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RNA sequencing uses next-generation sequencing (NGS) to uncover the presence and quantity of RNA for gene expression profiling across the transcriptome. Diagenode specializes in generating RNA-seq libraries which is optimal for low or degraded inputs including those from liquid biopsies,FFPE, exosomes, serum, or plasma.

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<li><strong>Required quantities</strong>: minimum 5 ng of total RNA (Human, Mouse, Rat) or 100 ng (Plant)</li>
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<h3 class="green">What our service includes</h3>
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<li><strong>RNA quality control</strong></li>
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<li>Determination of RIN value (RNA integration number)</li>
<li>Measurement of RNA concentration</li>
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<li><strong>Library preparation</strong></li>
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<li>Demultiplexing, read quality control, trimming and filtering</li>
<li>Alignment to reference genome</li>
<li>Quantification of coding and non-coding RNA biotypes with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>)</li>
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<li>Measurement of RNA concentration</li>
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<li><strong>Library preparation</strong></li>
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<li>Optional rRNA depletion</li>
<li>Stranded library construction</li>
<li>Quality control of libraries prior to sequencing</li>
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<li><strong>Sequencing on Illumina platforms</strong></li>
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<li>50 million raw reads per sample</li>
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<li><strong>Data analysis</strong> (optional for additional fee)</li>
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<li>Alignment to reference genome</li>
<li>Quantification of coding and non-coding RNA biotypes with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>)</li>
</ul>
<br />
<p><em>Provided files:</em></p>
<ul>
<li>Report with sequencing statistics</li>
<li>Raw data in FASTQ format</li>
<li>FastQC report</li>
<li>Alignment in BAM format</li>
<li>Un-normalized read counts</li>
</ul>
</td>
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<li>Differential expression analysis</li>
<li>Gene ontology terms analysis</li>
<li>Pathway analysis</li>
<li>Alternative splicing analysis</li>
<li>Novel transcript identification</li>
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			'description' => '<h2 class="text-center"><span class="diacol">New!</span> <br />Data mining using machine learning (AI) for unique epigenetic data insights</h2>
<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
</div>
<center><iframe width="560" height="315" src="https://www.youtube.com/embed/KXjnSHz3Jk8" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen="allowfullscreen"></iframe></center>
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<div class="row">
<div class="small-12 medium-12 large-12 columns">
<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
<div id="portal" class="main-portal">
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<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>
</nav></div>
</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><strong>Feasibility study</strong></li>
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<li>Prototypic analyses:</li>
<li>Initial feasibility report</li>
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<p></p>
<ol start="2">
<li><strong>Data Mining</strong></li>
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<li>Machine learning on data</li>
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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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		(int) 1 => array(
			'id' => '3088',
			'antibody_id' => null,
			'name' => 'mRNA-seq Service',
			'description' => '<p><strong>mRNA-seq</strong> is the method of choice to efficiently detect and precisely quantify coding RNAs present in a biological sample under specific circumstances. Accurate estimation of transcript abundance allows for differential expression analysis as well as the study of alternative splicing and discovery of new transcript isoforms.</p>
<p>Our Epigenomics Profiling Services assure a fully integrated service-workflow for generation of mRNA-seq libraries, multiplexed sequencing and data analysis. Serviced library preparation is based on specific poly A selection to produce Illumina compatible libraries enriched in mRNAs.</p>
<h2>High performance mRNA-seq</h2>
<ul class="square">
<li>Superior complexity and high transcript coverage</li>
<li>Accurate comparison of gene expression levels</li>
<li>Detection of novel transcripts and alternative splicing events</li>
<li>Suitable for a broad range of input amounts</li>
<li>Compatible with FFPE samples</li>
<li>Fast turnaround time (from 5 to 6 weeks)</li>
</ul>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/rna-seq-category">See our other RNA-seq Profiling Services</a></p>',
			'label1' => 'Sample Requirements',
			'info1' => '<div class="extra-spaced">
<ul>
<li><strong>Project size</strong>: minimum of 24 samples</li>
<li><strong>Sample type</strong>: total isolated RNA</li>
<li><strong>Required quantities</strong>: minimum 10 ng of total RNA</li>
<li><strong>Shipment conditions</strong>: shipped in 1.5 mL or 0.5 mL tubes on dry ice</li>
</ul>
</div>',
			'label2' => 'Services Workflow',
			'info2' => '<center><img alt="mRNA-seq Service" src="https://www.diagenode.com/img/product/services/mRNA-seq_workflow.png" /></center>
<div class="extra-spaced">
<h3 class="green">What our service includes</h3>
<ol>
<li><strong>RNA quality control</strong></li>
<ul>
<li>Determination of RIN value (RNA integration number)</li>
<li>Measurement of RNA concentration</li>
</ul>
<li><strong>Library preparation</strong></li>
<ul>
<li>Poly-A selection</li>
<li>Stranded library construction</li>
<li>Quality control of libraries prior to sequencing</li>
</ul>
<li><strong>Sequencing on Illumina platforms</strong></li>
<ul>
<li>25-30 million raw reads per sample</li>
</ul>
<li><strong>Data analysis</strong> (optional for additional fee)</li>
<ul>
<li>Standard bioinformatic analysis</li>
<li>Advanced bioinformatic analysis</li>
<li>Data delivery of raw data and all generated files through fast and secure file transfer or hard drive upon request</li>
</ul>
</ol>
</div>',
			'label3' => 'Bioinformatics Analysis',
			'info3' => '<table style="padding: 40px;">
<tbody>
<tr>
<td>
<h4 class="text-center"><strong>Standard bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
</td>
<td>
<h4 class="text-center"><strong>Advanced bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
</td>
</tr>
<tr>
<td style="vertical-align: top; padding-left: 20px;">
<ul>
<li>Demultiplexing, read quality control, trimming and filtering</li>
<li>Alignment to reference genome</li>
<li>Quantification of known transcripts</li>
</ul>
<br />
<p><em>Provided files:</em></p>
<ul>
<li>Report with sequencing statistics</li>
<li>Raw data in FASTQ format</li>
<li>FastQC report</li>
<li>Alignment in BAM format</li>
<li>Un-normalized read counts</li>
</ul>
</td>
<td style="vertical-align: top;">
<ul>
<li>Differential expression analysis</li>
<li>Gene ontology terms analysis</li>
<li>Pathway analysis</li>
<li>Alternative splicing analysis</li>
<li>Novel transcript identification</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p></p>',
			'format' => 'Custom',
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			'type' => 'ACC',
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			'price_JPY' => '42800',
			'price_CNY' => 'on request',
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			'slug' => 'mrna-seq-services',
			'meta_title' => 'mRNA-seq Service - Coding Transcriptome Analysis| Diagenode',
			'meta_keywords' => '',
			'meta_description' => 'mRNA-seq provides a complete view of the coding transcriptome. mRNA-Seq detects known and novel transcripts and measures transcript abundance for accurate, comprehensive analysis.',
			'modified' => '2020-12-24 12:47:26',
			'created' => '2020-07-13 10:52:47',
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		(int) 2 => array(
			'id' => '3089',
			'antibody_id' => null,
			'name' => 'Small RNA-seq Service',
			'description' => '<p><strong>Small RNA-seq</strong> is the method of choice to investigate the regulatory network of small non-coding RNAs (&lt;200 nt length), including but not limited to miRNA transcripts. It utilizes the proprietary technology D-Plex which ensures realistic representation of diverse small non-coding RNA transcripts with minimized bias. This approach is optimal for finding biomarkers from known transcripts and discover new RNA features.</p>
<p>Our Epigenomics Profiling Services assure a fully integrated service-workflow for generation of small RNA-seq libraries, multiplexed sequencing and data analysis. Serviced library preparation is based on a template-switching technology to produce Illumina compatible libraries enriched with the large variety of small non-coding RNAs.</p>
<h2>High transcript diversity with small RNA-seq</h2>
<ul class="square">
<li>Realistic representation of all small non-coding RNAs</li>
<li>Detection of novel transcripts and differentially-expressed small RNAs</li>
<li>Investigation of post-transcriptional regulation of gene expression</li>
<li>Leveraging the <a href="https://www.diagenode.com/en/pages/dplex">D-Plex technology</a></li>
<li>Working with ultra-low RNA inputs</li>
<li>Compatible with biological fluids</li>
</ul>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/rna-seq-category">See our other RNA-seq Profiling Services</a></p>',
			'label1' => 'Sample Requirements',
			'info1' => '<div class="extra-spaced">
<ul>
<li><strong>Project size</strong>: no minimum</li>
<li><strong>Sample type</strong>: total isolated RNA or small enriched RNA</li>
<li><strong>Required quantities</strong>: minimum 100 pg of total RNA or 10 pg of small RNA</li>
<li><strong>Shipment conditions</strong>: shipped in 1.5 mL or 0.5 mL tubes on dry ice</li>
</ul>
</div>',
			'label2' => 'Services Workflow',
			'info2' => '<center><img alt="Small RNA-seq Service" src="https://www.diagenode.com/img/product/services/sncRNA-seq_workflow.png" /></center>
<div class="extra-spaced">
<h3 class="green">What our service includes</h3>
<ol>
<li><strong>RNA quality control</strong></li>
<ul>
<li>Determination of RIN value (RNA integration number)</li>
<li>Measurement of RNA concentration</li>
</ul>
<li><strong>Library preparation</strong></li>
<ul>
<li>Capture and amplification by tailing and switching</li>
<li>Library construction</li>
<li>Quality control of libraries prior to sequencing</li>
</ul>
<li><strong>Sequencing on Illumina platforms</strong></li>
<ul>
<li>10-20 million raw reads per sample</li>
</ul>
<li><strong>Data analysis</strong> (optional for additional fee)</li>
<ul>
<li>Standard bioinformatic analysis</li>
<li>Advanced bioinformatic analysis</li>
<li>Data delivery of raw data and all generated files through fast and secure file transfer or hard drive upon request</li>
</ul>
</ol>
</div>',
			'label3' => 'Bioinformatics Analysis',
			'info3' => '<table style="padding: 40px;">
<tbody>
<tr>
<td>
<h4 class="text-center"><strong>Standard bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
</td>
<td>
<h4 class="text-center"><strong>Advanced bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
</td>
</tr>
<tr>
<td style="vertical-align: top; padding-left: 20px;">
<ul>
<li>Demultiplexing, read quality control, trimming and filtering</li>
<li>Alignment to reference genome</li>
<li>Quantification of small RNA biotypes with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>)</li>
</ul>
<br />
<p><em>Provided files:</em></p>
<ul>
<li>Report with sequencing statistics</li>
<li>Raw data in FASTQ format</li>
<li>FastQC report</li>
<li>Alignment in BAM format</li>
<li>Un-normalized read counts</li>
</ul>
</td>
<td style="vertical-align: top;">
<ul>
<li>Differential expression analysis</li>
<li>Gene ontology terms analysis</li>
<li>Pathway analysis</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p></p>',
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			'price_EUR' => 'on request',
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			'price_JPY' => '42800',
			'price_CNY' => 'on request',
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			'slug' => 'small-rna-seq-services',
			'meta_title' => 'Small RNA-seq Service - Small RNA and miRNA profiling | Diagenode',
			'meta_keywords' => '',
			'meta_description' => 'Small RNA-seq enables small RNA discovery, mutation characterization, and expression profiling of small RNAs by leveraging of sequencing technologies and data analysis pipeline.',
			'modified' => '2022-03-07 09:12:41',
			'created' => '2020-07-13 11:09:54',
			'ProductsRelated' => array(
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		(int) 3 => array(
			'id' => '3062',
			'antibody_id' => null,
			'name' => 'RNA Data Analysis',
			'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (&lt;100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p>
<div class="extra-spaced">
<h2>What do we provide with the analysis?</h2>
<p>This analysis provides information for either genes or isoforms with their expression levels.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</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>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul style="list-style-type: circle;">
<li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li>
<li>Functional gene annotation</li>
<li>Gene ontology enrichment analysis on DEGs</li>
<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
<li>Alternative splicing analysis</li>
<li>Gene fusion analysis</li>
<li>Novel transcript identification</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-3062">please consult with our expert bioinformatics team</a>.</p>
</div>
<div><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04544-3">MGcount</a>: Diagenode has developed a bioinformatics software for counting whole-transcriptome RNA-seq reads from one or more input alignment files. It is specially designed to incorporate multi-mapping and multi-overlapping reads in the quantification using a flexible methodology that is compatible with any biotype. At the end of its execution, it produces a count matrix, compatible with any downstream analysis.</p>
<center><img src="https://www.diagenode.com/img/product/services/mGcount-img.png" width="600px" /></center>',
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			'slug' => 'rna-data-analysis',
			'meta_title' => 'RNA Data Analysis Services | Diagenode',
			'meta_keywords' => '',
			'meta_description' => 'RNA-seq data analysis service is made to extract information from a large variety of  RNA-seq data (small RNA, mRNA and much more).',
			'modified' => '2022-11-22 15:26:24',
			'created' => '2020-03-26 10:04:58',
			'ProductsRelated' => array(
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			'id' => '51',
			'position' => '0',
			'parent_id' => '46',
			'name' => '小型RNAとmiRNA',
			'description' => '<ul>
<li><span> </span>遺伝子発現の転写後調節</li>
<li><span> </span>新規転写物および差示的に発現される小RNA</li>
<li><span> </span>腫瘍抑制遺伝子発現の調節に関与するmiRNA</li>
<li><span> </span>差次的miRNA発現</li>
</ul>',
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			'meta_title' => '',
			'modified' => '2018-01-12 15:03:17',
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		(int) 1 => array(
			'id' => '52',
			'position' => '0',
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			'name' => 'メッセンジャーRNA(ｍRNA)',
			'description' => '<ul>
<li><span> </span>特異的ポリA選定で<span>タンパク質コーディング</span><span>RNA</span><span></span><span></span></li>
<li><span> </span>がん関連mRNAシグネチャー</li>
<li><span> </span>癌の病因における異常なmRNA転換</li>
<li><span> </span>遺伝子発現プロファイリングおよび示差的に発現される遺伝子の同定（DEG）</li>
<li><span> </span>遺伝子発現のエピジェネティックな機構（トランスクリプトーム+ H3K36me3およびH3K4me3標識）</li>
<li><span> </span>コーディング遺伝子の機能的アノテーション</li>
</ul>',
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			'name' => '全トランスクリプトーム解析と長い非コードRNA（lncRNA）解析',
			'description' => '<ul>
<li><span> </span>トランスクリプトーム配列決定：任意のrRNA<span>除去</span>を伴う200ntより大きい<span>RNA</span><span>および</span>全トランスクリプトーム<span></span><span></span>の分析</li>
<li><span> </span>クロマチンリモデリングおよび転写/転写後調節に関与する非タンパク質コードRNA</li>
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<p><span>&nbsp;</span></p>',
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			'modified' => '2018-01-12 16:29:08',
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		(int) 0 => array(
			'id' => '900',
			'name' => 'The Diagenode Epigenetics custom service',
			'description' => '<h2><a href="https://www.diagenode.com/en/categories/Services">Complete workflows</a> for genome-scale DNA methylation and histone marks analysis</h2>
<p>Epigenetics is crucial for the regulation of gene expression and has broad relevance in biological processes like development, disease and response to the environment. For more than 10 years Diagenode has been developing innovative tools to study epigenetic marks such as post-translational modi cations of histones and DNA methylation. We are now utilizing our expertise by offering custom services. Our <a href="https://www.diagenode.com/en/categories/Services">services</a> include full work ows for ChIP-sequencing as well as reduced representation bisul te sequencing (RRBS) with our new optimized &ldquo;Premium RRBSTM technology. In addition, we also offer bioinformatic analysis of your results, both standard and customized. The <a href="https://www.diagenode.com/en/categories/Services">Diagenode Epigenetics Custom Services</a> helps you to complete your epigenetics work ow from your starting biological material to your nal results.</p>
<p><a href="https://www.diagenode.com/en/categories/Services">Learn more</a></p>',
			'image_id' => null,
			'type' => 'Poster',
			'url' => 'files/posters/diagenode-epigenetics-custom-service.pdf',
			'slug' => 'diagenode-epigenetics-custom-service',
			'meta_keywords' => '',
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			'modified' => '2021-04-29 09:26:52',
			'created' => '2016-04-27 15:27:04',
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		(int) 1 => array(
			'id' => '1052',
			'name' => 'Epigenomics Profiling Services',
			'description' => '<ul>
<li>Chromatin analysis</li>
<li>DNA methylation services</li>
<li>RNA-seq analysis</li>
</ul>',
			'image_id' => null,
			'type' => 'Flyer',
			'url' => 'files/flyers/epigenomics_profiling_services_flyer.pdf',
			'slug' => 'services-flyer',
			'meta_keywords' => '',
			'meta_description' => '',
			'modified' => '2020-04-23 16:22:55',
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		(int) 0 => array(
			'id' => '4913',
			'name' => 'Long-term effects of myo-inositol on traumatic brain injury: Epigenomic and transcriptomic studies',
			'authors' => 'Oganezovi N. et al.',
			'description' => '<h6>Background and purpose</h6>
<div class="section-paragraph">Traumatic brain injury (TBI) and its consequences remain great challenges for neurology. Consequences of TBI are associated with various alterations in the brain but little is known about long-term changes of epigenetic DNA methylation patterns. Moreover, nothing is known about potential treatments that can alter these epigenetic changes in beneficial ways. Therefore, we have examined myo-inositol (MI), which has positive effects on several pathological conditions.</div>
<h6></h6>
<h6>Methods</h6>
<div class="section-paragraph">TBI was induced in mice by controlled cortical impact (CCI). One group of CCI animals received saline injections for two months (TBI+SAL), another CCI group received MI treatment (TBI+MI) for the same period and one group served as a sham-operated control. Mice were sacrificed 4 months after CCI and changes in DNA methylome and transcriptomes were examined.</div>
<h6></h6>
<h6>Results</h6>
<div class="section-paragraph">For the first time we: (i) provide comprehensive map of long-term DNA methylation changes after CCI in the hippocampus; (ii) identify differences by methylation sites between the groups; (iii) characterize transcriptome changes; (iv) provide association between DNA methylation sites and gene expression. MI treatment is linked with upregulation of genes covering 33 biological processes, involved in immune response and inflammation. In support of these findings, we have shown that expression of BATF2, a transcription factor involved in immune-regulatory networks, is upregulated in the hippocampus of the TBI+MI group where the BATF2 gene is demethylated.</div>
<h6></h6>
<h6>Conclusion</h6>
<div class="section-paragraph">TBI is followed by long-term epigenetic and transcriptomic changes in hippocampus. MI treatment has a significant effect on these processes by modulation of immune response and biological pathways of inflammation.</div>',
			'date' => '2024-01-30',
			'pmid' => 'https://www.ibroneuroreports.org/article/S2667-2421(24)00013-7/fulltext',
			'doi' => 'https://doi.org/10.1016/j.ibneur.2024.01.009',
			'modified' => '2024-03-28 11:30:49',
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		(int) 1 => array(
			'id' => '4927',
			'name' => 'Inflammatory stress-mediated chromatin changes underlie dysfunction in endothelial cells',
			'authors' => 'Liu H. et al.',
			'description' => '<p><span>Inflammatory stresses underlie endothelial dysfunction and contribute to the development of chronic cardiovascular disorders such as atherosclerosis and vascular fibrosis. The initial transcriptional response of endothelial cells to pro-inflammatory cytokines such as TNF-alpha is well established. However, very few studies uncover the effects of inflammatory stresses on chromatin architecture. We used integrative analysis of ATAC-seq and RNA-seq data to investigate chromatin alterations in human endothelial cells in response to TNF-alpha and febrile-range heat stress exposure. Multi-omics data analysis suggests a correlation between the transcription of stress-related genes and endothelial dysfunction drivers with chromatin regions exhibiting differential accessibility. Moreover, microscopy identified the dynamics in the nuclear organization, specifically, the changes in a subset of heterochromatic nucleoli-associated chromatin domains, the centromeres. Upon inflammatory stress exposure, the centromeres decreased association with nucleoli in a p38-dependent manner and increased the number of transcripts from pericentromeric regions. Overall, we provide two lines of evidence that suggest chromatin alterations in vascular endothelial cells during inflammatory stresses.</span></p>',
			'date' => '2023-10-16',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614786/',
			'doi' => '10.1101/2023.10.11.561959',
			'modified' => '2024-03-27 15:15:21',
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            <h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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                         <a href="/cn/p/mrna-seq-services"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a>        </div>



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


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<a class="close-reveal-modal" aria-label="Close">&#215;</a></div><!-- END: QUOTE MODAL --><a href="#" id="small-rna-seq-services" data-reveal-id="quoteModal-3089" class="quote_btn" style="color:#B21329"><i class="fa fa-info-circle"></i></a>
            </div>

        </div>
        <div class="small-12 columns" >
            <h6 style="height:60px">Small RNA-seq Services</h6>
        </div>

    </div>
</li>
<li>
    <div class="row">
        <div class="small-12 columns">
                         <a href="/cn/p/rna-data-analysis"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a>        </div>



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        <h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>RNA Data Analysis</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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	'description' => '<p><span>Inflammatory stresses underlie endothelial dysfunction and contribute to the development of chronic cardiovascular disorders such as atherosclerosis and vascular fibrosis. The initial transcriptional response of endothelial cells to pro-inflammatory cytokines such as TNF-alpha is well established. However, very few studies uncover the effects of inflammatory stresses on chromatin architecture. We used integrative analysis of ATAC-seq and RNA-seq data to investigate chromatin alterations in human endothelial cells in response to TNF-alpha and febrile-range heat stress exposure. Multi-omics data analysis suggests a correlation between the transcription of stress-related genes and endothelial dysfunction drivers with chromatin regions exhibiting differential accessibility. Moreover, microscopy identified the dynamics in the nuclear organization, specifically, the changes in a subset of heterochromatic nucleoli-associated chromatin domains, the centromeres. Upon inflammatory stress exposure, the centromeres decreased association with nucleoli in a p38-dependent manner and increased the number of transcripts from pericentromeric regions. Overall, we provide two lines of evidence that suggest chromatin alterations in vascular endothelial cells during inflammatory stresses.</span></p>',
	'date' => '2023-10-16',
	'pmid' => 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614786/',
	'doi' => '10.1101/2023.10.11.561959',
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<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>
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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">
<div class="portal-inner"><nav class="portal-nav" style="text-align: left;">
<ul data-tab="" class="tips-menu">
<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
<li><a href="#panel2" class="tips portal button">Breast cancer</a></li>
</ul>
</nav></div>
</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>
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<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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			'slug' => 'data-mining-service',
			'meta_title' => 'Bioinformatics Data Mining Service | Diagenode',
			'meta_keywords' => '',
			'meta_description' => 'Diagenode Data Mining Services utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data.',
			'modified' => '2020-12-21 08:05:15',
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			'id' => '3088',
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			'name' => 'mRNA-seq Service',
			'description' => '<p><strong>mRNA-seq</strong> is the method of choice to efficiently detect and precisely quantify coding RNAs present in a biological sample under specific circumstances. Accurate estimation of transcript abundance allows for differential expression analysis as well as the study of alternative splicing and discovery of new transcript isoforms.</p>
<p>Our Epigenomics Profiling Services assure a fully integrated service-workflow for generation of mRNA-seq libraries, multiplexed sequencing and data analysis. Serviced library preparation is based on specific poly A selection to produce Illumina compatible libraries enriched in mRNAs.</p>
<h2>High performance mRNA-seq</h2>
<ul class="square">
<li>Superior complexity and high transcript coverage</li>
<li>Accurate comparison of gene expression levels</li>
<li>Detection of novel transcripts and alternative splicing events</li>
<li>Suitable for a broad range of input amounts</li>
<li>Compatible with FFPE samples</li>
<li>Fast turnaround time (from 5 to 6 weeks)</li>
</ul>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/rna-seq-category">See our other RNA-seq Profiling Services</a></p>',
			'label1' => 'Sample Requirements',
			'info1' => '<div class="extra-spaced">
<ul>
<li><strong>Project size</strong>: minimum of 24 samples</li>
<li><strong>Sample type</strong>: total isolated RNA</li>
<li><strong>Required quantities</strong>: minimum 10 ng of total RNA</li>
<li><strong>Shipment conditions</strong>: shipped in 1.5 mL or 0.5 mL tubes on dry ice</li>
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<div class="extra-spaced">
<h3 class="green">What our service includes</h3>
<ol>
<li><strong>RNA quality control</strong></li>
<ul>
<li>Determination of RIN value (RNA integration number)</li>
<li>Measurement of RNA concentration</li>
</ul>
<li><strong>Library preparation</strong></li>
<ul>
<li>Poly-A selection</li>
<li>Stranded library construction</li>
<li>Quality control of libraries prior to sequencing</li>
</ul>
<li><strong>Sequencing on Illumina platforms</strong></li>
<ul>
<li>25-30 million raw reads per sample</li>
</ul>
<li><strong>Data analysis</strong> (optional for additional fee)</li>
<ul>
<li>Standard bioinformatic analysis</li>
<li>Advanced bioinformatic analysis</li>
<li>Data delivery of raw data and all generated files through fast and secure file transfer or hard drive upon request</li>
</ul>
</ol>
</div>',
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			'info3' => '<table style="padding: 40px;">
<tbody>
<tr>
<td>
<h4 class="text-center"><strong>Standard bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
</td>
<td>
<h4 class="text-center"><strong>Advanced bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
</td>
</tr>
<tr>
<td style="vertical-align: top; padding-left: 20px;">
<ul>
<li>Demultiplexing, read quality control, trimming and filtering</li>
<li>Alignment to reference genome</li>
<li>Quantification of known transcripts</li>
</ul>
<br />
<p><em>Provided files:</em></p>
<ul>
<li>Report with sequencing statistics</li>
<li>Raw data in FASTQ format</li>
<li>FastQC report</li>
<li>Alignment in BAM format</li>
<li>Un-normalized read counts</li>
</ul>
</td>
<td style="vertical-align: top;">
<ul>
<li>Differential expression analysis</li>
<li>Gene ontology terms analysis</li>
<li>Pathway analysis</li>
<li>Alternative splicing analysis</li>
<li>Novel transcript identification</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p></p>',
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			'slug' => 'mrna-seq-services',
			'meta_title' => 'mRNA-seq Service - Coding Transcriptome Analysis| Diagenode',
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			'meta_description' => 'mRNA-seq provides a complete view of the coding transcriptome. mRNA-Seq detects known and novel transcripts and measures transcript abundance for accurate, comprehensive analysis.',
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			'id' => '3089',
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			'name' => 'Small RNA-seq Service',
			'description' => '<p><strong>Small RNA-seq</strong> is the method of choice to investigate the regulatory network of small non-coding RNAs (&lt;200 nt length), including but not limited to miRNA transcripts. It utilizes the proprietary technology D-Plex which ensures realistic representation of diverse small non-coding RNA transcripts with minimized bias. This approach is optimal for finding biomarkers from known transcripts and discover new RNA features.</p>
<p>Our Epigenomics Profiling Services assure a fully integrated service-workflow for generation of small RNA-seq libraries, multiplexed sequencing and data analysis. Serviced library preparation is based on a template-switching technology to produce Illumina compatible libraries enriched with the large variety of small non-coding RNAs.</p>
<h2>High transcript diversity with small RNA-seq</h2>
<ul class="square">
<li>Realistic representation of all small non-coding RNAs</li>
<li>Detection of novel transcripts and differentially-expressed small RNAs</li>
<li>Investigation of post-transcriptional regulation of gene expression</li>
<li>Leveraging the <a href="https://www.diagenode.com/en/pages/dplex">D-Plex technology</a></li>
<li>Working with ultra-low RNA inputs</li>
<li>Compatible with biological fluids</li>
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<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/rna-seq-category">See our other RNA-seq Profiling Services</a></p>',
			'label1' => 'Sample Requirements',
			'info1' => '<div class="extra-spaced">
<ul>
<li><strong>Project size</strong>: no minimum</li>
<li><strong>Sample type</strong>: total isolated RNA or small enriched RNA</li>
<li><strong>Required quantities</strong>: minimum 100 pg of total RNA or 10 pg of small RNA</li>
<li><strong>Shipment conditions</strong>: shipped in 1.5 mL or 0.5 mL tubes on dry ice</li>
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<div class="extra-spaced">
<h3 class="green">What our service includes</h3>
<ol>
<li><strong>RNA quality control</strong></li>
<ul>
<li>Determination of RIN value (RNA integration number)</li>
<li>Measurement of RNA concentration</li>
</ul>
<li><strong>Library preparation</strong></li>
<ul>
<li>Capture and amplification by tailing and switching</li>
<li>Library construction</li>
<li>Quality control of libraries prior to sequencing</li>
</ul>
<li><strong>Sequencing on Illumina platforms</strong></li>
<ul>
<li>10-20 million raw reads per sample</li>
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<li><strong>Data analysis</strong> (optional for additional fee)</li>
<ul>
<li>Standard bioinformatic analysis</li>
<li>Advanced bioinformatic analysis</li>
<li>Data delivery of raw data and all generated files through fast and secure file transfer or hard drive upon request</li>
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</div>',
			'label3' => 'Bioinformatics Analysis',
			'info3' => '<table style="padding: 40px;">
<tbody>
<tr>
<td>
<h4 class="text-center"><strong>Standard bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
</td>
<td>
<h4 class="text-center"><strong>Advanced bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
</td>
</tr>
<tr>
<td style="vertical-align: top; padding-left: 20px;">
<ul>
<li>Demultiplexing, read quality control, trimming and filtering</li>
<li>Alignment to reference genome</li>
<li>Quantification of small RNA biotypes with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>)</li>
</ul>
<br />
<p><em>Provided files:</em></p>
<ul>
<li>Report with sequencing statistics</li>
<li>Raw data in FASTQ format</li>
<li>FastQC report</li>
<li>Alignment in BAM format</li>
<li>Un-normalized read counts</li>
</ul>
</td>
<td style="vertical-align: top;">
<ul>
<li>Differential expression analysis</li>
<li>Gene ontology terms analysis</li>
<li>Pathway analysis</li>
</ul>
</td>
</tr>
</tbody>
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			'slug' => 'small-rna-seq-services',
			'meta_title' => 'Small RNA-seq Service - Small RNA and miRNA profiling | Diagenode',
			'meta_keywords' => '',
			'meta_description' => 'Small RNA-seq enables small RNA discovery, mutation characterization, and expression profiling of small RNAs by leveraging of sequencing technologies and data analysis pipeline.',
			'modified' => '2022-03-07 09:12:41',
			'created' => '2020-07-13 11:09:54',
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		(int) 3 => array(
			'id' => '3062',
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			'name' => 'RNA Data Analysis',
			'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (&lt;100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p>
<div class="extra-spaced">
<h2>What do we provide with the analysis?</h2>
<p>This analysis provides information for either genes or isoforms with their expression levels.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</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>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul style="list-style-type: circle;">
<li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li>
<li>Functional gene annotation</li>
<li>Gene ontology enrichment analysis on DEGs</li>
<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
<li>Alternative splicing analysis</li>
<li>Gene fusion analysis</li>
<li>Novel transcript identification</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-3062">please consult with our expert bioinformatics team</a>.</p>
</div>
<div><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04544-3">MGcount</a>: Diagenode has developed a bioinformatics software for counting whole-transcriptome RNA-seq reads from one or more input alignment files. It is specially designed to incorporate multi-mapping and multi-overlapping reads in the quantification using a flexible methodology that is compatible with any biotype. At the end of its execution, it produces a count matrix, compatible with any downstream analysis.</p>
<center><img src="https://www.diagenode.com/img/product/services/mGcount-img.png" width="600px" /></center>',
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			'slug' => 'rna-data-analysis',
			'meta_title' => 'RNA Data Analysis Services | Diagenode',
			'meta_keywords' => '',
			'meta_description' => 'RNA-seq data analysis service is made to extract information from a large variety of  RNA-seq data (small RNA, mRNA and much more).',
			'modified' => '2022-11-22 15:26:24',
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			'id' => '51',
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			'parent_id' => '46',
			'name' => '小型RNAとmiRNA',
			'description' => '<ul>
<li><span> </span>遺伝子発現の転写後調節</li>
<li><span> </span>新規転写物および差示的に発現される小RNA</li>
<li><span> </span>腫瘍抑制遺伝子発現の調節に関与するmiRNA</li>
<li><span> </span>差次的miRNA発現</li>
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			'modified' => '2018-01-12 15:03:17',
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			'name' => 'メッセンジャーRNA(ｍRNA)',
			'description' => '<ul>
<li><span> </span>特異的ポリA選定で<span>タンパク質コーディング</span><span>RNA</span><span></span><span></span></li>
<li><span> </span>がん関連mRNAシグネチャー</li>
<li><span> </span>癌の病因における異常なmRNA転換</li>
<li><span> </span>遺伝子発現プロファイリングおよび示差的に発現される遺伝子の同定（DEG）</li>
<li><span> </span>遺伝子発現のエピジェネティックな機構（トランスクリプトーム+ H3K36me3およびH3K4me3標識）</li>
<li><span> </span>コーディング遺伝子の機能的アノテーション</li>
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			'name' => '全トランスクリプトーム解析と長い非コードRNA（lncRNA）解析',
			'description' => '<ul>
<li><span> </span>トランスクリプトーム配列決定：任意のrRNA<span>除去</span>を伴う200ntより大きい<span>RNA</span><span>および</span>全トランスクリプトーム<span></span><span></span>の分析</li>
<li><span> </span>クロマチンリモデリングおよび転写/転写後調節に関与する非タンパク質コードRNA</li>
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<p><span>&nbsp;</span></p>',
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			'modified' => '2018-01-12 16:29:08',
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			'id' => '900',
			'name' => 'The Diagenode Epigenetics custom service',
			'description' => '<h2><a href="https://www.diagenode.com/en/categories/Services">Complete workflows</a> for genome-scale DNA methylation and histone marks analysis</h2>
<p>Epigenetics is crucial for the regulation of gene expression and has broad relevance in biological processes like development, disease and response to the environment. For more than 10 years Diagenode has been developing innovative tools to study epigenetic marks such as post-translational modi cations of histones and DNA methylation. We are now utilizing our expertise by offering custom services. Our <a href="https://www.diagenode.com/en/categories/Services">services</a> include full work ows for ChIP-sequencing as well as reduced representation bisul te sequencing (RRBS) with our new optimized &ldquo;Premium RRBSTM technology. In addition, we also offer bioinformatic analysis of your results, both standard and customized. The <a href="https://www.diagenode.com/en/categories/Services">Diagenode Epigenetics Custom Services</a> helps you to complete your epigenetics work ow from your starting biological material to your nal results.</p>
<p><a href="https://www.diagenode.com/en/categories/Services">Learn more</a></p>',
			'image_id' => null,
			'type' => 'Poster',
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			'slug' => 'diagenode-epigenetics-custom-service',
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			'modified' => '2021-04-29 09:26:52',
			'created' => '2016-04-27 15:27:04',
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		(int) 1 => array(
			'id' => '1052',
			'name' => 'Epigenomics Profiling Services',
			'description' => '<ul>
<li>Chromatin analysis</li>
<li>DNA methylation services</li>
<li>RNA-seq analysis</li>
</ul>',
			'image_id' => null,
			'type' => 'Flyer',
			'url' => 'files/flyers/epigenomics_profiling_services_flyer.pdf',
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			'modified' => '2020-04-23 16:22:55',
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		(int) 0 => array(
			'id' => '4913',
			'name' => 'Long-term effects of myo-inositol on traumatic brain injury: Epigenomic and transcriptomic studies',
			'authors' => 'Oganezovi N. et al.',
			'description' => '<h6>Background and purpose</h6>
<div class="section-paragraph">Traumatic brain injury (TBI) and its consequences remain great challenges for neurology. Consequences of TBI are associated with various alterations in the brain but little is known about long-term changes of epigenetic DNA methylation patterns. Moreover, nothing is known about potential treatments that can alter these epigenetic changes in beneficial ways. Therefore, we have examined myo-inositol (MI), which has positive effects on several pathological conditions.</div>
<h6></h6>
<h6>Methods</h6>
<div class="section-paragraph">TBI was induced in mice by controlled cortical impact (CCI). One group of CCI animals received saline injections for two months (TBI+SAL), another CCI group received MI treatment (TBI+MI) for the same period and one group served as a sham-operated control. Mice were sacrificed 4 months after CCI and changes in DNA methylome and transcriptomes were examined.</div>
<h6></h6>
<h6>Results</h6>
<div class="section-paragraph">For the first time we: (i) provide comprehensive map of long-term DNA methylation changes after CCI in the hippocampus; (ii) identify differences by methylation sites between the groups; (iii) characterize transcriptome changes; (iv) provide association between DNA methylation sites and gene expression. MI treatment is linked with upregulation of genes covering 33 biological processes, involved in immune response and inflammation. In support of these findings, we have shown that expression of BATF2, a transcription factor involved in immune-regulatory networks, is upregulated in the hippocampus of the TBI+MI group where the BATF2 gene is demethylated.</div>
<h6></h6>
<h6>Conclusion</h6>
<div class="section-paragraph">TBI is followed by long-term epigenetic and transcriptomic changes in hippocampus. MI treatment has a significant effect on these processes by modulation of immune response and biological pathways of inflammation.</div>',
			'date' => '2024-01-30',
			'pmid' => 'https://www.ibroneuroreports.org/article/S2667-2421(24)00013-7/fulltext',
			'doi' => 'https://doi.org/10.1016/j.ibneur.2024.01.009',
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			'description' => '<p><span>Inflammatory stresses underlie endothelial dysfunction and contribute to the development of chronic cardiovascular disorders such as atherosclerosis and vascular fibrosis. The initial transcriptional response of endothelial cells to pro-inflammatory cytokines such as TNF-alpha is well established. However, very few studies uncover the effects of inflammatory stresses on chromatin architecture. We used integrative analysis of ATAC-seq and RNA-seq data to investigate chromatin alterations in human endothelial cells in response to TNF-alpha and febrile-range heat stress exposure. Multi-omics data analysis suggests a correlation between the transcription of stress-related genes and endothelial dysfunction drivers with chromatin regions exhibiting differential accessibility. Moreover, microscopy identified the dynamics in the nuclear organization, specifically, the changes in a subset of heterochromatic nucleoli-associated chromatin domains, the centromeres. Upon inflammatory stress exposure, the centromeres decreased association with nucleoli in a p38-dependent manner and increased the number of transcripts from pericentromeric regions. Overall, we provide two lines of evidence that suggest chromatin alterations in vascular endothelial cells during inflammatory stresses.</span></p>',
			'date' => '2023-10-16',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614786/',
			'doi' => '10.1101/2023.10.11.561959',
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            </div>

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            <h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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                         <a href="/cn/p/mrna-seq-services"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a>        </div>



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            <div class="small-6 columns" style="padding-left:0px;padding-right:0px;margin-top:-6px;margin-left:-1px">
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        <h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>mRNA-seq Service</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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            <span class="prefix">Sample species</span>
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            <span class="prefix">Total number of samples (including replicates)</span>
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        <div class="small-12 columns" >
            <h6 style="height:60px">mRNA-seq Service</h6>
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        <h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>Small RNA-seq 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>

        </div>
        <div class="small-12 columns" >
            <h6 style="height:60px">Small RNA-seq Services</h6>
        </div>

    </div>
</li>
<li>
    <div class="row">
        <div class="small-12 columns">
                         <a href="/cn/p/rna-data-analysis"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a>        </div>



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        <h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>RNA Data Analysis</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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            <h6 style="height:60px">RNA Data Analysis</h6>
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<li>RNA-seq analysis</li>
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	'date' => '2023-10-16',
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	'doi' => '10.1101/2023.10.11.561959',
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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">
<div class="portal-inner"><nav class="portal-nav" style="text-align: left;">
<ul data-tab="" class="tips-menu">
<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
<li><a href="#panel2" class="tips portal button">Breast cancer</a></li>
</ul>
</nav></div>
</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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<ol start="2">
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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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			'slug' => 'data-mining-service',
			'meta_title' => 'Bioinformatics Data Mining Service | Diagenode',
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			'meta_description' => 'Diagenode Data Mining Services utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data.',
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			'description' => '<p><strong>mRNA-seq</strong> is the method of choice to efficiently detect and precisely quantify coding RNAs present in a biological sample under specific circumstances. Accurate estimation of transcript abundance allows for differential expression analysis as well as the study of alternative splicing and discovery of new transcript isoforms.</p>
<p>Our Epigenomics Profiling Services assure a fully integrated service-workflow for generation of mRNA-seq libraries, multiplexed sequencing and data analysis. Serviced library preparation is based on specific poly A selection to produce Illumina compatible libraries enriched in mRNAs.</p>
<h2>High performance mRNA-seq</h2>
<ul class="square">
<li>Superior complexity and high transcript coverage</li>
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<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/rna-seq-category">See our other RNA-seq Profiling Services</a></p>',
			'label1' => 'Sample Requirements',
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<ul>
<li><strong>Project size</strong>: minimum of 24 samples</li>
<li><strong>Sample type</strong>: total isolated RNA</li>
<li><strong>Required quantities</strong>: minimum 10 ng of total RNA</li>
<li><strong>Shipment conditions</strong>: shipped in 1.5 mL or 0.5 mL tubes on dry ice</li>
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<h3 class="green">What our service includes</h3>
<ol>
<li><strong>RNA quality control</strong></li>
<ul>
<li>Determination of RIN value (RNA integration number)</li>
<li>Measurement of RNA concentration</li>
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<li><strong>Library preparation</strong></li>
<ul>
<li>Poly-A selection</li>
<li>Stranded library construction</li>
<li>Quality control of libraries prior to sequencing</li>
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<li><strong>Sequencing on Illumina platforms</strong></li>
<ul>
<li>25-30 million raw reads per sample</li>
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<li><strong>Data analysis</strong> (optional for additional fee)</li>
<ul>
<li>Standard bioinformatic analysis</li>
<li>Advanced bioinformatic analysis</li>
<li>Data delivery of raw data and all generated files through fast and secure file transfer or hard drive upon request</li>
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<tr>
<td>
<h4 class="text-center"><strong>Standard bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
</td>
<td>
<h4 class="text-center"><strong>Advanced bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
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</tr>
<tr>
<td style="vertical-align: top; padding-left: 20px;">
<ul>
<li>Demultiplexing, read quality control, trimming and filtering</li>
<li>Alignment to reference genome</li>
<li>Quantification of known transcripts</li>
</ul>
<br />
<p><em>Provided files:</em></p>
<ul>
<li>Report with sequencing statistics</li>
<li>Raw data in FASTQ format</li>
<li>FastQC report</li>
<li>Alignment in BAM format</li>
<li>Un-normalized read counts</li>
</ul>
</td>
<td style="vertical-align: top;">
<ul>
<li>Differential expression analysis</li>
<li>Gene ontology terms analysis</li>
<li>Pathway analysis</li>
<li>Alternative splicing analysis</li>
<li>Novel transcript identification</li>
</ul>
</td>
</tr>
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			'slug' => 'mrna-seq-services',
			'meta_title' => 'mRNA-seq Service - Coding Transcriptome Analysis| Diagenode',
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			'meta_description' => 'mRNA-seq provides a complete view of the coding transcriptome. mRNA-Seq detects known and novel transcripts and measures transcript abundance for accurate, comprehensive analysis.',
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			'description' => '<p><strong>Small RNA-seq</strong> is the method of choice to investigate the regulatory network of small non-coding RNAs (&lt;200 nt length), including but not limited to miRNA transcripts. It utilizes the proprietary technology D-Plex which ensures realistic representation of diverse small non-coding RNA transcripts with minimized bias. This approach is optimal for finding biomarkers from known transcripts and discover new RNA features.</p>
<p>Our Epigenomics Profiling Services assure a fully integrated service-workflow for generation of small RNA-seq libraries, multiplexed sequencing and data analysis. Serviced library preparation is based on a template-switching technology to produce Illumina compatible libraries enriched with the large variety of small non-coding RNAs.</p>
<h2>High transcript diversity with small RNA-seq</h2>
<ul class="square">
<li>Realistic representation of all small non-coding RNAs</li>
<li>Detection of novel transcripts and differentially-expressed small RNAs</li>
<li>Investigation of post-transcriptional regulation of gene expression</li>
<li>Leveraging the <a href="https://www.diagenode.com/en/pages/dplex">D-Plex technology</a></li>
<li>Working with ultra-low RNA inputs</li>
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<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/rna-seq-category">See our other RNA-seq Profiling Services</a></p>',
			'label1' => 'Sample Requirements',
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<li><strong>Project size</strong>: no minimum</li>
<li><strong>Sample type</strong>: total isolated RNA or small enriched RNA</li>
<li><strong>Required quantities</strong>: minimum 100 pg of total RNA or 10 pg of small RNA</li>
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<h3 class="green">What our service includes</h3>
<ol>
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<ul>
<li>Determination of RIN value (RNA integration number)</li>
<li>Measurement of RNA concentration</li>
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<li><strong>Library preparation</strong></li>
<ul>
<li>Capture and amplification by tailing and switching</li>
<li>Library construction</li>
<li>Quality control of libraries prior to sequencing</li>
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<li><strong>Sequencing on Illumina platforms</strong></li>
<ul>
<li>10-20 million raw reads per sample</li>
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<li><strong>Data analysis</strong> (optional for additional fee)</li>
<ul>
<li>Standard bioinformatic analysis</li>
<li>Advanced bioinformatic analysis</li>
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			'info3' => '<table style="padding: 40px;">
<tbody>
<tr>
<td>
<h4 class="text-center"><strong>Standard bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
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<td>
<h4 class="text-center"><strong>Advanced bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
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<td style="vertical-align: top; padding-left: 20px;">
<ul>
<li>Demultiplexing, read quality control, trimming and filtering</li>
<li>Alignment to reference genome</li>
<li>Quantification of small RNA biotypes with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>)</li>
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<br />
<p><em>Provided files:</em></p>
<ul>
<li>Report with sequencing statistics</li>
<li>Raw data in FASTQ format</li>
<li>FastQC report</li>
<li>Alignment in BAM format</li>
<li>Un-normalized read counts</li>
</ul>
</td>
<td style="vertical-align: top;">
<ul>
<li>Differential expression analysis</li>
<li>Gene ontology terms analysis</li>
<li>Pathway analysis</li>
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</td>
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			'slug' => 'small-rna-seq-services',
			'meta_title' => 'Small RNA-seq Service - Small RNA and miRNA profiling | Diagenode',
			'meta_keywords' => '',
			'meta_description' => 'Small RNA-seq enables small RNA discovery, mutation characterization, and expression profiling of small RNAs by leveraging of sequencing technologies and data analysis pipeline.',
			'modified' => '2022-03-07 09:12:41',
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			'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (&lt;100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p>
<div class="extra-spaced">
<h2>What do we provide with the analysis?</h2>
<p>This analysis provides information for either genes or isoforms with their expression levels.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</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>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li>
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<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul style="list-style-type: circle;">
<li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li>
<li>Functional gene annotation</li>
<li>Gene ontology enrichment analysis on DEGs</li>
<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
<li>Alternative splicing analysis</li>
<li>Gene fusion analysis</li>
<li>Novel transcript identification</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-3062">please consult with our expert bioinformatics team</a>.</p>
</div>
<div><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04544-3">MGcount</a>: Diagenode has developed a bioinformatics software for counting whole-transcriptome RNA-seq reads from one or more input alignment files. It is specially designed to incorporate multi-mapping and multi-overlapping reads in the quantification using a flexible methodology that is compatible with any biotype. At the end of its execution, it produces a count matrix, compatible with any downstream analysis.</p>
<center><img src="https://www.diagenode.com/img/product/services/mGcount-img.png" width="600px" /></center>',
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			'slug' => 'rna-data-analysis',
			'meta_title' => 'RNA Data Analysis Services | Diagenode',
			'meta_keywords' => '',
			'meta_description' => 'RNA-seq data analysis service is made to extract information from a large variety of  RNA-seq data (small RNA, mRNA and much more).',
			'modified' => '2022-11-22 15:26:24',
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			'id' => '51',
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			'name' => '小型RNAとmiRNA',
			'description' => '<ul>
<li><span> </span>遺伝子発現の転写後調節</li>
<li><span> </span>新規転写物および差示的に発現される小RNA</li>
<li><span> </span>腫瘍抑制遺伝子発現の調節に関与するmiRNA</li>
<li><span> </span>差次的miRNA発現</li>
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<li><span> </span>特異的ポリA選定で<span>タンパク質コーディング</span><span>RNA</span><span></span><span></span></li>
<li><span> </span>がん関連mRNAシグネチャー</li>
<li><span> </span>癌の病因における異常なmRNA転換</li>
<li><span> </span>遺伝子発現プロファイリングおよび示差的に発現される遺伝子の同定（DEG）</li>
<li><span> </span>遺伝子発現のエピジェネティックな機構（トランスクリプトーム+ H3K36me3およびH3K4me3標識）</li>
<li><span> </span>コーディング遺伝子の機能的アノテーション</li>
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			'description' => '<ul>
<li><span> </span>トランスクリプトーム配列決定：任意のrRNA<span>除去</span>を伴う200ntより大きい<span>RNA</span><span>および</span>全トランスクリプトーム<span></span><span></span>の分析</li>
<li><span> </span>クロマチンリモデリングおよび転写/転写後調節に関与する非タンパク質コードRNA</li>
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<p><span>&nbsp;</span></p>',
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			'id' => '900',
			'name' => 'The Diagenode Epigenetics custom service',
			'description' => '<h2><a href="https://www.diagenode.com/en/categories/Services">Complete workflows</a> for genome-scale DNA methylation and histone marks analysis</h2>
<p>Epigenetics is crucial for the regulation of gene expression and has broad relevance in biological processes like development, disease and response to the environment. For more than 10 years Diagenode has been developing innovative tools to study epigenetic marks such as post-translational modi cations of histones and DNA methylation. We are now utilizing our expertise by offering custom services. Our <a href="https://www.diagenode.com/en/categories/Services">services</a> include full work ows for ChIP-sequencing as well as reduced representation bisul te sequencing (RRBS) with our new optimized &ldquo;Premium RRBSTM technology. In addition, we also offer bioinformatic analysis of your results, both standard and customized. The <a href="https://www.diagenode.com/en/categories/Services">Diagenode Epigenetics Custom Services</a> helps you to complete your epigenetics work ow from your starting biological material to your nal results.</p>
<p><a href="https://www.diagenode.com/en/categories/Services">Learn more</a></p>',
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			'slug' => 'diagenode-epigenetics-custom-service',
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		(int) 1 => array(
			'id' => '1052',
			'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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			'type' => 'Flyer',
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		(int) 0 => array(
			'id' => '4913',
			'name' => 'Long-term effects of myo-inositol on traumatic brain injury: Epigenomic and transcriptomic studies',
			'authors' => 'Oganezovi N. et al.',
			'description' => '<h6>Background and purpose</h6>
<div class="section-paragraph">Traumatic brain injury (TBI) and its consequences remain great challenges for neurology. Consequences of TBI are associated with various alterations in the brain but little is known about long-term changes of epigenetic DNA methylation patterns. Moreover, nothing is known about potential treatments that can alter these epigenetic changes in beneficial ways. Therefore, we have examined myo-inositol (MI), which has positive effects on several pathological conditions.</div>
<h6></h6>
<h6>Methods</h6>
<div class="section-paragraph">TBI was induced in mice by controlled cortical impact (CCI). One group of CCI animals received saline injections for two months (TBI+SAL), another CCI group received MI treatment (TBI+MI) for the same period and one group served as a sham-operated control. Mice were sacrificed 4 months after CCI and changes in DNA methylome and transcriptomes were examined.</div>
<h6></h6>
<h6>Results</h6>
<div class="section-paragraph">For the first time we: (i) provide comprehensive map of long-term DNA methylation changes after CCI in the hippocampus; (ii) identify differences by methylation sites between the groups; (iii) characterize transcriptome changes; (iv) provide association between DNA methylation sites and gene expression. MI treatment is linked with upregulation of genes covering 33 biological processes, involved in immune response and inflammation. In support of these findings, we have shown that expression of BATF2, a transcription factor involved in immune-regulatory networks, is upregulated in the hippocampus of the TBI+MI group where the BATF2 gene is demethylated.</div>
<h6></h6>
<h6>Conclusion</h6>
<div class="section-paragraph">TBI is followed by long-term epigenetic and transcriptomic changes in hippocampus. MI treatment has a significant effect on these processes by modulation of immune response and biological pathways of inflammation.</div>',
			'date' => '2024-01-30',
			'pmid' => 'https://www.ibroneuroreports.org/article/S2667-2421(24)00013-7/fulltext',
			'doi' => 'https://doi.org/10.1016/j.ibneur.2024.01.009',
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			'description' => '<p><span>Inflammatory stresses underlie endothelial dysfunction and contribute to the development of chronic cardiovascular disorders such as atherosclerosis and vascular fibrosis. The initial transcriptional response of endothelial cells to pro-inflammatory cytokines such as TNF-alpha is well established. However, very few studies uncover the effects of inflammatory stresses on chromatin architecture. We used integrative analysis of ATAC-seq and RNA-seq data to investigate chromatin alterations in human endothelial cells in response to TNF-alpha and febrile-range heat stress exposure. Multi-omics data analysis suggests a correlation between the transcription of stress-related genes and endothelial dysfunction drivers with chromatin regions exhibiting differential accessibility. Moreover, microscopy identified the dynamics in the nuclear organization, specifically, the changes in a subset of heterochromatic nucleoli-associated chromatin domains, the centromeres. Upon inflammatory stress exposure, the centromeres decreased association with nucleoli in a p38-dependent manner and increased the number of transcripts from pericentromeric regions. Overall, we provide two lines of evidence that suggest chromatin alterations in vascular endothelial cells during inflammatory stresses.</span></p>',
			'date' => '2023-10-16',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614786/',
			'doi' => '10.1101/2023.10.11.561959',
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            </div>

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        <div class="small-12 columns" >
            <h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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                         <a href="/cn/p/mrna-seq-services"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a>        </div>



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            <div class="small-6 columns" style="padding-left:0px;padding-right:0px;margin-top:-6px;margin-left:-1px">
                <span class="success label" style="">G02120000</span>
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        <h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>mRNA-seq Service</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][rna_seq_service][]" value="Total RNA-seq" id="QuoteRnaSeqServiceTotalRNASeq" /><label for="QuoteRnaSeqServiceTotalRNASeq">Total RNA-seq</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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            </div>

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        <div class="small-12 columns" >
            <h6 style="height:60px">mRNA-seq Service</h6>
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</li>
<li>
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                         <a href="/cn/p/small-rna-seq-services"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a>        </div>



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        <h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>Small RNA-seq 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>

        </div>
        <div class="small-12 columns" >
            <h6 style="height:60px">Small RNA-seq Services</h6>
        </div>

    </div>
</li>
<li>
    <div class="row">
        <div class="small-12 columns">
                         <a href="/cn/p/rna-data-analysis"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a>        </div>



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        <h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>RNA Data Analysis</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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            <h6 style="height:60px">RNA Data Analysis</h6>
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<h2>What do we provide with the analysis?</h2>
<p>This analysis provides information for either genes or isoforms with their expression levels.</p>
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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>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li>
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<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
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<li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li>
<li>Functional gene annotation</li>
<li>Gene ontology enrichment analysis on DEGs</li>
<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
<li>Alternative splicing analysis</li>
<li>Gene fusion analysis</li>
<li>Novel transcript identification</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-3062">please consult with our expert bioinformatics team</a>.</p>
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<div><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04544-3">MGcount</a>: Diagenode has developed a bioinformatics software for counting whole-transcriptome RNA-seq reads from one or more input alignment files. It is specially designed to incorporate multi-mapping and multi-overlapping reads in the quantification using a flexible methodology that is compatible with any biotype. At the end of its execution, it produces a count matrix, compatible with any downstream analysis.</p>
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<li>RNA-seq analysis</li>
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View::render() - CORE/Cake/View/View.php, line 473
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ProductsController::slug() - APP/Controller/ProductsController.php, line 1052
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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>
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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">
<div class="portal-inner"><nav class="portal-nav" style="text-align: left;">
<ul data-tab="" class="tips-menu">
<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
<li><a href="#panel2" class="tips portal button">Breast cancer</a></li>
</ul>
</nav></div>
</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>
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<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></p>
<ol start="2">
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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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			'meta_title' => 'Bioinformatics Data Mining Service | Diagenode',
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			'meta_description' => 'Diagenode Data Mining Services utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data.',
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<p>Our Epigenomics Profiling Services assure a fully integrated service-workflow for generation of mRNA-seq libraries, multiplexed sequencing and data analysis. Serviced library preparation is based on specific poly A selection to produce Illumina compatible libraries enriched in mRNAs.</p>
<h2>High performance mRNA-seq</h2>
<ul class="square">
<li>Superior complexity and high transcript coverage</li>
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<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/rna-seq-category">See our other RNA-seq Profiling Services</a></p>',
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<li><strong>Project size</strong>: minimum of 24 samples</li>
<li><strong>Sample type</strong>: total isolated RNA</li>
<li><strong>Required quantities</strong>: minimum 10 ng of total RNA</li>
<li><strong>Shipment conditions</strong>: shipped in 1.5 mL or 0.5 mL tubes on dry ice</li>
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<h3 class="green">What our service includes</h3>
<ol>
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<ul>
<li>Determination of RIN value (RNA integration number)</li>
<li>Measurement of RNA concentration</li>
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<li><strong>Library preparation</strong></li>
<ul>
<li>Poly-A selection</li>
<li>Stranded library construction</li>
<li>Quality control of libraries prior to sequencing</li>
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<li><strong>Sequencing on Illumina platforms</strong></li>
<ul>
<li>25-30 million raw reads per sample</li>
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<li><strong>Data analysis</strong> (optional for additional fee)</li>
<ul>
<li>Standard bioinformatic analysis</li>
<li>Advanced bioinformatic analysis</li>
<li>Data delivery of raw data and all generated files through fast and secure file transfer or hard drive upon request</li>
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<tr>
<td>
<h4 class="text-center"><strong>Standard bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
</td>
<td>
<h4 class="text-center"><strong>Advanced bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
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</tr>
<tr>
<td style="vertical-align: top; padding-left: 20px;">
<ul>
<li>Demultiplexing, read quality control, trimming and filtering</li>
<li>Alignment to reference genome</li>
<li>Quantification of known transcripts</li>
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<br />
<p><em>Provided files:</em></p>
<ul>
<li>Report with sequencing statistics</li>
<li>Raw data in FASTQ format</li>
<li>FastQC report</li>
<li>Alignment in BAM format</li>
<li>Un-normalized read counts</li>
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</td>
<td style="vertical-align: top;">
<ul>
<li>Differential expression analysis</li>
<li>Gene ontology terms analysis</li>
<li>Pathway analysis</li>
<li>Alternative splicing analysis</li>
<li>Novel transcript identification</li>
</ul>
</td>
</tr>
</tbody>
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			'slug' => 'mrna-seq-services',
			'meta_title' => 'mRNA-seq Service - Coding Transcriptome Analysis| Diagenode',
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			'meta_description' => 'mRNA-seq provides a complete view of the coding transcriptome. mRNA-Seq detects known and novel transcripts and measures transcript abundance for accurate, comprehensive analysis.',
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<p>Our Epigenomics Profiling Services assure a fully integrated service-workflow for generation of small RNA-seq libraries, multiplexed sequencing and data analysis. Serviced library preparation is based on a template-switching technology to produce Illumina compatible libraries enriched with the large variety of small non-coding RNAs.</p>
<h2>High transcript diversity with small RNA-seq</h2>
<ul class="square">
<li>Realistic representation of all small non-coding RNAs</li>
<li>Detection of novel transcripts and differentially-expressed small RNAs</li>
<li>Investigation of post-transcriptional regulation of gene expression</li>
<li>Leveraging the <a href="https://www.diagenode.com/en/pages/dplex">D-Plex technology</a></li>
<li>Working with ultra-low RNA inputs</li>
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<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/rna-seq-category">See our other RNA-seq Profiling Services</a></p>',
			'label1' => 'Sample Requirements',
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<ul>
<li><strong>Project size</strong>: no minimum</li>
<li><strong>Sample type</strong>: total isolated RNA or small enriched RNA</li>
<li><strong>Required quantities</strong>: minimum 100 pg of total RNA or 10 pg of small RNA</li>
<li><strong>Shipment conditions</strong>: shipped in 1.5 mL or 0.5 mL tubes on dry ice</li>
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<h3 class="green">What our service includes</h3>
<ol>
<li><strong>RNA quality control</strong></li>
<ul>
<li>Determination of RIN value (RNA integration number)</li>
<li>Measurement of RNA concentration</li>
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<li><strong>Library preparation</strong></li>
<ul>
<li>Capture and amplification by tailing and switching</li>
<li>Library construction</li>
<li>Quality control of libraries prior to sequencing</li>
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<li><strong>Sequencing on Illumina platforms</strong></li>
<ul>
<li>10-20 million raw reads per sample</li>
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<li><strong>Data analysis</strong> (optional for additional fee)</li>
<ul>
<li>Standard bioinformatic analysis</li>
<li>Advanced bioinformatic analysis</li>
<li>Data delivery of raw data and all generated files through fast and secure file transfer or hard drive upon request</li>
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			'info3' => '<table style="padding: 40px;">
<tbody>
<tr>
<td>
<h4 class="text-center"><strong>Standard bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
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<td>
<h4 class="text-center"><strong>Advanced bioinformatic analysis</strong><br /> (optional for additional fee)</h4>
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</tr>
<tr>
<td style="vertical-align: top; padding-left: 20px;">
<ul>
<li>Demultiplexing, read quality control, trimming and filtering</li>
<li>Alignment to reference genome</li>
<li>Quantification of small RNA biotypes with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>)</li>
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<br />
<p><em>Provided files:</em></p>
<ul>
<li>Report with sequencing statistics</li>
<li>Raw data in FASTQ format</li>
<li>FastQC report</li>
<li>Alignment in BAM format</li>
<li>Un-normalized read counts</li>
</ul>
</td>
<td style="vertical-align: top;">
<ul>
<li>Differential expression analysis</li>
<li>Gene ontology terms analysis</li>
<li>Pathway analysis</li>
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			'slug' => 'small-rna-seq-services',
			'meta_title' => 'Small RNA-seq Service - Small RNA and miRNA profiling | Diagenode',
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			'meta_description' => 'Small RNA-seq enables small RNA discovery, mutation characterization, and expression profiling of small RNAs by leveraging of sequencing technologies and data analysis pipeline.',
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			'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (&lt;100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p>
<div class="extra-spaced">
<h2>What do we provide with the analysis?</h2>
<p>This analysis provides information for either genes or isoforms with their expression levels.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</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>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li>
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<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul style="list-style-type: circle;">
<li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li>
<li>Functional gene annotation</li>
<li>Gene ontology enrichment analysis on DEGs</li>
<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
<li>Alternative splicing analysis</li>
<li>Gene fusion analysis</li>
<li>Novel transcript identification</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-3062">please consult with our expert bioinformatics team</a>.</p>
</div>
<div><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04544-3">MGcount</a>: Diagenode has developed a bioinformatics software for counting whole-transcriptome RNA-seq reads from one or more input alignment files. It is specially designed to incorporate multi-mapping and multi-overlapping reads in the quantification using a flexible methodology that is compatible with any biotype. At the end of its execution, it produces a count matrix, compatible with any downstream analysis.</p>
<center><img src="https://www.diagenode.com/img/product/services/mGcount-img.png" width="600px" /></center>',
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			'slug' => 'rna-data-analysis',
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			'meta_description' => 'RNA-seq data analysis service is made to extract information from a large variety of  RNA-seq data (small RNA, mRNA and much more).',
			'modified' => '2022-11-22 15:26:24',
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			'name' => '小型RNAとmiRNA',
			'description' => '<ul>
<li><span> </span>遺伝子発現の転写後調節</li>
<li><span> </span>新規転写物および差示的に発現される小RNA</li>
<li><span> </span>腫瘍抑制遺伝子発現の調節に関与するmiRNA</li>
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<li><span> </span>特異的ポリA選定で<span>タンパク質コーディング</span><span>RNA</span><span></span><span></span></li>
<li><span> </span>がん関連mRNAシグネチャー</li>
<li><span> </span>癌の病因における異常なmRNA転換</li>
<li><span> </span>遺伝子発現プロファイリングおよび示差的に発現される遺伝子の同定（DEG）</li>
<li><span> </span>遺伝子発現のエピジェネティックな機構（トランスクリプトーム+ H3K36me3およびH3K4me3標識）</li>
<li><span> </span>コーディング遺伝子の機能的アノテーション</li>
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			'description' => '<ul>
<li><span> </span>トランスクリプトーム配列決定：任意のrRNA<span>除去</span>を伴う200ntより大きい<span>RNA</span><span>および</span>全トランスクリプトーム<span></span><span></span>の分析</li>
<li><span> </span>クロマチンリモデリングおよび転写/転写後調節に関与する非タンパク質コードRNA</li>
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<p><span>&nbsp;</span></p>',
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			'name' => 'The Diagenode Epigenetics custom service',
			'description' => '<h2><a href="https://www.diagenode.com/en/categories/Services">Complete workflows</a> for genome-scale DNA methylation and histone marks analysis</h2>
<p>Epigenetics is crucial for the regulation of gene expression and has broad relevance in biological processes like development, disease and response to the environment. For more than 10 years Diagenode has been developing innovative tools to study epigenetic marks such as post-translational modi cations of histones and DNA methylation. We are now utilizing our expertise by offering custom services. Our <a href="https://www.diagenode.com/en/categories/Services">services</a> include full work ows for ChIP-sequencing as well as reduced representation bisul te sequencing (RRBS) with our new optimized &ldquo;Premium RRBSTM technology. In addition, we also offer bioinformatic analysis of your results, both standard and customized. The <a href="https://www.diagenode.com/en/categories/Services">Diagenode Epigenetics Custom Services</a> helps you to complete your epigenetics work ow from your starting biological material to your nal results.</p>
<p><a href="https://www.diagenode.com/en/categories/Services">Learn more</a></p>',
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<li>DNA methylation services</li>
<li>RNA-seq analysis</li>
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			'name' => 'Long-term effects of myo-inositol on traumatic brain injury: Epigenomic and transcriptomic studies',
			'authors' => 'Oganezovi N. et al.',
			'description' => '<h6>Background and purpose</h6>
<div class="section-paragraph">Traumatic brain injury (TBI) and its consequences remain great challenges for neurology. Consequences of TBI are associated with various alterations in the brain but little is known about long-term changes of epigenetic DNA methylation patterns. Moreover, nothing is known about potential treatments that can alter these epigenetic changes in beneficial ways. Therefore, we have examined myo-inositol (MI), which has positive effects on several pathological conditions.</div>
<h6></h6>
<h6>Methods</h6>
<div class="section-paragraph">TBI was induced in mice by controlled cortical impact (CCI). One group of CCI animals received saline injections for two months (TBI+SAL), another CCI group received MI treatment (TBI+MI) for the same period and one group served as a sham-operated control. Mice were sacrificed 4 months after CCI and changes in DNA methylome and transcriptomes were examined.</div>
<h6></h6>
<h6>Results</h6>
<div class="section-paragraph">For the first time we: (i) provide comprehensive map of long-term DNA methylation changes after CCI in the hippocampus; (ii) identify differences by methylation sites between the groups; (iii) characterize transcriptome changes; (iv) provide association between DNA methylation sites and gene expression. MI treatment is linked with upregulation of genes covering 33 biological processes, involved in immune response and inflammation. In support of these findings, we have shown that expression of BATF2, a transcription factor involved in immune-regulatory networks, is upregulated in the hippocampus of the TBI+MI group where the BATF2 gene is demethylated.</div>
<h6></h6>
<h6>Conclusion</h6>
<div class="section-paragraph">TBI is followed by long-term epigenetic and transcriptomic changes in hippocampus. MI treatment has a significant effect on these processes by modulation of immune response and biological pathways of inflammation.</div>',
			'date' => '2024-01-30',
			'pmid' => 'https://www.ibroneuroreports.org/article/S2667-2421(24)00013-7/fulltext',
			'doi' => 'https://doi.org/10.1016/j.ibneur.2024.01.009',
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			'description' => '<p><span>Inflammatory stresses underlie endothelial dysfunction and contribute to the development of chronic cardiovascular disorders such as atherosclerosis and vascular fibrosis. The initial transcriptional response of endothelial cells to pro-inflammatory cytokines such as TNF-alpha is well established. However, very few studies uncover the effects of inflammatory stresses on chromatin architecture. We used integrative analysis of ATAC-seq and RNA-seq data to investigate chromatin alterations in human endothelial cells in response to TNF-alpha and febrile-range heat stress exposure. Multi-omics data analysis suggests a correlation between the transcription of stress-related genes and endothelial dysfunction drivers with chromatin regions exhibiting differential accessibility. Moreover, microscopy identified the dynamics in the nuclear organization, specifically, the changes in a subset of heterochromatic nucleoli-associated chromatin domains, the centromeres. Upon inflammatory stress exposure, the centromeres decreased association with nucleoli in a p38-dependent manner and increased the number of transcripts from pericentromeric regions. Overall, we provide two lines of evidence that suggest chromatin alterations in vascular endothelial cells during inflammatory stresses.</span></p>',
			'date' => '2023-10-16',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614786/',
			'doi' => '10.1101/2023.10.11.561959',
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            <h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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            <span class="prefix">Sample species</span>
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            </div>

        </div>
        <div class="small-12 columns" >
            <h6 style="height:60px">mRNA-seq Service</h6>
        </div>

    </div>
</li>
<li>
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        <div class="small-12 columns">
                         <a href="/cn/p/small-rna-seq-services"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a>        </div>



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        <h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>Small RNA-seq 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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<a class="close-reveal-modal" aria-label="Close">&#215;</a></div><!-- END: QUOTE MODAL --><a href="#" id="small-rna-seq-services" data-reveal-id="quoteModal-3089" class="quote_btn" style="color:#B21329"><i class="fa fa-info-circle"></i></a>
            </div>

        </div>
        <div class="small-12 columns" >
            <h6 style="height:60px">Small RNA-seq Services</h6>
        </div>

    </div>
</li>
<li>
    <div class="row">
        <div class="small-12 columns">
                         <a href="/cn/p/rna-data-analysis"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a>        </div>



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        <h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>RNA Data Analysis</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>

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

    </div>
</li>
'
$related = array(
	'id' => '3062',
	'antibody_id' => null,
	'name' => 'RNA Data Analysis',
	'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (&lt;100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p>
<div class="extra-spaced">
<h2>What do we provide with the analysis?</h2>
<p>This analysis provides information for either genes or isoforms with their expression levels.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</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>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul style="list-style-type: circle;">
<li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li>
<li>Functional gene annotation</li>
<li>Gene ontology enrichment analysis on DEGs</li>
<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
<li>Alternative splicing analysis</li>
<li>Gene fusion analysis</li>
<li>Novel transcript identification</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-3062">please consult with our expert bioinformatics team</a>.</p>
</div>
<div><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04544-3">MGcount</a>: Diagenode has developed a bioinformatics software for counting whole-transcriptome RNA-seq reads from one or more input alignment files. It is specially designed to incorporate multi-mapping and multi-overlapping reads in the quantification using a flexible methodology that is compatible with any biotype. At the end of its execution, it produces a count matrix, compatible with any downstream analysis.</p>
<center><img src="https://www.diagenode.com/img/product/services/mGcount-img.png" width="600px" /></center>',
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