name: inverse layout: true class: center, middle, inverse <div class="my-header"><span> <a href="/training-material/topics/transcriptomics" title="Return to topic page" ><i class="fa fa-level-up" aria-hidden="true"></i></a> <a class="nav-link" href="https://github.com/galaxyproject/training-material/edit/main/topics/transcriptomics/tutorials/rna-seq-viz-with-cummerbund/slides.html"><i class="fa fa-pencil" aria-hidden="true"></i></a> </span></div> <div class="my-footer"><span> <img src="/training-material/assets/images/GTN-60px.png" alt="Galaxy Training Network" style="height: 40px;"/> </span></div> --- <img src="/training-material/assets/images/GTN.png" alt="Galaxy Training Network" class="cover-logo"/> # Visualization of RNA-Seq results with CummeRbund <div markdown="0"> <div class="contributors-line"> Authors: <a href="/training-material/hall-of-fame/bagnacan/" class="contributor-badge contributor-bagnacan"><img src="https://avatars.githubusercontent.com/bagnacan?s=27" alt="Avatar">Andrea Bagnacani</a> </div> </div> <!-- modified date --> <div class="footnote" style="bottom: 8em;"><i class="far fa-calendar" aria-hidden="true"></i><span class="visually-hidden">last_modification</span> Updated: Oct 9, 2018</div> <!-- other slide formats (video and plain-text) --> <div class="footnote" style="bottom: 6em;"> <i class="fas fa-file-alt" aria-hidden="true"></i><span class="visually-hidden">text-document</span><a href="slides-plain.html"> Plain-text slides</a> </div> <!-- usage tips --> <div class="footnote" style="bottom: 2em;"> <strong>Tip: </strong>press <kbd>P</kbd> to view the presenter notes | <i class="fa fa-arrows" aria-hidden="true"></i><span class="visually-hidden">arrow-keys</span> Use arrow keys to move between slides </div> ??? 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Useful when presenting. --- ## Requirements Before diving into this slide deck, we recommend you to have a look at: - [Introduction to Galaxy Analyses](/training-material/topics/introduction) - [Sequence analysis](/training-material/topics/sequence-analysis) - Quality Control: [<i class="fab fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/training-material/topics/sequence-analysis/tutorials/quality-control/slides.html) - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html) - Mapping: [<i class="fab fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/training-material/topics/sequence-analysis/tutorials/mapping/slides.html) - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/sequence-analysis/tutorials/mapping/tutorial.html) --- ### <i class="far fa-question-circle" aria-hidden="true"></i><span class="visually-hidden">question</span> Questions - How are RNA-Seq results stored? - Why are visualization techniques needed? - How to select our desired subjects for differential gene expression analysis? --- ### <i class="fas fa-bullseye" aria-hidden="true"></i><span class="visually-hidden">objectives</span> Objectives - Manage RNA-Seq results - Extract the desired subject for differential gene expression analysis - Visualize information --- # Why visualization? ??? Data alone does not bring any information: to carry information, data needs to be contextualized. Transcriptomic data is no exception, therefore to organize the growing body of knowledge pertaining RNA-Seq experiments and infer valuable insights, data needs to be organized, annotated, and ultimately visualized. --- ### Where is my data coming from?  <small>[*Wang et al, Nat Rev Genet, 2009*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949280/)</small> ??? - RNAs are converted into cDNA fragments through RNA fragmentation - sequencing adaptors (in blue) are then added to each cDNA fragment - cDNA sequences are obtained via NGS sequencing - the obtained reads are subsequently aligned against a reference genome or transcriptome, and classified in-silico as exonic reads, junction reads, and poly-A reads - these three types are then used to outline an expression profile for each gene This is the process that will: - reveal new genes and splice variants - help quantifying cell-specific gene expression within the genome under study But once this pipeline is implemented, how are sequence data going to be analysed and managed? --- ### Bioinformatic tools for RNA-Seq analysis Once the RNA-Seq pipeline is implemented, we still need to handle and analyse all data that is generated. This requires: - computer science skills to be handled - mathematical knowledge to be interpreted --- ### Bioinformatic tools for RNA-Seq analysis  <small>[*Trapnell et al, Nat Protoc, 2012*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3334321/)</small> ??? - first, reads from each condition are mapped to the reference genome using TopHat - the resulting alignment files are given to Cufflinks, which generates a transcriptome assembly for each condition - the two assemblies are then merged together to provides a uniform basis for calculating of gene and transcript expression in each condition - both reads and merged assemblies are fed to CuffDiff, to calculate expression levels via statistical significance test for the observed changes --- ### Bioinformatic tools for RNA-Seq analysis The last step in our RNA-Seq analysis is CuffDiff. Its output comprises multiple files containing the results of the differential expression analysis. - Gene expression levels are reported as <i>tab-separated</i> values: a simple tabular output that can be viewed with any spreadsheet application. Such files contain statistics, gene-related, and transcript-related attributes .image-75[] - Another way to collect all these data is to organize it within a dedicated database for later consultation. CuffDiff can be instructed to do so .image-50[] ??? - CuffDiff provides analyses of differential expression and regulation at the gene and transcript level - its results are reported in a tab separated format - the overall collection of data is difficult to read to obtain a bird's-eye view of the change of expression - the data can be organized in a SQLite database --- ### Bioinformatic tools for RNA-Seq analysis Whatever storage strategy you opted for, i.e. multiple tab-separated-value files or a SQLite database, all data is still retained within text format. We need to have a bird's-eye view of that data, and <i>make sense</i> of it --- # Visualization --- ### CummeRbund [CummeRbund](http://compbio.mit.edu/cummeRbund/) is an R package for visualizing the results of a CuffDiff output. - Manages, integrates, and visualizes all data produced by CuffDiff - Simplifies data exploration - Provides a bird's-eye view of the expression analysis - Helps creating publication-ready plots --- ### CummeRbund CummeRbund needs to be instructed on which data to be visualized: - <i>Extract CuffDiff</i>'s "Transcript differential expression testing" table - <i>Filter</i> the table on the column storing the significance of a differentially expressed gene - <i>Sort</i> all entries on the basis of most significant differentially expressed gene - Identify the most significant differentially expressed gene --- ### CummeRbund Once the most significant differentially expressed gene has been identified, CummeRbund can generate publication-ready plots to highlight... .image-50[] The expression of all isoforms of the single gene with replicate FPKMs --- ### CummeRbund Once the most significant differentially expressed gene has been identified, CummeRbund can generate publication-ready plots to highlight... .image-50[] The expression bar-plot of all isoforms of a gene with replicate FPKMs --- ### CummeRbund ...and many more .image-50[] Have a look at [CummerBund's tutorial](https://bioconductor.org/packages/2.11/bioc/vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf) to overview all possibilities! --- ### <i class="fas fa-key" aria-hidden="true"></i><span class="visually-hidden">keypoints</span> Key points - Extract informations from a SQLite CuffDiff database - Filter and sort results to highlight differential expressed genes of interest - Generate publication-ready visualizations for RNA-Seq analysis results --- ## Thank You! This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors! <div markdown="0"> <div class="contributors-line"> Authors: <a href="/training-material/hall-of-fame/bagnacan/" class="contributor-badge contributor-bagnacan"><img src="https://avatars.githubusercontent.com/bagnacan?s=27" alt="Avatar">Andrea Bagnacani</a> </div> </div> <div style="display: flex;flex-direction: row;align-items: center;justify-content: center;"> <img src="/training-material/assets/images/GTN.png" alt="Galaxy Training Network" style="height: 100px;"/> </div> <a rel="license" href="https://creativecommons.org/licenses/by/4.0/"> This material is licensed under the Creative Commons Attribution 4.0 International License</a>.