Visualization of RNA-Seq results with CummeRbund

Contributors

Authors: Andrea Bagnacani

Questions

• How are RNA-Seq results stored?

• Why are visualization techniques needed?

• How to select our desired subjects for differential gene expression analysis?

Objectives

• Manage RNA-Seq results

• Extract the desired subject for differential gene expression analysis

• Visualize information

Why visualization?

Speaker Notes 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?

Wang et al, Nat Rev Genet, 2009

Speaker Notes

• 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

Trapnell et al, Nat Protoc, 2012 Speaker Notes

• 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 tab-separated 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[]

Speaker Notes

• 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 make sense of it

Visualization

CummeRbund

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:

• Extract CuffDiff’s “Transcript differential expression testing” table

• Filter the table on the column storing the significance of a differentially expressed gene

• Sort 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 to overview all possibilities!

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!