RNA Data Analysis

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G02030005

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.

What do we provide with the analysis?

This analysis provides information for either genes or isoforms with their expression levels.

Standard Analysis

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.)
Trimmed and filtered reads in fastQ files after sequencing QC
BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)
Matrix with expression abundance obtained with specialized quantification tool MGCount (software developed by Diagenode). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.

Advanced Analysis

Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples
Functional gene annotation
Gene ontology enrichment analysis on DEGs
Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)
Alternative splicing analysis
Gene fusion analysis
Novel transcript identification

Customized Analysis

If you require a type of analysis that is not in the previous list, please consult with our expert bioinformatics team.

MGcount: Diagenode has developed a bioinformatics software for counting whole-transcriptome RNA-seq reads from one or more input alignment files. It is specially designed to incorporate multi-mapping and multi-overlapping reads in the quantification using a flexible methodology that is compatible with any biotype. At the end of its execution, it produces a count matrix, compatible with any downstream analysis.

Publications

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Using our products or services 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
Hita Andrea, Brocart Gilles, Fernandez Ana, Rehmsmeier Marc, Alemany Anna, Schvartzman Sol
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 biotyp...

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<div class="extra-spaced">
<h2>What do we provide with the analysis?</h2>
<p>This analysis provides information for either genes or isoforms with their expression levels.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li>
<li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul style="list-style-type: circle;">
<li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li>
<li>Functional gene annotation</li>
<li>Gene ontology enrichment analysis on DEGs</li>
<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
<li>Alternative splicing analysis</li>
<li>Gene fusion analysis</li>
<li>Novel transcript identification</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p>
</div>
<div><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04544-3">MGcount</a>: Diagenode has developed a bioinformatics software for counting whole-transcriptome RNA-seq reads from one or more input alignment files. It is specially designed to incorporate multi-mapping and multi-overlapping reads in the quantification using a flexible methodology that is compatible with any biotype. At the end of its execution, it produces a count matrix, compatible with any downstream analysis.</p>
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<div class="extra-spaced">
<h2>What do we provide with the analysis?</h2>
<p>This analysis provides information for either genes or isoforms with their expression levels.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li>
<li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul style="list-style-type: circle;">
<li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li>
<li>Functional gene annotation</li>
<li>Gene ontology enrichment analysis on DEGs</li>
<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
<li>Alternative splicing analysis</li>
<li>Gene fusion analysis</li>
<li>Novel transcript identification</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p>
</div>
<div><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04544-3">MGcount</a>: Diagenode has developed a bioinformatics software for counting whole-transcriptome RNA-seq reads from one or more input alignment files. It is specially designed to incorporate multi-mapping and multi-overlapping reads in the quantification using a flexible methodology that is compatible with any biotype. At the end of its execution, it produces a count matrix, compatible with any downstream analysis.</p>
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<div class="extra-spaced">
<h2>What do we provide with the analysis?</h2>
<p>This analysis provides information for either genes or isoforms with their expression levels.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3>
<ul style="list-style-type: circle;">
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates, number of genes detected, average read density per gene, number of highly expressed genes, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome or transcriptome (indexed bam files and bigwig files included)</li>
<li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul style="list-style-type: circle;">
<li>Comparative analysis (also called differential analysis) aimed at finding differentially expressed genes (DEGs) between two groups of samples</li>
<li>Functional gene annotation</li>
<li>Gene ontology enrichment analysis on DEGs</li>
<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
<li>Alternative splicing analysis</li>
<li>Gene fusion analysis</li>
<li>Novel transcript identification</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p>
</div>
<div><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04544-3">MGcount</a>: Diagenode has developed a bioinformatics software for counting whole-transcriptome RNA-seq reads from one or more input alignment files. It is specially designed to incorporate multi-mapping and multi-overlapping reads in the quantification using a flexible methodology that is compatible with any biotype. At the end of its execution, it produces a count matrix, compatible with any downstream analysis.</p>
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			'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>',
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View::_evaluate() - CORE/Cake/View/View.php, line 971
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 (Notice): Undefined variable: campaign_id [APP/View/Products/view.ctp, line 755]Code Context         $errorType = self::getErrorType($code);
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<div><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>
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<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>
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<li>Matrix with expression abundance obtained with specialized quantification tool MGCount (<a href="https://doi.org/10.1186/s12859-021-04544-3">software developed by Diagenode</a>). A table of MG communities linking each original feature in the GTF file with the resultant count matrix and metadata feature identifiers.</li>
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<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
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<div><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>
<p><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04544-3">MGcount</a>: Diagenode has developed a bioinformatics software for counting whole-transcriptome RNA-seq reads from one or more input alignment files. It is specially designed to incorporate multi-mapping and multi-overlapping reads in the quantification using a flexible methodology that is compatible with any biotype. At the end of its execution, it produces a count matrix, compatible with any downstream analysis.</p>
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View::_evaluate() - CORE/Cake/View/View.php, line 971
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