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Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (<100 nucleotides long) RNAs that operate as key regulators in cellular processes.
This analysis provides information for either genes or isoforms with their expression levels.
If you require a type of analysis that is not in the previous list, please consult with our expert bioinformatics team.
 How to properly cite this product in your workDiagenode strongly recommends using this: RNA Data Analysis (Diagenode Cat# G02030005). Click here to copy to clipboard. Using our products in your publication? Let us know! |
MGcount: a total RNA-seq quantification tool to address multi-mappingand multi-overlapping alignments ambiguity in non-coding transcripts |
Notice (8): Undefined variable: solution_of_interest [APP/View/Products/view.ctp, line 755]Code Context<!-- BEGIN: REQUEST_FORM MODAL --><div id="request_formModal" class="reveal-modal medium" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog"><?= $this->element('Forms/simple_form', array('solution_of_interest' => $solution_of_interest, 'header' => $header, 'message' => $message, 'campaign_id' => $campaign_id)) ?>$viewFile = '/home/website-server/www/app/View/Products/view.ctp' $dataForView = array( 'language' => 'jp', 'meta_keywords' => '', 'meta_description' => '', 'meta_title' => '', 'product' => array( 'Product' => array( 'id' => '3062', 'antibody_id' => null, 'name' => 'RNA Data Analysis', 'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (<100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p> <div class="extra-spaced"> <h2>What do we provide with the analysis?</h2> <p>This analysis provides information for either genes or isoforms with their expression levels.</p> <h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3> <ul style="list-style-type: circle;"> <li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</li> <li>Trimmed and filtered reads in fastQ files after sequencing QC</li> <li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li> <li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li> </ul> <h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3> <ul style="list-style-type: circle;"> <li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li> <li>Functional gene annotation</li> <li>Gene ontology enrichment analysis on DEGs</li> <li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li> <li>Alternative splicing analysis</li> <li>Gene fusion analysis</li> <li>Novel transcript identification</li> </ul> <h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3> <p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p> </div> <div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>', 'label1' => '', 'info1' => '', 'label2' => '', 'info2' => '', 'label3' => '', 'info3' => '', 'format' => '', 'catalog_number' => 'G02030005', 'old_catalog_number' => '', 'sf_code' => '', 'type' => 'ACC', 'search_order' => '', 'price_EUR' => '/', 'price_USD' => '/', 'price_GBP' => '/', 'price_JPY' => '42800', 'price_CNY' => '/', 'price_AUD' => '/', 'country' => 'ALL', 'except_countries' => 'None', 'quote' => true, 'in_stock' => false, 'featured' => true, 'no_promo' => false, 'online' => true, 'master' => true, 'last_datasheet_update' => '', 'slug' => 'rna-data-analysis', 'meta_title' => '', 'meta_keywords' => '', 'meta_description' => '', 'modified' => '2022-11-22 15:26:24', 'created' => '2020-03-26 10:04:58', 'locale' => 'jpn' ), 'Antibody' => array( 'host' => '*****', 'id' => null, 'name' => null, 'description' => null, 'clonality' => null, 'isotype' => null, 'lot' => null, 'concentration' => null, 'reactivity' => null, 'type' => null, 'purity' => null, 'classification' => null, 'application_table' => null, 'storage_conditions' => null, 'storage_buffer' => null, 'precautions' => null, 'uniprot_acc' => null, 'slug' => null, 'meta_keywords' => null, 'meta_description' => null, 'modified' => null, 'created' => null, 'select_label' => null ), 'Slave' => array(), 'Group' => array(), 'Related' => array(), 'Application' => array(), 'Category' => array(), 'Document' => array(), 'Feature' => array(), 'Image' => array(), 'Promotion' => array(), 'Protocol' => array(), 'Publication' => array( (int) 0 => array( [maximum depth reached] ) ), 'Testimonial' => array(), 'Area' => array(), 'SafetySheet' => array() ) ) $language = 'jp' $meta_keywords = '' $meta_description = '' $meta_title = '' $product = array( 'Product' => array( 'id' => '3062', 'antibody_id' => null, 'name' => 'RNA Data Analysis', 'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (<100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p> <div class="extra-spaced"> <h2>What do we provide with the analysis?</h2> <p>This analysis provides information for either genes or isoforms with their expression levels.</p> <h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3> <ul style="list-style-type: circle;"> <li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</li> <li>Trimmed and filtered reads in fastQ files after sequencing QC</li> <li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li> <li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li> </ul> <h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3> <ul style="list-style-type: circle;"> <li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li> <li>Functional gene annotation</li> <li>Gene ontology enrichment analysis on DEGs</li> <li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li> <li>Alternative splicing analysis</li> <li>Gene fusion analysis</li> <li>Novel transcript identification</li> </ul> <h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3> <p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p> </div> <div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>', 'label1' => '', 'info1' => '', 'label2' => '', 'info2' => '', 'label3' => '', 'info3' => '', 'format' => '', 'catalog_number' => 'G02030005', 'old_catalog_number' => '', 'sf_code' => '', 'type' => 'ACC', 'search_order' => '', 'price_EUR' => '/', 'price_USD' => '/', 'price_GBP' => '/', 'price_JPY' => '42800', 'price_CNY' => '/', 'price_AUD' => '/', 'country' => 'ALL', 'except_countries' => 'None', 'quote' => true, 'in_stock' => false, 'featured' => true, 'no_promo' => false, 'online' => true, 'master' => true, 'last_datasheet_update' => '', 'slug' => 'rna-data-analysis', 'meta_title' => '', 'meta_keywords' => '', 'meta_description' => '', 'modified' => '2022-11-22 15:26:24', 'created' => '2020-03-26 10:04:58', 'locale' => 'jpn' ), 'Antibody' => array( 'host' => '*****', 'id' => null, 'name' => null, 'description' => null, 'clonality' => null, 'isotype' => null, 'lot' => null, 'concentration' => null, 'reactivity' => null, 'type' => null, 'purity' => null, 'classification' => null, 'application_table' => null, 'storage_conditions' => null, 'storage_buffer' => null, 'precautions' => null, 'uniprot_acc' => null, 'slug' => null, 'meta_keywords' => null, 'meta_description' => null, 'modified' => null, 'created' => null, 'select_label' => null ), 'Slave' => array(), 'Group' => array(), 'Related' => array(), 'Application' => array(), 'Category' => array(), 'Document' => array(), 'Feature' => array(), 'Image' => array(), 'Promotion' => array(), 'Protocol' => array(), 'Publication' => array( (int) 0 => 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] ) ) ), 'Testimonial' => array(), 'Area' => array(), 'SafetySheet' => array() ) $country = 'US' $countries_allowed = array( (int) 0 => 'CA', (int) 1 => 'US', (int) 2 => 'IE', (int) 3 => 'GB', (int) 4 => 'DK', (int) 5 => 'NO', (int) 6 => 'SE', (int) 7 => 'FI', (int) 8 => 'NL', (int) 9 => 'BE', (int) 10 => 'LU', (int) 11 => 'FR', (int) 12 => 'DE', (int) 13 => 'CH', (int) 14 => 'AT', (int) 15 => 'ES', (int) 16 => 'IT', (int) 17 => 'PT' ) $outsource = false $other_formats = array() $edit = '' $testimonials = '' $featured_testimonials = '' $related_products = '' $rrbs_service = array( (int) 0 => (int) 1894, (int) 1 => (int) 1895 ) $chipseq_service = array( (int) 0 => (int) 2683, (int) 1 => (int) 1835, (int) 2 => (int) 1836, (int) 3 => (int) 2684, (int) 4 => (int) 1838, (int) 5 => (int) 1839, (int) 6 => (int) 1856 ) $labelize = object(Closure) { } $old_catalog_number = '' $label = '<img src="/img/banners/banner-customizer-back.png" alt=""/>' $publication = 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( 'id' => '5682', 'product_id' => '3062', 'publication_id' => '4252' ) ) $externalLink = ' <a href="https://www.ncbi.nlm.nih.gov/pubmed/35030988" target="_blank"><i class="fa fa-external-link"></i></a>'include - APP/View/Products/view.ctp, line 755 View::_evaluate() - CORE/Cake/View/View.php, line 971 View::_render() - CORE/Cake/View/View.php, line 933 View::render() - CORE/Cake/View/View.php, line 473 Controller::render() - CORE/Cake/Controller/Controller.php, line 963 ProductsController::slug() - APP/Controller/ProductsController.php, line 1052 ReflectionMethod::invokeArgs() - [internal], line ?? Controller::invokeAction() - CORE/Cake/Controller/Controller.php, line 491 Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 193 Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167 [main] - APP/webroot/index.php, line 118
Notice (8): Undefined variable: header [APP/View/Products/view.ctp, line 755]Code Context<!-- BEGIN: REQUEST_FORM MODAL --><div id="request_formModal" class="reveal-modal medium" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog"><?= $this->element('Forms/simple_form', array('solution_of_interest' => $solution_of_interest, 'header' => $header, 'message' => $message, 'campaign_id' => $campaign_id)) ?>$viewFile = '/home/website-server/www/app/View/Products/view.ctp' $dataForView = array( 'language' => 'jp', 'meta_keywords' => '', 'meta_description' => '', 'meta_title' => '', 'product' => array( 'Product' => array( 'id' => '3062', 'antibody_id' => null, 'name' => 'RNA Data Analysis', 'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (<100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p> <div class="extra-spaced"> <h2>What do we provide with the analysis?</h2> <p>This analysis provides information for either genes or isoforms with their expression levels.</p> <h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3> <ul style="list-style-type: circle;"> <li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</li> <li>Trimmed and filtered reads in fastQ files after sequencing QC</li> <li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li> <li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li> </ul> <h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3> <ul style="list-style-type: circle;"> <li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li> <li>Functional gene annotation</li> <li>Gene ontology enrichment analysis on DEGs</li> <li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li> <li>Alternative splicing analysis</li> <li>Gene fusion analysis</li> <li>Novel transcript identification</li> </ul> <h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3> <p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p> </div> <div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>', 'label1' => '', 'info1' => '', 'label2' => '', 'info2' => '', 'label3' => '', 'info3' => '', 'format' => '', 'catalog_number' => 'G02030005', 'old_catalog_number' => '', 'sf_code' => '', 'type' => 'ACC', 'search_order' => '', 'price_EUR' => '/', 'price_USD' => '/', 'price_GBP' => '/', 'price_JPY' => '42800', 'price_CNY' => '/', 'price_AUD' => '/', 'country' => 'ALL', 'except_countries' => 'None', 'quote' => true, 'in_stock' => false, 'featured' => true, 'no_promo' => false, 'online' => true, 'master' => true, 'last_datasheet_update' => '', 'slug' => 'rna-data-analysis', 'meta_title' => '', 'meta_keywords' => '', 'meta_description' => '', 'modified' => '2022-11-22 15:26:24', 'created' => '2020-03-26 10:04:58', 'locale' => 'jpn' ), 'Antibody' => array( 'host' => '*****', 'id' => null, 'name' => null, 'description' => null, 'clonality' => null, 'isotype' => null, 'lot' => null, 'concentration' => null, 'reactivity' => null, 'type' => null, 'purity' => null, 'classification' => null, 'application_table' => null, 'storage_conditions' => null, 'storage_buffer' => null, 'precautions' => null, 'uniprot_acc' => null, 'slug' => null, 'meta_keywords' => null, 'meta_description' => null, 'modified' => null, 'created' => null, 'select_label' => null ), 'Slave' => array(), 'Group' => array(), 'Related' => array(), 'Application' => array(), 'Category' => array(), 'Document' => array(), 'Feature' => array(), 'Image' => array(), 'Promotion' => array(), 'Protocol' => array(), 'Publication' => array( (int) 0 => array( [maximum depth reached] ) ), 'Testimonial' => array(), 'Area' => array(), 'SafetySheet' => array() ) ) $language = 'jp' $meta_keywords = '' $meta_description = '' $meta_title = '' $product = array( 'Product' => array( 'id' => '3062', 'antibody_id' => null, 'name' => 'RNA Data Analysis', 'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (<100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p> <div class="extra-spaced"> <h2>What do we provide with the analysis?</h2> <p>This analysis provides information for either genes or isoforms with their expression levels.</p> <h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3> <ul style="list-style-type: circle;"> <li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</li> <li>Trimmed and filtered reads in fastQ files after sequencing QC</li> <li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li> <li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li> </ul> <h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3> <ul style="list-style-type: circle;"> <li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li> <li>Functional gene annotation</li> <li>Gene ontology enrichment analysis on DEGs</li> <li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li> <li>Alternative splicing analysis</li> <li>Gene fusion analysis</li> <li>Novel transcript identification</li> </ul> <h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3> <p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p> </div> <div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>', 'label1' => '', 'info1' => '', 'label2' => '', 'info2' => '', 'label3' => '', 'info3' => '', 'format' => '', 'catalog_number' => 'G02030005', 'old_catalog_number' => '', 'sf_code' => '', 'type' => 'ACC', 'search_order' => '', 'price_EUR' => '/', 'price_USD' => '/', 'price_GBP' => '/', 'price_JPY' => '42800', 'price_CNY' => '/', 'price_AUD' => '/', 'country' => 'ALL', 'except_countries' => 'None', 'quote' => true, 'in_stock' => false, 'featured' => true, 'no_promo' => false, 'online' => true, 'master' => true, 'last_datasheet_update' => '', 'slug' => 'rna-data-analysis', 'meta_title' => '', 'meta_keywords' => '', 'meta_description' => '', 'modified' => '2022-11-22 15:26:24', 'created' => '2020-03-26 10:04:58', 'locale' => 'jpn' ), 'Antibody' => array( 'host' => '*****', 'id' => null, 'name' => null, 'description' => null, 'clonality' => null, 'isotype' => null, 'lot' => null, 'concentration' => null, 'reactivity' => null, 'type' => null, 'purity' => null, 'classification' => null, 'application_table' => null, 'storage_conditions' => null, 'storage_buffer' => null, 'precautions' => null, 'uniprot_acc' => null, 'slug' => null, 'meta_keywords' => null, 'meta_description' => null, 'modified' => null, 'created' => null, 'select_label' => null ), 'Slave' => array(), 'Group' => array(), 'Related' => array(), 'Application' => array(), 'Category' => array(), 'Document' => array(), 'Feature' => array(), 'Image' => array(), 'Promotion' => array(), 'Protocol' => array(), 'Publication' => array( (int) 0 => 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] ) ) ), 'Testimonial' => array(), 'Area' => array(), 'SafetySheet' => array() ) $country = 'US' $countries_allowed = array( (int) 0 => 'CA', (int) 1 => 'US', (int) 2 => 'IE', (int) 3 => 'GB', (int) 4 => 'DK', (int) 5 => 'NO', (int) 6 => 'SE', (int) 7 => 'FI', (int) 8 => 'NL', (int) 9 => 'BE', (int) 10 => 'LU', (int) 11 => 'FR', (int) 12 => 'DE', (int) 13 => 'CH', (int) 14 => 'AT', (int) 15 => 'ES', (int) 16 => 'IT', (int) 17 => 'PT' ) $outsource = false $other_formats = array() $edit = '' $testimonials = '' $featured_testimonials = '' $related_products = '' $rrbs_service = array( (int) 0 => (int) 1894, (int) 1 => (int) 1895 ) $chipseq_service = array( (int) 0 => (int) 2683, (int) 1 => (int) 1835, (int) 2 => (int) 1836, (int) 3 => (int) 2684, (int) 4 => (int) 1838, (int) 5 => (int) 1839, (int) 6 => (int) 1856 ) $labelize = object(Closure) { } $old_catalog_number = '' $label = '<img src="/img/banners/banner-customizer-back.png" alt=""/>' $publication = 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( 'id' => '5682', 'product_id' => '3062', 'publication_id' => '4252' ) ) $externalLink = ' <a href="https://www.ncbi.nlm.nih.gov/pubmed/35030988" target="_blank"><i class="fa fa-external-link"></i></a>'include - APP/View/Products/view.ctp, line 755 View::_evaluate() - CORE/Cake/View/View.php, line 971 View::_render() - CORE/Cake/View/View.php, line 933 View::render() - CORE/Cake/View/View.php, line 473 Controller::render() - CORE/Cake/Controller/Controller.php, line 963 ProductsController::slug() - APP/Controller/ProductsController.php, line 1052 ReflectionMethod::invokeArgs() - [internal], line ?? Controller::invokeAction() - CORE/Cake/Controller/Controller.php, line 491 Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 193 Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167 [main] - APP/webroot/index.php, line 118
Notice (8): Undefined variable: message [APP/View/Products/view.ctp, line 755]Code Context<!-- BEGIN: REQUEST_FORM MODAL --><div id="request_formModal" class="reveal-modal medium" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog"><?= $this->element('Forms/simple_form', array('solution_of_interest' => $solution_of_interest, 'header' => $header, 'message' => $message, 'campaign_id' => $campaign_id)) ?>$viewFile = '/home/website-server/www/app/View/Products/view.ctp' $dataForView = array( 'language' => 'jp', 'meta_keywords' => '', 'meta_description' => '', 'meta_title' => '', 'product' => array( 'Product' => array( 'id' => '3062', 'antibody_id' => null, 'name' => 'RNA Data Analysis', 'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (<100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p> <div class="extra-spaced"> <h2>What do we provide with the analysis?</h2> <p>This analysis provides information for either genes or isoforms with their expression levels.</p> <h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3> <ul style="list-style-type: circle;"> <li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</li> <li>Trimmed and filtered reads in fastQ files after sequencing QC</li> <li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li> <li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li> </ul> <h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3> <ul style="list-style-type: circle;"> <li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li> <li>Functional gene annotation</li> <li>Gene ontology enrichment analysis on DEGs</li> <li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li> <li>Alternative splicing analysis</li> <li>Gene fusion analysis</li> <li>Novel transcript identification</li> </ul> <h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3> <p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p> </div> <div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>', 'label1' => '', 'info1' => '', 'label2' => '', 'info2' => '', 'label3' => '', 'info3' => '', 'format' => '', 'catalog_number' => 'G02030005', 'old_catalog_number' => '', 'sf_code' => '', 'type' => 'ACC', 'search_order' => '', 'price_EUR' => '/', 'price_USD' => '/', 'price_GBP' => '/', 'price_JPY' => '42800', 'price_CNY' => '/', 'price_AUD' => '/', 'country' => 'ALL', 'except_countries' => 'None', 'quote' => true, 'in_stock' => false, 'featured' => true, 'no_promo' => false, 'online' => true, 'master' => true, 'last_datasheet_update' => '', 'slug' => 'rna-data-analysis', 'meta_title' => '', 'meta_keywords' => '', 'meta_description' => '', 'modified' => '2022-11-22 15:26:24', 'created' => '2020-03-26 10:04:58', 'locale' => 'jpn' ), 'Antibody' => array( 'host' => '*****', 'id' => null, 'name' => null, 'description' => null, 'clonality' => null, 'isotype' => null, 'lot' => null, 'concentration' => null, 'reactivity' => null, 'type' => null, 'purity' => null, 'classification' => null, 'application_table' => null, 'storage_conditions' => null, 'storage_buffer' => null, 'precautions' => null, 'uniprot_acc' => null, 'slug' => null, 'meta_keywords' => null, 'meta_description' => null, 'modified' => null, 'created' => null, 'select_label' => null ), 'Slave' => array(), 'Group' => array(), 'Related' => array(), 'Application' => array(), 'Category' => array(), 'Document' => array(), 'Feature' => array(), 'Image' => array(), 'Promotion' => array(), 'Protocol' => array(), 'Publication' => array( (int) 0 => array( [maximum depth reached] ) ), 'Testimonial' => array(), 'Area' => array(), 'SafetySheet' => array() ) ) $language = 'jp' $meta_keywords = '' $meta_description = '' $meta_title = '' $product = array( 'Product' => array( 'id' => '3062', 'antibody_id' => null, 'name' => 'RNA Data Analysis', 'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (<100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p> <div class="extra-spaced"> <h2>What do we provide with the analysis?</h2> <p>This analysis provides information for either genes or isoforms with their expression levels.</p> <h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3> <ul style="list-style-type: circle;"> <li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</li> <li>Trimmed and filtered reads in fastQ files after sequencing QC</li> <li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li> <li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li> </ul> <h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3> <ul style="list-style-type: circle;"> <li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li> <li>Functional gene annotation</li> <li>Gene ontology enrichment analysis on DEGs</li> <li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li> <li>Alternative splicing analysis</li> <li>Gene fusion analysis</li> <li>Novel transcript identification</li> </ul> <h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3> <p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p> </div> <div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>', 'label1' => '', 'info1' => '', 'label2' => '', 'info2' => '', 'label3' => '', 'info3' => '', 'format' => '', 'catalog_number' => 'G02030005', 'old_catalog_number' => '', 'sf_code' => '', 'type' => 'ACC', 'search_order' => '', 'price_EUR' => '/', 'price_USD' => '/', 'price_GBP' => '/', 'price_JPY' => '42800', 'price_CNY' => '/', 'price_AUD' => '/', 'country' => 'ALL', 'except_countries' => 'None', 'quote' => true, 'in_stock' => false, 'featured' => true, 'no_promo' => false, 'online' => true, 'master' => true, 'last_datasheet_update' => '', 'slug' => 'rna-data-analysis', 'meta_title' => '', 'meta_keywords' => '', 'meta_description' => '', 'modified' => '2022-11-22 15:26:24', 'created' => '2020-03-26 10:04:58', 'locale' => 'jpn' ), 'Antibody' => array( 'host' => '*****', 'id' => null, 'name' => null, 'description' => null, 'clonality' => null, 'isotype' => null, 'lot' => null, 'concentration' => null, 'reactivity' => null, 'type' => null, 'purity' => null, 'classification' => null, 'application_table' => null, 'storage_conditions' => null, 'storage_buffer' => null, 'precautions' => null, 'uniprot_acc' => null, 'slug' => null, 'meta_keywords' => null, 'meta_description' => null, 'modified' => null, 'created' => null, 'select_label' => null ), 'Slave' => array(), 'Group' => array(), 'Related' => array(), 'Application' => array(), 'Category' => array(), 'Document' => array(), 'Feature' => array(), 'Image' => array(), 'Promotion' => array(), 'Protocol' => array(), 'Publication' => array( (int) 0 => 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] ) ) ), 'Testimonial' => array(), 'Area' => array(), 'SafetySheet' => array() ) $country = 'US' $countries_allowed = array( (int) 0 => 'CA', (int) 1 => 'US', (int) 2 => 'IE', (int) 3 => 'GB', (int) 4 => 'DK', (int) 5 => 'NO', (int) 6 => 'SE', (int) 7 => 'FI', (int) 8 => 'NL', (int) 9 => 'BE', (int) 10 => 'LU', (int) 11 => 'FR', (int) 12 => 'DE', (int) 13 => 'CH', (int) 14 => 'AT', (int) 15 => 'ES', (int) 16 => 'IT', (int) 17 => 'PT' ) $outsource = false $other_formats = array() $edit = '' $testimonials = '' $featured_testimonials = '' $related_products = '' $rrbs_service = array( (int) 0 => (int) 1894, (int) 1 => (int) 1895 ) $chipseq_service = array( (int) 0 => (int) 2683, (int) 1 => (int) 1835, (int) 2 => (int) 1836, (int) 3 => (int) 2684, (int) 4 => (int) 1838, (int) 5 => (int) 1839, (int) 6 => (int) 1856 ) $labelize = object(Closure) { } $old_catalog_number = '' $label = '<img src="/img/banners/banner-customizer-back.png" alt=""/>' $publication = 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( 'id' => '5682', 'product_id' => '3062', 'publication_id' => '4252' ) ) $externalLink = ' <a href="https://www.ncbi.nlm.nih.gov/pubmed/35030988" target="_blank"><i class="fa fa-external-link"></i></a>'include - APP/View/Products/view.ctp, line 755 View::_evaluate() - CORE/Cake/View/View.php, line 971 View::_render() - CORE/Cake/View/View.php, line 933 View::render() - CORE/Cake/View/View.php, line 473 Controller::render() - CORE/Cake/Controller/Controller.php, line 963 ProductsController::slug() - APP/Controller/ProductsController.php, line 1052 ReflectionMethod::invokeArgs() - [internal], line ?? Controller::invokeAction() - CORE/Cake/Controller/Controller.php, line 491 Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 193 Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167 [main] - APP/webroot/index.php, line 118
Notice (8): Undefined variable: campaign_id [APP/View/Products/view.ctp, line 755]Code Context<!-- BEGIN: REQUEST_FORM MODAL --><div id="request_formModal" class="reveal-modal medium" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog"><?= $this->element('Forms/simple_form', array('solution_of_interest' => $solution_of_interest, 'header' => $header, 'message' => $message, 'campaign_id' => $campaign_id)) ?>$viewFile = '/home/website-server/www/app/View/Products/view.ctp' $dataForView = array( 'language' => 'jp', 'meta_keywords' => '', 'meta_description' => '', 'meta_title' => '', 'product' => array( 'Product' => array( 'id' => '3062', 'antibody_id' => null, 'name' => 'RNA Data Analysis', 'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (<100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p> <div class="extra-spaced"> <h2>What do we provide with the analysis?</h2> <p>This analysis provides information for either genes or isoforms with their expression levels.</p> <h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3> <ul style="list-style-type: circle;"> <li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</li> <li>Trimmed and filtered reads in fastQ files after sequencing QC</li> <li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li> <li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li> </ul> <h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3> <ul style="list-style-type: circle;"> <li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li> <li>Functional gene annotation</li> <li>Gene ontology enrichment analysis on DEGs</li> <li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li> <li>Alternative splicing analysis</li> <li>Gene fusion analysis</li> <li>Novel transcript identification</li> </ul> <h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3> <p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p> </div> <div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>', 'label1' => '', 'info1' => '', 'label2' => '', 'info2' => '', 'label3' => '', 'info3' => '', 'format' => '', 'catalog_number' => 'G02030005', 'old_catalog_number' => '', 'sf_code' => '', 'type' => 'ACC', 'search_order' => '', 'price_EUR' => '/', 'price_USD' => '/', 'price_GBP' => '/', 'price_JPY' => '42800', 'price_CNY' => '/', 'price_AUD' => '/', 'country' => 'ALL', 'except_countries' => 'None', 'quote' => true, 'in_stock' => false, 'featured' => true, 'no_promo' => false, 'online' => true, 'master' => true, 'last_datasheet_update' => '', 'slug' => 'rna-data-analysis', 'meta_title' => '', 'meta_keywords' => '', 'meta_description' => '', 'modified' => '2022-11-22 15:26:24', 'created' => '2020-03-26 10:04:58', 'locale' => 'jpn' ), 'Antibody' => array( 'host' => '*****', 'id' => null, 'name' => null, 'description' => null, 'clonality' => null, 'isotype' => null, 'lot' => null, 'concentration' => null, 'reactivity' => null, 'type' => null, 'purity' => null, 'classification' => null, 'application_table' => null, 'storage_conditions' => null, 'storage_buffer' => null, 'precautions' => null, 'uniprot_acc' => null, 'slug' => null, 'meta_keywords' => null, 'meta_description' => null, 'modified' => null, 'created' => null, 'select_label' => null ), 'Slave' => array(), 'Group' => array(), 'Related' => array(), 'Application' => array(), 'Category' => array(), 'Document' => array(), 'Feature' => array(), 'Image' => array(), 'Promotion' => array(), 'Protocol' => array(), 'Publication' => array( (int) 0 => array( [maximum depth reached] ) ), 'Testimonial' => array(), 'Area' => array(), 'SafetySheet' => array() ) ) $language = 'jp' $meta_keywords = '' $meta_description = '' $meta_title = '' $product = array( 'Product' => array( 'id' => '3062', 'antibody_id' => null, 'name' => 'RNA Data Analysis', 'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (<100 nucleotides long) RNAs that operate as key regulators in cellular processes.</p> <div class="extra-spaced"> <h2>What do we provide with the analysis?</h2> <p>This analysis provides information for either genes or isoforms with their expression levels.</p> <h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3> <ul style="list-style-type: circle;"> <li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</li> <li>Trimmed and filtered reads in fastQ files after sequencing QC</li> <li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li> <li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li> </ul> <h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3> <ul style="list-style-type: circle;"> <li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li> <li>Functional gene annotation</li> <li>Gene ontology enrichment analysis on DEGs</li> <li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li> <li>Alternative splicing analysis</li> <li>Gene fusion analysis</li> <li>Novel transcript identification</li> </ul> <h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3> <p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p> </div> <div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>', 'label1' => '', 'info1' => '', 'label2' => '', 'info2' => '', 'label3' => '', 'info3' => '', 'format' => '', 'catalog_number' => 'G02030005', 'old_catalog_number' => '', 'sf_code' => '', 'type' => 'ACC', 'search_order' => '', 'price_EUR' => '/', 'price_USD' => '/', 'price_GBP' => '/', 'price_JPY' => '42800', 'price_CNY' => '/', 'price_AUD' => '/', 'country' => 'ALL', 'except_countries' => 'None', 'quote' => true, 'in_stock' => false, 'featured' => true, 'no_promo' => false, 'online' => true, 'master' => true, 'last_datasheet_update' => '', 'slug' => 'rna-data-analysis', 'meta_title' => '', 'meta_keywords' => '', 'meta_description' => '', 'modified' => '2022-11-22 15:26:24', 'created' => '2020-03-26 10:04:58', 'locale' => 'jpn' ), 'Antibody' => array( 'host' => '*****', 'id' => null, 'name' => null, 'description' => null, 'clonality' => null, 'isotype' => null, 'lot' => null, 'concentration' => null, 'reactivity' => null, 'type' => null, 'purity' => null, 'classification' => null, 'application_table' => null, 'storage_conditions' => null, 'storage_buffer' => null, 'precautions' => null, 'uniprot_acc' => null, 'slug' => null, 'meta_keywords' => null, 'meta_description' => null, 'modified' => null, 'created' => null, 'select_label' => null ), 'Slave' => array(), 'Group' => array(), 'Related' => array(), 'Application' => array(), 'Category' => array(), 'Document' => array(), 'Feature' => array(), 'Image' => array(), 'Promotion' => array(), 'Protocol' => array(), 'Publication' => array( (int) 0 => 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] ) ) ), 'Testimonial' => array(), 'Area' => array(), 'SafetySheet' => array() ) $country = 'US' $countries_allowed = array( (int) 0 => 'CA', (int) 1 => 'US', (int) 2 => 'IE', (int) 3 => 'GB', (int) 4 => 'DK', (int) 5 => 'NO', (int) 6 => 'SE', (int) 7 => 'FI', (int) 8 => 'NL', (int) 9 => 'BE', (int) 10 => 'LU', (int) 11 => 'FR', (int) 12 => 'DE', (int) 13 => 'CH', (int) 14 => 'AT', (int) 15 => 'ES', (int) 16 => 'IT', (int) 17 => 'PT' ) $outsource = false $other_formats = array() $edit = '' $testimonials = '' $featured_testimonials = '' $related_products = '' $rrbs_service = array( (int) 0 => (int) 1894, (int) 1 => (int) 1895 ) $chipseq_service = array( (int) 0 => (int) 2683, (int) 1 => (int) 1835, (int) 2 => (int) 1836, (int) 3 => (int) 2684, (int) 4 => (int) 1838, (int) 5 => (int) 1839, (int) 6 => (int) 1856 ) $labelize = object(Closure) { } $old_catalog_number = '' $label = '<img src="/img/banners/banner-customizer-back.png" alt=""/>' $publication = 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( 'id' => '5682', 'product_id' => '3062', 'publication_id' => '4252' ) ) $externalLink = ' <a href="https://www.ncbi.nlm.nih.gov/pubmed/35030988" target="_blank"><i class="fa fa-external-link"></i></a>'include - APP/View/Products/view.ctp, line 755 View::_evaluate() - CORE/Cake/View/View.php, line 971 View::_render() - CORE/Cake/View/View.php, line 933 View::render() - CORE/Cake/View/View.php, line 473 Controller::render() - CORE/Cake/Controller/Controller.php, line 963 ProductsController::slug() - APP/Controller/ProductsController.php, line 1052 ReflectionMethod::invokeArgs() - [internal], line ?? 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