Module: Transcriptomics

RNA sequencing is used to assess the expression levels of genes. This video introduces the important concepts related to RNA-seq analysis.

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In this tutorial we will walk you through the process of an RNA-seq analysis, comparing gene expression levels between different conditions and assessing impacted gene pathways.

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• Slack: #transcriptomics_ref-based
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• Check a sequence quality report generated by FastQC for RNA-Seq data
• Explain the principle and specificity of mapping of RNA-Seq data to an eukaryotic reference genome
• Select and run a state of the art mapping tool for RNA-Seq data
• Evaluate the quality of mapping results
• Describe the process to estimate the library strandness
• Estimate the number of reads per genes
• Explain the count normalization to perform before sample comparison
• Construct and run a differential gene expression analysis
• Analyze the DESeq2 output to identify, annotate and visualize differentially expressed genes
• Perform a gene ontology enrichment analysis
• Perform and visualize an enrichment analysis for KEGG pathways

This tutorial will show you how you can create volcano plots from your RNA-Seq analysis results using Galaxy tools.

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• Slack: #transcriptomics_rna-seq-viz-with-volcanoplot
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• Create a volcano plot of RNA-seq data to visualize significant genes

In this tutorial, we will use de novo transcript reconstruction to infer transcript structures from aligned reads. We will identify and quantify transcripts present in two different cell state and determine which transcripts are differentially expressed between the two states.

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• Dynamic shifts in occupancy by TAL1 are guided by GATA factors and drive large-scale reprogramming of gene expression during hematopoiesis.
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• Analysis of RNA sequencing data using a reference genome
• Reconstruction of transcripts without reference transcriptome (de novo)
• Analysis of differentially expressed genes

This tutorial will show you how you can start Rstudio from within Galaxy. This option is only available on Galaxy EU for the time being. If you are working on a different Galaxy server, you can use Rstudio Cloud (https://rstudio.cloud/)

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This tutorial will provide an introduction to using R with Rstudio in Galaxy.

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• Slack: #data-science_r-basics
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• Know advantages of analyzing data using R within Galaxy.
• Compose an R script file containing comments, commands, objects, and functions.
• Be able to work with objects (i.e. applying mathematical and logical operators, subsetting, retrieving values, etc).

An advanced tutorial covering downstream analysis of RNA-seq data using R.

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• Slack: #data-science_r-advanced
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• Be able to load and explore the shape and contents of a tabular dataset using base R functions.
• Understand factors and how they can be used to store and work with categorical data.
• Apply common `dplyr` functions to manipulate data in R.
• Employ the ‘pipe’ operator to link together a sequence of functions.

In this tutorial, we will visualize our RNA-seq analysis results using R.

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• Slack: #transcriptomics_rna-seq-counts-to-viz-in-r
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• Describe the role of data, aesthetics, geoms, and layers in `ggplot` functions.
• Customize plot scales, titles, subtitles, themes, fonts, layout, and orientation.

The previous tutorial introduced volcano plots and showed how they can be generated with the Galaxy Volcano plot tool. In this tutorial we show how you can customise a plot using the R script output from the tool.

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• Slack: #transcriptomics_rna-seq-viz-with-volcanoplot
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• Learn how to use R to edit Volcano plot colours, points, labels and categories

Metatranscriptomics analysis examines how the microbiome responds to the environment by studying the taxonomic composition and functional analysis of genes expressed by the microbiome, using microbial community RNASeq data and subsequent metatranscriptomics workflows. This workshop will introduce researchers to the basic concepts and tools from the ASaiM-MT workflow. ASaiM-MT provides a curated collection of tools to explore and visualize taxonomic and functional information from metatranscriptomic sequences.

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• Slack: #metagenomics_metatranscriptomics
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