<li>mRNA-Seq with methylation data</li>
<li>sncRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>mRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>...and more</li>
</ul>
<p>この解析は様々な線形数学モデルを調査し、2セットのオミクスデータ間の関係を推定します。</p>
<h3 class="diacol" style="font-weight: 100;">完全な解析</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics for the different technologies used</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li>
<li>Outcome of interest:</li>
<ul>
<li>List of peaks with their enrichment level (ChIP-Seq, ATAC-Seq, MeDIP-Seq)</li>
<li>List of CpGs with their methylation percentage (WGBS, RRBS, EPIC, BSAS)</li>
<li>Matrix with expression abundance estimation (total, messenger and small non-coding RNA)</li>
</ul>
<li>Comparative analysis aimed at finding differential responses between two groups of samples</li>
<li>Functional gene annotation on features with differential responses</li>
<li>Integration of two sets of omics data - linear regression</li>
<li>Gene ontology enrichment analysis of correlated features</li>
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<p>進行中または神経変性のガンや慢性疾患などの病理学的過程などで観察される表現型の変化をよりよく理解するために、複数のエピゲノム技術を組み合わせる必要性が数多くの研究で証明されています。</p>
<p>癌細胞で観察される表現型の変化は、DNAメチル化とクロマチン修飾パターンの両方の変化と強く関連していて、遺伝子発現は、DNAメチル化とクロマチンダイナミクスによって強く制御されています。一般的に特定のタンパク質が調節する遺伝子を発見するために、 クロマチン修飾に関する結合部位とトランスクリプトームデータが統合されます。 トランスクリプトミクスデータをオープンクロマチン領域と統合することで、遺伝子調節パターンの正確な評価を可能にします。メチル化プロファイルは、他のエピゲノミクスまたはトランスクリプトミクス測定と統合的に分析することがで、遺伝子の過剰発現または過少発現によるメチル化の役割を明らかにします。さまざまな「オミクス」データが利用可能であるからこそ、複数のエピゲノム技術を組み合わせる事が推奨されています。</p>
</div>
<h2>どのような種類のデータを組み合わせることができますか？</h2>
<ul style="list-style-type: circle;">
<li>ChIP-Seq with methylation</li>
<li>ATAC-Seq with methylation</li>
<li>mRNA-Seq with methylation data</li>
<li>sncRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>mRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>...and more</li>
</ul>
<p>この解析は様々な線形数学モデルを調査し、2セットのオミクスデータ間の関係を推定します。</p>
<h3 class="diacol" style="font-weight: 100;">完全な解析</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics for the different technologies used</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li>
<li>Outcome of interest:</li>
<ul>
<li>List of peaks with their enrichment level (ChIP-Seq, ATAC-Seq, MeDIP-Seq)</li>
<li>List of CpGs with their methylation percentage (WGBS, RRBS, EPIC, BSAS)</li>
<li>Matrix with expression abundance estimation (total, messenger and small non-coding RNA)</li>
</ul>
<li>Comparative analysis aimed at finding differential responses between two groups of samples</li>
<li>Functional gene annotation on features with differential responses</li>
<li>Integration of two sets of omics data - linear regression</li>
<li>Gene ontology enrichment analysis of correlated features</li>
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<p>Epigenomic Services at Diagenode offers data analysis solutions for different application areas. These analysis services are open to everyone, whether you use our wet lab and sequencing service with us or not.</p>
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<h2 class="diacol">ADVANTAGES</h2>
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<li>The bioinformatics team works constantly to optimize pipelines to deliver biologically meaningful results within a short turnaround time</li>
<li>Flexible and communicative team work in close collaboration with you to adapt to your analysis requirements</li>
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<li>We have great computing capacity to undertake large projects.</li>
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<h2>Check out our service offerings</h2>
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<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/methylation-data-analysis">Methylation studies</a></li>
<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/rna-data-analysis">RNA studies</a></li>
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<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/data-mining-service">Data mining with machine learning techniques</a></li>
</ul>
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<p>進行中または神経変性のガンや慢性疾患などの病理学的過程などで観察される表現型の変化をよりよく理解するために、複数のエピゲノム技術を組み合わせる必要性が数多くの研究で証明されています。</p>
<p>癌細胞で観察される表現型の変化は、DNAメチル化とクロマチン修飾パターンの両方の変化と強く関連していて、遺伝子発現は、DNAメチル化とクロマチンダイナミクスによって強く制御されています。一般的に特定のタンパク質が調節する遺伝子を発見するために、 クロマチン修飾に関する結合部位とトランスクリプトームデータが統合されます。 トランスクリプトミクスデータをオープンクロマチン領域と統合することで、遺伝子調節パターンの正確な評価を可能にします。メチル化プロファイルは、他のエピゲノミクスまたはトランスクリプトミクス測定と統合的に分析することがで、遺伝子の過剰発現または過少発現によるメチル化の役割を明らかにします。さまざまな「オミクス」データが利用可能であるからこそ、複数のエピゲノム技術を組み合わせる事が推奨されています。</p>
</div>
<h2>どのような種類のデータを組み合わせることができますか？</h2>
<ul style="list-style-type: circle;">
<li>ChIP-Seq with methylation</li>
<li>ATAC-Seq with methylation</li>
<li>mRNA-Seq with methylation data</li>
<li>sncRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>mRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>...and more</li>
</ul>
<p>この解析は様々な線形数学モデルを調査し、2セットのオミクスデータ間の関係を推定します。</p>
<h3 class="diacol" style="font-weight: 100;">完全な解析</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics for the different technologies used</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li>
<li>Outcome of interest:</li>
<ul>
<li>List of peaks with their enrichment level (ChIP-Seq, ATAC-Seq, MeDIP-Seq)</li>
<li>List of CpGs with their methylation percentage (WGBS, RRBS, EPIC, BSAS)</li>
<li>Matrix with expression abundance estimation (total, messenger and small non-coding RNA)</li>
</ul>
<li>Comparative analysis aimed at finding differential responses between two groups of samples</li>
<li>Functional gene annotation on features with differential responses</li>
<li>Integration of two sets of omics data - linear regression</li>
<li>Gene ontology enrichment analysis of correlated features</li>
</ul>',
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</div>
<h2>どのような種類のデータを組み合わせることができますか？</h2>
<ul style="list-style-type: circle;">
<li>ChIP-Seq with methylation</li>
<li>ATAC-Seq with methylation</li>
<li>mRNA-Seq with methylation data</li>
<li>sncRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>mRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>...and more</li>
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</div>
<h2>どのような種類のデータを組み合わせることができますか？</h2>
<ul style="list-style-type: circle;">
<li>ChIP-Seq with methylation</li>
<li>ATAC-Seq with methylation</li>
<li>mRNA-Seq with methylation data</li>
<li>sncRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>mRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>...and more</li>
</ul>
<p>この解析は様々な線形数学モデルを調査し、2セットのオミクスデータ間の関係を推定します。</p>
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<ul style="list-style-type: circle;">
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<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li>
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<li>List of CpGs with their methylation percentage (WGBS, RRBS, EPIC, BSAS)</li>
<li>Matrix with expression abundance estimation (total, messenger and small non-coding RNA)</li>
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<li>Comparative analysis aimed at finding differential responses between two groups of samples</li>
<li>Functional gene annotation on features with differential responses</li>
<li>Integration of two sets of omics data - linear regression</li>
<li>Gene ontology enrichment analysis of correlated features</li>
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</div>
<h2>どのような種類のデータを組み合わせることができますか？</h2>
<ul style="list-style-type: circle;">
<li>ChIP-Seq with methylation</li>
<li>ATAC-Seq with methylation</li>
<li>mRNA-Seq with methylation data</li>
<li>sncRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>mRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>...and more</li>
</ul>
<p>この解析は様々な線形数学モデルを調査し、2セットのオミクスデータ間の関係を推定します。</p>
<h3 class="diacol" style="font-weight: 100;">完全な解析</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics for the different technologies used</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li>
<li>Outcome of interest:</li>
<ul>
<li>List of peaks with their enrichment level (ChIP-Seq, ATAC-Seq, MeDIP-Seq)</li>
<li>List of CpGs with their methylation percentage (WGBS, RRBS, EPIC, BSAS)</li>
<li>Matrix with expression abundance estimation (total, messenger and small non-coding RNA)</li>
</ul>
<li>Comparative analysis aimed at finding differential responses between two groups of samples</li>
<li>Functional gene annotation on features with differential responses</li>
<li>Integration of two sets of omics data - linear regression</li>
<li>Gene ontology enrichment analysis of correlated features</li>
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<div class="extra-spaced">
<h2 class="diacol">ADVANTAGES</h2>
<ol>
<li>The bioinformatics team works constantly to optimize pipelines to deliver biologically meaningful results within a short turnaround time</li>
<li>Flexible and communicative team work in close collaboration with you to adapt to your analysis requirements</li>
<li>We help you shape the methods section of your manuscripts</li>
<li>We back up our internal servers daily to protect your data</li>
<li>We have great computing capacity to undertake large projects.</li>
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<div class="extra-spaced">
<h2>Check out our service offerings</h2>
<ul class="nobullet">
<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/chromatin-data-analysis">Chromatin studies</a></li>
<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/methylation-data-analysis">Methylation studies</a></li>
<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/rna-data-analysis">RNA studies</a></li>
<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/integrative-analysis">Integrative analysis of multiple omics data</a></li>
<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/data-mining-service">Data mining with machine learning techniques</a></li>
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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
[main] - APP/webroot/index.php, line 118

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<p>進行中または神経変性のガンや慢性疾患などの病理学的過程などで観察される表現型の変化をよりよく理解するために、複数のエピゲノム技術を組み合わせる必要性が数多くの研究で証明されています。</p>
<p>癌細胞で観察される表現型の変化は、DNAメチル化とクロマチン修飾パターンの両方の変化と強く関連していて、遺伝子発現は、DNAメチル化とクロマチンダイナミクスによって強く制御されています。一般的に特定のタンパク質が調節する遺伝子を発見するために、 クロマチン修飾に関する結合部位とトランスクリプトームデータが統合されます。 トランスクリプトミクスデータをオープンクロマチン領域と統合することで、遺伝子調節パターンの正確な評価を可能にします。メチル化プロファイルは、他のエピゲノミクスまたはトランスクリプトミクス測定と統合的に分析することがで、遺伝子の過剰発現または過少発現によるメチル化の役割を明らかにします。さまざまな「オミクス」データが利用可能であるからこそ、複数のエピゲノム技術を組み合わせる事が推奨されています。</p>
</div>
<h2>どのような種類のデータを組み合わせることができますか？</h2>
<ul style="list-style-type: circle;">
<li>ChIP-Seq with methylation</li>
<li>ATAC-Seq with methylation</li>
<li>mRNA-Seq with methylation data</li>
<li>sncRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>mRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>...and more</li>
</ul>
<p>この解析は様々な線形数学モデルを調査し、2セットのオミクスデータ間の関係を推定します。</p>
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<ul style="list-style-type: circle;">
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<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li>
<li>Outcome of interest:</li>
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<li>List of CpGs with their methylation percentage (WGBS, RRBS, EPIC, BSAS)</li>
<li>Matrix with expression abundance estimation (total, messenger and small non-coding RNA)</li>
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<li>Comparative analysis aimed at finding differential responses between two groups of samples</li>
<li>Functional gene annotation on features with differential responses</li>
<li>Integration of two sets of omics data - linear regression</li>
<li>Gene ontology enrichment analysis of correlated features</li>
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<p>癌細胞で観察される表現型の変化は、DNAメチル化とクロマチン修飾パターンの両方の変化と強く関連していて、遺伝子発現は、DNAメチル化とクロマチンダイナミクスによって強く制御されています。一般的に特定のタンパク質が調節する遺伝子を発見するために、 クロマチン修飾に関する結合部位とトランスクリプトームデータが統合されます。 トランスクリプトミクスデータをオープンクロマチン領域と統合することで、遺伝子調節パターンの正確な評価を可能にします。メチル化プロファイルは、他のエピゲノミクスまたはトランスクリプトミクス測定と統合的に分析することがで、遺伝子の過剰発現または過少発現によるメチル化の役割を明らかにします。さまざまな「オミクス」データが利用可能であるからこそ、複数のエピゲノム技術を組み合わせる事が推奨されています。</p>
</div>
<h2>どのような種類のデータを組み合わせることができますか？</h2>
<ul style="list-style-type: circle;">
<li>ChIP-Seq with methylation</li>
<li>ATAC-Seq with methylation</li>
<li>mRNA-Seq with methylation data</li>
<li>sncRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>mRNA-Seq with ChIP-Seq or ATAC-Seq</li>
<li>...and more</li>
</ul>
<p>この解析は様々な線形数学モデルを調査し、2セットのオミクスデータ間の関係を推定します。</p>
<h3 class="diacol" style="font-weight: 100;">完全な解析</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics for the different technologies used</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li>
<li>Outcome of interest:</li>
<ul>
<li>List of peaks with their enrichment level (ChIP-Seq, ATAC-Seq, MeDIP-Seq)</li>
<li>List of CpGs with their methylation percentage (WGBS, RRBS, EPIC, BSAS)</li>
<li>Matrix with expression abundance estimation (total, messenger and small non-coding RNA)</li>
</ul>
<li>Comparative analysis aimed at finding differential responses between two groups of samples</li>
<li>Functional gene annotation on features with differential responses</li>
<li>Integration of two sets of omics data - linear regression</li>
<li>Gene ontology enrichment analysis of correlated features</li>
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<div class="extra-spaced">
<p>Epigenomic Services at Diagenode offers data analysis solutions for different application areas. These analysis services are open to everyone, whether you use our wet lab and sequencing service with us or not.</p>
</div>
<div class="extra-spaced">
<h2 class="diacol">ADVANTAGES</h2>
<ol>
<li>The bioinformatics team works constantly to optimize pipelines to deliver biologically meaningful results within a short turnaround time</li>
<li>Flexible and communicative team work in close collaboration with you to adapt to your analysis requirements</li>
<li>We help you shape the methods section of your manuscripts</li>
<li>We back up our internal servers daily to protect your data</li>
<li>We have great computing capacity to undertake large projects.</li>
</ol>
</div>
<div class="extra-spaced">
<h2>Check out our service offerings</h2>
<ul class="nobullet">
<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/chromatin-data-analysis">Chromatin studies</a></li>
<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/methylation-data-analysis">Methylation studies</a></li>
<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/rna-data-analysis">RNA studies</a></li>
<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/integrative-analysis">Integrative analysis of multiple omics data</a></li>
<li><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/p/data-mining-service">Data mining with machine learning techniques</a></li>
</ul>
</div>
<div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/tech-tab.png" /></center></div>',
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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
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APHL 2024
Milwaukee, Wisconsin, USA
May 6-May 9, 2024
London Calling 2024
London, UK
May 21-May 24, 2024
Symposium of the Young Scientist Association
Vienna, Austria
May 28-May 29, 2024
ESHG 2024
Berlin, Germany
Jun 1-Jun 4, 2024
CLEPIC 2024
Warsaw, Poland
Jun 5-Jun 7, 2024

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The European Regional Development Fund and Wallonia are investing in your future.

Extension of industrial buildings and new laboratories.

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どのような種類のデータを組み合わせることができますか？

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How to properly cite this product in your work

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