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<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p>
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<div class="extra-spaced">
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<h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3>
<ul style="list-style-type: circle;">
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<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
<li>Alternative splicing analysis</li>
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<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p>
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<div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>',
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<h3 class="diacol" style="font-weight: 100;">Standard Analysis</h3>
<ul style="list-style-type: circle;">
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<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
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</div>
<div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/RNA-theorie.png" /></center></div>',
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<div class="extra-spaced">
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<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;">
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<li>Pathway enrichment analysis on DEGs (KEGG or DOSE for human samples)</li>
<li>Alternative splicing analysis</li>
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<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3062">please consult with our expert bioinformatics team</a>.</p>
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<div 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>
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<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>
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<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>',
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'description' => '<p>Total RNA sequencing (RNA-Seq) detects both coding and noncoding RNAs and is typically used to measure gene and transcript abundance as well as to identify novel components of the transcriptome. Messenger RNA-Seq focuses on the quantification of gene expression, the identification of unknown transcripts, the discovery of alternative splicing and gene fusion events. And finally, small non-coding RNA sequencing (sncRNA-Seq) will detect small (<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>',
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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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