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<p style="text-align: center;"><span><img src="https://www.diagenode.com/img/banners/dplex-product-banner-small.jpg" alt="D-Plex Small RNA-seq library prep kit for Illumina" caption="false" width="728" height="90" /></span></p>
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<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/MA-D-Plex-V1_2020.pdf" target="_blank" title="D-Plex Small RNA library preparation user manual"><strong>Download the manual</strong></a></center>
<p><span>Diagenode’s latest <strong>innovation in </strong>RNA-seq, D-Plex, is based on a <strong>template-switching technology</strong> that delivers a</span><span><span> </span></span><strong>ligation-free method</strong> for RNA library preparation, ensuring realistic representation of diverse small RNA species (such as miRNA, piRNA, tRNA, and siRNA) with <strong>minimized bias</strong>.<strong> </strong>This solution allows you<span> to produce <strong>Illumina®-compatible</strong> DNA libraries for NGS from <strong>low input</strong> amounts of RNA with <strong>minimized PhiX</strong> spike-in quantity for the most representative results.</span></p>
<p><span>The D-Plex small RNA solution was optimized for <strong>10 pg</strong> - 10 ng enriched <strong>small RNA</strong> (or 100 pg - 100 ng total RNA) and contains our latest TSO (<strong>T</strong>emplate-<strong>S</strong>witch <strong>O</strong>ligonucleotides) technology including <strong>UMI</strong>s. The kit is provided with our new <strong>bioinformatics tool</strong> especially developed in parallel to get the best of your data.</span></p>
<ul class="accordion" data-accordion="" style="list-style-type: circle;">
<li class="accordion-navigation"><a href="#more" style="color: #13b29c; background-color: transparent; display: inline; padding: 0;"><strong>Read more</strong></a>
<div id="more" class="content">
<ul>
<li><strong>Truest representation</strong> - UMIs with ligation-free protocol ensures the most representative and unbiased data</li>
<li><strong>Easy</strong> - One-tube, one-day protocol Lowest inputs - Down to 10 pg enriched small RNA or 100 pg total RNA starting amount</li>
<li><strong>Clinical sample compatibility</strong> - Tested with RNA from biofluids</li>
<li><strong>Highest sequencing quality</strong> - Minimized PhiX quantity</li>
<li><strong>Enhanced data</strong> - Our new bioinformatics pipeline provided in the manual was optimized and developed specifically for getting the best out of your D-Plex data including UMI processing</li>
</ul>
<h3>miRMaster : your bioinformatics tool for miRNA</h3>
<div class="small-12 medium-3 large-3 columns"><center></center><center></center><center></center><center><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank"><img src="https://www.diagenode.com/img/product/kits/dplex/miRMaster_logo.png" /></a></center></div>
<div class="small-12 medium-9 large-9 columns">
<p style="text-align: justify;">Diagenode has collaborated with <strong>Andreas Keller</strong> and <strong>Tobias Fehlmann</strong> to develop a new bioinformatics pipeline in order to allow <strong>novel miRNA prediction.</strong><br /><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank">miRMaster</a> is a comprehensive analysis online free tool for NGS miRNA samples.</p>
</div>
</div>
</li>
</ul>
<p></p>
<div class="extra-spaced" align="center"><iframe width="700" height="394" src="https://www.youtube.com/embed/TmMM4gVpINo" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="allowfullscreen"></iframe></div>
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<div class="container">
<div class="row" style="background: rgba(255,255,255,0.1);">
<div class="large-12 columns slick">
<div>
<h3>Focused on your target</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/RNA_theorie.png" alt="small RNA sequencing kit" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>D-Plex: the highest yield technology to detect every kind of small RNA simultaneously (miRNA, snRNA, piRNA, …)</p>
</center></div>
</div>
<div>
<h3>Get rich content</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/biotyping_smallRNA.png" alt="small RNA diversity" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>One day to enrich your sample with a large variety of small RNAs in one tube. Identify your reads accurately with UMIs.</p>
</center></div>
</div>
<div>
<h3>Robust technology</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/correlation_inputs.png" alt="small non coding RNA" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Independent of your input amount -- go down to 10 pg small RNA as a starting amount.</p>
</center></div>
</div>
<div>
<h3>Raise your quality output</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/fastQ_DPlex_NovaSeq_human_mouse.png" alt="small RNA library preparation for Illumina" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Get the best of your data -- use our designed bioinformatics pipeline and guidelines. We provide everything you need to greatly simplify your analysis.</p>
</center></div>
</div>
</div>
</div>
</div>
</div>
</div>
<p style="text-align: center;">[ To check figures in details, click on "Figure" in the menu below ]</p>',
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'info2' => '<p>Specific D-Plex indexes <span>were designed and validated to fit the D-Plex technology for Illumina sequencing and </span>are not included in the kit. They can be bought separately according to your needs. Please choose the format that suits you best among the compatible references to:</p>
<p><strong>For D-Plex UDI library construction:</strong></p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex UDI Module - Set B" target="_blank"><span>C05030022</span> - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-C">C05030023 - D-Plex Unique Dual Indexes for Illumina - Set C</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-D">C05030024 - D-Plex Unique Dual Indexes for Illumina - Set D</a></li>
</ul>
<p><span>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</span></p>
<p><strong>For D-Plex SI library construction:</strong></p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-A" title="D-Plex SI Module - Set A" target="_blank">C05030010 - D-Plex Single Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-B" title="D-Plex SI Module - Set B" target="_blank">C05030011 - D-Plex Single Indexes for Illumina - Set B</a></li>
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'info3' => '<p>A specific bioinformatics pipeline has been developed to process the special sequences present in the D-Plex construct, namely the UMI, the A-tail, and the template switch motif. All guidelines and free softwares are shared in the user manual. Subject to the compatibility of D-Plex constructs, other specific pipelines can be used.</p>
<p><img src="https://www.diagenode.com/img/product/kits/dplex/bioinfoPipe.png" alt="small RNA sequencing bioinformatics pipeline" caption="false" width="925" height="196" /></p>',
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'description' => '<div class="extra-spaced">
<p style="text-align: center;"><span><img src="https://www.diagenode.com/img/banners/dplex-product-banner-small.jpg" alt="D-Plex Small RNA-seq library prep kit for Illumina" caption="false" width="728" height="90" /></span></p>
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<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/MA-D-Plex-V1_2020.pdf" target="_blank" title="D-Plex Small RNA library preparation user manual"><strong>Download the manual</strong></a></center>
<p><span>Diagenode’s latest <strong>innovation in </strong>RNA-seq, D-Plex, is based on a <strong>template-switching technology</strong> that delivers a</span><span><span> </span></span><strong>ligation-free method</strong> for RNA library preparation, ensuring realistic representation of diverse small RNA species (such as miRNA, piRNA, tRNA, and siRNA) with <strong>minimized bias</strong>.<strong> </strong>This solution allows you<span> to produce <strong>Illumina®-compatible</strong> DNA libraries for NGS from <strong>low input</strong> amounts of RNA with <strong>minimized PhiX</strong> spike-in quantity for the most representative results.</span></p>
<p><span>The D-Plex small RNA solution was optimized for <strong>10 pg</strong> - 10 ng enriched <strong>small RNA</strong> (or 100 pg - 100 ng total RNA) and contains our latest TSO (<strong>T</strong>emplate-<strong>S</strong>witch <strong>O</strong>ligonucleotides) technology including <strong>UMI</strong>s. The kit is provided with our new <strong>bioinformatics tool</strong> especially developed in parallel to get the best of your data.</span></p>
<ul class="accordion" data-accordion="" style="list-style-type: circle;">
<li class="accordion-navigation"><a href="#more" style="color: #13b29c; background-color: transparent; display: inline; padding: 0;"><strong>Read more</strong></a>
<div id="more" class="content">
<ul>
<li><strong>Truest representation</strong> - UMIs with ligation-free protocol ensures the most representative and unbiased data</li>
<li><strong>Easy</strong> - One-tube, one-day protocol Lowest inputs - Down to 10 pg enriched small RNA or 100 pg total RNA starting amount</li>
<li><strong>Clinical sample compatibility</strong> - Tested with RNA from biofluids</li>
<li><strong>Highest sequencing quality</strong> - Minimized PhiX quantity</li>
<li><strong>Enhanced data</strong> - Our new bioinformatics pipeline provided in the manual was optimized and developed specifically for getting the best out of your D-Plex data including UMI processing</li>
</ul>
<h3>miRMaster : your bioinformatics tool for miRNA</h3>
<div class="small-12 medium-3 large-3 columns"><center></center><center></center><center></center><center><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank"><img src="https://www.diagenode.com/img/product/kits/dplex/miRMaster_logo.png" /></a></center></div>
<div class="small-12 medium-9 large-9 columns">
<p style="text-align: justify;">Diagenode has collaborated with <strong>Andreas Keller</strong> and <strong>Tobias Fehlmann</strong> to develop a new bioinformatics pipeline in order to allow <strong>novel miRNA prediction.</strong><br /><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank">miRMaster</a> is a comprehensive analysis online free tool for NGS miRNA samples.</p>
</div>
</div>
</li>
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<p></p>
<div class="extra-spaced" align="center"><iframe width="700" height="394" src="https://www.youtube.com/embed/TmMM4gVpINo" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="allowfullscreen"></iframe></div>
<div class="row">
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<div class="container">
<div class="row" style="background: rgba(255,255,255,0.1);">
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<div>
<h3>Focused on your target</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/RNA_theorie.png" alt="small RNA sequencing kit" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>D-Plex: the highest yield technology to detect every kind of small RNA simultaneously (miRNA, snRNA, piRNA, …)</p>
</center></div>
</div>
<div>
<h3>Get rich content</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/biotyping_smallRNA.png" alt="small RNA diversity" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>One day to enrich your sample with a large variety of small RNAs in one tube. Identify your reads accurately with UMIs.</p>
</center></div>
</div>
<div>
<h3>Robust technology</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/correlation_inputs.png" alt="small non coding RNA" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Independent of your input amount -- go down to 10 pg small RNA as a starting amount.</p>
</center></div>
</div>
<div>
<h3>Raise your quality output</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/fastQ_DPlex_NovaSeq_human_mouse.png" alt="small RNA library preparation for Illumina" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Get the best of your data -- use our designed bioinformatics pipeline and guidelines. We provide everything you need to greatly simplify your analysis.</p>
</center></div>
</div>
</div>
</div>
</div>
</div>
</div>
<p style="text-align: center;">[ To check figures in details, click on "Figure" in the menu below ]</p>',
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'info2' => '<p>Specific D-Plex indexes <span>were designed and validated to fit the D-Plex technology for Illumina sequencing and </span>are not included in the kit. They can be bought separately according to your needs. Please choose the format that suits you best among the compatible references to:</p>
<p><strong>For D-Plex UDI library construction:</strong></p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex UDI Module - Set B" target="_blank"><span>C05030022</span> - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-C">C05030023 - D-Plex Unique Dual Indexes for Illumina - Set C</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-D">C05030024 - D-Plex Unique Dual Indexes for Illumina - Set D</a></li>
</ul>
<p><span>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</span></p>
<p><strong>For D-Plex SI library construction:</strong></p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-A" title="D-Plex SI Module - Set A" target="_blank">C05030010 - D-Plex Single Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-B" title="D-Plex SI Module - Set B" target="_blank">C05030011 - D-Plex Single Indexes for Illumina - Set B</a></li>
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'info3' => '<p>A specific bioinformatics pipeline has been developed to process the special sequences present in the D-Plex construct, namely the UMI, the A-tail, and the template switch motif. All guidelines and free softwares are shared in the user manual. Subject to the compatibility of D-Plex constructs, other specific pipelines can be used.</p>
<p><img src="https://www.diagenode.com/img/product/kits/dplex/bioinfoPipe.png" alt="small RNA sequencing bioinformatics pipeline" caption="false" width="925" height="196" /></p>',
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'meta_title' => 'D-Plex Small RNA-seq Library Prep Kit for Illumina',
'meta_keywords' => 'RNA-seq, UMI, low input, small RNA-seq, one tube, one day, challenging, miRNA, biofluid, RNA-seq library preparation, D-Plex, higher RNA diversity, easy, user-friendly',
'meta_description' => 'D-Plex Small RNA-seq Kit for Illumina contains UMI and is designed to generate rich small RNA-seq libraries from 100 pg of total RNA or 10 pg enriched small RNA.',
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'name' => 'D-Plex Small RNA DNBSEQ™ Kit for MGI Sequencing',
'description' => '<center><img src="https://www.diagenode.com/img/banners/banner-dplex-mgi.png" width="550" /></center>
<p></p>
<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/D-Plex-Small-RNA-DNBSEQ.pdf" target="_blank" title="D-Plex Small RNA DNBSEQ user manual"><strong>Download the manual</strong></a></center>
<p></p>
<p>D-Plex Small RNA DNBSEQ™ Kit is a tool designed for the study of the small non-coding transcriptome. The kit is using the <a href="https://www.diagenode.com/en/pages/dplex" target="_blank">D-Plex technology</a> to generate double-stranded DNA libraries ready to be used for the DNA single-strand circularization step required for DNBSEQ sequencing on MGI sequencers.</p>
<p>The D-Plex technology utilizes the innovative capture and amplification by tailing and switching, a ligation-free method for RNA library preparation from ultra-low input amounts, down to 10 pg for small RNAs and 100 pg for total RNAs. This innovative solution enables diverse and novel transcripts detection, even from challenging clinical samples such as liquid biopsies.</p>
<p><span>D-Plex Small RNA <span>DNBSEQ™</span> Kit offers a time saving protocol that can be completed within 5 hours and requires minimal hands-on time. </span><span>The library preparation takes place in a </span><span>single tube</span><span>, increasing the efficiency tremendously. This ensures high technical reliability and reproducibility<span>.</span></span></p>
<p>D-Plex Small RNA DNBSEQ™ Kit includes all buffers and enzymes necessary for the library preparation. Specific D-Plex DNBSEQ Barcodes were designed and validated to fit the D-Plex technology and are available separately:</p>
<ul>
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-a">C05030060 - D-Plex DNBSEQ Barcodes for MGI - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-b">C05030061 - D-Plex DNBSEQ Barcodes for MGI - Set B</a></li>
</ul>
<p><b><strong>D-Plex is also available for Illumina sequencing, check<span> </span><a href="https://www.diagenode.com/en/p/D-Plex-Small-RNA-seq-Library-Prep-x24" target="_blank">here</a>!</strong></b></p>
<div class="row">
<div class="carrousel" style="background-position: center;">
<div class="container">
<div class="row" style="background: rgba(255,255,255,0.1);">
<div class="large-12 columns slick">
<div>
<h3>Diverse and novel transcript detection</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px; display: block; margin-left: auto; margin-right: auto;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-transcript.png" alt="small RNA library preparation for Illumina" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p style="text-align: justify;">The D-Plex Small RNA DNBSEQ protocol generates complex RNA libraries deciphering the wide diversity of small non-coding RNA spectrum (including miRNAs, snoRNAs, snRNAs) in human plasma samples.</p>
</center></div>
</div>
<div>
<h3>Ultra-low input performance</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px; display: block; margin-left: auto; margin-right: auto;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-ultralow-input.png" alt="Ultra-low input performance" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p style="text-align: justify;">Sequencing data from circulating RNA samples of two input amounts (25 pg and 2.5 ng) were highly correlated (<i>R = 0.99</i>) when compared using Pearson correlation coefficient.</p>
</center></div>
</div>
<div>
<h3>High mapping efficiency</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px; display: block; margin-left: auto; margin-right: auto;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-mapping.png" alt="High mapping efficiency" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p style="text-align: justify;"><b></b>The D-Plex Small RNA DNBSEQ kit is compatible with clinical-relevant samples, such as human plasma, and ultra low range of circulating RNA input (down to 10 pg) and exhibits good read mapping of sequencing reads (up to 70% mapping rate).</p>
</center></div>
</div>
<div>
<h3>High quality DNBSEQ sequencing solution</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-dnbseq.png" alt="High quality DNBSEQ sequencing solution" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p><b></b>The D-Plex Small RNA DNBSEQ kit combines our best-in-class RNA library preparation – D-Plex technology – with MGI’s high-quality, cost-effective, DNA nanoballs – DNBSEQ – sequencing solution, creating a unified platform to support high quality small RNA sequencing.</p>
</center></div>
</div>
</div>
</div>
</div>
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<li><strong>Latest innovation in RNA-seq</strong>: unique D-Plex technology offering ligation-free protocol for library preparation</li>
<li><strong>Ultra-low input capability</strong>: down to 10 pg for small RNAs and 100 pg for total RNAs</li>
<li><strong>High library complexity</strong>:<strong> </strong>obtain a complete view of your small RNA transcriptome</li>
<li><strong>Optimal performance on clinical samples</strong>: validated with circulating RNAs from liquid biopsies</li>
<li><strong>Easy to use with minimal hands-on time</strong>: one day, one tube protocol</li>
<li><strong>Highest sequencing quality</strong>: specifically formatted for MGI DNBSEQ™ sequencers</li>
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'info2' => '<p>D-Plex DNBSEQ Barcodes are not included in the kit. Two sets are available separately:</p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-a">C05030060 - D-Plex 24 DNBSEQ Barcodes for MGI - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-b">C05030061 - D-Plex 24 DNBSEQ Barcodes for MGI - Set B</a></li>
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'slug' => 'd-plex-small-RNA-dnbseq-kit-x24',
'meta_title' => 'D-Plex Small RNA-seq Library Prep Kit for MGI Sequencing | Diagenode ',
'meta_keywords' => 'Small RNA-seq Library Prep Kit for MGI Sequencing',
'meta_description' => 'Small RNA-seq library preparation with D-Plex technology - Suitable for MGI sequencing platforms - Optimized for ultra-low input (100 pg total RNA) - Compatibility with plasma samples - User-friendly and fast protocol',
'modified' => '2021-05-26 11:03:01',
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'name' => 'MicroChIP DiaPure columns',
'description' => '<p>Diagenode’s MicroChIP DiaPure columns have been optimized for the purification and elution of very low amounts of DNA. This rapid method has been validated for epigenetic applications like low input ChIP (e.g. using the True MicroChIP kit) and CUT&Tag (e.g. using Diagenode’s pA-Tn5), but is also compatible with many other applications. The DNA can be eluted at high concentrations in volumes down to 6 μl and it is suitable for any downstream application (e.g. NGS).</p>
<p>Benefits of the MicroChIP DiaPure columns:</p>
<ul>
<li>Optimized for the purification of very low DNA amounts</li>
<li>Fast and easy protocol</li>
<li>Non-toxic</li>
<li>Validated for ChIP and Cut&Tag</li>
</ul>',
'label1' => 'Examples of results',
'info1' => '<h2 style="text-align: center;">MicroChIP DiaPure columns after ChIP</h2>
<p>Successful ChIP-seq results generated on 50,000 of K562 cells using True MicroChIP technology. ChIP has been performed accordingly to True MicroChIP protocol (Diagenode, Cat. No. C01010130), including DNA purification using the MicroChIP DiaPure columns. For the library preparation the MicroPlex Library Preparation Kit (Diagenode, Cat. No. C05010001) has been used. The below figure shows the peaks from ChIP-seq experiments using the following Diagenode antibodies: H3K4me1 (C15410194), H3K9/14ac (C15410200), H3K27ac (C15410196) and H3K36me3 (C15410192).</p>
<p><img src="https://www.diagenode.com/img/product/kits/figure-igv-microchip.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 1:</strong> Integrative genomics viewer (IGV) visualization of ChIP-seq experiments using 50,000 of K562 cells.</p>
<p></p>
<h2 style="text-align: center;"></h2>
<h2 style="text-align: center;">MicroChIP DiaPure columns after CUT&Tag</h2>
<p>Successful CUT&Tag results showing a low background with high region-specific enrichment has been generated using 50.000 of K562 cells, 1 µg of H3K27me3 antibody (Diagenode, Cat. No. C15410069) and proteinA-Tn5 (1:250) (Diagenode, Cat. No. C01070001). 1 µg of IgG (Diagenode, Cat. No. C15410206) was used as negative control. Samples were purified using the MicroChIP DiaPure columns or phenol-chloroform purification. The below figure presenst the comparison of two purification methods.</p>
<p><img src="https://www.diagenode.com/img/product/kits/figure-diapure-igv.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 2:</strong> Integrative genomics viewer (IGV) visualization of CUT&Tag experiments: MicroChIP DiaPure columns vs phenol-chloroform purification using the H3K27me3 antibody.</p>',
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'meta_description' => 'MicroChIP DiaPure columns',
'modified' => '2021-05-31 08:48:09',
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'name' => 'D-Plex Unique Dual Indexes for Illumina - Set A',
'description' => '<p style="text-align: left;"><span>D-Plex Unique Dual Indexes Module - Set A includes primer pairs with 24 unique dual barcodes (unique i5 and i7 indexes) for library multiplexing with the <a href="https://www.diagenode.com/en/p/D-Plex-Small-RNA-seq-Library-Prep-x24" target="_blank" title="D-Plex Small RNA-seq Kit">D-Plex Small RNA-seq Kit</a>. </span></p>
<p style="text-align: left;"><span>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</span></p>
<p><span>Four sets are available separately: </span></p>
<ul>
<li>C05030021 - D-Plex Unique Dual Indexes for Illumina - Set A</li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex 24 Single Indexes for Illumina - Set #B">C05030022 - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-C" title="D-Plex 24 Single Indexes for Illumina - Set #C">C05030023 - D-Plex Unique Dual Indexes for Illumina - Set C</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-D" title="D-Plex 24 Single Indexes for Illumina - Set #D">C05030024 - D-Plex Unique Dual Indexes for Illumina - Set D</a></li>
</ul>
<p><span>Each set can be used for library multiplexing up to 24. <span>Set A, B, C and D can be used simultaneously for library multiplexing up to 96.</span></span></p>
<p><span>Read more about the </span><a href="https://www.diagenode.com/en/pages/dplex" target="_blank">D-Plex technology</a><span>.</span><span> </span></p>',
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<li>Compatible with<span> </span><a href="https://www.diagenode.com/en/p/D-Plex-Small-RNA-seq-Library-Prep-x24">D-Plex Small RNA-seq kit for Illumina</a> </li>
<li>Multiplexing up to <strong>96 samples</strong> when combining Set A, B, C and D</li>
<li>Allows for identification of index hopping</li>
</ul>',
'label2' => 'Multiplexing',
'info2' => '<p><span>D-Plex RNA-seq UDI library constructs bear the TruSeq (Illumina) HT adapters. In case of a multiplexing scenario, it is therefore recommended to submit the D-Plex libraries as TruSeq HT libraries to your sequencing provider. </span><span>Further details are provided in the D-Plex Unique Dual Indexes Module<span> </span><a href="https://www.diagenode.com/files/products/kits/dplex-unique-dual-indexes-manual.pdf" target="_blank">manual</a>.</span></p>',
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'slug' => 'D-Plex-24-Unique-Dual-Indexes-Set-A',
'meta_title' => 'D-Plex Unique Dual Indexes for Illumina - RNA Library Prep Kit - Set A | Diagenode',
'meta_keywords' => 'RNA kit; small RNA kit; RNA-seq kit; low input RNA-seq kit; small RNA-seq, template switching kit, RNA-seq library preparation, D-Plex, higher RNA diversity',
'meta_description' => 'Compatible with D-Plex RNA-seq kits - Unique Dual Indexes for Illumina - Index hopping mitigation - Suitable for ultra-low input - Contains UMI - Multiplexing up to 48 samples',
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'created' => '2021-03-15 10:36:47',
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'name' => 'D-Plex Total RNA-seq Kit for Illumina',
'description' => '<div class="small-12 medium-12 large-12 columns" style="border: 3px solid #B02736; padding: 10px; margin: 10px;">
<h2>Product Features</h2>
<ul style="list-style-type: disc;">
<li><span style="font-weight: 400;">An innovative technology with template switching and UMIs</span></li>
<li><span style="font-weight: 400;">Ultra-low input capability, down to 50 pg for total RNAs</span></li>
<li><span style="font-weight: 400;">Capture the widest possible diversity of RNAs for rich content</span></li>
<li><span>Get high sensitivity data even from difficult samples, such as degraded, FFPE samples</span></li>
<li><span style="font-weight: 400;">Enjoy a fast, easy, single tube protocol</span></li>
</ul>
</div>
<p></p>
<p></p>
<h4></h4>
<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/dplex-total-manual.pdf" target="_blank" title="dplex total rnaseq user manual"><strong>Download the manual</strong></a></center>
<p></p>
<p>D-Plex Total RNA-seq Library Preparation Kit is a tool designed for the study of the whole coding and non-coding transcriptome. <span>The kit is using the</span><a href="https://www.diagenode.com/en/pages/dplex" target="_blank"><span> </span>D-Plex technology</a><span><span> </span>to generate directional libraries for Illumina sequencing directly from total RNAs, mRNAs that has already been enriched by poly(A) selection, or RNAs that has already been depleted of rRNAs.</span></p>
<p><span>The D-Plex technology utilizes two innovative ligation-free mechanisms - poly(A) tailing and template switching - to produce sequencing libraries from ultra-low input amounts, down to 50 pg for total RNAs, mRNAs or rRNA-depleted RNAs. Combined with unique molecular identifiers (UMI), this complete solution delivers a high-sensitivity detection method for comprehensive analysis of the transcriptome. It accurately measures gene and transcript abundance and captures the widest possible diversity of RNAs, including known and novel features in coding and non-coding RNAs.</span></p>
<p><span></span><span>D-Plex Total RNA-seq Kit offers a time saving protocol that can be completed within 5 hours and requires minimal hands-on time. <span>The library preparation takes place in a </span><span>single tube</span><span>, increasing the efficiency tremendously. This new solution has been extensively validated for both intact and highly degraded RNA samples, including that derived from FFPE preparations.</span></span></p>
<p><span><span>D-Plex Total RNA-seq Kit includes all buffers and enzymes necessary for the library preparation. Specific D-Plex Unique Dual Indexes were designed and validated to fit the D-Plex technology and are available separately:</span></span></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex UDI Module - Set B" target="_blank"><span>C05030022</span> - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
</ul>
<div class="extra-spaced" align="center"></div>
<div class="row">
<div class="carrousel" style="background-position: center;">
<div class="slick">
<div>
<h3>Greater sensitivity to detect novel transcripts</h3>
<div class="large-12 small-12 medium-12 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/CPM_totalrna.png" alt="total RNA kit" /></center></div>
<div class="large-12 small-12 medium-12 large-centered medium-centered small-centered columns">
<p></p>
<p>D-Plex Total RNA-seq kit enables great transcript detection, even when starting from very low RNA inputs or working with challenging FFPE samples. Increased number of transcripts detected at 1x coverage is an indicator of greater sensitivity.</p>
</div>
</div>
<div>
<h3>Highest diversity to facilitate biomarker discovery</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/HUR_rdep_biotypes.png" alt="total RNA diversity" /></center></div>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns">
<p></p>
<p>D-Plex Total RNA-seq libraries capture efficiently all RNA biotypes present in the sample of interest, both coding and non-coding RNAs or small and long RNAs.</p>
</div>
</div>
<div>
<h3>Consistent expression profiling across a wide range of RNA inputs</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/HUR_and_umeg_rdep_violin_tpm_10.png" alt="total RNA" width="450px" /></center></div>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns">
<p></p>
<p>Gene expression profiling (including protein coding exons and introns) obtained with D-Plex Total RNA-seq kit is conserved for decreasing RNA inputs, keeping transcript diversity across the range of RNA inputs.</p>
</div>
</div>
<div>
<h3>Superior library complexity at low RNA inputs</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/robustness_corr_HUR_HuMEg_rrna.png" alt="total RNA" /></center></div>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns">
<p></p>
<p>Correlation analysis of the protein coding genes detected indicates superior transcript expression correlation between low and high RNA inputs. This result demonstrates that D-Plex is an ideal solution for challenging samples such as FFPE preparations.</p>
</div>
</div>
</div>
</div>
</div>',
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<ul>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex UDI Module - Set B" target="_blank"><span>C05030022</span> - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
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<p>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</p>',
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<p><img src="https://www.diagenode.com/img/product/kits/dplex/bioinfoPipe.png" alt="Small RNA seq Bioinformatics pipeline" width="925" height="196" /></p>',
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<li>Ease of use</li>
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<p><span size="1" style="font-size: xx-small;">Representation of elution model. Elution changment may occur for different experimental models. Performed on HeLa cell culture meidium. </span></p>
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'description' => '<div class="row">
<div class="small-12 medium-12 large-12 columns">
<h2 style="font-size: 22px;">DNA断片化、ライブラリー調製、自動化：NGSのワンストップショップ</h2>
<table class="small-12 medium-12 large-12 columns">
<tbody>
<tr>
<th class="small-12 medium-12 large-12 columns">
<h4>1. 断片化装置を選択してください：150 bp〜75 kbの範囲でDNAを断片化します。</h4>
</th>
</tr>
<tr style="background-color: #ffffff;">
<td class="small-12 medium-12 large-12 columns"></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns"><a href="../p/bioruptor-pico-sonication-device"><img src="https://www.diagenode.com/img/product/shearing_technologies/bioruptor_pico.jpg" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/megaruptor2-1-unit"><img src="https://www.diagenode.com/img/product/shearing_technologies/B06010001_megaruptor2.jpg" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/bioruptor-one-sonication-device"><img src="https://www.diagenode.com/img/product/shearing_technologies/br-one-profil.png" /></a></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns">5μlまで断片化：150 bp〜2 kb<br />NGS DNAライブラリー調製およびFFPE核酸抽出に最適で、</td>
<td class="small-4 medium-4 large-4 columns">2 kb〜75 kbの範囲をできます。<br />メイトペアライブラリー調製および長いフラグメントDNAシーケンシングに最適で、この軽量デスクトップデバイスで</td>
<td class="small-4 medium-4 large-4 columns">20または50μlの断片化が可能です。</td>
</tr>
</tbody>
</table>
<table class="small-12 medium-12 large-12 columns">
<tbody>
<tr>
<th class="small-8 medium-8 large-8 columns">
<h4>2. 最適化されたライブラリー調整キットを選択してください。</h4>
</th>
<th class="small-4 medium-4 large-4 columns">
<h4>3. ライブラリー前処理自動化を選択して、比類のないデータ再現性を実感</h4>
</th>
</tr>
<tr style="background-color: #ffffff;">
<td class="small-12 medium-12 large-12 columns"></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns"><a href="../p/microplex-library-preparation-kit-v2-x12-12-indices-12-rxns"><img src="https://www.diagenode.com/img/product/kits/microPlex_library_preparation.png" style="display: block; margin-left: auto; margin-right: auto;" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/ideal-library-preparation-kit-x24-incl-index-primer-set-1-24-rxns"><img src="https://www.diagenode.com/img/product/kits/box_kit.jpg" style="display: block; margin-left: auto; margin-right: auto;" height="173" width="250" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/sx-8g-ip-star-compact-automated-system-1-unit"><img src="https://www.diagenode.com/img/product/automation/B03000002%20_ipstar_compact.png" style="display: block; margin-left: auto; margin-right: auto;" /></a></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns" style="text-align: center;">50pgの低入力：MicroPlex Library Preparation Kit</td>
<td class="small-4 medium-4 large-4 columns" style="text-align: center;">5ng以上：iDeal Library Preparation Kit</td>
<td class="small-4 medium-4 large-4 columns" style="text-align: center;">Achieve great NGS data easily</td>
</tr>
</tbody>
</table>
</div>
</div>
<blockquote>
<div class="row">
<div class="small-12 medium-12 large-12 columns"><span class="label" style="margin-bottom: 16px; margin-left: -22px; font-size: 15px;">DiagenodeがNGS研究にぴったりなプロバイダーである理由</span>
<p>Diagenodeは15年以上もエピジェネティクス研究に専念、専門としています。 ChIP研究クロマチン用のユニークな断片化システムの開発から始まり、 専門知識を活かし、5μlのせん断体積まで可能で、NGS DNAライブラリーの調製に最適な最先端DNA断片化装置の開発にたどり着きました。 我々は以来、ChIP-seq、Methyl-seq、NGSライブラリー調製用キットを研究開発し、業界をリードする免疫沈降研究と同様に、ライブラリー調製を自動化および完結させる独自の自動化システムを開発にも成功しました。</p>
<ul>
<li>信頼されるせん断装置</li>
<li>様々なインプットからのライブラリ作成キット</li>
<li>独自の自動化デバイス</li>
</ul>
</div>
</div>
</blockquote>
<div class="row">
<div class="small-12 columns">
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#panel1a">次世代シーケンシングへの理解とその専門知識</a>
<div id="panel1a" class="content">
<div class="row">
<div class="small-12 medium-12 large-12 columns">
<p><strong>次世代シーケンシング (NGS)</strong> ）は、著しいスケールとハイスループットでシーケンシングを行い、1日に数十億もの塩基生成を可能にします。 NGSのハイスループットは迅速でありながら正確で、再現性のあるデータセットを実現し、さらにシーケンシング費用を削減します。 NGSは、ゲノムシーケンシング、ゲノム再シーケンシング、デノボシーケンシング、トランスクリプトームシーケンシング、その他にDNA-タンパク質相互作用の検出やエピゲノムなどを示します。 指数関数的に増加するシーケンシングデータの需要は、計算分析の障害や解釈、データストレージなどの課題を解決します。</p>
<p>アプリケーションおよび出発物質に応じて、数百万から数十億の鋳型DNA分子を大規模に並行してシーケンシングすることが可能です。その為に、異なる化学物質を使用するいくつかの市販のNGSプラットフォームを利用することができます。 NGSプラットフォームの種類によっては、事前準備とライブラリー作成が必要です。</p>
<p>NGSにとっても、特にデータ処理と分析に関した大きな課題はあります。第3世代技術はゲノミクス研究にさらに革命を起こすであろうと大きく期待されています。</p>
</div>
</div>
<div class="row">
<div class="small-6 medium-6 large-6 columns">
<p><strong>NGS アプリケーション</strong></p>
<ul>
<li>全ゲノム配列決定</li>
<li>デノボシーケンシング</li>
<li>標的配列</li>
<li>Exomeシーケンシング</li>
<li>トランスクリプトーム配列決定</li>
<li>ゲノム配列決定</li>
<li>ミトコンドリア配列決定</li>
<li>DNA-タンパク質相互作用（ChIP-seq</li>
<li>バリアント検出</li>
<li>ゲノム仕上げ</li>
</ul>
</div>
<div class="small-6 medium-6 large-6 columns">
<p><strong>研究分野におけるNGS:</strong></p>
<ul>
<li>腫瘍学</li>
<li>リプロダクティブ・ヘルス</li>
<li>法医学ゲノミクス</li>
<li>アグリゲノミックス</li>
<li>複雑な病気</li>
<li>微生物ゲノミクス</li>
<li>食品・環境ゲノミクス</li>
<li>創薬ゲノミクス - パーソナライズド・メディカル</li>
</ul>
</div>
<div class="small-12 medium-12 large-12 columns">
<p><strong>NGSの用語</strong></p>
<dl>
<dt>リード（読み取り）</dt>
<dd>この装置から得られた連続した単一のストレッチ</dd>
<dt>断片リード</dt>
<dd>フラグメントライブラリからの読み込み。 シーケンシングプラットフォームに応じて、読み取りは通常約100〜300bp。</dd>
<dt>断片ペアエンドリード</dt>
<dd>断片ライブラリーからDNA断片の各末端2つの読み取り。</dd>
<dt>メイトペアリード</dt>
<dd>大きなDNA断片（通常は予め定義されたサイズ範囲）の各末端から2つの読み取り。</dd>
<dt>カバレッジ（例）</dt>
<dd>30×適用範囲とは、参照ゲノム中の各塩基対が平均30回の読み取りを示す。</dd>
</dl>
</div>
</div>
<div class="row">
<div class="small-12 medium-12 large-12 columns">
<h2>NGSプラットフォーム</h2>
<h3><a href="http://www.illumina.com" target="_blank">イルミナ</a></h3>
<p>イルミナは、クローン的に増幅された鋳型DNA（クラスター）上に位置する、蛍光標識された可逆的鎖ターミネーターヌクレオチドを用いた配列別合成技術を使用。 DNAクラスターは、ガラスフローセルの表面上に固定化され、 ワークフローは、4つのヌクレオチド（それぞれ異なる蛍光色素で標識された）の組み込み、4色イメージング、色素や末端基の切断、取り込み、イメージングなどを繰り返します。フローセルは大規模な並列配列決定を受ける。 この方法により、単一蛍光標識されたヌクレオチドの制御添加によるモノヌクレオチドのエラーを回避する可能性があります。 読み取りの長さは、通常約100〜150 bpです。</p>
<h3><a href="http://www.lifetechnologies.com" target="_blank">イオン トレント</a></h3>
<p>イオントレントは、半導体技術チップを用いて、合成中にヌクレオチドを取り込む際に放出されたプロトンを検出します。 これは、イオン球粒子と呼ばれるビーズの表面にエマルションPCR（emPCR）を使用し、リンクされた特定のアダプターを用いてDNA断片を増幅します。 各ビーズは1種類のDNA断片で覆われていて、異なるDNA断片を有するビーズは次いで、チップの陽子感知ウェル内に配置されます。 チップには一度に4つのヌクレオチドのうちの1つが浸水し、このプロセスは異なるヌクレオチドで15秒ごとに繰り返されます。 配列決定の間に4つの塩基の各々が1つずつ導入されます、組み込みの場合はプロトンが放出され、電圧信号が取り込みに比例して検出されます。.</p>
<h3><a href="http://www.pacificbiosciences.com" target="_blank">パシフィック バイオサイエンス</a></h3>
<p>パシフィックバイオサイエンスでは、20kbを超える塩基対の読み取りも、単一分子リアルタイム（SMRT）シーケンシングによる構造および細胞タイプの変化を観察することができます。 このプラットフォームでは、超長鎖二本鎖DNA（dsDNA）断片が、Megaruptor（登録商標）のようなDiagenode装置を用いたランダムシアリングまたは目的の標的領域の増幅によって生成されます。 SMRTbellライブラリーは、ユニバーサルヘアピンアダプターをDNA断片の各末端に連結することによって生成します。 サイズ選択条件による洗浄ステップの後、配列決定プライマーをSMRTbellテンプレートにアニーリングし、鋳型DNAに結合したDNAポリメラーゼを含む配列決定を、蛍光標識ヌクレオチドの存在下で開始。 各塩基が取り込まれると、異なる蛍光のパルスをリアルタイムで検出します。</p>
<h3><a href="https://nanoporetech.com" target="_blank">オックスフォード ナノポア</a></h3>
<p>Oxford Nanoporeは、単一のDNA分子配列決定に基づく技術を開発します。その技術により生物学的分子、すなわちDNAが一群の電気抵抗性高分子膜として位置するナノスケールの孔（ナノ細孔）またはその近くを通過し、イオン電流が変化します。 この変化に関する情報は、例えば4つのヌクレオチド（AまたはG r CまたはT）ならびに修飾されたヌクレオチドすべてを区別することによって分子情報に訳されます。 シーケンシングミニオンデバイスのフローセルは、数百個のナノポアチャネルのセンサアレイを含みます。 DNAサンプルは、Diagenode社のMegaruptor（登録商標）を用いてランダムシアリングによって生成され得る超長鎖DNAフラグメントが必要です。</p>
<h3><a href="http://www.lifetechnologies.com/be/en/home/life-science/sequencing/next-generation-sequencing/solid-next-generation-sequencing.html" target="_blank">SOLiD</a></h3>
<p>SOLiDは、ユニークな化学作用により、何千という個々のDNA分子の同時配列決定を可能にします。 それは、アダプター対ライブラリーのフラグメントが適切で、せん断されたゲノムDNAへのアダプターのライゲーションによるライブラリー作製から始まります。 次のステップでは、エマルジョンPCR（emPCR）を実施して、ビーズの表面上の個々の鋳型DNA分子をクローン的に増幅。 emPCRでは、個々の鋳型DNAをPCR試薬と混合し、水中油型エマルジョン内の疎水性シェルで囲まれた水性液滴内のプライマーコートビーズを、配列決定のためにロードするスライドガラスの表面にランダムに付着。 この技術は、シークエンシングプライマーへのライゲーションで競合する4つの蛍光標識されたジ塩基プローブのセットを使用します。</p>
<h3><a href="http://454.com/products/technology.asp" target="_blank">454</a></h3>
<p>454は、大規模並列パイロシーケンシングを利用しています。 始めに全ゲノムDNAまたは標的遺伝子断片の300〜800bp断片のライブラリー調製します。 次に、DNAフラグメントへのアダプターの付着および単一のDNA鎖の分離。 その後アダプターに連結されたDNAフラグメントをエマルジョンベースのクローン増幅（emPCR）で処理し、DNAライブラリーフラグメントをミクロンサイズのビーズ上に配置します。 各DNA結合ビーズを光ファイバーチップ上のウェルに入れ、器具に挿入します。 4つのDNAヌクレオチドは、配列決定操作中に固定された順序で連続して加えられ、並行して配列決定されます。</p>
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<div class="small-12 medium-12 large-12 columns">
<p><span style="font-weight: 400;">Most of the major next-generation sequencing platforms require ligation of specific adaptor oligos to </span><a href="../applications/dna-rna-shearing"><span style="font-weight: 400;">fragmented DNA or RNA</span></a><span style="font-weight: 400;"> prior to sequencing</span></p>
<p><span style="font-weight: 400;">After input DNA has been fragmented, it is end-repaired and blunt-ended</span><span style="font-weight: 400;">. The next step is a A-tailing in which dAMP is added to the 3´ end of the blunt phosphorylated DNA fragments to prevent concatemerization and to allow the ligation of adaptors with complementary dT overhangs. In addition, barcoded adapters can be incorporated to facilitate multiplexing prior to or during amplification.</span></p>
<center><img src="https://www.diagenode.com/img/categories/library-prep/flux.png" /></center>
<p><span style="font-weight: 400;">Diagenode offers a comprehensive product portfolio for library preparation:<br /></span></p>
<strong><a href="https://www.diagenode.com/en/categories/Library-preparation-for-RNA-seq">D-Plex RNA-seq Library Preparation Kits</a></strong><br />
<p><span style="font-weight: 400;">Diagenode’s new RNA-sequencing solutions utilize the innovative c</span><span style="font-weight: 400;">apture and a</span><span style="font-weight: 400;">mplification by t</span><span style="font-weight: 400;">ailing and s</span><span style="font-weight: 400;">witching”</span><span style="font-weight: 400;">, a ligation-free method to produce DNA libraries for next generation sequencing from low input amounts of RNA. </span><span style="font-weight: 400;"></span><a href="../categories/Library-preparation-for-RNA-seq">Learn more</a></p>
<strong><a href="../categories/library-preparation-for-ChIP-seq">ChIP-seq and DNA sequencing library preparation solutions</a></strong><br />
<p><span style="font-weight: 400;">Our kits have been optimized for DNA library preparation used for next generation sequencing for a wide range of inputs. Using a simple three-step protocols, our</span><a href="http://www.diagenode.com/p/microplex-library-preparation-kit-v2-x12-12-indices-12-rxns"><span style="font-weight: 400;"> </span></a><span style="font-weight: 400;">kits are an optimal choice for library preparation from DNA inputs down to 50 pg. </span><a href="../categories/library-preparation-for-ChIP-seq">Learn more</a></p>
<a href="../p/bioruptor-pico-sonication-device"><span style="font-weight: 400;"></span><strong>Bioruptor Pico - short fragments</strong></a><a href="../categories/library-preparation-for-ChIP-seq-and-DNA-sequencing"><span style="font-weight: 400;"></span></a><br />
<p><span style="font-weight: 400;"></span><span style="font-weight: 400;">Our well-cited Bioruptor Pico is the shearing device of choice for chromatin and DNA fragmentation. Obtain uniform and tight fragment distributions between 150bp -2kb. </span><a href="../p/bioruptor-pico-sonication-device">Learn more</a></p>
<strong><a href="../p/megaruptor2-1-unit"><span href="../p/bioruptor-pico-sonication-device">Megaruptor</span>® - long fragments</a></strong><a href="../p/bioruptor-pico-sonication-device"><span style="font-weight: 400;"></span></a><a href="../categories/library-preparation-for-ChIP-seq-and-DNA-sequencing"><span style="font-weight: 400;"></span></a><br />
<p><span style="font-weight: 400;"></span><span style="font-weight: 400;">The Megaruptor is designed to shear DNA from 3kb-75kb for long-read sequencing. <a href="../p/megaruptor2-1-unit">Learn more</a></span></p>
<span href="../p/bioruptor-pico-sonication-device"></span><span style="font-weight: 400;"></span></div>
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'id' => '4491',
'name' => 'The piRNA-pathway factor FKBP6 is essential for spermatogenesis butdispensable for control of meiotic LINE-1 expression in humans.',
'authors' => 'Wyrwoll M.J. et al.',
'description' => '<p>Infertility affects around 7\% of the male population and can be due to severe spermatogenic failure (SPGF), resulting in no or very few sperm in the ejaculate. We initially identified a homozygous frameshift variant in FKBP6 in a man with extreme oligozoospermia. Subsequently, we screened a total of 2,699 men with SPGF and detected rare bi-allelic loss-of-function variants in FKBP6 in five additional persons. All six individuals had no or extremely few sperm in the ejaculate, which were not suitable for medically assisted reproduction. Evaluation of testicular tissue revealed an arrest at the stage of round spermatids. Lack of FKBP6 expression in the testis was confirmed by RT-qPCR and immunofluorescence staining. In mice, Fkbp6 is essential for spermatogenesis and has been described as being involved in piRNA biogenesis and formation of the synaptonemal complex (SC). We did not detect FKBP6 as part of the SC in normal human spermatocytes, but small RNA sequencing revealed that loss of FKBP6 severely impacted piRNA levels, supporting a role for FKBP6 in piRNA biogenesis in humans. In contrast to findings in piRNA-pathway mouse models, we did not detect an increase in LINE-1 expression in men with pathogenic FKBP6 variants. Based on our findings, FKBP6 reaches a "strong" level of evidence for being associated with male infertility according to the ClinGen criteria, making it directly applicable for clinical diagnostics. This will improve patient care by providing a causal diagnosis and will help to predict chances for successful surgical sperm retrieval.</p>',
'date' => '2022-10-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36150389',
'doi' => '10.1016/j.ajhg.2022.09.002',
'modified' => '2022-11-16 09:28:27',
'created' => '2022-11-15 09:26:20',
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(int) 1 => array(
'id' => '4467',
'name' => 'The seminal plasma microbiome of men with testicular germ cell tumours described by small RNA sequencing',
'authors' => 'Nina Mørup, Ailsa Maria Main, Niels Jørgensen, Gedske Daugaard, Anders Juul, Kristian Almstrup ',
'description' => '<p><strong class="sub-title">Background:<span> </span></strong>It has been estimated that microorganisms are involved in the pathogenesis of approximately 20% of all cancers. Testicular germ cell tumours (TGCTs) are the most common type of malignancy in young men and arise from the precursor cell, Germ Cell Neoplasia in Situ (GCNIS). The microbiome of seminal plasma and testicular tissue has not been thoroughly investigated in regard to TGCTs.</p>
<p><strong class="sub-title">Objectives:<span> </span></strong>To investigate the differences in the seminal plasma microbiome between men with TGCT or GCNIS-only compared with controls.</p>
<p><strong class="sub-title">Materials and methods:<span> </span></strong>The study population consisted of patients with GCNIS-only (n = 5), TGCT (n = 18), and controls (n = 25) with different levels of sperm counts in the ejaculate. RNA was isolated from the seminal plasma and sequenced. Reads not mapping to the human genome were aligned against a set of 2784 bacterial/archaeal and 4336 viral genomes using the Kraken pipeline.</p>
<p><strong class="sub-title">Results:<span> </span></strong>We identified reads from 2172 species and most counts were from Alteromonas mediterranea, Falconid herpesvirus 1, and Stigmatella aurantiaca. Six species (Acaryochloris marina, Halovirus HGTV-1, Thermaerobacter marianensis, Thioalkalivibrio sp. K90mix, Burkholderia sp. YI23, and Desulfurivibrio alkaliphilus) were found in significantly (q-value <0.05) higher levels in the seminal plasma of TGCT and GCNIS-only patients compared with controls. In contrast, Streptomyces phage VWB, was found at significantly higher levels among controls compared with TGCT and GCNIS-only patients combined.</p>
<p><strong class="sub-title">Discussion:<span> </span></strong>Often the microbiome is analysed by shotgun or 16S ribosomal sequencing whereas our present data builds on small RNA sequencing. This allowed us to identify more viruses and phages compared to previous studies, but also makes the results difficult to directly compare.</p>
<p><strong class="sub-title">Conclusion:<span> </span></strong>Our study is the first to report identification of the microbiome species in seminal plasma of men with TGCT and GCNIS-only, which potentially could be involved in the pathogenesis of TGCTs. Further studies are, however, needed to confirm our findings. This article is protected by copyright. All rights reserved.</p>',
'date' => '2022-09-28',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/36168917/',
'doi' => '10.1111/andr.13305',
'modified' => '2022-10-20 06:51:58',
'created' => '2022-10-20 06:51:58',
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(int) 2 => array(
'id' => '4375',
'name' => 'Neutral sphingomyelinase 2 inhibition attenuates extracellular vesiclerelease and improves neurobehavioral deficits in murine HIV.',
'authors' => 'Zhu X. et al.',
'description' => '<p>People living with HIV (PLH) have significantly higher rates of cognitive impairment (CI) and major depressive disorder (MDD) versus the general population. The enzyme neutral sphingomyelinase 2 (nSMase2) is involved in the biogenesis of ceramide and extracellular vesicles (EVs), both of which are dysregulated in PLH, CI, and MDD. Here we evaluated EcoHIV-infected mice for behavioral abnormalities relevant to depression and cognition deficits, and assessed the behavioral and biochemical effects of nSMase2 inhibition. Mice were infected with EcoHIV and daily treatment with either vehicle or the nSMase2 inhibitor (R)-(1-(3-(3,4-dimethoxyphenyl)-2,6-dimethylimidazo[1,2-b]pyridazin-8-yl)pyrrolidin-3-yl)-carbamate (PDDC) began 3 weeks post-infection. After 2 weeks of treatment, mice were subjected to behavior tests. EcoHIV-infected mice exhibited behavioral abnormalities relevant to MDD and CI that were reversed by PDDC treatment. EcoHIV infection significantly increased cortical brain nSMase2 activity, resulting in trend changes in sphingomyelin and ceramide levels that were normalized by PDDC treatment. EcoHIV-infected mice also exhibited increased levels of brain-derived EVs and altered microRNA cargo, including miR-183-5p, miR-200c-3p, miR-200b-3p, and miR-429-3p, known to be associated with MDD and CI; all were normalized by PDDC. In conclusion, inhibition of nSMase2 represents a possible new therapeutic strategy for the treatment of HIV-associated CI and MDD.</p>',
'date' => '2022-07-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35462006',
'doi' => '10.1016/j.nbd.2022.105734',
'modified' => '2022-08-04 15:59:55',
'created' => '2022-08-04 14:55:36',
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(int) 3 => array(
'id' => '4422',
'name' => 'A novel, essential trans-splicing protein connects the nematode SL1snRNP to the CBC-ARS2 complex.',
'authors' => 'Fasimoye Rotimi Yemi et al.',
'description' => '<p>Spliced leader trans-splicing is essential for gene expression in many eukaryotes. To elucidate the molecular mechanism of this process, we characterise the molecules associated with the Caenorhabditis elegans major spliced leader snRNP (SL1 snRNP), which donates the spliced leader that replaces the 5' untranslated region of most pre-mRNAs. Using a GFP-tagged version of the SL1 snRNP protein SNA-1 created by CRISPR-mediated genome engineering, we immunoprecipitate and identify RNAs and protein components by RIP-Seq and mass spectrometry. This reveals the composition of the SL1 snRNP and identifies associations with spliceosome components PRP-8 and PRP-19. Significantly, we identify a novel, nematode-specific protein required for SL1 trans-splicing, which we designate SNA-3. SNA-3 is an essential, nuclear protein with three NADAR domains whose function is unknown. Mutation of key residues in NADAR domains inactivates the protein, indicating that domain function is required for activity. SNA-3 interacts with the CBC-ARS2 complex and other factors involved in RNA metabolism, including SUT-1 protein, through RNA or protein-mediated contacts revealed by yeast two-hybrid assays, localisation studies and immunoprecipitations. Our data are compatible with a role for SNA-3 in coordinating trans-splicing with target pre-mRNA transcription or in the processing of the Y-branch product of the trans-splicing reaction.</p>',
'date' => '2022-07-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35736244',
'doi' => '10.1093/nar/gkac534',
'modified' => '2022-09-27 14:48:51',
'created' => '2022-09-08 16:32:20',
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(int) 4 => array(
'id' => '4428',
'name' => 'Interspecies effectors of a transgenerational memory of bacterial infection in Caenorhabditis elegans .',
'authors' => 'Legüe Marcela et al.',
'description' => '<p>The inheritance of memory is an adaptive trait. Microbes challenge the immunity of organisms and trigger behavioral adaptations that can be inherited, but how bacteria produce inheritance of a trait is unknown. We use and its bacteria to study the transgenerational RNA dynamics of interspecies crosstalk leading to a heritable behavior. A heritable response of to microbes is the pathogen-induced diapause (PIDF), a state of suspended animation to evade infection. We identify RsmY, a small RNA involved in quorum sensing in as a trigger of PIDF. The histone methyltransferase (HMT) SET-18/SMYD3 and the argonaute HRDE-1, which promotes multi-generational silencing in the germline, are also needed for PIDF initiation The HMT SET-25/EHMT2 is necessary for memory maintenance in the transgenerational lineage. Our work is a starting point to understanding microbiome-induced inheritance of acquired traits, and the transgenerational influence of microbes in health and disease.</p>',
'date' => '2022-07-01',
'pmid' => 'https://www.sciencedirect.com/science/article/pii/S2589004222008999',
'doi' => '10.1016/j.isci.2022.104627',
'modified' => '2022-09-28 09:00:02',
'created' => '2022-09-08 16:32:20',
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[maximum depth reached]
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(int) 5 => array(
'id' => '4471',
'name' => 'Diverse Monogenic Subforms of Human Spermatogenic Failure',
'authors' => 'Nagirnaja L. et al. ',
'description' => '<p>Non-obstructive azoospermia (NOA) is the most severe form of male infertility and typically incurable with current medicine. Due to the biological complexity of sperm production, defining the genetic basis of NOA has proven challenging, and to date, the most advanced classification of NOA subforms is based on simple description of testis histology. In this study, we exome-sequenced over 1,000 clinically diagnosed NOA cases and identified a plausible recessive Mendelian cause in 20\%. Population-based testing against fertile controls identified 27 genes as significantly associated with azoospermia. The disrupted genes are primarily on the autosomes, enriched for undescribed human “knockouts”, and, for the most part, have yet to be linked to a Mendelian trait. Integration with single-cell RNA sequencing of adult testes shows that, rather than affecting a single cell type or pathway, azoospermia genes can be grouped into molecular subforms with highly synchronized expression patterns, and analogs of these subforms exist in mice. This analysis framework identifies groups of genes with known roles in spermatogenesis but also reveals unrecognized subforms, such as a set of genes expressed specifically in mitotic divisions of type B spermatogonia. Our findings highlight NOA as an understudied Mendelian disorder and provide a conceptual structure for organizing the complex genetics of male infertility, which may serve as a basis for disease classification more advanced than histology.</p>',
'date' => '2022-07-01',
'pmid' => 'https://doi.org/10.1101%2F2022.07.19.22271581',
'doi' => '10.1101/2022.07.19.22271581',
'modified' => '2022-11-18 12:14:08',
'created' => '2022-11-15 09:26:20',
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[maximum depth reached]
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(int) 6 => array(
'id' => '4390',
'name' => 'Ultrasensitive Ribo-seq reveals translational landscapes duringmammalian oocyte-to-embryo transition and pre-implantation development.',
'authors' => 'Xiong Zhuqing et al.',
'description' => '<p>In mammals, translational control plays critical roles during oocyte-to-embryo transition (OET) when transcription ceases. However, the underlying regulatory mechanisms remain challenging to study. Here, using low-input Ribo-seq (Ribo-lite), we investigated translational landscapes during OET using 30-150 mouse oocytes or embryos per stage. Ribo-lite can also accommodate single oocytes. Combining PAIso-seq to interrogate poly(A) tail lengths, we found a global switch of translatome that closely parallels changes of poly(A) tails upon meiotic resumption. Translation activation correlates with polyadenylation and is supported by polyadenylation signal proximal cytoplasmic polyadenylation elements (papCPEs) in 3' untranslated regions. By contrast, translation repression parallels global de-adenylation. The latter includes transcripts containing no CPEs or non-papCPEs, which encode many transcription regulators that are preferentially re-activated before zygotic genome activation. CCR4-NOT, the major de-adenylation complex, and its key adaptor protein BTG4 regulate translation downregulation often independent of RNA decay. BTG4 is not essential for global de-adenylation but is required for selective gene de-adenylation and production of very short-tailed transcripts. In sum, our data reveal intimate interplays among translation, RNA stability and poly(A) tail length regulation underlying mammalian OET.</p>',
'date' => '2022-06-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35697785',
'doi' => '10.1038/s41556-022-00928-6',
'modified' => '2022-08-11 14:17:11',
'created' => '2022-08-11 12:14:50',
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[maximum depth reached]
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(int) 7 => array(
'id' => '4421',
'name' => 'Translation is a key determinant controlling the fate of cytoplasmic longnon-coding RNAs',
'authors' => 'Andjus Sara et al.',
'description' => '<p>Despite predicted to lack coding potential, cytoplasmic long non-coding (lnc)RNAs can associate with ribosomes, resulting in some cases into the production of functional peptides. However, the biological and mechanistic relevance of this pervasive lncRNAs translation remains poorly studied. In yeast, cytoplasmic Xrn1-sensitive lncRNAs (XUTs) are targeted by the Nonsense-Mediated mRNA Decay (NMD), suggesting a translation-dependent degradation process. Here, we report that XUTs are translated, which impacts their abundance. We show that XUTs globally accumulate upon translation elongation inhibition, but not when initial ribosome loading is impaired. Translation also affects XUTs independently of NMD, by interfering with their decapping. Ribo-Seq confirmed ribosomes binding to XUTs and identified actively translated small ORFs in their 5’-proximal region. Mechanistic analyses revealed that their NMD-sensitivity depends on the 3’-untranslated region length. Finally, we detected the peptide derived from the translation of an NMD-sensitive XUT reporter in NMD-competent cells. Our work highlights the role of translation in the metabolism of XUTs, which could contribute to expose genetic novelty to the natural selection, while NMD restricts their expression.</p>',
'date' => '2022-05-01',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2022.05.25.493276v1',
'doi' => '10.1101/2022.05.25.493276',
'modified' => '2022-09-27 14:48:05',
'created' => '2022-09-08 16:32:20',
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[maximum depth reached]
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(int) 8 => array(
'id' => '4252',
'name' => 'MGcount: a total RNA-seq quantification tool to address multi-mappingand multi-overlapping alignments ambiguity in non-coding transcripts',
'authors' => 'Hita Andrea, Brocart Gilles, Fernandez Ana, Rehmsmeier Marc, Alemany Anna, Schvartzman Sol',
'description' => '<p>Background Total-RNA sequencing (total-RNA-seq) allows the simultaneous study of both the coding and the non-coding transcriptome. Yet, computational pipelines have traditionally focused on particular biotypes, making assumptions that are not fullfilled by total-RNA-seq datasets. Transcripts from distinct RNA biotypes vary in length, biogenesis, and function, can overlap in a genomic region, and may be present in the genome with a high copy number. Consequently, reads from total-RNA-seq libraries may cause ambiguous genomic alignments, demanding for flexible quantification approaches. Results Here we present Multi-Graph count (MGcount), a total-RNA-seq quantification tool combining two strategies for handling ambiguous alignments. First, MGcount assigns reads hierarchically to small-RNA and long-RNA features to account for length disparity when transcripts overlap in the same genomic position. Next, MGcount aggregates RNA products with similar sequences where reads systematically multi-map using a graph-based approach. MGcount outputs a transcriptomic count matrix compatible with RNA-sequencing downstream analysis pipelines, with both bulk and single-cell resolution, and the graphs that model repeated transcript structures for different biotypes. The software can be used as a python module or as a single-file executable program. Conclusions MGcount is a flexible total-RNA-seq quantification tool that successfully integrates reads that align to multiple genomic locations or that overlap with multiple gene features. Its approach is suitable for the simultaneous estimation of protein-coding, long non-coding and small non-coding transcript concentration, in both precursor and processed forms. Both source code and compiled software are available at https://github.com/hitaandrea/MGcount. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04544-3.</p>',
'date' => '2022-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35030988',
'doi' => '10.1186/s12859-021-04544-3',
'modified' => '2022-05-20 09:42:23',
'created' => '2022-05-19 10:41:50',
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[maximum depth reached]
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(int) 9 => array(
'id' => '4223',
'name' => 'Single cell quantification of ribosome occupancy in early mouse development',
'authors' => 'Tori Tonn et al.',
'description' => '<p><span>Technological limitations precluded transcriptome-wide analyses of translation at single cell resolution. To solve this challenge, we developed a novel microfluidic isotachophoresis approach, named RIBOsome profiling via IsoTachoPhoresis (Ribo-ITP), and characterized translation in single oocytes and embryos during early mouse development. We identified differential translation efficiency as a key regulatory mechanism of genes involved in centrosome organization and N</span><sup>6</sup><span>-methyladenosine modification of RNAs. Our high coverage measurements enabled the first analysis of allele-specific ribosome engagement in early development and led to the discovery of stage-specific differential engagement of zygotic RNAs with ribosomes. Finally, by integrating our measurements with proteomics data, we discovered that ribosome occupancy in germinal vesicle stage oocytes is the predominant determinant of protein abundance in the zygote. Taken together, these findings resolve the long-standing paradox of low correlation between RNA expression and protein abundance in early embryonic development. The novel Ribo-ITP approach will enable numerous applications by providing high coverage and high resolution ribosome occupancy measurements from ultra-low input samples including single cells.</span></p>',
'date' => '2021-12-09',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.12.07.471408v1.abstract',
'doi' => 'https://doi.org/10.1101/2021.12.07.471408',
'modified' => '2022-04-29 11:39:09',
'created' => '2022-04-29 11:39:09',
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(int) 10 => array(
'id' => '4456',
'name' => 'Single cell quantification of ribosome occupancy in early mousedevelopment',
'authors' => 'Tonn T. et al.',
'description' => '<p>Technological limitations precluded transcriptome-wide analyses of translation at single cell resolution. To solve this challenge, we developed a novel microfluidic isotachophoresis approach, named RIBOsome profiling via IsoTachoPhoresis (Ribo-ITP), and characterized translation in single oocytes and embryos during early mouse development. We identified differential translation efficiency as a key regulatory mechanism of genes involved in centrosome organization and N6-methyladenosine modification of RNAs. Our high coverage measurements enabled the first analysis of allele-specific ribosome engagement in early development and led to the discovery of stage-specific differential engagement of zygotic RNAs with ribosomes. Finally, by integrating our measurements with proteomics data, we discovered that ribosome occupancy in germinal vesicle stage oocytes is the predominant determinant of protein abundance in the zygote. Taken together, these findings resolve the long-standing paradox of low correlation between RNA expression and protein abundance in early embryonic development. The novel Ribo-ITP approach will enable numerous applications by providing high coverage and high resolution ribosome occupancy measurements from ultra-low input samples including single cells.</p>',
'date' => '2021-12-01',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.12.07.471408v1',
'doi' => '10.1101/2021.12.07.471408',
'modified' => '2022-10-21 09:42:29',
'created' => '2022-09-28 09:53:13',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 11 => array(
'id' => '4216',
'name' => 'Functional microRNA targetome undergoes degeneration-induced shift in the retina',
'authors' => 'Chu-Tan JA et al.',
'description' => '<div class="abstract-content selected" id="enc-abstract">
<p><strong class="sub-title">Background:<span> </span></strong>MicroRNA (miRNA) play a significant role in the pathogenesis of complex neurodegenerative diseases including age-related macular degeneration (AMD), acting as post-transcriptional gene suppressors through their association with argonaute 2 (AGO2) - a key member of the RNA Induced Silencing Complex (RISC). Identifying the retinal miRNA/mRNA interactions in health and disease will provide important insight into the key pathways miRNA regulate in disease pathogenesis and may lead to potential therapeutic targets to mediate retinal degeneration.</p>
<p><strong class="sub-title">Methods:<span> </span></strong>To identify the active miRnome targetome interactions in the healthy and degenerating retina, AGO2 HITS-CLIP was performed using a rodent model of photoreceptor degeneration. Analysis of publicly available single-cell RNA sequencing (scRNAseq) data was performed to identify the cellular location of AGO2 and key members of the microRNA targetome in the retina. AGO2 findings were verified by in situ hybridization (RNA) and immunohistochemistry (protein).</p>
<p><strong class="sub-title">Results:<span> </span></strong>Analysis revealed a similar miRnome between healthy and damaged retinas, however, a shift in the active targetome was observed with an enrichment of miRNA involvement in inflammatory pathways. This shift was further demonstrated by a change in the seed binding regions of miR-124-3p, the most abundant retinal AGO2-bound miRNA, and has known roles in regulating retinal inflammation. Additionally, photoreceptor cluster miR-183/96/182 were all among the most highly abundant miRNA bound to AGO2. Following damage, AGO2 expression was localized to the inner retinal layers and more in the OLM than in healthy retinas, indicating a locational miRNA response to retinal damage.</p>
<p><strong class="sub-title">Conclusions:<span> </span></strong>This study provides important insight into the alteration of miRNA regulatory activity that occurs as a response to retinal degeneration and explores the miRNA-mRNA targetome as a consequence of retinal degenerations. Further characterisation of these miRNA/mRNA interactions in the context of the degenerating retina may provide an important insight into the active role these miRNA may play in diseases such as AMD.</p>
</div>
<p><strong class="sub-title">Keywords:<span> </span></strong>Argonaute; HITS-CLIP; Inflammation; Retina; Retinal degeneration; Transcriptome; mRNA; microRNA.</p>',
'date' => '2021-08-31',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34465369/',
'doi' => '10.1186/s13024-021-00478-9',
'modified' => '2022-03-28 11:05:53',
'created' => '2022-03-28 11:03:41',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 12 => array(
'id' => '4429',
'name' => 'Functional microRNA targetome undergoes degeneration-induced shift inthe retina.',
'authors' => 'Chu-Tan Joshua A et al.',
'description' => '<p>BACKGROUND: MicroRNA (miRNA) play a significant role in the pathogenesis of complex neurodegenerative diseases including age-related macular degeneration (AMD), acting as post-transcriptional gene suppressors through their association with argonaute 2 (AGO2) - a key member of the RNA Induced Silencing Complex (RISC). Identifying the retinal miRNA/mRNA interactions in health and disease will provide important insight into the key pathways miRNA regulate in disease pathogenesis and may lead to potential therapeutic targets to mediate retinal degeneration. METHODS: To identify the active miRnome targetome interactions in the healthy and degenerating retina, AGO2 HITS-CLIP was performed using a rodent model of photoreceptor degeneration. Analysis of publicly available single-cell RNA sequencing (scRNAseq) data was performed to identify the cellular location of AGO2 and key members of the microRNA targetome in the retina. AGO2 findings were verified by in situ hybridization (RNA) and immunohistochemistry (protein). RESULTS: Analysis revealed a similar miRnome between healthy and damaged retinas, however, a shift in the active targetome was observed with an enrichment of miRNA involvement in inflammatory pathways. This shift was further demonstrated by a change in the seed binding regions of miR-124-3p, the most abundant retinal AGO2-bound miRNA, and has known roles in regulating retinal inflammation. Additionally, photoreceptor cluster miR-183/96/182 were all among the most highly abundant miRNA bound to AGO2. Following damage, AGO2 expression was localized to the inner retinal layers and more in the OLM than in healthy retinas, indicating a locational miRNA response to retinal damage. CONCLUSIONS: This study provides important insight into the alteration of miRNA regulatory activity that occurs as a response to retinal degeneration and explores the miRNA-mRNA targetome as a consequence of retinal degenerations. Further characterisation of these miRNA/mRNA interactions in the context of the degenerating retina may provide an important insight into the active role these miRNA may play in diseases such as AMD.</p>',
'date' => '2021-08-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34465369',
'doi' => '10.1186/s13024-021-00478-9',
'modified' => '2022-09-28 09:01:43',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 13 => array(
'id' => '4334',
'name' => 'Single-cell microRNA sequencing method comparison and application tocell lines and circulating lung tumor cells',
'authors' => 'Hücker S. et al. ',
'description' => '<p>Molecular single cell analyses provide insights into physiological and pathological processes. Here, in a stepwise approach, we first evaluate 19 protocols for single cell small RNA sequencing on MCF7 cells spiked with 1 pg of 1,006 miRNAs. Second, we analyze MCF7 single cell equivalents of the eight best protocols. Third, we sequence single cells from eight different cell lines and 67 circulating tumor cells (CTCs) from seven SCLC patients. Altogether, we analyze 244 different samples. We observe high reproducibility within protocols and reads covered a broad spectrum of RNAs. For the 67 CTCs, we detect a median of 68 miRNAs, with 10 miRNAs being expressed in 90\% of tested cells. Enrichment analysis suggested the lung as the most likely organ of origin and enrichment of cancer-related categories. Even the identification of non-annotated candidate miRNAs was feasible, underlining the potential of single cell small RNA sequencing.</p>',
'date' => '2021-07-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34262050',
'doi' => '10.1038/s41467-021-24611-w',
'modified' => '2022-08-03 16:15:42',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 14 => array(
'id' => '4219',
'name' => 'Small RNAs in Seminal Plasma as Novel Biomarkers for Germ Cell Tumors',
'authors' => 'Mørup N. et al.',
'description' => '<p><span>Circulating miRNAs secreted by testicular germ cell tumors (TGCT) show great potential as novel non-invasive biomarkers for diagnosis of TGCT. Seminal plasma (SP) represents a biofluid closer to the primary site. Here, we investigate whether small RNAs in SP can be used to diagnose men with TGCTs or the precursor lesions, germ cell neoplasia in situ (GCNIS). Small RNAs isolated from SP from men with TGCTs (</span><i>n</i><span><span> </span>= 18), GCNIS-only (</span><i>n</i><span><span> </span>= 5), and controls (</span><i>n</i><span><span> </span>= 25) were sequenced. SP from men with TGCT/GCNIS (</span><i>n</i><span><span> </span>= 37) and controls (</span><i>n</i><span><span> </span>= 22) were used for validation by RT-qPCR. In general, piRNAs were found at lower levels in SP from men with TGCTs. Ten small RNAs were found at significantly (q-value < 0.05) different levels in SP from men with TGCT/GCNIS than controls. Random forests classification identified sets of small RNAs that could detect either TGCT/GCNIS or GCNIS-only with an area under the curve of 0.98 and 1 in ROC analyses, respectively. RT-qPCR validated hsa-miR-6782-5p to be present at 2.3-fold lower levels (</span><i>p</i><span><span> </span>= 0.02) in the SP from men with TGCTs compared with controls. Small RNAs in SP show potential as novel biomarkers for diagnosing men with TGCT/GCNIS but validation in larger cohorts is needed.</span></p>',
'date' => '2021-05-13',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34067956/',
'doi' => '10.3390/cancers13102346',
'modified' => '2022-04-19 15:29:47',
'created' => '2022-04-19 15:29:47',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => array(
'id' => '4099',
'name' => 'Vesicle-bound regulatory RNAs are associated with tissue aging',
'authors' => 'F. Kern, T. Kuhn, N. Ludwig, M. Simon, L. Gröger, N. Fabis, A. Salhab, T. Fehlmann, O. Hahn, A. Engel, M. Koch, J. Koehler, K. Winek, H. Soreq, G. Fuhrmann, T. Wyss-Coray, E. Meese, M. W. Laschke and A. Keller',
'description' => '<p><span>Previous work on murine models and human demonstrated global as well as tissue-specific molecular aging trajectories in solid tissues and body fluids</span><sup><a id="xref-ref-1-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-1">1</a>–<a id="xref-ref-8-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-8">8</a></sup><span>. Extracellular vesicles like exosomes play a crucial role in communication and information exchange in between such systemic factors and solid tissues</span><sup><a id="xref-ref-9-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-9">9</a>,<a id="xref-ref-10-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-10">10</a></sup><span>. We sequenced freely circulating and vesicle-bound small regulatory RNAs in mice at five time points across the average life span from 2 to 18 months. Intriguingly, each small RNA class exhibits unique aging patterns, which showed differential signatures between vesicle-bound and freely circulating molecules. In particular, tRNA fragments showed overall highest correlation with aging which also matched well between sample types, facilitating age prediction with non-negative matrix factorization (86% accuracy). Interestingly, rRNAs exhibited inverse correlation trajectories between vesicles and plasma while vesicle-bound microRNAs (miRNAs) were exceptionally strong associated with aging. Affected miRNAs regulate the inflammatory response and transcriptional processes, and adipose tissues show considerable effects in associated gene regulatory modules. Finally, nanoparticle tracking and electron microscopy suggest a shift from overall many small to fewer but larger vesicles in aged plasma, potentially contributing to systemic aging trajectories and affecting the molecular aging of organs.</span></p>',
'date' => '2021-05-08',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1',
'doi' => '10.1101/2021.05.07.443093',
'modified' => '2022-01-06 14:25:33',
'created' => '2021-05-17 10:44:33',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 16 => array(
'id' => '4427',
'name' => 'Small RNAs in Seminal Plasma as Novel Biomarkers for GermCell Tumors.',
'authors' => 'Mørup Nina et al.',
'description' => '<p>Circulating miRNAs secreted by testicular germ cell tumors (TGCT) show great potential as novel non-invasive biomarkers for diagnosis of TGCT. Seminal plasma (SP) represents a biofluid closer to the primary site. Here, we investigate whether small RNAs in SP can be used to diagnose men with TGCTs or the precursor lesions, germ cell neoplasia in situ (GCNIS). Small RNAs isolated from SP from men with TGCTs ( = 18), GCNIS-only ( = 5), and controls ( = 25) were sequenced. SP from men with TGCT/GCNIS ( = 37) and controls ( = 22) were used for validation by RT-qPCR. In general, piRNAs were found at lower levels in SP from men with TGCTs. Ten small RNAs were found at significantly (q-value < 0.05) different levels in SP from men with TGCT/GCNIS than controls. Random forests classification identified sets of small RNAs that could detect either TGCT/GCNIS or GCNIS-only with an area under the curve of 0.98 and 1 in ROC analyses, respectively. RT-qPCR validated hsa-miR-6782-5p to be present at 2.3-fold lower levels ( = 0.02) in the SP from men with TGCTs compared with controls. Small RNAs in SP show potential as novel biomarkers for diagnosing men with TGCT/GCNIS but validation in larger cohorts is needed.</p>',
'date' => '2021-05-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34067956',
'doi' => '10.3390/cancers13102346',
'modified' => '2022-09-28 09:03:57',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 17 => array(
'id' => '4102',
'name' => 'miRMaster 2.0: multi-species non-coding RNA sequencing analyses at scale',
'authors' => 'Tobias Fehlmann, Fabian Kern, Omar Laham, Christina Backes, Jeffrey Solomon, Pascal Hirsch, Carsten Volz, Rolf Müller, Andreas Keller',
'description' => '<p><span>Analyzing all features of small non-coding RNA sequencing data can be demanding and challenging. To facilitate this process, we developed miRMaster. After the analysis of over 125 000 human samples and 1.5 trillion human small RNA reads over 4 years, we present miRMaster 2 with a wide range of updates and new features. We extended our reference data sets so that miRMaster 2 now supports the analysis of eight species (e.g. human, mouse, chicken, dog, cow) and 10 non-coding RNA classes (e.g. microRNAs, piRNAs, tRNAs, rRNAs, circRNAs). We also incorporated new downstream analysis modules such as batch effect analysis or sample embeddings using UMAP, and updated annotation data bases included by default (miRBase, Ensembl, GtRNAdb). To accommodate the increasing popularity of single cell small-RNA sequencing data, we incorporated a module for unique molecular identifier (UMI) processing. Further, the output tables and graphics have been improved based on user feedback and new output formats that emerged in the community are now supported (e.g. miRGFF3). Finally, we integrated differential expression analysis with the miRNA enrichment analysis tool miEAA. miRMaster is freely available at </span><a href="https://www.ccb.uni-saarland.de/mirmaster2" title="https://www.ccb.uni-saarland.de/mirmaster2">https://www.ccb.uni-saarland.de/mirmaster2</a><span>.</span></p>',
'date' => '2021-04-19',
'pmid' => ' https://doi.org/10.1093/nar/gkab268',
'doi' => '10.1093/nar/gkab268',
'modified' => '2021-06-28 11:45:48',
'created' => '2021-06-28 11:42:21',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 18 => array(
'id' => '4455',
'name' => 'Bacterial small RNAs and host epigenetic effectors of atransgenerational memory of pathogens in C. elegans',
'authors' => 'Legüe M. et al.',
'description' => '<p>The inheritance of memories is adaptive for survival. Microbes interact with all organisms challenging their immunity and triggering behavioral adaptations. Some of these behaviors induced by bacteria can be inherited although the mechanisms of action are largely unexplored. In this work, we use C. elegans and its bacteria to study the transgenerational RNA dynamics of an interspecies crosstalk leading to a heritable behavior. Heritable responses to bacterial pathogens in the nematode include avoidance and pathogen-induced diapause (PIDF), a state of suspended animation to evade the pathogen threat. We identify a small RNA RsmY, involved in quorum sensing from P. aeruginosa as required for initiation of PIDF. Histone methyltransferase SET-18/SMYD3 is also needed for PIDF initiation in C. elegans. In contrast, SET-25/EHMT2 is necessary for the maintenance of the memory of pathogen exposure in the transgenerational lineage. This work can be a starting point to understanding microbiome-induced inheritance of acquired traits.</p>',
'date' => '2021-03-01',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.03.26.437277v1',
'doi' => '10.1101/2021.03.26.437277',
'modified' => '2022-10-21 09:41:13',
'created' => '2022-09-28 09:53:13',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 19 => array(
'id' => '4425',
'name' => 'Interspecies RNA Interactome of Pathogen and Host in aHeritable Defensive Strategy.',
'authors' => 'Legüe Marcela et al.',
'description' => '<p>Communication with bacteria deeply impacts the life history traits of their hosts. Through specific molecules and metabolites, bacteria can promote short- and long-term phenotypic and behavioral changes in the nematode . The chronic exposure of to pathogens promotes the adaptive behavior in the host's progeny called pathogen-induced diapause formation (PIDF). PIDF is a pathogen avoidance strategy induced in the second generation of animals infected and can be recalled transgenerationally. This behavior requires the RNA interference machinery and specific nematode and bacteria small RNAs (sRNAs). In this work, we assume that RNAs from both species co-exist and can interact with each other. Under this principle, we explore the potential interspecies RNA interactions during PIDF-triggering conditions, using transcriptomic data from the holobiont. We study two transcriptomics datasets: first, the dual sRNA expression of PAO1 and in a transgenerational paradigm for six generations and second, the simultaneous expression of sRNAs and mRNA in intergenerational PIDF. We focus on those bacterial sRNAs that are systematically overexpressed in the intestines of animals compared with sRNAs expressed in host-naïve bacteria. We selected diverse methods that represent putative mechanisms of RNA-mediated interspecies interaction. These interactions are as follows: heterologous perfect and incomplete pairing between bacterial RNA and host mRNA; sRNAs of similar sequence expressed in both species that could mimic each other; and known or predicted eukaryotic motifs present in bacterial transcripts. We conclude that a broad spectrum of tools can be applied for the identification of potential sRNA and mRNA targets of the interspecies RNA interaction that can be subsequently tested experimentally.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34367078',
'doi' => '10.3389/fmicb.2021.649858',
'modified' => '2022-09-28 09:06:04',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 20 => array(
'id' => '4426',
'name' => 'Distinct Extracellular RNA Profiles in Different PlasmaComponents.',
'authors' => 'Jia Jing et al.',
'description' => '<p>Circulating extracellular RNAs (exRNAs) have great potential to serve as biomarkers for a wide range of diagnostic, therapeutic, and prognostic applications. So far, knowledge of the difference among different sources of exRNAs is limited. To address this issue, we performed a sequential physical and biochemical precipitation to collect four fractions (platelets and cell debris, the thrombin-induced precipitates, extracellular vesicles, and supernatant) from each of 10 plasma samples. From total RNAs of the 40 fractions, we prepared ligation-free libraries to profile full spectrum of all RNA species, without size selection and rRNA reduction. Due to complicated RNA composition in these libraries, we utilized a successive stepwise alignment strategy to map the RNA sequences to different RNA categories, including miRNAs, piwi-interacting RNAs, tRNAs, rRNAs, lincRNAs, snoRNAs, snRNAs, other ncRNAs, protein coding RNAs, and circRNAs. Our data showed that each plasma fraction had its own unique distribution of RNA species. Hierarchical cluster analyses using transcript abundance demonstrated similarities in the same plasma fraction and significant differences between different fractions. In addition, we observed various unique transcripts, and novel predicted miRNAs among these plasma fractions. These results demonstrate that the distribution of RNA species and functional RNA transcripts is plasma fraction-dependent. Appropriate plasma preparation and thorough inspection of different plasma fractions are necessary for an exRNA-based biomarker study.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34234804',
'doi' => '10.3389/fgene.2021.564780',
'modified' => '2022-09-28 09:06:47',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 21 => array(
'id' => '4008',
'name' => 'Genes with 5′ terminal oligopyrimidine tracts preferentially escapeglobal suppression of translation by the SARS-CoV-2 NSP1 protein',
'authors' => 'Shilpa, Rao and Ian, Hoskins and P., Daniela Garcia and Tori, Tonn andHakan, Ozadam and Elif, Sarinay Cenik and ProfileCan, Cenik',
'description' => '<p>Viruses rely on the host translation machinery to synthesize their own proteins. Consequently, they have evolved varied mechanisms to co-opt host translation for their survival. SARS-CoV-2 relies on a non-structural protein, NSP1, for shutting down host translation. Despite this, it is currently unknown how viral proteins and host factors critical for viral replication can escape a global shutdown of host translation. Here, using a novel FACS-based assay called MeTAFlow, we report a dose-dependent reduction in both nascent protein synthesis and mRNA abundance in cells expressing NSP1. We perform RNA-Seq and matched ribosome profiling experiments to identify gene-specific changes both at the mRNA expression and translation level. We discover a functionally-coherent subset of human genes preferentially translated in the context of NSP1 expression. These genes include the translation machinery components, RNA binding proteins, and others important for viral pathogenicity. Importantly, we also uncover potential mechanisms of preferential translation through the presence of shared sites for specific RNA binding proteins and a remarkable enrichment for 5′ terminal oligo-pyrimidine tracts. Collectively, the present study suggests fine tuning of host gene expression and translation by NSP1 despite its global repressive effect on host protein synthesis.</p>',
'date' => '2020-09-14',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2020.09.13.295493v1',
'doi' => '10.1101/2020.09.13.295493.',
'modified' => '2020-12-16 17:08:02',
'created' => '2020-10-12 14:54:59',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 22 => array(
'id' => '3949',
'name' => 'Repeat RNAs associate with replication forks and post-replicative DNA.',
'authors' => 'Gylling HM, Gonzalez-Aguilera C, Smith MA, Kaczorowski DC, Groth A, Lund AH',
'description' => '<p>Non-coding RNA has a proven ability to direct and regulate chromatin modifications by acting as scaffolds between DNA and histone-modifying complexes. However, it is unknown if ncRNA plays any role in DNA replication and epigenome maintenance, including histone eviction and re-instalment of histone-modifications after genome duplication. Isolation of nascent chromatin has identified a large number of RNA-binding proteins in addition to unknown components of the replication and epigenetic maintenance machinery. Here, we isolated and characterized long and short RNAs associated with nascent chromatin at active replication forks and track RNA composition during chromatin maturation across the cell cycle. Shortly after fork passage, GA-rich-, Alpha- and TElomeric Repeat-containing RNAs (TERRA) are associated with replicated DNA. These repeat containing RNAs arise from loci undergoing replication, suggesting an interaction in cis. Post-replication during chromatin maturation, and even after mitosis in G1, the repeats remain enriched on DNA. This suggests that specific types of repeat RNAs are transcribed shortly after DNA replication and stably associate with their loci of origin throughout cell cycle. The presented method and data enables studies of RNA interactions with replication forks and post-replicative chromatin and provides insights into how repeat RNAs and their engagement with chromatin are regulated with respect to DNA replication and across the cell cycle.</p>',
'date' => '2020-05-11',
'pmid' => 'http://www.pubmed.gov/32393525',
'doi' => '10.1261/rna.074757.120',
'modified' => '2020-08-17 10:03:46',
'created' => '2020-08-10 12:12:25',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 23 => array(
'id' => '3697',
'name' => 'The sncRNA Zoo: a repository for circulating small noncoding RNAs in animals.',
'authors' => 'Fehlmann T, Backes C, Pirritano M, Laufer T, Galata V, Kern F, Kahraman M, Gasparoni G, Ludwig N, Lenhof HP, Gregersen HA, Francke R, Meese E, Simon M, Keller A',
'description' => '<p>The repertoire of small noncoding RNAs (sncRNAs), particularly miRNAs, in animals is considered to be evolutionarily conserved. Studies on sncRNAs are often largely based on homology-based information, relying on genomic sequence similarity and excluding actual expression data. To obtain information on sncRNA expression (including miRNAs, snoRNAs, YRNAs and tRNAs), we performed low-input-volume next-generation sequencing of 500 pg of RNA from 21 animals at two German zoological gardens. Notably, none of the species under investigation were previously annotated in any miRNA reference database. Sequencing was performed on blood cells as they are amongst the most accessible, stable and abundant sources of the different sncRNA classes. We evaluated and compared the composition and nature of sncRNAs across the different species by computational approaches. While the distribution of sncRNAs in the different RNA classes varied significantly, general evolutionary patterns were maintained. In particular, miRNA sequences and expression were found to be even more conserved than previously assumed. To make the results available for other researchers, all data, including expression profiles at the species and family levels, and different tools for viewing, filtering and searching the data are freely available in the online resource ASRA (Animal sncRNA Atlas) at https://www.ccb.uni-saarland.de/asra/.</p>',
'date' => '2019-05-21',
'pmid' => 'http://www.pubmed.gov/30937442',
'doi' => '10.1093/nar/gkz227',
'modified' => '2019-06-28 13:44:35',
'created' => '2019-06-21 14:55:31',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 24 => array(
'id' => '3387',
'name' => 'NGS analysis of total small non coding RNAs from low input RNA from dried blood sampling',
'authors' => 'Marcello Pirritano, Tobias Fehlmann, Thomas Laufer, Nicole Ludwig, Gilles Gasparoni, Yongping Li, Eckart Meese, Andreas Keller, and Martin Simon',
'description' => '<p><span>Circulating miRNAs are favored for biomarker candidates as they can reflect tissue specific miRNA dysregulation in disease contexts. Moreover, they have additional advantages that they can be monitored in a minimal invasive manner. Blood-borne miRNAs are therefore currently characterized to identify, describe and validate their potential suitability for a biomarker, however, sampling and as well miRNA detection methods limit these studies in terms of sensitivity but also practicability in clinical, at-home or low-resource sampling of high quality circulating RNA samples. We describe here a novel and innovative method of circulating RNA microsampling from minimal volume dried blood spots with direct enrichment for small RNA fractions in combination with ligation free library preparation. We evaluated crucial parameters for efficient library preparation from low RNA inputs of 50pg for efficient dissection not only of miRNAs but also isomiRs, piRNAs, and lincRNAs. We compared these data to classical microarrays and characterize the technical reproducibility and its sensitivity. We demonstrate and evaluate a method for easy low resource sampling and NGS analysis of circulating RNAs providing a powerful tool for massive cohort and remote patient monitoring.</span></p>',
'date' => '2018-09-10',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/?term=NGS+analysis+of+total+small+non+coding+RNAs+from+low+input+RNA+from+dried+blood+sampling',
'doi' => '',
'modified' => '2018-12-31 11:32:28',
'created' => '2018-09-20 11:18:35',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 25 => array(
'id' => '4103',
'name' => 'Web-based NGS data analysis using miRMaster: a large-scale meta-analysis of human miRNAs',
'authors' => 'Tobias Fehlmann, Christina Backes, Mustafa Kahraman, Jan Haas, Nicole Ludwig, Andreas E. Posch, Maximilian L. Würstle, Matthias Hübenthal, Andre Franke, Benjamin Meder, Eckart Meese, Andreas Keller',
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<p style="text-align: center;"><span><img src="https://www.diagenode.com/img/banners/dplex-product-banner-small.jpg" alt="D-Plex Small RNA-seq library prep kit for Illumina" caption="false" width="728" height="90" /></span></p>
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<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/MA-D-Plex-V1_2020.pdf" target="_blank" title="D-Plex Small RNA library preparation user manual"><strong>Download the manual</strong></a></center>
<p><span>Diagenode’s latest <strong>innovation in </strong>RNA-seq, D-Plex, is based on a <strong>template-switching technology</strong> that delivers a</span><span><span> </span></span><strong>ligation-free method</strong> for RNA library preparation, ensuring realistic representation of diverse small RNA species (such as miRNA, piRNA, tRNA, and siRNA) with <strong>minimized bias</strong>.<strong> </strong>This solution allows you<span> to produce <strong>Illumina®-compatible</strong> DNA libraries for NGS from <strong>low input</strong> amounts of RNA with <strong>minimized PhiX</strong> spike-in quantity for the most representative results.</span></p>
<p><span>The D-Plex small RNA solution was optimized for <strong>10 pg</strong> - 10 ng enriched <strong>small RNA</strong> (or 100 pg - 100 ng total RNA) and contains our latest TSO (<strong>T</strong>emplate-<strong>S</strong>witch <strong>O</strong>ligonucleotides) technology including <strong>UMI</strong>s. The kit is provided with our new <strong>bioinformatics tool</strong> especially developed in parallel to get the best of your data.</span></p>
<ul class="accordion" data-accordion="" style="list-style-type: circle;">
<li class="accordion-navigation"><a href="#more" style="color: #13b29c; background-color: transparent; display: inline; padding: 0;"><strong>Read more</strong></a>
<div id="more" class="content">
<ul>
<li><strong>Truest representation</strong> - UMIs with ligation-free protocol ensures the most representative and unbiased data</li>
<li><strong>Easy</strong> - One-tube, one-day protocol Lowest inputs - Down to 10 pg enriched small RNA or 100 pg total RNA starting amount</li>
<li><strong>Clinical sample compatibility</strong> - Tested with RNA from biofluids</li>
<li><strong>Highest sequencing quality</strong> - Minimized PhiX quantity</li>
<li><strong>Enhanced data</strong> - Our new bioinformatics pipeline provided in the manual was optimized and developed specifically for getting the best out of your D-Plex data including UMI processing</li>
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<h3>miRMaster : your bioinformatics tool for miRNA</h3>
<div class="small-12 medium-3 large-3 columns"><center></center><center></center><center></center><center><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank"><img src="https://www.diagenode.com/img/product/kits/dplex/miRMaster_logo.png" /></a></center></div>
<div class="small-12 medium-9 large-9 columns">
<p style="text-align: justify;">Diagenode has collaborated with <strong>Andreas Keller</strong> and <strong>Tobias Fehlmann</strong> to develop a new bioinformatics pipeline in order to allow <strong>novel miRNA prediction.</strong><br /><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank">miRMaster</a> is a comprehensive analysis online free tool for NGS miRNA samples.</p>
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<h3>Focused on your target</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/RNA_theorie.png" alt="small RNA sequencing kit" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>D-Plex: the highest yield technology to detect every kind of small RNA simultaneously (miRNA, snRNA, piRNA, …)</p>
</center></div>
</div>
<div>
<h3>Get rich content</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/biotyping_smallRNA.png" alt="small RNA diversity" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>One day to enrich your sample with a large variety of small RNAs in one tube. Identify your reads accurately with UMIs.</p>
</center></div>
</div>
<div>
<h3>Robust technology</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/correlation_inputs.png" alt="small non coding RNA" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Independent of your input amount -- go down to 10 pg small RNA as a starting amount.</p>
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</div>
<div>
<h3>Raise your quality output</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/fastQ_DPlex_NovaSeq_human_mouse.png" alt="small RNA library preparation for Illumina" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Get the best of your data -- use our designed bioinformatics pipeline and guidelines. We provide everything you need to greatly simplify your analysis.</p>
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<p style="text-align: center;">[ To check figures in details, click on "Figure" in the menu below ]</p>',
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<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex UDI Module - Set B" target="_blank"><span>C05030022</span> - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-C">C05030023 - D-Plex Unique Dual Indexes for Illumina - Set C</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-D">C05030024 - D-Plex Unique Dual Indexes for Illumina - Set D</a></li>
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<p><span>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</span></p>
<p><strong>For D-Plex SI library construction:</strong></p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-A" title="D-Plex SI Module - Set A" target="_blank">C05030010 - D-Plex Single Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-B" title="D-Plex SI Module - Set B" target="_blank">C05030011 - D-Plex Single Indexes for Illumina - Set B</a></li>
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<p><img src="https://www.diagenode.com/img/product/kits/dplex/bioinfoPipe.png" alt="small RNA sequencing bioinformatics pipeline" caption="false" width="925" height="196" /></p>',
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<p style="text-align: center;"><span><img src="https://www.diagenode.com/img/banners/dplex-product-banner-small.jpg" alt="D-Plex Small RNA-seq library prep kit for Illumina" caption="false" width="728" height="90" /></span></p>
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<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/MA-D-Plex-V1_2020.pdf" target="_blank" title="D-Plex Small RNA library preparation user manual"><strong>Download the manual</strong></a></center>
<p><span>Diagenode’s latest <strong>innovation in </strong>RNA-seq, D-Plex, is based on a <strong>template-switching technology</strong> that delivers a</span><span><span> </span></span><strong>ligation-free method</strong> for RNA library preparation, ensuring realistic representation of diverse small RNA species (such as miRNA, piRNA, tRNA, and siRNA) with <strong>minimized bias</strong>.<strong> </strong>This solution allows you<span> to produce <strong>Illumina®-compatible</strong> DNA libraries for NGS from <strong>low input</strong> amounts of RNA with <strong>minimized PhiX</strong> spike-in quantity for the most representative results.</span></p>
<p><span>The D-Plex small RNA solution was optimized for <strong>10 pg</strong> - 10 ng enriched <strong>small RNA</strong> (or 100 pg - 100 ng total RNA) and contains our latest TSO (<strong>T</strong>emplate-<strong>S</strong>witch <strong>O</strong>ligonucleotides) technology including <strong>UMI</strong>s. The kit is provided with our new <strong>bioinformatics tool</strong> especially developed in parallel to get the best of your data.</span></p>
<ul class="accordion" data-accordion="" style="list-style-type: circle;">
<li class="accordion-navigation"><a href="#more" style="color: #13b29c; background-color: transparent; display: inline; padding: 0;"><strong>Read more</strong></a>
<div id="more" class="content">
<ul>
<li><strong>Truest representation</strong> - UMIs with ligation-free protocol ensures the most representative and unbiased data</li>
<li><strong>Easy</strong> - One-tube, one-day protocol Lowest inputs - Down to 10 pg enriched small RNA or 100 pg total RNA starting amount</li>
<li><strong>Clinical sample compatibility</strong> - Tested with RNA from biofluids</li>
<li><strong>Highest sequencing quality</strong> - Minimized PhiX quantity</li>
<li><strong>Enhanced data</strong> - Our new bioinformatics pipeline provided in the manual was optimized and developed specifically for getting the best out of your D-Plex data including UMI processing</li>
</ul>
<h3>miRMaster : your bioinformatics tool for miRNA</h3>
<div class="small-12 medium-3 large-3 columns"><center></center><center></center><center></center><center><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank"><img src="https://www.diagenode.com/img/product/kits/dplex/miRMaster_logo.png" /></a></center></div>
<div class="small-12 medium-9 large-9 columns">
<p style="text-align: justify;">Diagenode has collaborated with <strong>Andreas Keller</strong> and <strong>Tobias Fehlmann</strong> to develop a new bioinformatics pipeline in order to allow <strong>novel miRNA prediction.</strong><br /><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank">miRMaster</a> is a comprehensive analysis online free tool for NGS miRNA samples.</p>
</div>
</div>
</li>
</ul>
<p></p>
<div class="extra-spaced" align="center"><iframe width="700" height="394" src="https://www.youtube.com/embed/TmMM4gVpINo" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="allowfullscreen"></iframe></div>
<div class="row">
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<div class="container">
<div class="row" style="background: rgba(255,255,255,0.1);">
<div class="large-12 columns slick">
<div>
<h3>Focused on your target</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/RNA_theorie.png" alt="small RNA sequencing kit" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>D-Plex: the highest yield technology to detect every kind of small RNA simultaneously (miRNA, snRNA, piRNA, …)</p>
</center></div>
</div>
<div>
<h3>Get rich content</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/biotyping_smallRNA.png" alt="small RNA diversity" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>One day to enrich your sample with a large variety of small RNAs in one tube. Identify your reads accurately with UMIs.</p>
</center></div>
</div>
<div>
<h3>Robust technology</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/correlation_inputs.png" alt="small non coding RNA" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Independent of your input amount -- go down to 10 pg small RNA as a starting amount.</p>
</center></div>
</div>
<div>
<h3>Raise your quality output</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/fastQ_DPlex_NovaSeq_human_mouse.png" alt="small RNA library preparation for Illumina" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Get the best of your data -- use our designed bioinformatics pipeline and guidelines. We provide everything you need to greatly simplify your analysis.</p>
</center></div>
</div>
</div>
</div>
</div>
</div>
</div>
<p style="text-align: center;">[ To check figures in details, click on "Figure" in the menu below ]</p>',
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'info2' => '<p>Specific D-Plex indexes <span>were designed and validated to fit the D-Plex technology for Illumina sequencing and </span>are not included in the kit. They can be bought separately according to your needs. Please choose the format that suits you best among the compatible references to:</p>
<p><strong>For D-Plex UDI library construction:</strong></p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex UDI Module - Set B" target="_blank"><span>C05030022</span> - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-C">C05030023 - D-Plex Unique Dual Indexes for Illumina - Set C</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-D">C05030024 - D-Plex Unique Dual Indexes for Illumina - Set D</a></li>
</ul>
<p><span>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</span></p>
<p><strong>For D-Plex SI library construction:</strong></p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-A" title="D-Plex SI Module - Set A" target="_blank">C05030010 - D-Plex Single Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-B" title="D-Plex SI Module - Set B" target="_blank">C05030011 - D-Plex Single Indexes for Illumina - Set B</a></li>
</ul>
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'info3' => '<p>A specific bioinformatics pipeline has been developed to process the special sequences present in the D-Plex construct, namely the UMI, the A-tail, and the template switch motif. All guidelines and free softwares are shared in the user manual. Subject to the compatibility of D-Plex constructs, other specific pipelines can be used.</p>
<p><img src="https://www.diagenode.com/img/product/kits/dplex/bioinfoPipe.png" alt="small RNA sequencing bioinformatics pipeline" caption="false" width="925" height="196" /></p>',
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'meta_title' => 'D-Plex Small RNA-seq Library Prep Kit for Illumina',
'meta_keywords' => 'RNA-seq, UMI, low input, small RNA-seq, one tube, one day, challenging, miRNA, biofluid, RNA-seq library preparation, D-Plex, higher RNA diversity, easy, user-friendly',
'meta_description' => 'D-Plex Small RNA-seq Kit for Illumina contains UMI and is designed to generate rich small RNA-seq libraries from 100 pg of total RNA or 10 pg enriched small RNA.',
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'name' => 'D-Plex Small RNA DNBSEQ™ Kit for MGI Sequencing',
'description' => '<center><img src="https://www.diagenode.com/img/banners/banner-dplex-mgi.png" width="550" /></center>
<p></p>
<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/D-Plex-Small-RNA-DNBSEQ.pdf" target="_blank" title="D-Plex Small RNA DNBSEQ user manual"><strong>Download the manual</strong></a></center>
<p></p>
<p>D-Plex Small RNA DNBSEQ™ Kit is a tool designed for the study of the small non-coding transcriptome. The kit is using the <a href="https://www.diagenode.com/en/pages/dplex" target="_blank">D-Plex technology</a> to generate double-stranded DNA libraries ready to be used for the DNA single-strand circularization step required for DNBSEQ sequencing on MGI sequencers.</p>
<p>The D-Plex technology utilizes the innovative capture and amplification by tailing and switching, a ligation-free method for RNA library preparation from ultra-low input amounts, down to 10 pg for small RNAs and 100 pg for total RNAs. This innovative solution enables diverse and novel transcripts detection, even from challenging clinical samples such as liquid biopsies.</p>
<p><span>D-Plex Small RNA <span>DNBSEQ™</span> Kit offers a time saving protocol that can be completed within 5 hours and requires minimal hands-on time. </span><span>The library preparation takes place in a </span><span>single tube</span><span>, increasing the efficiency tremendously. This ensures high technical reliability and reproducibility<span>.</span></span></p>
<p>D-Plex Small RNA DNBSEQ™ Kit includes all buffers and enzymes necessary for the library preparation. Specific D-Plex DNBSEQ Barcodes were designed and validated to fit the D-Plex technology and are available separately:</p>
<ul>
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-a">C05030060 - D-Plex DNBSEQ Barcodes for MGI - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-b">C05030061 - D-Plex DNBSEQ Barcodes for MGI - Set B</a></li>
</ul>
<p><b><strong>D-Plex is also available for Illumina sequencing, check<span> </span><a href="https://www.diagenode.com/en/p/D-Plex-Small-RNA-seq-Library-Prep-x24" target="_blank">here</a>!</strong></b></p>
<div class="row">
<div class="carrousel" style="background-position: center;">
<div class="container">
<div class="row" style="background: rgba(255,255,255,0.1);">
<div class="large-12 columns slick">
<div>
<h3>Diverse and novel transcript detection</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px; display: block; margin-left: auto; margin-right: auto;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-transcript.png" alt="small RNA library preparation for Illumina" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p style="text-align: justify;">The D-Plex Small RNA DNBSEQ protocol generates complex RNA libraries deciphering the wide diversity of small non-coding RNA spectrum (including miRNAs, snoRNAs, snRNAs) in human plasma samples.</p>
</center></div>
</div>
<div>
<h3>Ultra-low input performance</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px; display: block; margin-left: auto; margin-right: auto;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-ultralow-input.png" alt="Ultra-low input performance" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p style="text-align: justify;">Sequencing data from circulating RNA samples of two input amounts (25 pg and 2.5 ng) were highly correlated (<i>R = 0.99</i>) when compared using Pearson correlation coefficient.</p>
</center></div>
</div>
<div>
<h3>High mapping efficiency</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px; display: block; margin-left: auto; margin-right: auto;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-mapping.png" alt="High mapping efficiency" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p style="text-align: justify;"><b></b>The D-Plex Small RNA DNBSEQ kit is compatible with clinical-relevant samples, such as human plasma, and ultra low range of circulating RNA input (down to 10 pg) and exhibits good read mapping of sequencing reads (up to 70% mapping rate).</p>
</center></div>
</div>
<div>
<h3>High quality DNBSEQ sequencing solution</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-dnbseq.png" alt="High quality DNBSEQ sequencing solution" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p><b></b>The D-Plex Small RNA DNBSEQ kit combines our best-in-class RNA library preparation – D-Plex technology – with MGI’s high-quality, cost-effective, DNA nanoballs – DNBSEQ – sequencing solution, creating a unified platform to support high quality small RNA sequencing.</p>
</center></div>
</div>
</div>
</div>
</div>
</div>
</div>
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<li><strong>Latest innovation in RNA-seq</strong>: unique D-Plex technology offering ligation-free protocol for library preparation</li>
<li><strong>Ultra-low input capability</strong>: down to 10 pg for small RNAs and 100 pg for total RNAs</li>
<li><strong>High library complexity</strong>:<strong> </strong>obtain a complete view of your small RNA transcriptome</li>
<li><strong>Optimal performance on clinical samples</strong>: validated with circulating RNAs from liquid biopsies</li>
<li><strong>Easy to use with minimal hands-on time</strong>: one day, one tube protocol</li>
<li><strong>Highest sequencing quality</strong>: specifically formatted for MGI DNBSEQ™ sequencers</li>
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'info2' => '<p>D-Plex DNBSEQ Barcodes are not included in the kit. Two sets are available separately:</p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-a">C05030060 - D-Plex 24 DNBSEQ Barcodes for MGI - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-b">C05030061 - D-Plex 24 DNBSEQ Barcodes for MGI - Set B</a></li>
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'meta_title' => 'D-Plex Small RNA-seq Library Prep Kit for MGI Sequencing | Diagenode ',
'meta_keywords' => 'Small RNA-seq Library Prep Kit for MGI Sequencing',
'meta_description' => 'Small RNA-seq library preparation with D-Plex technology - Suitable for MGI sequencing platforms - Optimized for ultra-low input (100 pg total RNA) - Compatibility with plasma samples - User-friendly and fast protocol',
'modified' => '2021-05-26 11:03:01',
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'name' => 'MicroChIP DiaPure columns',
'description' => '<p>Diagenode’s MicroChIP DiaPure columns have been optimized for the purification and elution of very low amounts of DNA. This rapid method has been validated for epigenetic applications like low input ChIP (e.g. using the True MicroChIP kit) and CUT&Tag (e.g. using Diagenode’s pA-Tn5), but is also compatible with many other applications. The DNA can be eluted at high concentrations in volumes down to 6 μl and it is suitable for any downstream application (e.g. NGS).</p>
<p>Benefits of the MicroChIP DiaPure columns:</p>
<ul>
<li>Optimized for the purification of very low DNA amounts</li>
<li>Fast and easy protocol</li>
<li>Non-toxic</li>
<li>Validated for ChIP and Cut&Tag</li>
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'label1' => 'Examples of results',
'info1' => '<h2 style="text-align: center;">MicroChIP DiaPure columns after ChIP</h2>
<p>Successful ChIP-seq results generated on 50,000 of K562 cells using True MicroChIP technology. ChIP has been performed accordingly to True MicroChIP protocol (Diagenode, Cat. No. C01010130), including DNA purification using the MicroChIP DiaPure columns. For the library preparation the MicroPlex Library Preparation Kit (Diagenode, Cat. No. C05010001) has been used. The below figure shows the peaks from ChIP-seq experiments using the following Diagenode antibodies: H3K4me1 (C15410194), H3K9/14ac (C15410200), H3K27ac (C15410196) and H3K36me3 (C15410192).</p>
<p><img src="https://www.diagenode.com/img/product/kits/figure-igv-microchip.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 1:</strong> Integrative genomics viewer (IGV) visualization of ChIP-seq experiments using 50,000 of K562 cells.</p>
<p></p>
<h2 style="text-align: center;"></h2>
<h2 style="text-align: center;">MicroChIP DiaPure columns after CUT&Tag</h2>
<p>Successful CUT&Tag results showing a low background with high region-specific enrichment has been generated using 50.000 of K562 cells, 1 µg of H3K27me3 antibody (Diagenode, Cat. No. C15410069) and proteinA-Tn5 (1:250) (Diagenode, Cat. No. C01070001). 1 µg of IgG (Diagenode, Cat. No. C15410206) was used as negative control. Samples were purified using the MicroChIP DiaPure columns or phenol-chloroform purification. The below figure presenst the comparison of two purification methods.</p>
<p><img src="https://www.diagenode.com/img/product/kits/figure-diapure-igv.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 2:</strong> Integrative genomics viewer (IGV) visualization of CUT&Tag experiments: MicroChIP DiaPure columns vs phenol-chloroform purification using the H3K27me3 antibody.</p>',
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'name' => 'D-Plex Unique Dual Indexes for Illumina - Set A',
'description' => '<p style="text-align: left;"><span>D-Plex Unique Dual Indexes Module - Set A includes primer pairs with 24 unique dual barcodes (unique i5 and i7 indexes) for library multiplexing with the <a href="https://www.diagenode.com/en/p/D-Plex-Small-RNA-seq-Library-Prep-x24" target="_blank" title="D-Plex Small RNA-seq Kit">D-Plex Small RNA-seq Kit</a>. </span></p>
<p style="text-align: left;"><span>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</span></p>
<p><span>Four sets are available separately: </span></p>
<ul>
<li>C05030021 - D-Plex Unique Dual Indexes for Illumina - Set A</li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex 24 Single Indexes for Illumina - Set #B">C05030022 - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-C" title="D-Plex 24 Single Indexes for Illumina - Set #C">C05030023 - D-Plex Unique Dual Indexes for Illumina - Set C</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-D" title="D-Plex 24 Single Indexes for Illumina - Set #D">C05030024 - D-Plex Unique Dual Indexes for Illumina - Set D</a></li>
</ul>
<p><span>Each set can be used for library multiplexing up to 24. <span>Set A, B, C and D can be used simultaneously for library multiplexing up to 96.</span></span></p>
<p><span>Read more about the </span><a href="https://www.diagenode.com/en/pages/dplex" target="_blank">D-Plex technology</a><span>.</span><span> </span></p>',
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<li>Multiplexing up to <strong>96 samples</strong> when combining Set A, B, C and D</li>
<li>Allows for identification of index hopping</li>
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'info2' => '<p><span>D-Plex RNA-seq UDI library constructs bear the TruSeq (Illumina) HT adapters. In case of a multiplexing scenario, it is therefore recommended to submit the D-Plex libraries as TruSeq HT libraries to your sequencing provider. </span><span>Further details are provided in the D-Plex Unique Dual Indexes Module<span> </span><a href="https://www.diagenode.com/files/products/kits/dplex-unique-dual-indexes-manual.pdf" target="_blank">manual</a>.</span></p>',
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'meta_title' => 'D-Plex Unique Dual Indexes for Illumina - RNA Library Prep Kit - Set A | Diagenode',
'meta_keywords' => 'RNA kit; small RNA kit; RNA-seq kit; low input RNA-seq kit; small RNA-seq, template switching kit, RNA-seq library preparation, D-Plex, higher RNA diversity',
'meta_description' => 'Compatible with D-Plex RNA-seq kits - Unique Dual Indexes for Illumina - Index hopping mitigation - Suitable for ultra-low input - Contains UMI - Multiplexing up to 48 samples',
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'name' => 'D-Plex Total RNA-seq Kit for Illumina',
'description' => '<div class="small-12 medium-12 large-12 columns" style="border: 3px solid #B02736; padding: 10px; margin: 10px;">
<h2>Product Features</h2>
<ul style="list-style-type: disc;">
<li><span style="font-weight: 400;">An innovative technology with template switching and UMIs</span></li>
<li><span style="font-weight: 400;">Ultra-low input capability, down to 50 pg for total RNAs</span></li>
<li><span style="font-weight: 400;">Capture the widest possible diversity of RNAs for rich content</span></li>
<li><span>Get high sensitivity data even from difficult samples, such as degraded, FFPE samples</span></li>
<li><span style="font-weight: 400;">Enjoy a fast, easy, single tube protocol</span></li>
</ul>
</div>
<p></p>
<p></p>
<h4></h4>
<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/dplex-total-manual.pdf" target="_blank" title="dplex total rnaseq user manual"><strong>Download the manual</strong></a></center>
<p></p>
<p>D-Plex Total RNA-seq Library Preparation Kit is a tool designed for the study of the whole coding and non-coding transcriptome. <span>The kit is using the</span><a href="https://www.diagenode.com/en/pages/dplex" target="_blank"><span> </span>D-Plex technology</a><span><span> </span>to generate directional libraries for Illumina sequencing directly from total RNAs, mRNAs that has already been enriched by poly(A) selection, or RNAs that has already been depleted of rRNAs.</span></p>
<p><span>The D-Plex technology utilizes two innovative ligation-free mechanisms - poly(A) tailing and template switching - to produce sequencing libraries from ultra-low input amounts, down to 50 pg for total RNAs, mRNAs or rRNA-depleted RNAs. Combined with unique molecular identifiers (UMI), this complete solution delivers a high-sensitivity detection method for comprehensive analysis of the transcriptome. It accurately measures gene and transcript abundance and captures the widest possible diversity of RNAs, including known and novel features in coding and non-coding RNAs.</span></p>
<p><span></span><span>D-Plex Total RNA-seq Kit offers a time saving protocol that can be completed within 5 hours and requires minimal hands-on time. <span>The library preparation takes place in a </span><span>single tube</span><span>, increasing the efficiency tremendously. This new solution has been extensively validated for both intact and highly degraded RNA samples, including that derived from FFPE preparations.</span></span></p>
<p><span><span>D-Plex Total RNA-seq Kit includes all buffers and enzymes necessary for the library preparation. Specific D-Plex Unique Dual Indexes were designed and validated to fit the D-Plex technology and are available separately:</span></span></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex UDI Module - Set B" target="_blank"><span>C05030022</span> - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
</ul>
<div class="extra-spaced" align="center"></div>
<div class="row">
<div class="carrousel" style="background-position: center;">
<div class="slick">
<div>
<h3>Greater sensitivity to detect novel transcripts</h3>
<div class="large-12 small-12 medium-12 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/CPM_totalrna.png" alt="total RNA kit" /></center></div>
<div class="large-12 small-12 medium-12 large-centered medium-centered small-centered columns">
<p></p>
<p>D-Plex Total RNA-seq kit enables great transcript detection, even when starting from very low RNA inputs or working with challenging FFPE samples. Increased number of transcripts detected at 1x coverage is an indicator of greater sensitivity.</p>
</div>
</div>
<div>
<h3>Highest diversity to facilitate biomarker discovery</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/HUR_rdep_biotypes.png" alt="total RNA diversity" /></center></div>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns">
<p></p>
<p>D-Plex Total RNA-seq libraries capture efficiently all RNA biotypes present in the sample of interest, both coding and non-coding RNAs or small and long RNAs.</p>
</div>
</div>
<div>
<h3>Consistent expression profiling across a wide range of RNA inputs</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/HUR_and_umeg_rdep_violin_tpm_10.png" alt="total RNA" width="450px" /></center></div>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns">
<p></p>
<p>Gene expression profiling (including protein coding exons and introns) obtained with D-Plex Total RNA-seq kit is conserved for decreasing RNA inputs, keeping transcript diversity across the range of RNA inputs.</p>
</div>
</div>
<div>
<h3>Superior library complexity at low RNA inputs</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/robustness_corr_HUR_HuMEg_rrna.png" alt="total RNA" /></center></div>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns">
<p></p>
<p>Correlation analysis of the protein coding genes detected indicates superior transcript expression correlation between low and high RNA inputs. This result demonstrates that D-Plex is an ideal solution for challenging samples such as FFPE preparations.</p>
</div>
</div>
</div>
</div>
</div>',
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<ul>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex UDI Module - Set B" target="_blank"><span>C05030022</span> - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
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<p>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</p>',
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<p><img src="https://www.diagenode.com/img/product/kits/dplex/bioinfoPipe.png" alt="Small RNA seq Bioinformatics pipeline" width="925" height="196" /></p>',
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<p>EVCleaner has been designed to collect enriched extracellular vesicles(EVs) from samples. </p>
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<h6>Advantages of EVCleaner :</h6>
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<ul>
<li>Fast, within 30 minutes</li>
<li>Ease of use</li>
<li>Reproducibility</li>
<li>Suitable for a wide range of sample </li>
<li>Suitable for routine EVs isolation</li>
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<h4>Performance Data</h4>
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<p>The EVCleaner size exclusion column has been especially designed to enrich samples in extracellular vesicles. Thanks to a careful optimization, over 95% of soluble proteins in the sample can be discarded through the elution while a gentle recovery of all the exosomes is obtained.</p>
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<p><span size="1" style="font-size: xx-small;">Representation of elution model. Elution changment may occur for different experimental models. Performed on HeLa cell culture meidium. </span></p>
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<p></p>
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'description' => '<div class="row">
<div class="small-12 medium-12 large-12 columns">
<h2 style="font-size: 22px;">DNA断片化、ライブラリー調製、自動化：NGSのワンストップショップ</h2>
<table class="small-12 medium-12 large-12 columns">
<tbody>
<tr>
<th class="small-12 medium-12 large-12 columns">
<h4>1. 断片化装置を選択してください：150 bp〜75 kbの範囲でDNAを断片化します。</h4>
</th>
</tr>
<tr style="background-color: #ffffff;">
<td class="small-12 medium-12 large-12 columns"></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns"><a href="../p/bioruptor-pico-sonication-device"><img src="https://www.diagenode.com/img/product/shearing_technologies/bioruptor_pico.jpg" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/megaruptor2-1-unit"><img src="https://www.diagenode.com/img/product/shearing_technologies/B06010001_megaruptor2.jpg" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/bioruptor-one-sonication-device"><img src="https://www.diagenode.com/img/product/shearing_technologies/br-one-profil.png" /></a></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns">5μlまで断片化：150 bp〜2 kb<br />NGS DNAライブラリー調製およびFFPE核酸抽出に最適で、</td>
<td class="small-4 medium-4 large-4 columns">2 kb〜75 kbの範囲をできます。<br />メイトペアライブラリー調製および長いフラグメントDNAシーケンシングに最適で、この軽量デスクトップデバイスで</td>
<td class="small-4 medium-4 large-4 columns">20または50μlの断片化が可能です。</td>
</tr>
</tbody>
</table>
<table class="small-12 medium-12 large-12 columns">
<tbody>
<tr>
<th class="small-8 medium-8 large-8 columns">
<h4>2. 最適化されたライブラリー調整キットを選択してください。</h4>
</th>
<th class="small-4 medium-4 large-4 columns">
<h4>3. ライブラリー前処理自動化を選択して、比類のないデータ再現性を実感</h4>
</th>
</tr>
<tr style="background-color: #ffffff;">
<td class="small-12 medium-12 large-12 columns"></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns"><a href="../p/microplex-library-preparation-kit-v2-x12-12-indices-12-rxns"><img src="https://www.diagenode.com/img/product/kits/microPlex_library_preparation.png" style="display: block; margin-left: auto; margin-right: auto;" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/ideal-library-preparation-kit-x24-incl-index-primer-set-1-24-rxns"><img src="https://www.diagenode.com/img/product/kits/box_kit.jpg" style="display: block; margin-left: auto; margin-right: auto;" height="173" width="250" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/sx-8g-ip-star-compact-automated-system-1-unit"><img src="https://www.diagenode.com/img/product/automation/B03000002%20_ipstar_compact.png" style="display: block; margin-left: auto; margin-right: auto;" /></a></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns" style="text-align: center;">50pgの低入力：MicroPlex Library Preparation Kit</td>
<td class="small-4 medium-4 large-4 columns" style="text-align: center;">5ng以上：iDeal Library Preparation Kit</td>
<td class="small-4 medium-4 large-4 columns" style="text-align: center;">Achieve great NGS data easily</td>
</tr>
</tbody>
</table>
</div>
</div>
<blockquote>
<div class="row">
<div class="small-12 medium-12 large-12 columns"><span class="label" style="margin-bottom: 16px; margin-left: -22px; font-size: 15px;">DiagenodeがNGS研究にぴったりなプロバイダーである理由</span>
<p>Diagenodeは15年以上もエピジェネティクス研究に専念、専門としています。 ChIP研究クロマチン用のユニークな断片化システムの開発から始まり、 専門知識を活かし、5μlのせん断体積まで可能で、NGS DNAライブラリーの調製に最適な最先端DNA断片化装置の開発にたどり着きました。 我々は以来、ChIP-seq、Methyl-seq、NGSライブラリー調製用キットを研究開発し、業界をリードする免疫沈降研究と同様に、ライブラリー調製を自動化および完結させる独自の自動化システムを開発にも成功しました。</p>
<ul>
<li>信頼されるせん断装置</li>
<li>様々なインプットからのライブラリ作成キット</li>
<li>独自の自動化デバイス</li>
</ul>
</div>
</div>
</blockquote>
<div class="row">
<div class="small-12 columns">
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#panel1a">次世代シーケンシングへの理解とその専門知識</a>
<div id="panel1a" class="content">
<div class="row">
<div class="small-12 medium-12 large-12 columns">
<p><strong>次世代シーケンシング (NGS)</strong> ）は、著しいスケールとハイスループットでシーケンシングを行い、1日に数十億もの塩基生成を可能にします。 NGSのハイスループットは迅速でありながら正確で、再現性のあるデータセットを実現し、さらにシーケンシング費用を削減します。 NGSは、ゲノムシーケンシング、ゲノム再シーケンシング、デノボシーケンシング、トランスクリプトームシーケンシング、その他にDNA-タンパク質相互作用の検出やエピゲノムなどを示します。 指数関数的に増加するシーケンシングデータの需要は、計算分析の障害や解釈、データストレージなどの課題を解決します。</p>
<p>アプリケーションおよび出発物質に応じて、数百万から数十億の鋳型DNA分子を大規模に並行してシーケンシングすることが可能です。その為に、異なる化学物質を使用するいくつかの市販のNGSプラットフォームを利用することができます。 NGSプラットフォームの種類によっては、事前準備とライブラリー作成が必要です。</p>
<p>NGSにとっても、特にデータ処理と分析に関した大きな課題はあります。第3世代技術はゲノミクス研究にさらに革命を起こすであろうと大きく期待されています。</p>
</div>
</div>
<div class="row">
<div class="small-6 medium-6 large-6 columns">
<p><strong>NGS アプリケーション</strong></p>
<ul>
<li>全ゲノム配列決定</li>
<li>デノボシーケンシング</li>
<li>標的配列</li>
<li>Exomeシーケンシング</li>
<li>トランスクリプトーム配列決定</li>
<li>ゲノム配列決定</li>
<li>ミトコンドリア配列決定</li>
<li>DNA-タンパク質相互作用（ChIP-seq</li>
<li>バリアント検出</li>
<li>ゲノム仕上げ</li>
</ul>
</div>
<div class="small-6 medium-6 large-6 columns">
<p><strong>研究分野におけるNGS:</strong></p>
<ul>
<li>腫瘍学</li>
<li>リプロダクティブ・ヘルス</li>
<li>法医学ゲノミクス</li>
<li>アグリゲノミックス</li>
<li>複雑な病気</li>
<li>微生物ゲノミクス</li>
<li>食品・環境ゲノミクス</li>
<li>創薬ゲノミクス - パーソナライズド・メディカル</li>
</ul>
</div>
<div class="small-12 medium-12 large-12 columns">
<p><strong>NGSの用語</strong></p>
<dl>
<dt>リード（読み取り）</dt>
<dd>この装置から得られた連続した単一のストレッチ</dd>
<dt>断片リード</dt>
<dd>フラグメントライブラリからの読み込み。 シーケンシングプラットフォームに応じて、読み取りは通常約100〜300bp。</dd>
<dt>断片ペアエンドリード</dt>
<dd>断片ライブラリーからDNA断片の各末端2つの読み取り。</dd>
<dt>メイトペアリード</dt>
<dd>大きなDNA断片（通常は予め定義されたサイズ範囲）の各末端から2つの読み取り。</dd>
<dt>カバレッジ（例）</dt>
<dd>30×適用範囲とは、参照ゲノム中の各塩基対が平均30回の読み取りを示す。</dd>
</dl>
</div>
</div>
<div class="row">
<div class="small-12 medium-12 large-12 columns">
<h2>NGSプラットフォーム</h2>
<h3><a href="http://www.illumina.com" target="_blank">イルミナ</a></h3>
<p>イルミナは、クローン的に増幅された鋳型DNA（クラスター）上に位置する、蛍光標識された可逆的鎖ターミネーターヌクレオチドを用いた配列別合成技術を使用。 DNAクラスターは、ガラスフローセルの表面上に固定化され、 ワークフローは、4つのヌクレオチド（それぞれ異なる蛍光色素で標識された）の組み込み、4色イメージング、色素や末端基の切断、取り込み、イメージングなどを繰り返します。フローセルは大規模な並列配列決定を受ける。 この方法により、単一蛍光標識されたヌクレオチドの制御添加によるモノヌクレオチドのエラーを回避する可能性があります。 読み取りの長さは、通常約100〜150 bpです。</p>
<h3><a href="http://www.lifetechnologies.com" target="_blank">イオン トレント</a></h3>
<p>イオントレントは、半導体技術チップを用いて、合成中にヌクレオチドを取り込む際に放出されたプロトンを検出します。 これは、イオン球粒子と呼ばれるビーズの表面にエマルションPCR（emPCR）を使用し、リンクされた特定のアダプターを用いてDNA断片を増幅します。 各ビーズは1種類のDNA断片で覆われていて、異なるDNA断片を有するビーズは次いで、チップの陽子感知ウェル内に配置されます。 チップには一度に4つのヌクレオチドのうちの1つが浸水し、このプロセスは異なるヌクレオチドで15秒ごとに繰り返されます。 配列決定の間に4つの塩基の各々が1つずつ導入されます、組み込みの場合はプロトンが放出され、電圧信号が取り込みに比例して検出されます。.</p>
<h3><a href="http://www.pacificbiosciences.com" target="_blank">パシフィック バイオサイエンス</a></h3>
<p>パシフィックバイオサイエンスでは、20kbを超える塩基対の読み取りも、単一分子リアルタイム（SMRT）シーケンシングによる構造および細胞タイプの変化を観察することができます。 このプラットフォームでは、超長鎖二本鎖DNA（dsDNA）断片が、Megaruptor（登録商標）のようなDiagenode装置を用いたランダムシアリングまたは目的の標的領域の増幅によって生成されます。 SMRTbellライブラリーは、ユニバーサルヘアピンアダプターをDNA断片の各末端に連結することによって生成します。 サイズ選択条件による洗浄ステップの後、配列決定プライマーをSMRTbellテンプレートにアニーリングし、鋳型DNAに結合したDNAポリメラーゼを含む配列決定を、蛍光標識ヌクレオチドの存在下で開始。 各塩基が取り込まれると、異なる蛍光のパルスをリアルタイムで検出します。</p>
<h3><a href="https://nanoporetech.com" target="_blank">オックスフォード ナノポア</a></h3>
<p>Oxford Nanoporeは、単一のDNA分子配列決定に基づく技術を開発します。その技術により生物学的分子、すなわちDNAが一群の電気抵抗性高分子膜として位置するナノスケールの孔（ナノ細孔）またはその近くを通過し、イオン電流が変化します。 この変化に関する情報は、例えば4つのヌクレオチド（AまたはG r CまたはT）ならびに修飾されたヌクレオチドすべてを区別することによって分子情報に訳されます。 シーケンシングミニオンデバイスのフローセルは、数百個のナノポアチャネルのセンサアレイを含みます。 DNAサンプルは、Diagenode社のMegaruptor（登録商標）を用いてランダムシアリングによって生成され得る超長鎖DNAフラグメントが必要です。</p>
<h3><a href="http://www.lifetechnologies.com/be/en/home/life-science/sequencing/next-generation-sequencing/solid-next-generation-sequencing.html" target="_blank">SOLiD</a></h3>
<p>SOLiDは、ユニークな化学作用により、何千という個々のDNA分子の同時配列決定を可能にします。 それは、アダプター対ライブラリーのフラグメントが適切で、せん断されたゲノムDNAへのアダプターのライゲーションによるライブラリー作製から始まります。 次のステップでは、エマルジョンPCR（emPCR）を実施して、ビーズの表面上の個々の鋳型DNA分子をクローン的に増幅。 emPCRでは、個々の鋳型DNAをPCR試薬と混合し、水中油型エマルジョン内の疎水性シェルで囲まれた水性液滴内のプライマーコートビーズを、配列決定のためにロードするスライドガラスの表面にランダムに付着。 この技術は、シークエンシングプライマーへのライゲーションで競合する4つの蛍光標識されたジ塩基プローブのセットを使用します。</p>
<h3><a href="http://454.com/products/technology.asp" target="_blank">454</a></h3>
<p>454は、大規模並列パイロシーケンシングを利用しています。 始めに全ゲノムDNAまたは標的遺伝子断片の300〜800bp断片のライブラリー調製します。 次に、DNAフラグメントへのアダプターの付着および単一のDNA鎖の分離。 その後アダプターに連結されたDNAフラグメントをエマルジョンベースのクローン増幅（emPCR）で処理し、DNAライブラリーフラグメントをミクロンサイズのビーズ上に配置します。 各DNA結合ビーズを光ファイバーチップ上のウェルに入れ、器具に挿入します。 4つのDNAヌクレオチドは、配列決定操作中に固定された順序で連続して加えられ、並行して配列決定されます。</p>
</div>
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<div class="small-12 medium-12 large-12 columns">
<p><span style="font-weight: 400;">Most of the major next-generation sequencing platforms require ligation of specific adaptor oligos to </span><a href="../applications/dna-rna-shearing"><span style="font-weight: 400;">fragmented DNA or RNA</span></a><span style="font-weight: 400;"> prior to sequencing</span></p>
<p><span style="font-weight: 400;">After input DNA has been fragmented, it is end-repaired and blunt-ended</span><span style="font-weight: 400;">. The next step is a A-tailing in which dAMP is added to the 3´ end of the blunt phosphorylated DNA fragments to prevent concatemerization and to allow the ligation of adaptors with complementary dT overhangs. In addition, barcoded adapters can be incorporated to facilitate multiplexing prior to or during amplification.</span></p>
<center><img src="https://www.diagenode.com/img/categories/library-prep/flux.png" /></center>
<p><span style="font-weight: 400;">Diagenode offers a comprehensive product portfolio for library preparation:<br /></span></p>
<strong><a href="https://www.diagenode.com/en/categories/Library-preparation-for-RNA-seq">D-Plex RNA-seq Library Preparation Kits</a></strong><br />
<p><span style="font-weight: 400;">Diagenode’s new RNA-sequencing solutions utilize the innovative c</span><span style="font-weight: 400;">apture and a</span><span style="font-weight: 400;">mplification by t</span><span style="font-weight: 400;">ailing and s</span><span style="font-weight: 400;">witching”</span><span style="font-weight: 400;">, a ligation-free method to produce DNA libraries for next generation sequencing from low input amounts of RNA. </span><span style="font-weight: 400;"></span><a href="../categories/Library-preparation-for-RNA-seq">Learn more</a></p>
<strong><a href="../categories/library-preparation-for-ChIP-seq">ChIP-seq and DNA sequencing library preparation solutions</a></strong><br />
<p><span style="font-weight: 400;">Our kits have been optimized for DNA library preparation used for next generation sequencing for a wide range of inputs. Using a simple three-step protocols, our</span><a href="http://www.diagenode.com/p/microplex-library-preparation-kit-v2-x12-12-indices-12-rxns"><span style="font-weight: 400;"> </span></a><span style="font-weight: 400;">kits are an optimal choice for library preparation from DNA inputs down to 50 pg. </span><a href="../categories/library-preparation-for-ChIP-seq">Learn more</a></p>
<a href="../p/bioruptor-pico-sonication-device"><span style="font-weight: 400;"></span><strong>Bioruptor Pico - short fragments</strong></a><a href="../categories/library-preparation-for-ChIP-seq-and-DNA-sequencing"><span style="font-weight: 400;"></span></a><br />
<p><span style="font-weight: 400;"></span><span style="font-weight: 400;">Our well-cited Bioruptor Pico is the shearing device of choice for chromatin and DNA fragmentation. Obtain uniform and tight fragment distributions between 150bp -2kb. </span><a href="../p/bioruptor-pico-sonication-device">Learn more</a></p>
<strong><a href="../p/megaruptor2-1-unit"><span href="../p/bioruptor-pico-sonication-device">Megaruptor</span>® - long fragments</a></strong><a href="../p/bioruptor-pico-sonication-device"><span style="font-weight: 400;"></span></a><a href="../categories/library-preparation-for-ChIP-seq-and-DNA-sequencing"><span style="font-weight: 400;"></span></a><br />
<p><span style="font-weight: 400;"></span><span style="font-weight: 400;">The Megaruptor is designed to shear DNA from 3kb-75kb for long-read sequencing. <a href="../p/megaruptor2-1-unit">Learn more</a></span></p>
<span href="../p/bioruptor-pico-sonication-device"></span><span style="font-weight: 400;"></span></div>
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'name' => 'The piRNA-pathway factor FKBP6 is essential for spermatogenesis butdispensable for control of meiotic LINE-1 expression in humans.',
'authors' => 'Wyrwoll M.J. et al.',
'description' => '<p>Infertility affects around 7\% of the male population and can be due to severe spermatogenic failure (SPGF), resulting in no or very few sperm in the ejaculate. We initially identified a homozygous frameshift variant in FKBP6 in a man with extreme oligozoospermia. Subsequently, we screened a total of 2,699 men with SPGF and detected rare bi-allelic loss-of-function variants in FKBP6 in five additional persons. All six individuals had no or extremely few sperm in the ejaculate, which were not suitable for medically assisted reproduction. Evaluation of testicular tissue revealed an arrest at the stage of round spermatids. Lack of FKBP6 expression in the testis was confirmed by RT-qPCR and immunofluorescence staining. In mice, Fkbp6 is essential for spermatogenesis and has been described as being involved in piRNA biogenesis and formation of the synaptonemal complex (SC). We did not detect FKBP6 as part of the SC in normal human spermatocytes, but small RNA sequencing revealed that loss of FKBP6 severely impacted piRNA levels, supporting a role for FKBP6 in piRNA biogenesis in humans. In contrast to findings in piRNA-pathway mouse models, we did not detect an increase in LINE-1 expression in men with pathogenic FKBP6 variants. Based on our findings, FKBP6 reaches a "strong" level of evidence for being associated with male infertility according to the ClinGen criteria, making it directly applicable for clinical diagnostics. This will improve patient care by providing a causal diagnosis and will help to predict chances for successful surgical sperm retrieval.</p>',
'date' => '2022-10-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36150389',
'doi' => '10.1016/j.ajhg.2022.09.002',
'modified' => '2022-11-16 09:28:27',
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'name' => 'The seminal plasma microbiome of men with testicular germ cell tumours described by small RNA sequencing',
'authors' => 'Nina Mørup, Ailsa Maria Main, Niels Jørgensen, Gedske Daugaard, Anders Juul, Kristian Almstrup ',
'description' => '<p><strong class="sub-title">Background:<span> </span></strong>It has been estimated that microorganisms are involved in the pathogenesis of approximately 20% of all cancers. Testicular germ cell tumours (TGCTs) are the most common type of malignancy in young men and arise from the precursor cell, Germ Cell Neoplasia in Situ (GCNIS). The microbiome of seminal plasma and testicular tissue has not been thoroughly investigated in regard to TGCTs.</p>
<p><strong class="sub-title">Objectives:<span> </span></strong>To investigate the differences in the seminal plasma microbiome between men with TGCT or GCNIS-only compared with controls.</p>
<p><strong class="sub-title">Materials and methods:<span> </span></strong>The study population consisted of patients with GCNIS-only (n = 5), TGCT (n = 18), and controls (n = 25) with different levels of sperm counts in the ejaculate. RNA was isolated from the seminal plasma and sequenced. Reads not mapping to the human genome were aligned against a set of 2784 bacterial/archaeal and 4336 viral genomes using the Kraken pipeline.</p>
<p><strong class="sub-title">Results:<span> </span></strong>We identified reads from 2172 species and most counts were from Alteromonas mediterranea, Falconid herpesvirus 1, and Stigmatella aurantiaca. Six species (Acaryochloris marina, Halovirus HGTV-1, Thermaerobacter marianensis, Thioalkalivibrio sp. K90mix, Burkholderia sp. YI23, and Desulfurivibrio alkaliphilus) were found in significantly (q-value <0.05) higher levels in the seminal plasma of TGCT and GCNIS-only patients compared with controls. In contrast, Streptomyces phage VWB, was found at significantly higher levels among controls compared with TGCT and GCNIS-only patients combined.</p>
<p><strong class="sub-title">Discussion:<span> </span></strong>Often the microbiome is analysed by shotgun or 16S ribosomal sequencing whereas our present data builds on small RNA sequencing. This allowed us to identify more viruses and phages compared to previous studies, but also makes the results difficult to directly compare.</p>
<p><strong class="sub-title">Conclusion:<span> </span></strong>Our study is the first to report identification of the microbiome species in seminal plasma of men with TGCT and GCNIS-only, which potentially could be involved in the pathogenesis of TGCTs. Further studies are, however, needed to confirm our findings. This article is protected by copyright. All rights reserved.</p>',
'date' => '2022-09-28',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/36168917/',
'doi' => '10.1111/andr.13305',
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'name' => 'Neutral sphingomyelinase 2 inhibition attenuates extracellular vesiclerelease and improves neurobehavioral deficits in murine HIV.',
'authors' => 'Zhu X. et al.',
'description' => '<p>People living with HIV (PLH) have significantly higher rates of cognitive impairment (CI) and major depressive disorder (MDD) versus the general population. The enzyme neutral sphingomyelinase 2 (nSMase2) is involved in the biogenesis of ceramide and extracellular vesicles (EVs), both of which are dysregulated in PLH, CI, and MDD. Here we evaluated EcoHIV-infected mice for behavioral abnormalities relevant to depression and cognition deficits, and assessed the behavioral and biochemical effects of nSMase2 inhibition. Mice were infected with EcoHIV and daily treatment with either vehicle or the nSMase2 inhibitor (R)-(1-(3-(3,4-dimethoxyphenyl)-2,6-dimethylimidazo[1,2-b]pyridazin-8-yl)pyrrolidin-3-yl)-carbamate (PDDC) began 3 weeks post-infection. After 2 weeks of treatment, mice were subjected to behavior tests. EcoHIV-infected mice exhibited behavioral abnormalities relevant to MDD and CI that were reversed by PDDC treatment. EcoHIV infection significantly increased cortical brain nSMase2 activity, resulting in trend changes in sphingomyelin and ceramide levels that were normalized by PDDC treatment. EcoHIV-infected mice also exhibited increased levels of brain-derived EVs and altered microRNA cargo, including miR-183-5p, miR-200c-3p, miR-200b-3p, and miR-429-3p, known to be associated with MDD and CI; all were normalized by PDDC. In conclusion, inhibition of nSMase2 represents a possible new therapeutic strategy for the treatment of HIV-associated CI and MDD.</p>',
'date' => '2022-07-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35462006',
'doi' => '10.1016/j.nbd.2022.105734',
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'description' => '<p>Spliced leader trans-splicing is essential for gene expression in many eukaryotes. To elucidate the molecular mechanism of this process, we characterise the molecules associated with the Caenorhabditis elegans major spliced leader snRNP (SL1 snRNP), which donates the spliced leader that replaces the 5' untranslated region of most pre-mRNAs. Using a GFP-tagged version of the SL1 snRNP protein SNA-1 created by CRISPR-mediated genome engineering, we immunoprecipitate and identify RNAs and protein components by RIP-Seq and mass spectrometry. This reveals the composition of the SL1 snRNP and identifies associations with spliceosome components PRP-8 and PRP-19. Significantly, we identify a novel, nematode-specific protein required for SL1 trans-splicing, which we designate SNA-3. SNA-3 is an essential, nuclear protein with three NADAR domains whose function is unknown. Mutation of key residues in NADAR domains inactivates the protein, indicating that domain function is required for activity. SNA-3 interacts with the CBC-ARS2 complex and other factors involved in RNA metabolism, including SUT-1 protein, through RNA or protein-mediated contacts revealed by yeast two-hybrid assays, localisation studies and immunoprecipitations. Our data are compatible with a role for SNA-3 in coordinating trans-splicing with target pre-mRNA transcription or in the processing of the Y-branch product of the trans-splicing reaction.</p>',
'date' => '2022-07-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35736244',
'doi' => '10.1093/nar/gkac534',
'modified' => '2022-09-27 14:48:51',
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'name' => 'Interspecies effectors of a transgenerational memory of bacterial infection in Caenorhabditis elegans .',
'authors' => 'Legüe Marcela et al.',
'description' => '<p>The inheritance of memory is an adaptive trait. Microbes challenge the immunity of organisms and trigger behavioral adaptations that can be inherited, but how bacteria produce inheritance of a trait is unknown. We use and its bacteria to study the transgenerational RNA dynamics of interspecies crosstalk leading to a heritable behavior. A heritable response of to microbes is the pathogen-induced diapause (PIDF), a state of suspended animation to evade infection. We identify RsmY, a small RNA involved in quorum sensing in as a trigger of PIDF. The histone methyltransferase (HMT) SET-18/SMYD3 and the argonaute HRDE-1, which promotes multi-generational silencing in the germline, are also needed for PIDF initiation The HMT SET-25/EHMT2 is necessary for memory maintenance in the transgenerational lineage. Our work is a starting point to understanding microbiome-induced inheritance of acquired traits, and the transgenerational influence of microbes in health and disease.</p>',
'date' => '2022-07-01',
'pmid' => 'https://www.sciencedirect.com/science/article/pii/S2589004222008999',
'doi' => '10.1016/j.isci.2022.104627',
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'name' => 'Diverse Monogenic Subforms of Human Spermatogenic Failure',
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'description' => '<p>Non-obstructive azoospermia (NOA) is the most severe form of male infertility and typically incurable with current medicine. Due to the biological complexity of sperm production, defining the genetic basis of NOA has proven challenging, and to date, the most advanced classification of NOA subforms is based on simple description of testis histology. In this study, we exome-sequenced over 1,000 clinically diagnosed NOA cases and identified a plausible recessive Mendelian cause in 20\%. Population-based testing against fertile controls identified 27 genes as significantly associated with azoospermia. The disrupted genes are primarily on the autosomes, enriched for undescribed human “knockouts”, and, for the most part, have yet to be linked to a Mendelian trait. Integration with single-cell RNA sequencing of adult testes shows that, rather than affecting a single cell type or pathway, azoospermia genes can be grouped into molecular subforms with highly synchronized expression patterns, and analogs of these subforms exist in mice. This analysis framework identifies groups of genes with known roles in spermatogenesis but also reveals unrecognized subforms, such as a set of genes expressed specifically in mitotic divisions of type B spermatogonia. Our findings highlight NOA as an understudied Mendelian disorder and provide a conceptual structure for organizing the complex genetics of male infertility, which may serve as a basis for disease classification more advanced than histology.</p>',
'date' => '2022-07-01',
'pmid' => 'https://doi.org/10.1101%2F2022.07.19.22271581',
'doi' => '10.1101/2022.07.19.22271581',
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'name' => 'Ultrasensitive Ribo-seq reveals translational landscapes duringmammalian oocyte-to-embryo transition and pre-implantation development.',
'authors' => 'Xiong Zhuqing et al.',
'description' => '<p>In mammals, translational control plays critical roles during oocyte-to-embryo transition (OET) when transcription ceases. However, the underlying regulatory mechanisms remain challenging to study. Here, using low-input Ribo-seq (Ribo-lite), we investigated translational landscapes during OET using 30-150 mouse oocytes or embryos per stage. Ribo-lite can also accommodate single oocytes. Combining PAIso-seq to interrogate poly(A) tail lengths, we found a global switch of translatome that closely parallels changes of poly(A) tails upon meiotic resumption. Translation activation correlates with polyadenylation and is supported by polyadenylation signal proximal cytoplasmic polyadenylation elements (papCPEs) in 3' untranslated regions. By contrast, translation repression parallels global de-adenylation. The latter includes transcripts containing no CPEs or non-papCPEs, which encode many transcription regulators that are preferentially re-activated before zygotic genome activation. CCR4-NOT, the major de-adenylation complex, and its key adaptor protein BTG4 regulate translation downregulation often independent of RNA decay. BTG4 is not essential for global de-adenylation but is required for selective gene de-adenylation and production of very short-tailed transcripts. In sum, our data reveal intimate interplays among translation, RNA stability and poly(A) tail length regulation underlying mammalian OET.</p>',
'date' => '2022-06-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35697785',
'doi' => '10.1038/s41556-022-00928-6',
'modified' => '2022-08-11 14:17:11',
'created' => '2022-08-11 12:14:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 7 => array(
'id' => '4421',
'name' => 'Translation is a key determinant controlling the fate of cytoplasmic longnon-coding RNAs',
'authors' => 'Andjus Sara et al.',
'description' => '<p>Despite predicted to lack coding potential, cytoplasmic long non-coding (lnc)RNAs can associate with ribosomes, resulting in some cases into the production of functional peptides. However, the biological and mechanistic relevance of this pervasive lncRNAs translation remains poorly studied. In yeast, cytoplasmic Xrn1-sensitive lncRNAs (XUTs) are targeted by the Nonsense-Mediated mRNA Decay (NMD), suggesting a translation-dependent degradation process. Here, we report that XUTs are translated, which impacts their abundance. We show that XUTs globally accumulate upon translation elongation inhibition, but not when initial ribosome loading is impaired. Translation also affects XUTs independently of NMD, by interfering with their decapping. Ribo-Seq confirmed ribosomes binding to XUTs and identified actively translated small ORFs in their 5’-proximal region. Mechanistic analyses revealed that their NMD-sensitivity depends on the 3’-untranslated region length. Finally, we detected the peptide derived from the translation of an NMD-sensitive XUT reporter in NMD-competent cells. Our work highlights the role of translation in the metabolism of XUTs, which could contribute to expose genetic novelty to the natural selection, while NMD restricts their expression.</p>',
'date' => '2022-05-01',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2022.05.25.493276v1',
'doi' => '10.1101/2022.05.25.493276',
'modified' => '2022-09-27 14:48:05',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 8 => array(
'id' => '4252',
'name' => 'MGcount: a total RNA-seq quantification tool to address multi-mappingand multi-overlapping alignments ambiguity in non-coding transcripts',
'authors' => 'Hita Andrea, Brocart Gilles, Fernandez Ana, Rehmsmeier Marc, Alemany Anna, Schvartzman Sol',
'description' => '<p>Background Total-RNA sequencing (total-RNA-seq) allows the simultaneous study of both the coding and the non-coding transcriptome. Yet, computational pipelines have traditionally focused on particular biotypes, making assumptions that are not fullfilled by total-RNA-seq datasets. Transcripts from distinct RNA biotypes vary in length, biogenesis, and function, can overlap in a genomic region, and may be present in the genome with a high copy number. Consequently, reads from total-RNA-seq libraries may cause ambiguous genomic alignments, demanding for flexible quantification approaches. Results Here we present Multi-Graph count (MGcount), a total-RNA-seq quantification tool combining two strategies for handling ambiguous alignments. First, MGcount assigns reads hierarchically to small-RNA and long-RNA features to account for length disparity when transcripts overlap in the same genomic position. Next, MGcount aggregates RNA products with similar sequences where reads systematically multi-map using a graph-based approach. MGcount outputs a transcriptomic count matrix compatible with RNA-sequencing downstream analysis pipelines, with both bulk and single-cell resolution, and the graphs that model repeated transcript structures for different biotypes. The software can be used as a python module or as a single-file executable program. Conclusions MGcount is a flexible total-RNA-seq quantification tool that successfully integrates reads that align to multiple genomic locations or that overlap with multiple gene features. Its approach is suitable for the simultaneous estimation of protein-coding, long non-coding and small non-coding transcript concentration, in both precursor and processed forms. Both source code and compiled software are available at https://github.com/hitaandrea/MGcount. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04544-3.</p>',
'date' => '2022-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35030988',
'doi' => '10.1186/s12859-021-04544-3',
'modified' => '2022-05-20 09:42:23',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 9 => array(
'id' => '4223',
'name' => 'Single cell quantification of ribosome occupancy in early mouse development',
'authors' => 'Tori Tonn et al.',
'description' => '<p><span>Technological limitations precluded transcriptome-wide analyses of translation at single cell resolution. To solve this challenge, we developed a novel microfluidic isotachophoresis approach, named RIBOsome profiling via IsoTachoPhoresis (Ribo-ITP), and characterized translation in single oocytes and embryos during early mouse development. We identified differential translation efficiency as a key regulatory mechanism of genes involved in centrosome organization and N</span><sup>6</sup><span>-methyladenosine modification of RNAs. Our high coverage measurements enabled the first analysis of allele-specific ribosome engagement in early development and led to the discovery of stage-specific differential engagement of zygotic RNAs with ribosomes. Finally, by integrating our measurements with proteomics data, we discovered that ribosome occupancy in germinal vesicle stage oocytes is the predominant determinant of protein abundance in the zygote. Taken together, these findings resolve the long-standing paradox of low correlation between RNA expression and protein abundance in early embryonic development. The novel Ribo-ITP approach will enable numerous applications by providing high coverage and high resolution ribosome occupancy measurements from ultra-low input samples including single cells.</span></p>',
'date' => '2021-12-09',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.12.07.471408v1.abstract',
'doi' => 'https://doi.org/10.1101/2021.12.07.471408',
'modified' => '2022-04-29 11:39:09',
'created' => '2022-04-29 11:39:09',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 10 => array(
'id' => '4456',
'name' => 'Single cell quantification of ribosome occupancy in early mousedevelopment',
'authors' => 'Tonn T. et al.',
'description' => '<p>Technological limitations precluded transcriptome-wide analyses of translation at single cell resolution. To solve this challenge, we developed a novel microfluidic isotachophoresis approach, named RIBOsome profiling via IsoTachoPhoresis (Ribo-ITP), and characterized translation in single oocytes and embryos during early mouse development. We identified differential translation efficiency as a key regulatory mechanism of genes involved in centrosome organization and N6-methyladenosine modification of RNAs. Our high coverage measurements enabled the first analysis of allele-specific ribosome engagement in early development and led to the discovery of stage-specific differential engagement of zygotic RNAs with ribosomes. Finally, by integrating our measurements with proteomics data, we discovered that ribosome occupancy in germinal vesicle stage oocytes is the predominant determinant of protein abundance in the zygote. Taken together, these findings resolve the long-standing paradox of low correlation between RNA expression and protein abundance in early embryonic development. The novel Ribo-ITP approach will enable numerous applications by providing high coverage and high resolution ribosome occupancy measurements from ultra-low input samples including single cells.</p>',
'date' => '2021-12-01',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.12.07.471408v1',
'doi' => '10.1101/2021.12.07.471408',
'modified' => '2022-10-21 09:42:29',
'created' => '2022-09-28 09:53:13',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 11 => array(
'id' => '4216',
'name' => 'Functional microRNA targetome undergoes degeneration-induced shift in the retina',
'authors' => 'Chu-Tan JA et al.',
'description' => '<div class="abstract-content selected" id="enc-abstract">
<p><strong class="sub-title">Background:<span> </span></strong>MicroRNA (miRNA) play a significant role in the pathogenesis of complex neurodegenerative diseases including age-related macular degeneration (AMD), acting as post-transcriptional gene suppressors through their association with argonaute 2 (AGO2) - a key member of the RNA Induced Silencing Complex (RISC). Identifying the retinal miRNA/mRNA interactions in health and disease will provide important insight into the key pathways miRNA regulate in disease pathogenesis and may lead to potential therapeutic targets to mediate retinal degeneration.</p>
<p><strong class="sub-title">Methods:<span> </span></strong>To identify the active miRnome targetome interactions in the healthy and degenerating retina, AGO2 HITS-CLIP was performed using a rodent model of photoreceptor degeneration. Analysis of publicly available single-cell RNA sequencing (scRNAseq) data was performed to identify the cellular location of AGO2 and key members of the microRNA targetome in the retina. AGO2 findings were verified by in situ hybridization (RNA) and immunohistochemistry (protein).</p>
<p><strong class="sub-title">Results:<span> </span></strong>Analysis revealed a similar miRnome between healthy and damaged retinas, however, a shift in the active targetome was observed with an enrichment of miRNA involvement in inflammatory pathways. This shift was further demonstrated by a change in the seed binding regions of miR-124-3p, the most abundant retinal AGO2-bound miRNA, and has known roles in regulating retinal inflammation. Additionally, photoreceptor cluster miR-183/96/182 were all among the most highly abundant miRNA bound to AGO2. Following damage, AGO2 expression was localized to the inner retinal layers and more in the OLM than in healthy retinas, indicating a locational miRNA response to retinal damage.</p>
<p><strong class="sub-title">Conclusions:<span> </span></strong>This study provides important insight into the alteration of miRNA regulatory activity that occurs as a response to retinal degeneration and explores the miRNA-mRNA targetome as a consequence of retinal degenerations. Further characterisation of these miRNA/mRNA interactions in the context of the degenerating retina may provide an important insight into the active role these miRNA may play in diseases such as AMD.</p>
</div>
<p><strong class="sub-title">Keywords:<span> </span></strong>Argonaute; HITS-CLIP; Inflammation; Retina; Retinal degeneration; Transcriptome; mRNA; microRNA.</p>',
'date' => '2021-08-31',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34465369/',
'doi' => '10.1186/s13024-021-00478-9',
'modified' => '2022-03-28 11:05:53',
'created' => '2022-03-28 11:03:41',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 12 => array(
'id' => '4429',
'name' => 'Functional microRNA targetome undergoes degeneration-induced shift inthe retina.',
'authors' => 'Chu-Tan Joshua A et al.',
'description' => '<p>BACKGROUND: MicroRNA (miRNA) play a significant role in the pathogenesis of complex neurodegenerative diseases including age-related macular degeneration (AMD), acting as post-transcriptional gene suppressors through their association with argonaute 2 (AGO2) - a key member of the RNA Induced Silencing Complex (RISC). Identifying the retinal miRNA/mRNA interactions in health and disease will provide important insight into the key pathways miRNA regulate in disease pathogenesis and may lead to potential therapeutic targets to mediate retinal degeneration. METHODS: To identify the active miRnome targetome interactions in the healthy and degenerating retina, AGO2 HITS-CLIP was performed using a rodent model of photoreceptor degeneration. Analysis of publicly available single-cell RNA sequencing (scRNAseq) data was performed to identify the cellular location of AGO2 and key members of the microRNA targetome in the retina. AGO2 findings were verified by in situ hybridization (RNA) and immunohistochemistry (protein). RESULTS: Analysis revealed a similar miRnome between healthy and damaged retinas, however, a shift in the active targetome was observed with an enrichment of miRNA involvement in inflammatory pathways. This shift was further demonstrated by a change in the seed binding regions of miR-124-3p, the most abundant retinal AGO2-bound miRNA, and has known roles in regulating retinal inflammation. Additionally, photoreceptor cluster miR-183/96/182 were all among the most highly abundant miRNA bound to AGO2. Following damage, AGO2 expression was localized to the inner retinal layers and more in the OLM than in healthy retinas, indicating a locational miRNA response to retinal damage. CONCLUSIONS: This study provides important insight into the alteration of miRNA regulatory activity that occurs as a response to retinal degeneration and explores the miRNA-mRNA targetome as a consequence of retinal degenerations. Further characterisation of these miRNA/mRNA interactions in the context of the degenerating retina may provide an important insight into the active role these miRNA may play in diseases such as AMD.</p>',
'date' => '2021-08-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34465369',
'doi' => '10.1186/s13024-021-00478-9',
'modified' => '2022-09-28 09:01:43',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 13 => array(
'id' => '4334',
'name' => 'Single-cell microRNA sequencing method comparison and application tocell lines and circulating lung tumor cells',
'authors' => 'Hücker S. et al. ',
'description' => '<p>Molecular single cell analyses provide insights into physiological and pathological processes. Here, in a stepwise approach, we first evaluate 19 protocols for single cell small RNA sequencing on MCF7 cells spiked with 1 pg of 1,006 miRNAs. Second, we analyze MCF7 single cell equivalents of the eight best protocols. Third, we sequence single cells from eight different cell lines and 67 circulating tumor cells (CTCs) from seven SCLC patients. Altogether, we analyze 244 different samples. We observe high reproducibility within protocols and reads covered a broad spectrum of RNAs. For the 67 CTCs, we detect a median of 68 miRNAs, with 10 miRNAs being expressed in 90\% of tested cells. Enrichment analysis suggested the lung as the most likely organ of origin and enrichment of cancer-related categories. Even the identification of non-annotated candidate miRNAs was feasible, underlining the potential of single cell small RNA sequencing.</p>',
'date' => '2021-07-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34262050',
'doi' => '10.1038/s41467-021-24611-w',
'modified' => '2022-08-03 16:15:42',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 14 => array(
'id' => '4219',
'name' => 'Small RNAs in Seminal Plasma as Novel Biomarkers for Germ Cell Tumors',
'authors' => 'Mørup N. et al.',
'description' => '<p><span>Circulating miRNAs secreted by testicular germ cell tumors (TGCT) show great potential as novel non-invasive biomarkers for diagnosis of TGCT. Seminal plasma (SP) represents a biofluid closer to the primary site. Here, we investigate whether small RNAs in SP can be used to diagnose men with TGCTs or the precursor lesions, germ cell neoplasia in situ (GCNIS). Small RNAs isolated from SP from men with TGCTs (</span><i>n</i><span><span> </span>= 18), GCNIS-only (</span><i>n</i><span><span> </span>= 5), and controls (</span><i>n</i><span><span> </span>= 25) were sequenced. SP from men with TGCT/GCNIS (</span><i>n</i><span><span> </span>= 37) and controls (</span><i>n</i><span><span> </span>= 22) were used for validation by RT-qPCR. In general, piRNAs were found at lower levels in SP from men with TGCTs. Ten small RNAs were found at significantly (q-value < 0.05) different levels in SP from men with TGCT/GCNIS than controls. Random forests classification identified sets of small RNAs that could detect either TGCT/GCNIS or GCNIS-only with an area under the curve of 0.98 and 1 in ROC analyses, respectively. RT-qPCR validated hsa-miR-6782-5p to be present at 2.3-fold lower levels (</span><i>p</i><span><span> </span>= 0.02) in the SP from men with TGCTs compared with controls. Small RNAs in SP show potential as novel biomarkers for diagnosing men with TGCT/GCNIS but validation in larger cohorts is needed.</span></p>',
'date' => '2021-05-13',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34067956/',
'doi' => '10.3390/cancers13102346',
'modified' => '2022-04-19 15:29:47',
'created' => '2022-04-19 15:29:47',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => array(
'id' => '4099',
'name' => 'Vesicle-bound regulatory RNAs are associated with tissue aging',
'authors' => 'F. Kern, T. Kuhn, N. Ludwig, M. Simon, L. Gröger, N. Fabis, A. Salhab, T. Fehlmann, O. Hahn, A. Engel, M. Koch, J. Koehler, K. Winek, H. Soreq, G. Fuhrmann, T. Wyss-Coray, E. Meese, M. W. Laschke and A. Keller',
'description' => '<p><span>Previous work on murine models and human demonstrated global as well as tissue-specific molecular aging trajectories in solid tissues and body fluids</span><sup><a id="xref-ref-1-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-1">1</a>–<a id="xref-ref-8-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-8">8</a></sup><span>. Extracellular vesicles like exosomes play a crucial role in communication and information exchange in between such systemic factors and solid tissues</span><sup><a id="xref-ref-9-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-9">9</a>,<a id="xref-ref-10-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-10">10</a></sup><span>. We sequenced freely circulating and vesicle-bound small regulatory RNAs in mice at five time points across the average life span from 2 to 18 months. Intriguingly, each small RNA class exhibits unique aging patterns, which showed differential signatures between vesicle-bound and freely circulating molecules. In particular, tRNA fragments showed overall highest correlation with aging which also matched well between sample types, facilitating age prediction with non-negative matrix factorization (86% accuracy). Interestingly, rRNAs exhibited inverse correlation trajectories between vesicles and plasma while vesicle-bound microRNAs (miRNAs) were exceptionally strong associated with aging. Affected miRNAs regulate the inflammatory response and transcriptional processes, and adipose tissues show considerable effects in associated gene regulatory modules. Finally, nanoparticle tracking and electron microscopy suggest a shift from overall many small to fewer but larger vesicles in aged plasma, potentially contributing to systemic aging trajectories and affecting the molecular aging of organs.</span></p>',
'date' => '2021-05-08',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1',
'doi' => '10.1101/2021.05.07.443093',
'modified' => '2022-01-06 14:25:33',
'created' => '2021-05-17 10:44:33',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 16 => array(
'id' => '4427',
'name' => 'Small RNAs in Seminal Plasma as Novel Biomarkers for GermCell Tumors.',
'authors' => 'Mørup Nina et al.',
'description' => '<p>Circulating miRNAs secreted by testicular germ cell tumors (TGCT) show great potential as novel non-invasive biomarkers for diagnosis of TGCT. Seminal plasma (SP) represents a biofluid closer to the primary site. Here, we investigate whether small RNAs in SP can be used to diagnose men with TGCTs or the precursor lesions, germ cell neoplasia in situ (GCNIS). Small RNAs isolated from SP from men with TGCTs ( = 18), GCNIS-only ( = 5), and controls ( = 25) were sequenced. SP from men with TGCT/GCNIS ( = 37) and controls ( = 22) were used for validation by RT-qPCR. In general, piRNAs were found at lower levels in SP from men with TGCTs. Ten small RNAs were found at significantly (q-value < 0.05) different levels in SP from men with TGCT/GCNIS than controls. Random forests classification identified sets of small RNAs that could detect either TGCT/GCNIS or GCNIS-only with an area under the curve of 0.98 and 1 in ROC analyses, respectively. RT-qPCR validated hsa-miR-6782-5p to be present at 2.3-fold lower levels ( = 0.02) in the SP from men with TGCTs compared with controls. Small RNAs in SP show potential as novel biomarkers for diagnosing men with TGCT/GCNIS but validation in larger cohorts is needed.</p>',
'date' => '2021-05-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34067956',
'doi' => '10.3390/cancers13102346',
'modified' => '2022-09-28 09:03:57',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 17 => array(
'id' => '4102',
'name' => 'miRMaster 2.0: multi-species non-coding RNA sequencing analyses at scale',
'authors' => 'Tobias Fehlmann, Fabian Kern, Omar Laham, Christina Backes, Jeffrey Solomon, Pascal Hirsch, Carsten Volz, Rolf Müller, Andreas Keller',
'description' => '<p><span>Analyzing all features of small non-coding RNA sequencing data can be demanding and challenging. To facilitate this process, we developed miRMaster. After the analysis of over 125 000 human samples and 1.5 trillion human small RNA reads over 4 years, we present miRMaster 2 with a wide range of updates and new features. We extended our reference data sets so that miRMaster 2 now supports the analysis of eight species (e.g. human, mouse, chicken, dog, cow) and 10 non-coding RNA classes (e.g. microRNAs, piRNAs, tRNAs, rRNAs, circRNAs). We also incorporated new downstream analysis modules such as batch effect analysis or sample embeddings using UMAP, and updated annotation data bases included by default (miRBase, Ensembl, GtRNAdb). To accommodate the increasing popularity of single cell small-RNA sequencing data, we incorporated a module for unique molecular identifier (UMI) processing. Further, the output tables and graphics have been improved based on user feedback and new output formats that emerged in the community are now supported (e.g. miRGFF3). Finally, we integrated differential expression analysis with the miRNA enrichment analysis tool miEAA. miRMaster is freely available at </span><a href="https://www.ccb.uni-saarland.de/mirmaster2" title="https://www.ccb.uni-saarland.de/mirmaster2">https://www.ccb.uni-saarland.de/mirmaster2</a><span>.</span></p>',
'date' => '2021-04-19',
'pmid' => ' https://doi.org/10.1093/nar/gkab268',
'doi' => '10.1093/nar/gkab268',
'modified' => '2021-06-28 11:45:48',
'created' => '2021-06-28 11:42:21',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 18 => array(
'id' => '4455',
'name' => 'Bacterial small RNAs and host epigenetic effectors of atransgenerational memory of pathogens in C. elegans',
'authors' => 'Legüe M. et al.',
'description' => '<p>The inheritance of memories is adaptive for survival. Microbes interact with all organisms challenging their immunity and triggering behavioral adaptations. Some of these behaviors induced by bacteria can be inherited although the mechanisms of action are largely unexplored. In this work, we use C. elegans and its bacteria to study the transgenerational RNA dynamics of an interspecies crosstalk leading to a heritable behavior. Heritable responses to bacterial pathogens in the nematode include avoidance and pathogen-induced diapause (PIDF), a state of suspended animation to evade the pathogen threat. We identify a small RNA RsmY, involved in quorum sensing from P. aeruginosa as required for initiation of PIDF. Histone methyltransferase SET-18/SMYD3 is also needed for PIDF initiation in C. elegans. In contrast, SET-25/EHMT2 is necessary for the maintenance of the memory of pathogen exposure in the transgenerational lineage. This work can be a starting point to understanding microbiome-induced inheritance of acquired traits.</p>',
'date' => '2021-03-01',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.03.26.437277v1',
'doi' => '10.1101/2021.03.26.437277',
'modified' => '2022-10-21 09:41:13',
'created' => '2022-09-28 09:53:13',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 19 => array(
'id' => '4425',
'name' => 'Interspecies RNA Interactome of Pathogen and Host in aHeritable Defensive Strategy.',
'authors' => 'Legüe Marcela et al.',
'description' => '<p>Communication with bacteria deeply impacts the life history traits of their hosts. Through specific molecules and metabolites, bacteria can promote short- and long-term phenotypic and behavioral changes in the nematode . The chronic exposure of to pathogens promotes the adaptive behavior in the host's progeny called pathogen-induced diapause formation (PIDF). PIDF is a pathogen avoidance strategy induced in the second generation of animals infected and can be recalled transgenerationally. This behavior requires the RNA interference machinery and specific nematode and bacteria small RNAs (sRNAs). In this work, we assume that RNAs from both species co-exist and can interact with each other. Under this principle, we explore the potential interspecies RNA interactions during PIDF-triggering conditions, using transcriptomic data from the holobiont. We study two transcriptomics datasets: first, the dual sRNA expression of PAO1 and in a transgenerational paradigm for six generations and second, the simultaneous expression of sRNAs and mRNA in intergenerational PIDF. We focus on those bacterial sRNAs that are systematically overexpressed in the intestines of animals compared with sRNAs expressed in host-naïve bacteria. We selected diverse methods that represent putative mechanisms of RNA-mediated interspecies interaction. These interactions are as follows: heterologous perfect and incomplete pairing between bacterial RNA and host mRNA; sRNAs of similar sequence expressed in both species that could mimic each other; and known or predicted eukaryotic motifs present in bacterial transcripts. We conclude that a broad spectrum of tools can be applied for the identification of potential sRNA and mRNA targets of the interspecies RNA interaction that can be subsequently tested experimentally.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34367078',
'doi' => '10.3389/fmicb.2021.649858',
'modified' => '2022-09-28 09:06:04',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 20 => array(
'id' => '4426',
'name' => 'Distinct Extracellular RNA Profiles in Different PlasmaComponents.',
'authors' => 'Jia Jing et al.',
'description' => '<p>Circulating extracellular RNAs (exRNAs) have great potential to serve as biomarkers for a wide range of diagnostic, therapeutic, and prognostic applications. So far, knowledge of the difference among different sources of exRNAs is limited. To address this issue, we performed a sequential physical and biochemical precipitation to collect four fractions (platelets and cell debris, the thrombin-induced precipitates, extracellular vesicles, and supernatant) from each of 10 plasma samples. From total RNAs of the 40 fractions, we prepared ligation-free libraries to profile full spectrum of all RNA species, without size selection and rRNA reduction. Due to complicated RNA composition in these libraries, we utilized a successive stepwise alignment strategy to map the RNA sequences to different RNA categories, including miRNAs, piwi-interacting RNAs, tRNAs, rRNAs, lincRNAs, snoRNAs, snRNAs, other ncRNAs, protein coding RNAs, and circRNAs. Our data showed that each plasma fraction had its own unique distribution of RNA species. Hierarchical cluster analyses using transcript abundance demonstrated similarities in the same plasma fraction and significant differences between different fractions. In addition, we observed various unique transcripts, and novel predicted miRNAs among these plasma fractions. These results demonstrate that the distribution of RNA species and functional RNA transcripts is plasma fraction-dependent. Appropriate plasma preparation and thorough inspection of different plasma fractions are necessary for an exRNA-based biomarker study.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34234804',
'doi' => '10.3389/fgene.2021.564780',
'modified' => '2022-09-28 09:06:47',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 21 => array(
'id' => '4008',
'name' => 'Genes with 5′ terminal oligopyrimidine tracts preferentially escapeglobal suppression of translation by the SARS-CoV-2 NSP1 protein',
'authors' => 'Shilpa, Rao and Ian, Hoskins and P., Daniela Garcia and Tori, Tonn andHakan, Ozadam and Elif, Sarinay Cenik and ProfileCan, Cenik',
'description' => '<p>Viruses rely on the host translation machinery to synthesize their own proteins. Consequently, they have evolved varied mechanisms to co-opt host translation for their survival. SARS-CoV-2 relies on a non-structural protein, NSP1, for shutting down host translation. Despite this, it is currently unknown how viral proteins and host factors critical for viral replication can escape a global shutdown of host translation. Here, using a novel FACS-based assay called MeTAFlow, we report a dose-dependent reduction in both nascent protein synthesis and mRNA abundance in cells expressing NSP1. We perform RNA-Seq and matched ribosome profiling experiments to identify gene-specific changes both at the mRNA expression and translation level. We discover a functionally-coherent subset of human genes preferentially translated in the context of NSP1 expression. These genes include the translation machinery components, RNA binding proteins, and others important for viral pathogenicity. Importantly, we also uncover potential mechanisms of preferential translation through the presence of shared sites for specific RNA binding proteins and a remarkable enrichment for 5′ terminal oligo-pyrimidine tracts. Collectively, the present study suggests fine tuning of host gene expression and translation by NSP1 despite its global repressive effect on host protein synthesis.</p>',
'date' => '2020-09-14',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2020.09.13.295493v1',
'doi' => '10.1101/2020.09.13.295493.',
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'authors' => 'Gylling HM, Gonzalez-Aguilera C, Smith MA, Kaczorowski DC, Groth A, Lund AH',
'description' => '<p>Non-coding RNA has a proven ability to direct and regulate chromatin modifications by acting as scaffolds between DNA and histone-modifying complexes. However, it is unknown if ncRNA plays any role in DNA replication and epigenome maintenance, including histone eviction and re-instalment of histone-modifications after genome duplication. Isolation of nascent chromatin has identified a large number of RNA-binding proteins in addition to unknown components of the replication and epigenetic maintenance machinery. Here, we isolated and characterized long and short RNAs associated with nascent chromatin at active replication forks and track RNA composition during chromatin maturation across the cell cycle. Shortly after fork passage, GA-rich-, Alpha- and TElomeric Repeat-containing RNAs (TERRA) are associated with replicated DNA. These repeat containing RNAs arise from loci undergoing replication, suggesting an interaction in cis. Post-replication during chromatin maturation, and even after mitosis in G1, the repeats remain enriched on DNA. This suggests that specific types of repeat RNAs are transcribed shortly after DNA replication and stably associate with their loci of origin throughout cell cycle. The presented method and data enables studies of RNA interactions with replication forks and post-replicative chromatin and provides insights into how repeat RNAs and their engagement with chromatin are regulated with respect to DNA replication and across the cell cycle.</p>',
'date' => '2020-05-11',
'pmid' => 'http://www.pubmed.gov/32393525',
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'authors' => 'Fehlmann T, Backes C, Pirritano M, Laufer T, Galata V, Kern F, Kahraman M, Gasparoni G, Ludwig N, Lenhof HP, Gregersen HA, Francke R, Meese E, Simon M, Keller A',
'description' => '<p>The repertoire of small noncoding RNAs (sncRNAs), particularly miRNAs, in animals is considered to be evolutionarily conserved. Studies on sncRNAs are often largely based on homology-based information, relying on genomic sequence similarity and excluding actual expression data. To obtain information on sncRNA expression (including miRNAs, snoRNAs, YRNAs and tRNAs), we performed low-input-volume next-generation sequencing of 500 pg of RNA from 21 animals at two German zoological gardens. Notably, none of the species under investigation were previously annotated in any miRNA reference database. Sequencing was performed on blood cells as they are amongst the most accessible, stable and abundant sources of the different sncRNA classes. We evaluated and compared the composition and nature of sncRNAs across the different species by computational approaches. While the distribution of sncRNAs in the different RNA classes varied significantly, general evolutionary patterns were maintained. In particular, miRNA sequences and expression were found to be even more conserved than previously assumed. To make the results available for other researchers, all data, including expression profiles at the species and family levels, and different tools for viewing, filtering and searching the data are freely available in the online resource ASRA (Animal sncRNA Atlas) at https://www.ccb.uni-saarland.de/asra/.</p>',
'date' => '2019-05-21',
'pmid' => 'http://www.pubmed.gov/30937442',
'doi' => '10.1093/nar/gkz227',
'modified' => '2019-06-28 13:44:35',
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'authors' => 'Marcello Pirritano, Tobias Fehlmann, Thomas Laufer, Nicole Ludwig, Gilles Gasparoni, Yongping Li, Eckart Meese, Andreas Keller, and Martin Simon',
'description' => '<p><span>Circulating miRNAs are favored for biomarker candidates as they can reflect tissue specific miRNA dysregulation in disease contexts. Moreover, they have additional advantages that they can be monitored in a minimal invasive manner. Blood-borne miRNAs are therefore currently characterized to identify, describe and validate their potential suitability for a biomarker, however, sampling and as well miRNA detection methods limit these studies in terms of sensitivity but also practicability in clinical, at-home or low-resource sampling of high quality circulating RNA samples. We describe here a novel and innovative method of circulating RNA microsampling from minimal volume dried blood spots with direct enrichment for small RNA fractions in combination with ligation free library preparation. We evaluated crucial parameters for efficient library preparation from low RNA inputs of 50pg for efficient dissection not only of miRNAs but also isomiRs, piRNAs, and lincRNAs. We compared these data to classical microarrays and characterize the technical reproducibility and its sensitivity. We demonstrate and evaluate a method for easy low resource sampling and NGS analysis of circulating RNAs providing a powerful tool for massive cohort and remote patient monitoring.</span></p>',
'date' => '2018-09-10',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/?term=NGS+analysis+of+total+small+non+coding+RNAs+from+low+input+RNA+from+dried+blood+sampling',
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'authors' => 'Tobias Fehlmann, Christina Backes, Mustafa Kahraman, Jan Haas, Nicole Ludwig, Andreas E. Posch, Maximilian L. Würstle, Matthias Hübenthal, Andre Franke, Benjamin Meder, Eckart Meese, Andreas Keller',
'description' => '<p><span>The analysis of small RNA NGS data together with the discovery of new small RNAs is among the foremost challenges in life science. For the analysis of raw high-throughput sequencing data we implemented the fast, accurate and comprehensive web-based tool miRMaster. Our toolbox provides a wide range of modules for quantification of miRNAs and other non-coding RNAs, discovering new miRNAs, isomiRs, mutations, exogenous RNAs and motifs. Use-cases comprising hundreds of samples are processed in less than 5 h with an accuracy of 99.4%. An integrative analysis of small RNAs from 1836 data sets (20 billion reads) indicated that context-specific miRNAs (e.g. miRNAs present only in one or few different tissues / cell types) still remain to be discovered while broadly expressed miRNAs appear to be largely known. In total, our analysis of known and novel miRNAs indicated nearly 22 000 candidates of precursors with one or two mature forms. Based on these, we designed a custom microarray comprising 11 872 potential mature miRNAs to assess the quality of our prediction. MiRMaster is a convenient-to-use tool for the comprehensive and fast analysis of miRNA NGS data. In addition, our predicted miRNA candidates provided as custom array will allow researchers to perform in depth validation of candidates interesting to them.</span></p>',
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'description' => '<div class="row"><div class="small-12 medium-4 large-4 columns"><div class="panel"><h3><em>lncRNAs in cancer</em></h3><p class="text-left">Gastrointestinal cancer: Gastrointestinal cancers occur as a result of dysregulated signaling pathways and cellular processes, such as the cell cycle or apoptosis. LncRNAs can regulate proliferation of gastrointestinal cancer through interacting with RNA targets, localization to chromatin, or binding to proteins. Read more about Long non-coding RNAs: crucial regulators of gastrointestinal cancer cell proliferation</p><h3><em>microRNAs in cancer</em></h3><p class="text-left">miR-155 has been found overexpressed in many cancer types including hematopoietic cancers, breast, lung and colon cancer<br /><br /> <a href="https://www.cell.com/trends/molecular-medicine/pdf/S1471-4914(14)00101-4.pdf">https://www.cell.com/trends/molecular-medicine/pdf/S1471-4914(14)00101-4.pdf</a></p></div></div><div class="small-12 medium-8 large-8 columns"><center><img src="https://www.diagenode.com/img/cancer/long-non-coding-rna.jpg" width="550" height="345" /></center><p></p><p>Various non-coding RNAs (ncRNAs) have been found to function as key regulators for transcription, chromatin remodelling, and post-transcriptional modification, thus making them relevant in oncogenesis, both as tumor suppressors and as drivers. For example, a number of microRNAs (miRNAs), one of the more well-studied types of ncRNAs, have been identified as potential cancer biomarkers and clinical targets. Other ncRNAs, such as long noncoding RNAs are involved in cancer regulation and proliferation. Understanding the role of ncRNAs will be critical for cancer research and therapeutic development.</p></div></div>',
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<h6 style="height:60px">D-Plex Total RNA-seq Kit for Illumina</h6>
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<a href="/cn/p/evcleaner-1-pc"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a> </div>
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<p style="text-align: center;"><span><img src="https://www.diagenode.com/img/banners/dplex-product-banner-small.jpg" alt="D-Plex Small RNA-seq library prep kit for Illumina" caption="false" width="728" height="90" /></span></p>
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<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/MA-D-Plex-V1_2020.pdf" target="_blank" title="D-Plex Small RNA library preparation user manual"><strong>Download the manual</strong></a></center>
<p><span>Diagenode’s latest <strong>innovation in </strong>RNA-seq, D-Plex, is based on a <strong>template-switching technology</strong> that delivers a</span><span><span> </span></span><strong>ligation-free method</strong> for RNA library preparation, ensuring realistic representation of diverse small RNA species (such as miRNA, piRNA, tRNA, and siRNA) with <strong>minimized bias</strong>.<strong> </strong>This solution allows you<span> to produce <strong>Illumina®-compatible</strong> DNA libraries for NGS from <strong>low input</strong> amounts of RNA with <strong>minimized PhiX</strong> spike-in quantity for the most representative results.</span></p>
<p><span>The D-Plex small RNA solution was optimized for <strong>10 pg</strong> - 10 ng enriched <strong>small RNA</strong> (or 100 pg - 100 ng total RNA) and contains our latest TSO (<strong>T</strong>emplate-<strong>S</strong>witch <strong>O</strong>ligonucleotides) technology including <strong>UMI</strong>s. The kit is provided with our new <strong>bioinformatics tool</strong> especially developed in parallel to get the best of your data.</span></p>
<ul class="accordion" data-accordion="" style="list-style-type: circle;">
<li class="accordion-navigation"><a href="#more" style="color: #13b29c; background-color: transparent; display: inline; padding: 0;"><strong>Read more</strong></a>
<div id="more" class="content">
<ul>
<li><strong>Truest representation</strong> - UMIs with ligation-free protocol ensures the most representative and unbiased data</li>
<li><strong>Easy</strong> - One-tube, one-day protocol Lowest inputs - Down to 10 pg enriched small RNA or 100 pg total RNA starting amount</li>
<li><strong>Clinical sample compatibility</strong> - Tested with RNA from biofluids</li>
<li><strong>Highest sequencing quality</strong> - Minimized PhiX quantity</li>
<li><strong>Enhanced data</strong> - Our new bioinformatics pipeline provided in the manual was optimized and developed specifically for getting the best out of your D-Plex data including UMI processing</li>
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<h3>miRMaster : your bioinformatics tool for miRNA</h3>
<div class="small-12 medium-3 large-3 columns"><center></center><center></center><center></center><center><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank"><img src="https://www.diagenode.com/img/product/kits/dplex/miRMaster_logo.png" /></a></center></div>
<div class="small-12 medium-9 large-9 columns">
<p style="text-align: justify;">Diagenode has collaborated with <strong>Andreas Keller</strong> and <strong>Tobias Fehlmann</strong> to develop a new bioinformatics pipeline in order to allow <strong>novel miRNA prediction.</strong><br /><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank">miRMaster</a> is a comprehensive analysis online free tool for NGS miRNA samples.</p>
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<h3>Focused on your target</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/RNA_theorie.png" alt="small RNA sequencing kit" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>D-Plex: the highest yield technology to detect every kind of small RNA simultaneously (miRNA, snRNA, piRNA, …)</p>
</center></div>
</div>
<div>
<h3>Get rich content</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/biotyping_smallRNA.png" alt="small RNA diversity" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>One day to enrich your sample with a large variety of small RNAs in one tube. Identify your reads accurately with UMIs.</p>
</center></div>
</div>
<div>
<h3>Robust technology</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/correlation_inputs.png" alt="small non coding RNA" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Independent of your input amount -- go down to 10 pg small RNA as a starting amount.</p>
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</div>
<div>
<h3>Raise your quality output</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/fastQ_DPlex_NovaSeq_human_mouse.png" alt="small RNA library preparation for Illumina" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Get the best of your data -- use our designed bioinformatics pipeline and guidelines. We provide everything you need to greatly simplify your analysis.</p>
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</div>
</div>
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<p style="text-align: center;">[ To check figures in details, click on "Figure" in the menu below ]</p>',
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<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
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<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-C">C05030023 - D-Plex Unique Dual Indexes for Illumina - Set C</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-D">C05030024 - D-Plex Unique Dual Indexes for Illumina - Set D</a></li>
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<p><span>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</span></p>
<p><strong>For D-Plex SI library construction:</strong></p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-A" title="D-Plex SI Module - Set A" target="_blank">C05030010 - D-Plex Single Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-B" title="D-Plex SI Module - Set B" target="_blank">C05030011 - D-Plex Single Indexes for Illumina - Set B</a></li>
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<p><img src="https://www.diagenode.com/img/product/kits/dplex/bioinfoPipe.png" alt="small RNA sequencing bioinformatics pipeline" caption="false" width="925" height="196" /></p>',
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$meta_description = 'D-Plex Small RNA-seq Kit for Illumina contains UMI and is designed to generate rich small RNA-seq libraries from 100 pg of total RNA or 10 pg enriched small RNA.'
$meta_title = 'D-Plex Small RNA-seq Library Prep Kit for Illumina'
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'description' => '<div class="extra-spaced">
<p style="text-align: center;"><span><img src="https://www.diagenode.com/img/banners/dplex-product-banner-small.jpg" alt="D-Plex Small RNA-seq library prep kit for Illumina" caption="false" width="728" height="90" /></span></p>
</div>
<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/MA-D-Plex-V1_2020.pdf" target="_blank" title="D-Plex Small RNA library preparation user manual"><strong>Download the manual</strong></a></center>
<p><span>Diagenode’s latest <strong>innovation in </strong>RNA-seq, D-Plex, is based on a <strong>template-switching technology</strong> that delivers a</span><span><span> </span></span><strong>ligation-free method</strong> for RNA library preparation, ensuring realistic representation of diverse small RNA species (such as miRNA, piRNA, tRNA, and siRNA) with <strong>minimized bias</strong>.<strong> </strong>This solution allows you<span> to produce <strong>Illumina®-compatible</strong> DNA libraries for NGS from <strong>low input</strong> amounts of RNA with <strong>minimized PhiX</strong> spike-in quantity for the most representative results.</span></p>
<p><span>The D-Plex small RNA solution was optimized for <strong>10 pg</strong> - 10 ng enriched <strong>small RNA</strong> (or 100 pg - 100 ng total RNA) and contains our latest TSO (<strong>T</strong>emplate-<strong>S</strong>witch <strong>O</strong>ligonucleotides) technology including <strong>UMI</strong>s. The kit is provided with our new <strong>bioinformatics tool</strong> especially developed in parallel to get the best of your data.</span></p>
<ul class="accordion" data-accordion="" style="list-style-type: circle;">
<li class="accordion-navigation"><a href="#more" style="color: #13b29c; background-color: transparent; display: inline; padding: 0;"><strong>Read more</strong></a>
<div id="more" class="content">
<ul>
<li><strong>Truest representation</strong> - UMIs with ligation-free protocol ensures the most representative and unbiased data</li>
<li><strong>Easy</strong> - One-tube, one-day protocol Lowest inputs - Down to 10 pg enriched small RNA or 100 pg total RNA starting amount</li>
<li><strong>Clinical sample compatibility</strong> - Tested with RNA from biofluids</li>
<li><strong>Highest sequencing quality</strong> - Minimized PhiX quantity</li>
<li><strong>Enhanced data</strong> - Our new bioinformatics pipeline provided in the manual was optimized and developed specifically for getting the best out of your D-Plex data including UMI processing</li>
</ul>
<h3>miRMaster : your bioinformatics tool for miRNA</h3>
<div class="small-12 medium-3 large-3 columns"><center></center><center></center><center></center><center><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank"><img src="https://www.diagenode.com/img/product/kits/dplex/miRMaster_logo.png" /></a></center></div>
<div class="small-12 medium-9 large-9 columns">
<p style="text-align: justify;">Diagenode has collaborated with <strong>Andreas Keller</strong> and <strong>Tobias Fehlmann</strong> to develop a new bioinformatics pipeline in order to allow <strong>novel miRNA prediction.</strong><br /><a href="https://ccb-compute.cs.uni-saarland.de/mirmaster" target="_blank">miRMaster</a> is a comprehensive analysis online free tool for NGS miRNA samples.</p>
</div>
</div>
</li>
</ul>
<p></p>
<div class="extra-spaced" align="center"><iframe width="700" height="394" src="https://www.youtube.com/embed/TmMM4gVpINo" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="allowfullscreen"></iframe></div>
<div class="row">
<div class="carrousel" style="background-position: center;">
<div class="container">
<div class="row" style="background: rgba(255,255,255,0.1);">
<div class="large-12 columns slick">
<div>
<h3>Focused on your target</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/RNA_theorie.png" alt="small RNA sequencing kit" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>D-Plex: the highest yield technology to detect every kind of small RNA simultaneously (miRNA, snRNA, piRNA, …)</p>
</center></div>
</div>
<div>
<h3>Get rich content</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/biotyping_smallRNA.png" alt="small RNA diversity" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>One day to enrich your sample with a large variety of small RNAs in one tube. Identify your reads accurately with UMIs.</p>
</center></div>
</div>
<div>
<h3>Robust technology</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/correlation_inputs.png" alt="small non coding RNA" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Independent of your input amount -- go down to 10 pg small RNA as a starting amount.</p>
</center></div>
</div>
<div>
<h3>Raise your quality output</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img src="https://www.diagenode.com/img/product/kits/dplex/fastQ_DPlex_NovaSeq_human_mouse.png" alt="small RNA library preparation for Illumina" /></div>
<div class="large-6 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p>Get the best of your data -- use our designed bioinformatics pipeline and guidelines. We provide everything you need to greatly simplify your analysis.</p>
</center></div>
</div>
</div>
</div>
</div>
</div>
</div>
<p style="text-align: center;">[ To check figures in details, click on "Figure" in the menu below ]</p>',
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'info2' => '<p>Specific D-Plex indexes <span>were designed and validated to fit the D-Plex technology for Illumina sequencing and </span>are not included in the kit. They can be bought separately according to your needs. Please choose the format that suits you best among the compatible references to:</p>
<p><strong>For D-Plex UDI library construction:</strong></p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex UDI Module - Set B" target="_blank"><span>C05030022</span> - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-C">C05030023 - D-Plex Unique Dual Indexes for Illumina - Set C</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-D">C05030024 - D-Plex Unique Dual Indexes for Illumina - Set D</a></li>
</ul>
<p><span>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</span></p>
<p><strong>For D-Plex SI library construction:</strong></p>
<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-A" title="D-Plex SI Module - Set A" target="_blank">C05030010 - D-Plex Single Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Single-Indexes-Set-B" title="D-Plex SI Module - Set B" target="_blank">C05030011 - D-Plex Single Indexes for Illumina - Set B</a></li>
</ul>
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'info3' => '<p>A specific bioinformatics pipeline has been developed to process the special sequences present in the D-Plex construct, namely the UMI, the A-tail, and the template switch motif. All guidelines and free softwares are shared in the user manual. Subject to the compatibility of D-Plex constructs, other specific pipelines can be used.</p>
<p><img src="https://www.diagenode.com/img/product/kits/dplex/bioinfoPipe.png" alt="small RNA sequencing bioinformatics pipeline" caption="false" width="925" height="196" /></p>',
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'meta_title' => 'D-Plex Small RNA-seq Library Prep Kit for Illumina',
'meta_keywords' => 'RNA-seq, UMI, low input, small RNA-seq, one tube, one day, challenging, miRNA, biofluid, RNA-seq library preparation, D-Plex, higher RNA diversity, easy, user-friendly',
'meta_description' => 'D-Plex Small RNA-seq Kit for Illumina contains UMI and is designed to generate rich small RNA-seq libraries from 100 pg of total RNA or 10 pg enriched small RNA.',
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'name' => 'D-Plex Small RNA DNBSEQ™ Kit for MGI Sequencing',
'description' => '<center><img src="https://www.diagenode.com/img/banners/banner-dplex-mgi.png" width="550" /></center>
<p></p>
<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/D-Plex-Small-RNA-DNBSEQ.pdf" target="_blank" title="D-Plex Small RNA DNBSEQ user manual"><strong>Download the manual</strong></a></center>
<p></p>
<p>D-Plex Small RNA DNBSEQ™ Kit is a tool designed for the study of the small non-coding transcriptome. The kit is using the <a href="https://www.diagenode.com/en/pages/dplex" target="_blank">D-Plex technology</a> to generate double-stranded DNA libraries ready to be used for the DNA single-strand circularization step required for DNBSEQ sequencing on MGI sequencers.</p>
<p>The D-Plex technology utilizes the innovative capture and amplification by tailing and switching, a ligation-free method for RNA library preparation from ultra-low input amounts, down to 10 pg for small RNAs and 100 pg for total RNAs. This innovative solution enables diverse and novel transcripts detection, even from challenging clinical samples such as liquid biopsies.</p>
<p><span>D-Plex Small RNA <span>DNBSEQ™</span> Kit offers a time saving protocol that can be completed within 5 hours and requires minimal hands-on time. </span><span>The library preparation takes place in a </span><span>single tube</span><span>, increasing the efficiency tremendously. This ensures high technical reliability and reproducibility<span>.</span></span></p>
<p>D-Plex Small RNA DNBSEQ™ Kit includes all buffers and enzymes necessary for the library preparation. Specific D-Plex DNBSEQ Barcodes were designed and validated to fit the D-Plex technology and are available separately:</p>
<ul>
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-a">C05030060 - D-Plex DNBSEQ Barcodes for MGI - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-b">C05030061 - D-Plex DNBSEQ Barcodes for MGI - Set B</a></li>
</ul>
<p><b><strong>D-Plex is also available for Illumina sequencing, check<span> </span><a href="https://www.diagenode.com/en/p/D-Plex-Small-RNA-seq-Library-Prep-x24" target="_blank">here</a>!</strong></b></p>
<div class="row">
<div class="carrousel" style="background-position: center;">
<div class="container">
<div class="row" style="background: rgba(255,255,255,0.1);">
<div class="large-12 columns slick">
<div>
<h3>Diverse and novel transcript detection</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px; display: block; margin-left: auto; margin-right: auto;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-transcript.png" alt="small RNA library preparation for Illumina" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p style="text-align: justify;">The D-Plex Small RNA DNBSEQ protocol generates complex RNA libraries deciphering the wide diversity of small non-coding RNA spectrum (including miRNAs, snoRNAs, snRNAs) in human plasma samples.</p>
</center></div>
</div>
<div>
<h3>Ultra-low input performance</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px; display: block; margin-left: auto; margin-right: auto;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-ultralow-input.png" alt="Ultra-low input performance" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p style="text-align: justify;">Sequencing data from circulating RNA samples of two input amounts (25 pg and 2.5 ng) were highly correlated (<i>R = 0.99</i>) when compared using Pearson correlation coefficient.</p>
</center></div>
</div>
<div>
<h3>High mapping efficiency</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px; display: block; margin-left: auto; margin-right: auto;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-mapping.png" alt="High mapping efficiency" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p style="text-align: justify;"><b></b>The D-Plex Small RNA DNBSEQ kit is compatible with clinical-relevant samples, such as human plasma, and ultra low range of circulating RNA input (down to 10 pg) and exhibits good read mapping of sequencing reads (up to 70% mapping rate).</p>
</center></div>
</div>
<div>
<h3>High quality DNBSEQ sequencing solution</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><img style="height: 400px;" src="https://www.diagenode.com/img/product/kits/dplex/dplex-dnbseq.png" alt="High quality DNBSEQ sequencing solution" /></div>
<div class="large-8 small-12 medium-6 large-centered medium-centered small-centered columns"><center>
<p><b></b>The D-Plex Small RNA DNBSEQ kit combines our best-in-class RNA library preparation – D-Plex technology – with MGI’s high-quality, cost-effective, DNA nanoballs – DNBSEQ – sequencing solution, creating a unified platform to support high quality small RNA sequencing.</p>
</center></div>
</div>
</div>
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<li><strong>Latest innovation in RNA-seq</strong>: unique D-Plex technology offering ligation-free protocol for library preparation</li>
<li><strong>Ultra-low input capability</strong>: down to 10 pg for small RNAs and 100 pg for total RNAs</li>
<li><strong>High library complexity</strong>:<strong> </strong>obtain a complete view of your small RNA transcriptome</li>
<li><strong>Optimal performance on clinical samples</strong>: validated with circulating RNAs from liquid biopsies</li>
<li><strong>Easy to use with minimal hands-on time</strong>: one day, one tube protocol</li>
<li><strong>Highest sequencing quality</strong>: specifically formatted for MGI DNBSEQ™ sequencers</li>
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<ul style="list-style-type: disc;">
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-a">C05030060 - D-Plex 24 DNBSEQ Barcodes for MGI - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/d-plex-24-dnbseq-barcodes-set-b">C05030061 - D-Plex 24 DNBSEQ Barcodes for MGI - Set B</a></li>
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'meta_keywords' => 'Small RNA-seq Library Prep Kit for MGI Sequencing',
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'name' => 'MicroChIP DiaPure columns',
'description' => '<p>Diagenode’s MicroChIP DiaPure columns have been optimized for the purification and elution of very low amounts of DNA. This rapid method has been validated for epigenetic applications like low input ChIP (e.g. using the True MicroChIP kit) and CUT&Tag (e.g. using Diagenode’s pA-Tn5), but is also compatible with many other applications. The DNA can be eluted at high concentrations in volumes down to 6 μl and it is suitable for any downstream application (e.g. NGS).</p>
<p>Benefits of the MicroChIP DiaPure columns:</p>
<ul>
<li>Optimized for the purification of very low DNA amounts</li>
<li>Fast and easy protocol</li>
<li>Non-toxic</li>
<li>Validated for ChIP and Cut&Tag</li>
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'label1' => 'Examples of results',
'info1' => '<h2 style="text-align: center;">MicroChIP DiaPure columns after ChIP</h2>
<p>Successful ChIP-seq results generated on 50,000 of K562 cells using True MicroChIP technology. ChIP has been performed accordingly to True MicroChIP protocol (Diagenode, Cat. No. C01010130), including DNA purification using the MicroChIP DiaPure columns. For the library preparation the MicroPlex Library Preparation Kit (Diagenode, Cat. No. C05010001) has been used. The below figure shows the peaks from ChIP-seq experiments using the following Diagenode antibodies: H3K4me1 (C15410194), H3K9/14ac (C15410200), H3K27ac (C15410196) and H3K36me3 (C15410192).</p>
<p><img src="https://www.diagenode.com/img/product/kits/figure-igv-microchip.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 1:</strong> Integrative genomics viewer (IGV) visualization of ChIP-seq experiments using 50,000 of K562 cells.</p>
<p></p>
<h2 style="text-align: center;"></h2>
<h2 style="text-align: center;">MicroChIP DiaPure columns after CUT&Tag</h2>
<p>Successful CUT&Tag results showing a low background with high region-specific enrichment has been generated using 50.000 of K562 cells, 1 µg of H3K27me3 antibody (Diagenode, Cat. No. C15410069) and proteinA-Tn5 (1:250) (Diagenode, Cat. No. C01070001). 1 µg of IgG (Diagenode, Cat. No. C15410206) was used as negative control. Samples were purified using the MicroChIP DiaPure columns or phenol-chloroform purification. The below figure presenst the comparison of two purification methods.</p>
<p><img src="https://www.diagenode.com/img/product/kits/figure-diapure-igv.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 2:</strong> Integrative genomics viewer (IGV) visualization of CUT&Tag experiments: MicroChIP DiaPure columns vs phenol-chloroform purification using the H3K27me3 antibody.</p>',
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'name' => 'D-Plex Unique Dual Indexes for Illumina - Set A',
'description' => '<p style="text-align: left;"><span>D-Plex Unique Dual Indexes Module - Set A includes primer pairs with 24 unique dual barcodes (unique i5 and i7 indexes) for library multiplexing with the <a href="https://www.diagenode.com/en/p/D-Plex-Small-RNA-seq-Library-Prep-x24" target="_blank" title="D-Plex Small RNA-seq Kit">D-Plex Small RNA-seq Kit</a>. </span></p>
<p style="text-align: left;"><span>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</span></p>
<p><span>Four sets are available separately: </span></p>
<ul>
<li>C05030021 - D-Plex Unique Dual Indexes for Illumina - Set A</li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex 24 Single Indexes for Illumina - Set #B">C05030022 - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-C" title="D-Plex 24 Single Indexes for Illumina - Set #C">C05030023 - D-Plex Unique Dual Indexes for Illumina - Set C</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-D" title="D-Plex 24 Single Indexes for Illumina - Set #D">C05030024 - D-Plex Unique Dual Indexes for Illumina - Set D</a></li>
</ul>
<p><span>Each set can be used for library multiplexing up to 24. <span>Set A, B, C and D can be used simultaneously for library multiplexing up to 96.</span></span></p>
<p><span>Read more about the </span><a href="https://www.diagenode.com/en/pages/dplex" target="_blank">D-Plex technology</a><span>.</span><span> </span></p>',
'label1' => 'Characteristics',
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<li>Compatible with<span> </span><a href="https://www.diagenode.com/en/p/D-Plex-Small-RNA-seq-Library-Prep-x24">D-Plex Small RNA-seq kit for Illumina</a> </li>
<li>Multiplexing up to <strong>96 samples</strong> when combining Set A, B, C and D</li>
<li>Allows for identification of index hopping</li>
</ul>',
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'info2' => '<p><span>D-Plex RNA-seq UDI library constructs bear the TruSeq (Illumina) HT adapters. In case of a multiplexing scenario, it is therefore recommended to submit the D-Plex libraries as TruSeq HT libraries to your sequencing provider. </span><span>Further details are provided in the D-Plex Unique Dual Indexes Module<span> </span><a href="https://www.diagenode.com/files/products/kits/dplex-unique-dual-indexes-manual.pdf" target="_blank">manual</a>.</span></p>',
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'meta_title' => 'D-Plex Unique Dual Indexes for Illumina - RNA Library Prep Kit - Set A | Diagenode',
'meta_keywords' => 'RNA kit; small RNA kit; RNA-seq kit; low input RNA-seq kit; small RNA-seq, template switching kit, RNA-seq library preparation, D-Plex, higher RNA diversity',
'meta_description' => 'Compatible with D-Plex RNA-seq kits - Unique Dual Indexes for Illumina - Index hopping mitigation - Suitable for ultra-low input - Contains UMI - Multiplexing up to 48 samples',
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'name' => 'D-Plex Total RNA-seq Kit for Illumina',
'description' => '<div class="small-12 medium-12 large-12 columns" style="border: 3px solid #B02736; padding: 10px; margin: 10px;">
<h2>Product Features</h2>
<ul style="list-style-type: disc;">
<li><span style="font-weight: 400;">An innovative technology with template switching and UMIs</span></li>
<li><span style="font-weight: 400;">Ultra-low input capability, down to 50 pg for total RNAs</span></li>
<li><span style="font-weight: 400;">Capture the widest possible diversity of RNAs for rich content</span></li>
<li><span>Get high sensitivity data even from difficult samples, such as degraded, FFPE samples</span></li>
<li><span style="font-weight: 400;">Enjoy a fast, easy, single tube protocol</span></li>
</ul>
</div>
<p></p>
<p></p>
<h4></h4>
<center><a class="chip diahome button" href="https://www.diagenode.com/files/products/kits/dplex-total-manual.pdf" target="_blank" title="dplex total rnaseq user manual"><strong>Download the manual</strong></a></center>
<p></p>
<p>D-Plex Total RNA-seq Library Preparation Kit is a tool designed for the study of the whole coding and non-coding transcriptome. <span>The kit is using the</span><a href="https://www.diagenode.com/en/pages/dplex" target="_blank"><span> </span>D-Plex technology</a><span><span> </span>to generate directional libraries for Illumina sequencing directly from total RNAs, mRNAs that has already been enriched by poly(A) selection, or RNAs that has already been depleted of rRNAs.</span></p>
<p><span>The D-Plex technology utilizes two innovative ligation-free mechanisms - poly(A) tailing and template switching - to produce sequencing libraries from ultra-low input amounts, down to 50 pg for total RNAs, mRNAs or rRNA-depleted RNAs. Combined with unique molecular identifiers (UMI), this complete solution delivers a high-sensitivity detection method for comprehensive analysis of the transcriptome. It accurately measures gene and transcript abundance and captures the widest possible diversity of RNAs, including known and novel features in coding and non-coding RNAs.</span></p>
<p><span></span><span>D-Plex Total RNA-seq Kit offers a time saving protocol that can be completed within 5 hours and requires minimal hands-on time. <span>The library preparation takes place in a </span><span>single tube</span><span>, increasing the efficiency tremendously. This new solution has been extensively validated for both intact and highly degraded RNA samples, including that derived from FFPE preparations.</span></span></p>
<p><span><span>D-Plex Total RNA-seq Kit includes all buffers and enzymes necessary for the library preparation. Specific D-Plex Unique Dual Indexes were designed and validated to fit the D-Plex technology and are available separately:</span></span></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex UDI Module - Set B" target="_blank"><span>C05030022</span> - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
</ul>
<div class="extra-spaced" align="center"></div>
<div class="row">
<div class="carrousel" style="background-position: center;">
<div class="slick">
<div>
<h3>Greater sensitivity to detect novel transcripts</h3>
<div class="large-12 small-12 medium-12 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/CPM_totalrna.png" alt="total RNA kit" /></center></div>
<div class="large-12 small-12 medium-12 large-centered medium-centered small-centered columns">
<p></p>
<p>D-Plex Total RNA-seq kit enables great transcript detection, even when starting from very low RNA inputs or working with challenging FFPE samples. Increased number of transcripts detected at 1x coverage is an indicator of greater sensitivity.</p>
</div>
</div>
<div>
<h3>Highest diversity to facilitate biomarker discovery</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/HUR_rdep_biotypes.png" alt="total RNA diversity" /></center></div>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns">
<p></p>
<p>D-Plex Total RNA-seq libraries capture efficiently all RNA biotypes present in the sample of interest, both coding and non-coding RNAs or small and long RNAs.</p>
</div>
</div>
<div>
<h3>Consistent expression profiling across a wide range of RNA inputs</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/HUR_and_umeg_rdep_violin_tpm_10.png" alt="total RNA" width="450px" /></center></div>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns">
<p></p>
<p>Gene expression profiling (including protein coding exons and introns) obtained with D-Plex Total RNA-seq kit is conserved for decreasing RNA inputs, keeping transcript diversity across the range of RNA inputs.</p>
</div>
</div>
<div>
<h3>Superior library complexity at low RNA inputs</h3>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns"><center><img src="https://www.diagenode.com/img/product/kits/dplex/robustness_corr_HUR_HuMEg_rrna.png" alt="total RNA" /></center></div>
<div class="large-10 small-12 medium-10 large-centered medium-centered small-centered columns">
<p></p>
<p>Correlation analysis of the protein coding genes detected indicates superior transcript expression correlation between low and high RNA inputs. This result demonstrates that D-Plex is an ideal solution for challenging samples such as FFPE preparations.</p>
</div>
</div>
</div>
</div>
</div>',
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'label2' => 'Indexes',
'info2' => '<p><span>Specific D-Plex indexes </span><span>were designed and validated to fit the D-Plex technology for Illumina sequencing and </span><span>are not included in the kit. They can be bought separately according to your needs. Please choose the format that suits you best among the compatible references to:</span></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-A" title="D-Plex UDI Module - Set A" target="_blank"><span>C05030021</span> - D-Plex Unique Dual Indexes for Illumina - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/D-Plex-24-Unique-Dual-Indexes-Set-B" title="D-Plex UDI Module - Set B" target="_blank"><span>C05030022</span> - D-Plex Unique Dual Indexes for Illumina - Set B</a></li>
</ul>
<p></p>
<p>The use of UDI is highly recommended to mitigate errors introduced by read misassignment, including index hopping frequently observed with patterned flow cells such as Illumina’s NovaSeq system.</p>',
'label3' => 'Data analysis',
'info3' => '<p><span>A specific bioinformatics pipeline has been developed to process the special sequences present in the D-Plex construct, namely the UMI, the A-tail, and the template switch motif. All guidelines and free softwares are shared in the user manual. Subject to the compatibility of D-Plex constructs, other specific pipelines can be used.</span></p>
<p><img src="https://www.diagenode.com/img/product/kits/dplex/bioinfoPipe.png" alt="Small RNA seq Bioinformatics pipeline" width="925" height="196" /></p>',
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'meta_keywords' => 'RNA-seq kit, RNA-seq library preparation; low input RNA-seq, UMI RNA-seq, UDI RNA-seq, D-Plex, higher RNA sensitivity',
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'label1' => 'Exosomes',
'info1' => '<p><span>Exosomes are small <strong>extracellular vesicules</strong> of 30-100 nm constitutively released under certain stimuli in biological fluids including blood, urine, saliva, breast milk, culture medium of cell cultures, etc.</span></p>
<p><span>Exosomes contain various molecular constituents of their cell of origin, including <strong>proteins</strong> and <strong>microRNA</strong>. Circulant exosomes can potentially act as cargo messenger and transfer molecules from one cell to another via membrane vesicle trafficking.</span></p>
<p><span>Exosomes are suitable for a variety of downstream applications including RNA analysis, protein analysis and functional use.</span></p>',
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<p></p>
<p>EVCleaner has been designed to collect enriched extracellular vesicles(EVs) from samples. </p>
<h6></h6>
<h6>Advantages of EVCleaner :</h6>
<p></p>
<ul>
<li>Fast, within 30 minutes</li>
<li>Ease of use</li>
<li>Reproducibility</li>
<li>Suitable for a wide range of sample </li>
<li>Suitable for routine EVs isolation</li>
<li>Intact and global exosome isolation</li>
</ul>
<h4></h4>
<p></p>
<h4>Performance Data</h4>
<p></p>
<p>The EVCleaner size exclusion column has been especially designed to enrich samples in extracellular vesicles. Thanks to a careful optimization, over 95% of soluble proteins in the sample can be discarded through the elution while a gentle recovery of all the exosomes is obtained.</p>
<p></p>
<div class="row">
<div class="small-4 medium-4 large-4 columns text-center"><img src="https://www.diagenode.com/img/product/kits/C28030001-exosome-1.png" caption="false" width="276" height="203" /></div>
<div class="small-8 medium-8 large-8 columns">
<p><span size="1" style="font-size: xx-small;">Representation of elution model. Elution changment may occur for different experimental models. Performed on HeLa cell culture meidium. </span></p>
<p><small><br /><br /></small></p>
<p></p>
</div>
</div>',
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'description' => '<div class="row">
<div class="small-12 medium-12 large-12 columns">
<h2 style="font-size: 22px;">DNA断片化、ライブラリー調製、自動化：NGSのワンストップショップ</h2>
<table class="small-12 medium-12 large-12 columns">
<tbody>
<tr>
<th class="small-12 medium-12 large-12 columns">
<h4>1. 断片化装置を選択してください：150 bp〜75 kbの範囲でDNAを断片化します。</h4>
</th>
</tr>
<tr style="background-color: #ffffff;">
<td class="small-12 medium-12 large-12 columns"></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns"><a href="../p/bioruptor-pico-sonication-device"><img src="https://www.diagenode.com/img/product/shearing_technologies/bioruptor_pico.jpg" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/megaruptor2-1-unit"><img src="https://www.diagenode.com/img/product/shearing_technologies/B06010001_megaruptor2.jpg" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/bioruptor-one-sonication-device"><img src="https://www.diagenode.com/img/product/shearing_technologies/br-one-profil.png" /></a></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns">5μlまで断片化：150 bp〜2 kb<br />NGS DNAライブラリー調製およびFFPE核酸抽出に最適で、</td>
<td class="small-4 medium-4 large-4 columns">2 kb〜75 kbの範囲をできます。<br />メイトペアライブラリー調製および長いフラグメントDNAシーケンシングに最適で、この軽量デスクトップデバイスで</td>
<td class="small-4 medium-4 large-4 columns">20または50μlの断片化が可能です。</td>
</tr>
</tbody>
</table>
<table class="small-12 medium-12 large-12 columns">
<tbody>
<tr>
<th class="small-8 medium-8 large-8 columns">
<h4>2. 最適化されたライブラリー調整キットを選択してください。</h4>
</th>
<th class="small-4 medium-4 large-4 columns">
<h4>3. ライブラリー前処理自動化を選択して、比類のないデータ再現性を実感</h4>
</th>
</tr>
<tr style="background-color: #ffffff;">
<td class="small-12 medium-12 large-12 columns"></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns"><a href="../p/microplex-library-preparation-kit-v2-x12-12-indices-12-rxns"><img src="https://www.diagenode.com/img/product/kits/microPlex_library_preparation.png" style="display: block; margin-left: auto; margin-right: auto;" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/ideal-library-preparation-kit-x24-incl-index-primer-set-1-24-rxns"><img src="https://www.diagenode.com/img/product/kits/box_kit.jpg" style="display: block; margin-left: auto; margin-right: auto;" height="173" width="250" /></a></td>
<td class="small-4 medium-4 large-4 columns"><a href="../p/sx-8g-ip-star-compact-automated-system-1-unit"><img src="https://www.diagenode.com/img/product/automation/B03000002%20_ipstar_compact.png" style="display: block; margin-left: auto; margin-right: auto;" /></a></td>
</tr>
<tr>
<td class="small-4 medium-4 large-4 columns" style="text-align: center;">50pgの低入力：MicroPlex Library Preparation Kit</td>
<td class="small-4 medium-4 large-4 columns" style="text-align: center;">5ng以上：iDeal Library Preparation Kit</td>
<td class="small-4 medium-4 large-4 columns" style="text-align: center;">Achieve great NGS data easily</td>
</tr>
</tbody>
</table>
</div>
</div>
<blockquote>
<div class="row">
<div class="small-12 medium-12 large-12 columns"><span class="label" style="margin-bottom: 16px; margin-left: -22px; font-size: 15px;">DiagenodeがNGS研究にぴったりなプロバイダーである理由</span>
<p>Diagenodeは15年以上もエピジェネティクス研究に専念、専門としています。 ChIP研究クロマチン用のユニークな断片化システムの開発から始まり、 専門知識を活かし、5μlのせん断体積まで可能で、NGS DNAライブラリーの調製に最適な最先端DNA断片化装置の開発にたどり着きました。 我々は以来、ChIP-seq、Methyl-seq、NGSライブラリー調製用キットを研究開発し、業界をリードする免疫沈降研究と同様に、ライブラリー調製を自動化および完結させる独自の自動化システムを開発にも成功しました。</p>
<ul>
<li>信頼されるせん断装置</li>
<li>様々なインプットからのライブラリ作成キット</li>
<li>独自の自動化デバイス</li>
</ul>
</div>
</div>
</blockquote>
<div class="row">
<div class="small-12 columns">
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#panel1a">次世代シーケンシングへの理解とその専門知識</a>
<div id="panel1a" class="content">
<div class="row">
<div class="small-12 medium-12 large-12 columns">
<p><strong>次世代シーケンシング (NGS)</strong> ）は、著しいスケールとハイスループットでシーケンシングを行い、1日に数十億もの塩基生成を可能にします。 NGSのハイスループットは迅速でありながら正確で、再現性のあるデータセットを実現し、さらにシーケンシング費用を削減します。 NGSは、ゲノムシーケンシング、ゲノム再シーケンシング、デノボシーケンシング、トランスクリプトームシーケンシング、その他にDNA-タンパク質相互作用の検出やエピゲノムなどを示します。 指数関数的に増加するシーケンシングデータの需要は、計算分析の障害や解釈、データストレージなどの課題を解決します。</p>
<p>アプリケーションおよび出発物質に応じて、数百万から数十億の鋳型DNA分子を大規模に並行してシーケンシングすることが可能です。その為に、異なる化学物質を使用するいくつかの市販のNGSプラットフォームを利用することができます。 NGSプラットフォームの種類によっては、事前準備とライブラリー作成が必要です。</p>
<p>NGSにとっても、特にデータ処理と分析に関した大きな課題はあります。第3世代技術はゲノミクス研究にさらに革命を起こすであろうと大きく期待されています。</p>
</div>
</div>
<div class="row">
<div class="small-6 medium-6 large-6 columns">
<p><strong>NGS アプリケーション</strong></p>
<ul>
<li>全ゲノム配列決定</li>
<li>デノボシーケンシング</li>
<li>標的配列</li>
<li>Exomeシーケンシング</li>
<li>トランスクリプトーム配列決定</li>
<li>ゲノム配列決定</li>
<li>ミトコンドリア配列決定</li>
<li>DNA-タンパク質相互作用（ChIP-seq</li>
<li>バリアント検出</li>
<li>ゲノム仕上げ</li>
</ul>
</div>
<div class="small-6 medium-6 large-6 columns">
<p><strong>研究分野におけるNGS:</strong></p>
<ul>
<li>腫瘍学</li>
<li>リプロダクティブ・ヘルス</li>
<li>法医学ゲノミクス</li>
<li>アグリゲノミックス</li>
<li>複雑な病気</li>
<li>微生物ゲノミクス</li>
<li>食品・環境ゲノミクス</li>
<li>創薬ゲノミクス - パーソナライズド・メディカル</li>
</ul>
</div>
<div class="small-12 medium-12 large-12 columns">
<p><strong>NGSの用語</strong></p>
<dl>
<dt>リード（読み取り）</dt>
<dd>この装置から得られた連続した単一のストレッチ</dd>
<dt>断片リード</dt>
<dd>フラグメントライブラリからの読み込み。 シーケンシングプラットフォームに応じて、読み取りは通常約100〜300bp。</dd>
<dt>断片ペアエンドリード</dt>
<dd>断片ライブラリーからDNA断片の各末端2つの読み取り。</dd>
<dt>メイトペアリード</dt>
<dd>大きなDNA断片（通常は予め定義されたサイズ範囲）の各末端から2つの読み取り。</dd>
<dt>カバレッジ（例）</dt>
<dd>30×適用範囲とは、参照ゲノム中の各塩基対が平均30回の読み取りを示す。</dd>
</dl>
</div>
</div>
<div class="row">
<div class="small-12 medium-12 large-12 columns">
<h2>NGSプラットフォーム</h2>
<h3><a href="http://www.illumina.com" target="_blank">イルミナ</a></h3>
<p>イルミナは、クローン的に増幅された鋳型DNA（クラスター）上に位置する、蛍光標識された可逆的鎖ターミネーターヌクレオチドを用いた配列別合成技術を使用。 DNAクラスターは、ガラスフローセルの表面上に固定化され、 ワークフローは、4つのヌクレオチド（それぞれ異なる蛍光色素で標識された）の組み込み、4色イメージング、色素や末端基の切断、取り込み、イメージングなどを繰り返します。フローセルは大規模な並列配列決定を受ける。 この方法により、単一蛍光標識されたヌクレオチドの制御添加によるモノヌクレオチドのエラーを回避する可能性があります。 読み取りの長さは、通常約100〜150 bpです。</p>
<h3><a href="http://www.lifetechnologies.com" target="_blank">イオン トレント</a></h3>
<p>イオントレントは、半導体技術チップを用いて、合成中にヌクレオチドを取り込む際に放出されたプロトンを検出します。 これは、イオン球粒子と呼ばれるビーズの表面にエマルションPCR（emPCR）を使用し、リンクされた特定のアダプターを用いてDNA断片を増幅します。 各ビーズは1種類のDNA断片で覆われていて、異なるDNA断片を有するビーズは次いで、チップの陽子感知ウェル内に配置されます。 チップには一度に4つのヌクレオチドのうちの1つが浸水し、このプロセスは異なるヌクレオチドで15秒ごとに繰り返されます。 配列決定の間に4つの塩基の各々が1つずつ導入されます、組み込みの場合はプロトンが放出され、電圧信号が取り込みに比例して検出されます。.</p>
<h3><a href="http://www.pacificbiosciences.com" target="_blank">パシフィック バイオサイエンス</a></h3>
<p>パシフィックバイオサイエンスでは、20kbを超える塩基対の読み取りも、単一分子リアルタイム（SMRT）シーケンシングによる構造および細胞タイプの変化を観察することができます。 このプラットフォームでは、超長鎖二本鎖DNA（dsDNA）断片が、Megaruptor（登録商標）のようなDiagenode装置を用いたランダムシアリングまたは目的の標的領域の増幅によって生成されます。 SMRTbellライブラリーは、ユニバーサルヘアピンアダプターをDNA断片の各末端に連結することによって生成します。 サイズ選択条件による洗浄ステップの後、配列決定プライマーをSMRTbellテンプレートにアニーリングし、鋳型DNAに結合したDNAポリメラーゼを含む配列決定を、蛍光標識ヌクレオチドの存在下で開始。 各塩基が取り込まれると、異なる蛍光のパルスをリアルタイムで検出します。</p>
<h3><a href="https://nanoporetech.com" target="_blank">オックスフォード ナノポア</a></h3>
<p>Oxford Nanoporeは、単一のDNA分子配列決定に基づく技術を開発します。その技術により生物学的分子、すなわちDNAが一群の電気抵抗性高分子膜として位置するナノスケールの孔（ナノ細孔）またはその近くを通過し、イオン電流が変化します。 この変化に関する情報は、例えば4つのヌクレオチド（AまたはG r CまたはT）ならびに修飾されたヌクレオチドすべてを区別することによって分子情報に訳されます。 シーケンシングミニオンデバイスのフローセルは、数百個のナノポアチャネルのセンサアレイを含みます。 DNAサンプルは、Diagenode社のMegaruptor（登録商標）を用いてランダムシアリングによって生成され得る超長鎖DNAフラグメントが必要です。</p>
<h3><a href="http://www.lifetechnologies.com/be/en/home/life-science/sequencing/next-generation-sequencing/solid-next-generation-sequencing.html" target="_blank">SOLiD</a></h3>
<p>SOLiDは、ユニークな化学作用により、何千という個々のDNA分子の同時配列決定を可能にします。 それは、アダプター対ライブラリーのフラグメントが適切で、せん断されたゲノムDNAへのアダプターのライゲーションによるライブラリー作製から始まります。 次のステップでは、エマルジョンPCR（emPCR）を実施して、ビーズの表面上の個々の鋳型DNA分子をクローン的に増幅。 emPCRでは、個々の鋳型DNAをPCR試薬と混合し、水中油型エマルジョン内の疎水性シェルで囲まれた水性液滴内のプライマーコートビーズを、配列決定のためにロードするスライドガラスの表面にランダムに付着。 この技術は、シークエンシングプライマーへのライゲーションで競合する4つの蛍光標識されたジ塩基プローブのセットを使用します。</p>
<h3><a href="http://454.com/products/technology.asp" target="_blank">454</a></h3>
<p>454は、大規模並列パイロシーケンシングを利用しています。 始めに全ゲノムDNAまたは標的遺伝子断片の300〜800bp断片のライブラリー調製します。 次に、DNAフラグメントへのアダプターの付着および単一のDNA鎖の分離。 その後アダプターに連結されたDNAフラグメントをエマルジョンベースのクローン増幅（emPCR）で処理し、DNAライブラリーフラグメントをミクロンサイズのビーズ上に配置します。 各DNA結合ビーズを光ファイバーチップ上のウェルに入れ、器具に挿入します。 4つのDNAヌクレオチドは、配列決定操作中に固定された順序で連続して加えられ、並行して配列決定されます。</p>
</div>
</div>
</div>
</li>
</ul>
</div>
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<div class="small-12 medium-12 large-12 columns">
<p><span style="font-weight: 400;">Most of the major next-generation sequencing platforms require ligation of specific adaptor oligos to </span><a href="../applications/dna-rna-shearing"><span style="font-weight: 400;">fragmented DNA or RNA</span></a><span style="font-weight: 400;"> prior to sequencing</span></p>
<p><span style="font-weight: 400;">After input DNA has been fragmented, it is end-repaired and blunt-ended</span><span style="font-weight: 400;">. The next step is a A-tailing in which dAMP is added to the 3´ end of the blunt phosphorylated DNA fragments to prevent concatemerization and to allow the ligation of adaptors with complementary dT overhangs. In addition, barcoded adapters can be incorporated to facilitate multiplexing prior to or during amplification.</span></p>
<center><img src="https://www.diagenode.com/img/categories/library-prep/flux.png" /></center>
<p><span style="font-weight: 400;">Diagenode offers a comprehensive product portfolio for library preparation:<br /></span></p>
<strong><a href="https://www.diagenode.com/en/categories/Library-preparation-for-RNA-seq">D-Plex RNA-seq Library Preparation Kits</a></strong><br />
<p><span style="font-weight: 400;">Diagenode’s new RNA-sequencing solutions utilize the innovative c</span><span style="font-weight: 400;">apture and a</span><span style="font-weight: 400;">mplification by t</span><span style="font-weight: 400;">ailing and s</span><span style="font-weight: 400;">witching”</span><span style="font-weight: 400;">, a ligation-free method to produce DNA libraries for next generation sequencing from low input amounts of RNA. </span><span style="font-weight: 400;"></span><a href="../categories/Library-preparation-for-RNA-seq">Learn more</a></p>
<strong><a href="../categories/library-preparation-for-ChIP-seq">ChIP-seq and DNA sequencing library preparation solutions</a></strong><br />
<p><span style="font-weight: 400;">Our kits have been optimized for DNA library preparation used for next generation sequencing for a wide range of inputs. Using a simple three-step protocols, our</span><a href="http://www.diagenode.com/p/microplex-library-preparation-kit-v2-x12-12-indices-12-rxns"><span style="font-weight: 400;"> </span></a><span style="font-weight: 400;">kits are an optimal choice for library preparation from DNA inputs down to 50 pg. </span><a href="../categories/library-preparation-for-ChIP-seq">Learn more</a></p>
<a href="../p/bioruptor-pico-sonication-device"><span style="font-weight: 400;"></span><strong>Bioruptor Pico - short fragments</strong></a><a href="../categories/library-preparation-for-ChIP-seq-and-DNA-sequencing"><span style="font-weight: 400;"></span></a><br />
<p><span style="font-weight: 400;"></span><span style="font-weight: 400;">Our well-cited Bioruptor Pico is the shearing device of choice for chromatin and DNA fragmentation. Obtain uniform and tight fragment distributions between 150bp -2kb. </span><a href="../p/bioruptor-pico-sonication-device">Learn more</a></p>
<strong><a href="../p/megaruptor2-1-unit"><span href="../p/bioruptor-pico-sonication-device">Megaruptor</span>® - long fragments</a></strong><a href="../p/bioruptor-pico-sonication-device"><span style="font-weight: 400;"></span></a><a href="../categories/library-preparation-for-ChIP-seq-and-DNA-sequencing"><span style="font-weight: 400;"></span></a><br />
<p><span style="font-weight: 400;"></span><span style="font-weight: 400;">The Megaruptor is designed to shear DNA from 3kb-75kb for long-read sequencing. <a href="../p/megaruptor2-1-unit">Learn more</a></span></p>
<span href="../p/bioruptor-pico-sonication-device"></span><span style="font-weight: 400;"></span></div>
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'name' => 'The piRNA-pathway factor FKBP6 is essential for spermatogenesis butdispensable for control of meiotic LINE-1 expression in humans.',
'authors' => 'Wyrwoll M.J. et al.',
'description' => '<p>Infertility affects around 7\% of the male population and can be due to severe spermatogenic failure (SPGF), resulting in no or very few sperm in the ejaculate. We initially identified a homozygous frameshift variant in FKBP6 in a man with extreme oligozoospermia. Subsequently, we screened a total of 2,699 men with SPGF and detected rare bi-allelic loss-of-function variants in FKBP6 in five additional persons. All six individuals had no or extremely few sperm in the ejaculate, which were not suitable for medically assisted reproduction. Evaluation of testicular tissue revealed an arrest at the stage of round spermatids. Lack of FKBP6 expression in the testis was confirmed by RT-qPCR and immunofluorescence staining. In mice, Fkbp6 is essential for spermatogenesis and has been described as being involved in piRNA biogenesis and formation of the synaptonemal complex (SC). We did not detect FKBP6 as part of the SC in normal human spermatocytes, but small RNA sequencing revealed that loss of FKBP6 severely impacted piRNA levels, supporting a role for FKBP6 in piRNA biogenesis in humans. In contrast to findings in piRNA-pathway mouse models, we did not detect an increase in LINE-1 expression in men with pathogenic FKBP6 variants. Based on our findings, FKBP6 reaches a "strong" level of evidence for being associated with male infertility according to the ClinGen criteria, making it directly applicable for clinical diagnostics. This will improve patient care by providing a causal diagnosis and will help to predict chances for successful surgical sperm retrieval.</p>',
'date' => '2022-10-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36150389',
'doi' => '10.1016/j.ajhg.2022.09.002',
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'name' => 'The seminal plasma microbiome of men with testicular germ cell tumours described by small RNA sequencing',
'authors' => 'Nina Mørup, Ailsa Maria Main, Niels Jørgensen, Gedske Daugaard, Anders Juul, Kristian Almstrup ',
'description' => '<p><strong class="sub-title">Background:<span> </span></strong>It has been estimated that microorganisms are involved in the pathogenesis of approximately 20% of all cancers. Testicular germ cell tumours (TGCTs) are the most common type of malignancy in young men and arise from the precursor cell, Germ Cell Neoplasia in Situ (GCNIS). The microbiome of seminal plasma and testicular tissue has not been thoroughly investigated in regard to TGCTs.</p>
<p><strong class="sub-title">Objectives:<span> </span></strong>To investigate the differences in the seminal plasma microbiome between men with TGCT or GCNIS-only compared with controls.</p>
<p><strong class="sub-title">Materials and methods:<span> </span></strong>The study population consisted of patients with GCNIS-only (n = 5), TGCT (n = 18), and controls (n = 25) with different levels of sperm counts in the ejaculate. RNA was isolated from the seminal plasma and sequenced. Reads not mapping to the human genome were aligned against a set of 2784 bacterial/archaeal and 4336 viral genomes using the Kraken pipeline.</p>
<p><strong class="sub-title">Results:<span> </span></strong>We identified reads from 2172 species and most counts were from Alteromonas mediterranea, Falconid herpesvirus 1, and Stigmatella aurantiaca. Six species (Acaryochloris marina, Halovirus HGTV-1, Thermaerobacter marianensis, Thioalkalivibrio sp. K90mix, Burkholderia sp. YI23, and Desulfurivibrio alkaliphilus) were found in significantly (q-value <0.05) higher levels in the seminal plasma of TGCT and GCNIS-only patients compared with controls. In contrast, Streptomyces phage VWB, was found at significantly higher levels among controls compared with TGCT and GCNIS-only patients combined.</p>
<p><strong class="sub-title">Discussion:<span> </span></strong>Often the microbiome is analysed by shotgun or 16S ribosomal sequencing whereas our present data builds on small RNA sequencing. This allowed us to identify more viruses and phages compared to previous studies, but also makes the results difficult to directly compare.</p>
<p><strong class="sub-title">Conclusion:<span> </span></strong>Our study is the first to report identification of the microbiome species in seminal plasma of men with TGCT and GCNIS-only, which potentially could be involved in the pathogenesis of TGCTs. Further studies are, however, needed to confirm our findings. This article is protected by copyright. All rights reserved.</p>',
'date' => '2022-09-28',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/36168917/',
'doi' => '10.1111/andr.13305',
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'name' => 'Neutral sphingomyelinase 2 inhibition attenuates extracellular vesiclerelease and improves neurobehavioral deficits in murine HIV.',
'authors' => 'Zhu X. et al.',
'description' => '<p>People living with HIV (PLH) have significantly higher rates of cognitive impairment (CI) and major depressive disorder (MDD) versus the general population. The enzyme neutral sphingomyelinase 2 (nSMase2) is involved in the biogenesis of ceramide and extracellular vesicles (EVs), both of which are dysregulated in PLH, CI, and MDD. Here we evaluated EcoHIV-infected mice for behavioral abnormalities relevant to depression and cognition deficits, and assessed the behavioral and biochemical effects of nSMase2 inhibition. Mice were infected with EcoHIV and daily treatment with either vehicle or the nSMase2 inhibitor (R)-(1-(3-(3,4-dimethoxyphenyl)-2,6-dimethylimidazo[1,2-b]pyridazin-8-yl)pyrrolidin-3-yl)-carbamate (PDDC) began 3 weeks post-infection. After 2 weeks of treatment, mice were subjected to behavior tests. EcoHIV-infected mice exhibited behavioral abnormalities relevant to MDD and CI that were reversed by PDDC treatment. EcoHIV infection significantly increased cortical brain nSMase2 activity, resulting in trend changes in sphingomyelin and ceramide levels that were normalized by PDDC treatment. EcoHIV-infected mice also exhibited increased levels of brain-derived EVs and altered microRNA cargo, including miR-183-5p, miR-200c-3p, miR-200b-3p, and miR-429-3p, known to be associated with MDD and CI; all were normalized by PDDC. In conclusion, inhibition of nSMase2 represents a possible new therapeutic strategy for the treatment of HIV-associated CI and MDD.</p>',
'date' => '2022-07-01',
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'authors' => 'Fasimoye Rotimi Yemi et al.',
'description' => '<p>Spliced leader trans-splicing is essential for gene expression in many eukaryotes. To elucidate the molecular mechanism of this process, we characterise the molecules associated with the Caenorhabditis elegans major spliced leader snRNP (SL1 snRNP), which donates the spliced leader that replaces the 5' untranslated region of most pre-mRNAs. Using a GFP-tagged version of the SL1 snRNP protein SNA-1 created by CRISPR-mediated genome engineering, we immunoprecipitate and identify RNAs and protein components by RIP-Seq and mass spectrometry. This reveals the composition of the SL1 snRNP and identifies associations with spliceosome components PRP-8 and PRP-19. Significantly, we identify a novel, nematode-specific protein required for SL1 trans-splicing, which we designate SNA-3. SNA-3 is an essential, nuclear protein with three NADAR domains whose function is unknown. Mutation of key residues in NADAR domains inactivates the protein, indicating that domain function is required for activity. SNA-3 interacts with the CBC-ARS2 complex and other factors involved in RNA metabolism, including SUT-1 protein, through RNA or protein-mediated contacts revealed by yeast two-hybrid assays, localisation studies and immunoprecipitations. Our data are compatible with a role for SNA-3 in coordinating trans-splicing with target pre-mRNA transcription or in the processing of the Y-branch product of the trans-splicing reaction.</p>',
'date' => '2022-07-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35736244',
'doi' => '10.1093/nar/gkac534',
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'name' => 'Interspecies effectors of a transgenerational memory of bacterial infection in Caenorhabditis elegans .',
'authors' => 'Legüe Marcela et al.',
'description' => '<p>The inheritance of memory is an adaptive trait. Microbes challenge the immunity of organisms and trigger behavioral adaptations that can be inherited, but how bacteria produce inheritance of a trait is unknown. We use and its bacteria to study the transgenerational RNA dynamics of interspecies crosstalk leading to a heritable behavior. A heritable response of to microbes is the pathogen-induced diapause (PIDF), a state of suspended animation to evade infection. We identify RsmY, a small RNA involved in quorum sensing in as a trigger of PIDF. The histone methyltransferase (HMT) SET-18/SMYD3 and the argonaute HRDE-1, which promotes multi-generational silencing in the germline, are also needed for PIDF initiation The HMT SET-25/EHMT2 is necessary for memory maintenance in the transgenerational lineage. Our work is a starting point to understanding microbiome-induced inheritance of acquired traits, and the transgenerational influence of microbes in health and disease.</p>',
'date' => '2022-07-01',
'pmid' => 'https://www.sciencedirect.com/science/article/pii/S2589004222008999',
'doi' => '10.1016/j.isci.2022.104627',
'modified' => '2022-09-28 09:00:02',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 5 => array(
'id' => '4471',
'name' => 'Diverse Monogenic Subforms of Human Spermatogenic Failure',
'authors' => 'Nagirnaja L. et al. ',
'description' => '<p>Non-obstructive azoospermia (NOA) is the most severe form of male infertility and typically incurable with current medicine. Due to the biological complexity of sperm production, defining the genetic basis of NOA has proven challenging, and to date, the most advanced classification of NOA subforms is based on simple description of testis histology. In this study, we exome-sequenced over 1,000 clinically diagnosed NOA cases and identified a plausible recessive Mendelian cause in 20\%. Population-based testing against fertile controls identified 27 genes as significantly associated with azoospermia. The disrupted genes are primarily on the autosomes, enriched for undescribed human “knockouts”, and, for the most part, have yet to be linked to a Mendelian trait. Integration with single-cell RNA sequencing of adult testes shows that, rather than affecting a single cell type or pathway, azoospermia genes can be grouped into molecular subforms with highly synchronized expression patterns, and analogs of these subforms exist in mice. This analysis framework identifies groups of genes with known roles in spermatogenesis but also reveals unrecognized subforms, such as a set of genes expressed specifically in mitotic divisions of type B spermatogonia. Our findings highlight NOA as an understudied Mendelian disorder and provide a conceptual structure for organizing the complex genetics of male infertility, which may serve as a basis for disease classification more advanced than histology.</p>',
'date' => '2022-07-01',
'pmid' => 'https://doi.org/10.1101%2F2022.07.19.22271581',
'doi' => '10.1101/2022.07.19.22271581',
'modified' => '2022-11-18 12:14:08',
'created' => '2022-11-15 09:26:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 6 => array(
'id' => '4390',
'name' => 'Ultrasensitive Ribo-seq reveals translational landscapes duringmammalian oocyte-to-embryo transition and pre-implantation development.',
'authors' => 'Xiong Zhuqing et al.',
'description' => '<p>In mammals, translational control plays critical roles during oocyte-to-embryo transition (OET) when transcription ceases. However, the underlying regulatory mechanisms remain challenging to study. Here, using low-input Ribo-seq (Ribo-lite), we investigated translational landscapes during OET using 30-150 mouse oocytes or embryos per stage. Ribo-lite can also accommodate single oocytes. Combining PAIso-seq to interrogate poly(A) tail lengths, we found a global switch of translatome that closely parallels changes of poly(A) tails upon meiotic resumption. Translation activation correlates with polyadenylation and is supported by polyadenylation signal proximal cytoplasmic polyadenylation elements (papCPEs) in 3' untranslated regions. By contrast, translation repression parallels global de-adenylation. The latter includes transcripts containing no CPEs or non-papCPEs, which encode many transcription regulators that are preferentially re-activated before zygotic genome activation. CCR4-NOT, the major de-adenylation complex, and its key adaptor protein BTG4 regulate translation downregulation often independent of RNA decay. BTG4 is not essential for global de-adenylation but is required for selective gene de-adenylation and production of very short-tailed transcripts. In sum, our data reveal intimate interplays among translation, RNA stability and poly(A) tail length regulation underlying mammalian OET.</p>',
'date' => '2022-06-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35697785',
'doi' => '10.1038/s41556-022-00928-6',
'modified' => '2022-08-11 14:17:11',
'created' => '2022-08-11 12:14:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 7 => array(
'id' => '4421',
'name' => 'Translation is a key determinant controlling the fate of cytoplasmic longnon-coding RNAs',
'authors' => 'Andjus Sara et al.',
'description' => '<p>Despite predicted to lack coding potential, cytoplasmic long non-coding (lnc)RNAs can associate with ribosomes, resulting in some cases into the production of functional peptides. However, the biological and mechanistic relevance of this pervasive lncRNAs translation remains poorly studied. In yeast, cytoplasmic Xrn1-sensitive lncRNAs (XUTs) are targeted by the Nonsense-Mediated mRNA Decay (NMD), suggesting a translation-dependent degradation process. Here, we report that XUTs are translated, which impacts their abundance. We show that XUTs globally accumulate upon translation elongation inhibition, but not when initial ribosome loading is impaired. Translation also affects XUTs independently of NMD, by interfering with their decapping. Ribo-Seq confirmed ribosomes binding to XUTs and identified actively translated small ORFs in their 5’-proximal region. Mechanistic analyses revealed that their NMD-sensitivity depends on the 3’-untranslated region length. Finally, we detected the peptide derived from the translation of an NMD-sensitive XUT reporter in NMD-competent cells. Our work highlights the role of translation in the metabolism of XUTs, which could contribute to expose genetic novelty to the natural selection, while NMD restricts their expression.</p>',
'date' => '2022-05-01',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2022.05.25.493276v1',
'doi' => '10.1101/2022.05.25.493276',
'modified' => '2022-09-27 14:48:05',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 8 => array(
'id' => '4252',
'name' => 'MGcount: a total RNA-seq quantification tool to address multi-mappingand multi-overlapping alignments ambiguity in non-coding transcripts',
'authors' => 'Hita Andrea, Brocart Gilles, Fernandez Ana, Rehmsmeier Marc, Alemany Anna, Schvartzman Sol',
'description' => '<p>Background Total-RNA sequencing (total-RNA-seq) allows the simultaneous study of both the coding and the non-coding transcriptome. Yet, computational pipelines have traditionally focused on particular biotypes, making assumptions that are not fullfilled by total-RNA-seq datasets. Transcripts from distinct RNA biotypes vary in length, biogenesis, and function, can overlap in a genomic region, and may be present in the genome with a high copy number. Consequently, reads from total-RNA-seq libraries may cause ambiguous genomic alignments, demanding for flexible quantification approaches. Results Here we present Multi-Graph count (MGcount), a total-RNA-seq quantification tool combining two strategies for handling ambiguous alignments. First, MGcount assigns reads hierarchically to small-RNA and long-RNA features to account for length disparity when transcripts overlap in the same genomic position. Next, MGcount aggregates RNA products with similar sequences where reads systematically multi-map using a graph-based approach. MGcount outputs a transcriptomic count matrix compatible with RNA-sequencing downstream analysis pipelines, with both bulk and single-cell resolution, and the graphs that model repeated transcript structures for different biotypes. The software can be used as a python module or as a single-file executable program. Conclusions MGcount is a flexible total-RNA-seq quantification tool that successfully integrates reads that align to multiple genomic locations or that overlap with multiple gene features. Its approach is suitable for the simultaneous estimation of protein-coding, long non-coding and small non-coding transcript concentration, in both precursor and processed forms. Both source code and compiled software are available at https://github.com/hitaandrea/MGcount. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04544-3.</p>',
'date' => '2022-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35030988',
'doi' => '10.1186/s12859-021-04544-3',
'modified' => '2022-05-20 09:42:23',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 9 => array(
'id' => '4223',
'name' => 'Single cell quantification of ribosome occupancy in early mouse development',
'authors' => 'Tori Tonn et al.',
'description' => '<p><span>Technological limitations precluded transcriptome-wide analyses of translation at single cell resolution. To solve this challenge, we developed a novel microfluidic isotachophoresis approach, named RIBOsome profiling via IsoTachoPhoresis (Ribo-ITP), and characterized translation in single oocytes and embryos during early mouse development. We identified differential translation efficiency as a key regulatory mechanism of genes involved in centrosome organization and N</span><sup>6</sup><span>-methyladenosine modification of RNAs. Our high coverage measurements enabled the first analysis of allele-specific ribosome engagement in early development and led to the discovery of stage-specific differential engagement of zygotic RNAs with ribosomes. Finally, by integrating our measurements with proteomics data, we discovered that ribosome occupancy in germinal vesicle stage oocytes is the predominant determinant of protein abundance in the zygote. Taken together, these findings resolve the long-standing paradox of low correlation between RNA expression and protein abundance in early embryonic development. The novel Ribo-ITP approach will enable numerous applications by providing high coverage and high resolution ribosome occupancy measurements from ultra-low input samples including single cells.</span></p>',
'date' => '2021-12-09',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.12.07.471408v1.abstract',
'doi' => 'https://doi.org/10.1101/2021.12.07.471408',
'modified' => '2022-04-29 11:39:09',
'created' => '2022-04-29 11:39:09',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 10 => array(
'id' => '4456',
'name' => 'Single cell quantification of ribosome occupancy in early mousedevelopment',
'authors' => 'Tonn T. et al.',
'description' => '<p>Technological limitations precluded transcriptome-wide analyses of translation at single cell resolution. To solve this challenge, we developed a novel microfluidic isotachophoresis approach, named RIBOsome profiling via IsoTachoPhoresis (Ribo-ITP), and characterized translation in single oocytes and embryos during early mouse development. We identified differential translation efficiency as a key regulatory mechanism of genes involved in centrosome organization and N6-methyladenosine modification of RNAs. Our high coverage measurements enabled the first analysis of allele-specific ribosome engagement in early development and led to the discovery of stage-specific differential engagement of zygotic RNAs with ribosomes. Finally, by integrating our measurements with proteomics data, we discovered that ribosome occupancy in germinal vesicle stage oocytes is the predominant determinant of protein abundance in the zygote. Taken together, these findings resolve the long-standing paradox of low correlation between RNA expression and protein abundance in early embryonic development. The novel Ribo-ITP approach will enable numerous applications by providing high coverage and high resolution ribosome occupancy measurements from ultra-low input samples including single cells.</p>',
'date' => '2021-12-01',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.12.07.471408v1',
'doi' => '10.1101/2021.12.07.471408',
'modified' => '2022-10-21 09:42:29',
'created' => '2022-09-28 09:53:13',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 11 => array(
'id' => '4216',
'name' => 'Functional microRNA targetome undergoes degeneration-induced shift in the retina',
'authors' => 'Chu-Tan JA et al.',
'description' => '<div class="abstract-content selected" id="enc-abstract">
<p><strong class="sub-title">Background:<span> </span></strong>MicroRNA (miRNA) play a significant role in the pathogenesis of complex neurodegenerative diseases including age-related macular degeneration (AMD), acting as post-transcriptional gene suppressors through their association with argonaute 2 (AGO2) - a key member of the RNA Induced Silencing Complex (RISC). Identifying the retinal miRNA/mRNA interactions in health and disease will provide important insight into the key pathways miRNA regulate in disease pathogenesis and may lead to potential therapeutic targets to mediate retinal degeneration.</p>
<p><strong class="sub-title">Methods:<span> </span></strong>To identify the active miRnome targetome interactions in the healthy and degenerating retina, AGO2 HITS-CLIP was performed using a rodent model of photoreceptor degeneration. Analysis of publicly available single-cell RNA sequencing (scRNAseq) data was performed to identify the cellular location of AGO2 and key members of the microRNA targetome in the retina. AGO2 findings were verified by in situ hybridization (RNA) and immunohistochemistry (protein).</p>
<p><strong class="sub-title">Results:<span> </span></strong>Analysis revealed a similar miRnome between healthy and damaged retinas, however, a shift in the active targetome was observed with an enrichment of miRNA involvement in inflammatory pathways. This shift was further demonstrated by a change in the seed binding regions of miR-124-3p, the most abundant retinal AGO2-bound miRNA, and has known roles in regulating retinal inflammation. Additionally, photoreceptor cluster miR-183/96/182 were all among the most highly abundant miRNA bound to AGO2. Following damage, AGO2 expression was localized to the inner retinal layers and more in the OLM than in healthy retinas, indicating a locational miRNA response to retinal damage.</p>
<p><strong class="sub-title">Conclusions:<span> </span></strong>This study provides important insight into the alteration of miRNA regulatory activity that occurs as a response to retinal degeneration and explores the miRNA-mRNA targetome as a consequence of retinal degenerations. Further characterisation of these miRNA/mRNA interactions in the context of the degenerating retina may provide an important insight into the active role these miRNA may play in diseases such as AMD.</p>
</div>
<p><strong class="sub-title">Keywords:<span> </span></strong>Argonaute; HITS-CLIP; Inflammation; Retina; Retinal degeneration; Transcriptome; mRNA; microRNA.</p>',
'date' => '2021-08-31',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34465369/',
'doi' => '10.1186/s13024-021-00478-9',
'modified' => '2022-03-28 11:05:53',
'created' => '2022-03-28 11:03:41',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 12 => array(
'id' => '4429',
'name' => 'Functional microRNA targetome undergoes degeneration-induced shift inthe retina.',
'authors' => 'Chu-Tan Joshua A et al.',
'description' => '<p>BACKGROUND: MicroRNA (miRNA) play a significant role in the pathogenesis of complex neurodegenerative diseases including age-related macular degeneration (AMD), acting as post-transcriptional gene suppressors through their association with argonaute 2 (AGO2) - a key member of the RNA Induced Silencing Complex (RISC). Identifying the retinal miRNA/mRNA interactions in health and disease will provide important insight into the key pathways miRNA regulate in disease pathogenesis and may lead to potential therapeutic targets to mediate retinal degeneration. METHODS: To identify the active miRnome targetome interactions in the healthy and degenerating retina, AGO2 HITS-CLIP was performed using a rodent model of photoreceptor degeneration. Analysis of publicly available single-cell RNA sequencing (scRNAseq) data was performed to identify the cellular location of AGO2 and key members of the microRNA targetome in the retina. AGO2 findings were verified by in situ hybridization (RNA) and immunohistochemistry (protein). RESULTS: Analysis revealed a similar miRnome between healthy and damaged retinas, however, a shift in the active targetome was observed with an enrichment of miRNA involvement in inflammatory pathways. This shift was further demonstrated by a change in the seed binding regions of miR-124-3p, the most abundant retinal AGO2-bound miRNA, and has known roles in regulating retinal inflammation. Additionally, photoreceptor cluster miR-183/96/182 were all among the most highly abundant miRNA bound to AGO2. Following damage, AGO2 expression was localized to the inner retinal layers and more in the OLM than in healthy retinas, indicating a locational miRNA response to retinal damage. CONCLUSIONS: This study provides important insight into the alteration of miRNA regulatory activity that occurs as a response to retinal degeneration and explores the miRNA-mRNA targetome as a consequence of retinal degenerations. Further characterisation of these miRNA/mRNA interactions in the context of the degenerating retina may provide an important insight into the active role these miRNA may play in diseases such as AMD.</p>',
'date' => '2021-08-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34465369',
'doi' => '10.1186/s13024-021-00478-9',
'modified' => '2022-09-28 09:01:43',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 13 => array(
'id' => '4334',
'name' => 'Single-cell microRNA sequencing method comparison and application tocell lines and circulating lung tumor cells',
'authors' => 'Hücker S. et al. ',
'description' => '<p>Molecular single cell analyses provide insights into physiological and pathological processes. Here, in a stepwise approach, we first evaluate 19 protocols for single cell small RNA sequencing on MCF7 cells spiked with 1 pg of 1,006 miRNAs. Second, we analyze MCF7 single cell equivalents of the eight best protocols. Third, we sequence single cells from eight different cell lines and 67 circulating tumor cells (CTCs) from seven SCLC patients. Altogether, we analyze 244 different samples. We observe high reproducibility within protocols and reads covered a broad spectrum of RNAs. For the 67 CTCs, we detect a median of 68 miRNAs, with 10 miRNAs being expressed in 90\% of tested cells. Enrichment analysis suggested the lung as the most likely organ of origin and enrichment of cancer-related categories. Even the identification of non-annotated candidate miRNAs was feasible, underlining the potential of single cell small RNA sequencing.</p>',
'date' => '2021-07-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34262050',
'doi' => '10.1038/s41467-021-24611-w',
'modified' => '2022-08-03 16:15:42',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 14 => array(
'id' => '4219',
'name' => 'Small RNAs in Seminal Plasma as Novel Biomarkers for Germ Cell Tumors',
'authors' => 'Mørup N. et al.',
'description' => '<p><span>Circulating miRNAs secreted by testicular germ cell tumors (TGCT) show great potential as novel non-invasive biomarkers for diagnosis of TGCT. Seminal plasma (SP) represents a biofluid closer to the primary site. Here, we investigate whether small RNAs in SP can be used to diagnose men with TGCTs or the precursor lesions, germ cell neoplasia in situ (GCNIS). Small RNAs isolated from SP from men with TGCTs (</span><i>n</i><span><span> </span>= 18), GCNIS-only (</span><i>n</i><span><span> </span>= 5), and controls (</span><i>n</i><span><span> </span>= 25) were sequenced. SP from men with TGCT/GCNIS (</span><i>n</i><span><span> </span>= 37) and controls (</span><i>n</i><span><span> </span>= 22) were used for validation by RT-qPCR. In general, piRNAs were found at lower levels in SP from men with TGCTs. Ten small RNAs were found at significantly (q-value < 0.05) different levels in SP from men with TGCT/GCNIS than controls. Random forests classification identified sets of small RNAs that could detect either TGCT/GCNIS or GCNIS-only with an area under the curve of 0.98 and 1 in ROC analyses, respectively. RT-qPCR validated hsa-miR-6782-5p to be present at 2.3-fold lower levels (</span><i>p</i><span><span> </span>= 0.02) in the SP from men with TGCTs compared with controls. Small RNAs in SP show potential as novel biomarkers for diagnosing men with TGCT/GCNIS but validation in larger cohorts is needed.</span></p>',
'date' => '2021-05-13',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34067956/',
'doi' => '10.3390/cancers13102346',
'modified' => '2022-04-19 15:29:47',
'created' => '2022-04-19 15:29:47',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => array(
'id' => '4099',
'name' => 'Vesicle-bound regulatory RNAs are associated with tissue aging',
'authors' => 'F. Kern, T. Kuhn, N. Ludwig, M. Simon, L. Gröger, N. Fabis, A. Salhab, T. Fehlmann, O. Hahn, A. Engel, M. Koch, J. Koehler, K. Winek, H. Soreq, G. Fuhrmann, T. Wyss-Coray, E. Meese, M. W. Laschke and A. Keller',
'description' => '<p><span>Previous work on murine models and human demonstrated global as well as tissue-specific molecular aging trajectories in solid tissues and body fluids</span><sup><a id="xref-ref-1-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-1">1</a>–<a id="xref-ref-8-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-8">8</a></sup><span>. Extracellular vesicles like exosomes play a crucial role in communication and information exchange in between such systemic factors and solid tissues</span><sup><a id="xref-ref-9-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-9">9</a>,<a id="xref-ref-10-1" class="xref-bibr" href="https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1#ref-10">10</a></sup><span>. We sequenced freely circulating and vesicle-bound small regulatory RNAs in mice at five time points across the average life span from 2 to 18 months. Intriguingly, each small RNA class exhibits unique aging patterns, which showed differential signatures between vesicle-bound and freely circulating molecules. In particular, tRNA fragments showed overall highest correlation with aging which also matched well between sample types, facilitating age prediction with non-negative matrix factorization (86% accuracy). Interestingly, rRNAs exhibited inverse correlation trajectories between vesicles and plasma while vesicle-bound microRNAs (miRNAs) were exceptionally strong associated with aging. Affected miRNAs regulate the inflammatory response and transcriptional processes, and adipose tissues show considerable effects in associated gene regulatory modules. Finally, nanoparticle tracking and electron microscopy suggest a shift from overall many small to fewer but larger vesicles in aged plasma, potentially contributing to systemic aging trajectories and affecting the molecular aging of organs.</span></p>',
'date' => '2021-05-08',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.05.07.443093v1',
'doi' => '10.1101/2021.05.07.443093',
'modified' => '2022-01-06 14:25:33',
'created' => '2021-05-17 10:44:33',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 16 => array(
'id' => '4427',
'name' => 'Small RNAs in Seminal Plasma as Novel Biomarkers for GermCell Tumors.',
'authors' => 'Mørup Nina et al.',
'description' => '<p>Circulating miRNAs secreted by testicular germ cell tumors (TGCT) show great potential as novel non-invasive biomarkers for diagnosis of TGCT. Seminal plasma (SP) represents a biofluid closer to the primary site. Here, we investigate whether small RNAs in SP can be used to diagnose men with TGCTs or the precursor lesions, germ cell neoplasia in situ (GCNIS). Small RNAs isolated from SP from men with TGCTs ( = 18), GCNIS-only ( = 5), and controls ( = 25) were sequenced. SP from men with TGCT/GCNIS ( = 37) and controls ( = 22) were used for validation by RT-qPCR. In general, piRNAs were found at lower levels in SP from men with TGCTs. Ten small RNAs were found at significantly (q-value < 0.05) different levels in SP from men with TGCT/GCNIS than controls. Random forests classification identified sets of small RNAs that could detect either TGCT/GCNIS or GCNIS-only with an area under the curve of 0.98 and 1 in ROC analyses, respectively. RT-qPCR validated hsa-miR-6782-5p to be present at 2.3-fold lower levels ( = 0.02) in the SP from men with TGCTs compared with controls. Small RNAs in SP show potential as novel biomarkers for diagnosing men with TGCT/GCNIS but validation in larger cohorts is needed.</p>',
'date' => '2021-05-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34067956',
'doi' => '10.3390/cancers13102346',
'modified' => '2022-09-28 09:03:57',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 17 => array(
'id' => '4102',
'name' => 'miRMaster 2.0: multi-species non-coding RNA sequencing analyses at scale',
'authors' => 'Tobias Fehlmann, Fabian Kern, Omar Laham, Christina Backes, Jeffrey Solomon, Pascal Hirsch, Carsten Volz, Rolf Müller, Andreas Keller',
'description' => '<p><span>Analyzing all features of small non-coding RNA sequencing data can be demanding and challenging. To facilitate this process, we developed miRMaster. After the analysis of over 125 000 human samples and 1.5 trillion human small RNA reads over 4 years, we present miRMaster 2 with a wide range of updates and new features. We extended our reference data sets so that miRMaster 2 now supports the analysis of eight species (e.g. human, mouse, chicken, dog, cow) and 10 non-coding RNA classes (e.g. microRNAs, piRNAs, tRNAs, rRNAs, circRNAs). We also incorporated new downstream analysis modules such as batch effect analysis or sample embeddings using UMAP, and updated annotation data bases included by default (miRBase, Ensembl, GtRNAdb). To accommodate the increasing popularity of single cell small-RNA sequencing data, we incorporated a module for unique molecular identifier (UMI) processing. Further, the output tables and graphics have been improved based on user feedback and new output formats that emerged in the community are now supported (e.g. miRGFF3). Finally, we integrated differential expression analysis with the miRNA enrichment analysis tool miEAA. miRMaster is freely available at </span><a href="https://www.ccb.uni-saarland.de/mirmaster2" title="https://www.ccb.uni-saarland.de/mirmaster2">https://www.ccb.uni-saarland.de/mirmaster2</a><span>.</span></p>',
'date' => '2021-04-19',
'pmid' => ' https://doi.org/10.1093/nar/gkab268',
'doi' => '10.1093/nar/gkab268',
'modified' => '2021-06-28 11:45:48',
'created' => '2021-06-28 11:42:21',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 18 => array(
'id' => '4455',
'name' => 'Bacterial small RNAs and host epigenetic effectors of atransgenerational memory of pathogens in C. elegans',
'authors' => 'Legüe M. et al.',
'description' => '<p>The inheritance of memories is adaptive for survival. Microbes interact with all organisms challenging their immunity and triggering behavioral adaptations. Some of these behaviors induced by bacteria can be inherited although the mechanisms of action are largely unexplored. In this work, we use C. elegans and its bacteria to study the transgenerational RNA dynamics of an interspecies crosstalk leading to a heritable behavior. Heritable responses to bacterial pathogens in the nematode include avoidance and pathogen-induced diapause (PIDF), a state of suspended animation to evade the pathogen threat. We identify a small RNA RsmY, involved in quorum sensing from P. aeruginosa as required for initiation of PIDF. Histone methyltransferase SET-18/SMYD3 is also needed for PIDF initiation in C. elegans. In contrast, SET-25/EHMT2 is necessary for the maintenance of the memory of pathogen exposure in the transgenerational lineage. This work can be a starting point to understanding microbiome-induced inheritance of acquired traits.</p>',
'date' => '2021-03-01',
'pmid' => 'https://www.biorxiv.org/content/10.1101/2021.03.26.437277v1',
'doi' => '10.1101/2021.03.26.437277',
'modified' => '2022-10-21 09:41:13',
'created' => '2022-09-28 09:53:13',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 19 => array(
'id' => '4425',
'name' => 'Interspecies RNA Interactome of Pathogen and Host in aHeritable Defensive Strategy.',
'authors' => 'Legüe Marcela et al.',
'description' => '<p>Communication with bacteria deeply impacts the life history traits of their hosts. Through specific molecules and metabolites, bacteria can promote short- and long-term phenotypic and behavioral changes in the nematode . The chronic exposure of to pathogens promotes the adaptive behavior in the host's progeny called pathogen-induced diapause formation (PIDF). PIDF is a pathogen avoidance strategy induced in the second generation of animals infected and can be recalled transgenerationally. This behavior requires the RNA interference machinery and specific nematode and bacteria small RNAs (sRNAs). In this work, we assume that RNAs from both species co-exist and can interact with each other. Under this principle, we explore the potential interspecies RNA interactions during PIDF-triggering conditions, using transcriptomic data from the holobiont. We study two transcriptomics datasets: first, the dual sRNA expression of PAO1 and in a transgenerational paradigm for six generations and second, the simultaneous expression of sRNAs and mRNA in intergenerational PIDF. We focus on those bacterial sRNAs that are systematically overexpressed in the intestines of animals compared with sRNAs expressed in host-naïve bacteria. We selected diverse methods that represent putative mechanisms of RNA-mediated interspecies interaction. These interactions are as follows: heterologous perfect and incomplete pairing between bacterial RNA and host mRNA; sRNAs of similar sequence expressed in both species that could mimic each other; and known or predicted eukaryotic motifs present in bacterial transcripts. We conclude that a broad spectrum of tools can be applied for the identification of potential sRNA and mRNA targets of the interspecies RNA interaction that can be subsequently tested experimentally.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34367078',
'doi' => '10.3389/fmicb.2021.649858',
'modified' => '2022-09-28 09:06:04',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
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(int) 20 => array(
'id' => '4426',
'name' => 'Distinct Extracellular RNA Profiles in Different PlasmaComponents.',
'authors' => 'Jia Jing et al.',
'description' => '<p>Circulating extracellular RNAs (exRNAs) have great potential to serve as biomarkers for a wide range of diagnostic, therapeutic, and prognostic applications. So far, knowledge of the difference among different sources of exRNAs is limited. To address this issue, we performed a sequential physical and biochemical precipitation to collect four fractions (platelets and cell debris, the thrombin-induced precipitates, extracellular vesicles, and supernatant) from each of 10 plasma samples. From total RNAs of the 40 fractions, we prepared ligation-free libraries to profile full spectrum of all RNA species, without size selection and rRNA reduction. Due to complicated RNA composition in these libraries, we utilized a successive stepwise alignment strategy to map the RNA sequences to different RNA categories, including miRNAs, piwi-interacting RNAs, tRNAs, rRNAs, lincRNAs, snoRNAs, snRNAs, other ncRNAs, protein coding RNAs, and circRNAs. Our data showed that each plasma fraction had its own unique distribution of RNA species. Hierarchical cluster analyses using transcript abundance demonstrated similarities in the same plasma fraction and significant differences between different fractions. In addition, we observed various unique transcripts, and novel predicted miRNAs among these plasma fractions. These results demonstrate that the distribution of RNA species and functional RNA transcripts is plasma fraction-dependent. Appropriate plasma preparation and thorough inspection of different plasma fractions are necessary for an exRNA-based biomarker study.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34234804',
'doi' => '10.3389/fgene.2021.564780',
'modified' => '2022-09-28 09:06:47',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
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),
(int) 21 => array(
'id' => '4008',
'name' => 'Genes with 5′ terminal oligopyrimidine tracts preferentially escapeglobal suppression of translation by the SARS-CoV-2 NSP1 protein',
'authors' => 'Shilpa, Rao and Ian, Hoskins and P., Daniela Garcia and Tori, Tonn andHakan, Ozadam and Elif, Sarinay Cenik and ProfileCan, Cenik',
'description' => '<p>Viruses rely on the host translation machinery to synthesize their own proteins. Consequently, they have evolved varied mechanisms to co-opt host translation for their survival. SARS-CoV-2 relies on a non-structural protein, NSP1, for shutting down host translation. Despite this, it is currently unknown how viral proteins and host factors critical for viral replication can escape a global shutdown of host translation. Here, using a novel FACS-based assay called MeTAFlow, we report a dose-dependent reduction in both nascent protein synthesis and mRNA abundance in cells expressing NSP1. We perform RNA-Seq and matched ribosome profiling experiments to identify gene-specific changes both at the mRNA expression and translation level. We discover a functionally-coherent subset of human genes preferentially translated in the context of NSP1 expression. These genes include the translation machinery components, RNA binding proteins, and others important for viral pathogenicity. Importantly, we also uncover potential mechanisms of preferential translation through the presence of shared sites for specific RNA binding proteins and a remarkable enrichment for 5′ terminal olig
