Genome Spot

There is a need to make bioinformatics tools more user friendly and accessible to a wider audience. We have seen that Galaxy, GEO2R, Genevestigator and GenePattern have each developed a huge following in the molecular biology community, and this trend will continue with introduction of new RNA-seq analysis tools. Previously, I posted about differential gene expression analysis of RNA-seq performed by the DEB online tool. In this post, I introduce Degust, an online app to analyse gene expression count data and determine which genes are differentially expressed. Degust was written by David R. Powell (@d_r_powell) and was Supported by Victorian Bioinformatics Consortium, Monash University and VLSCI's Life Sciences Computation Centre.

In this test, I'll be using the azacitidine mRNA-seq data set that I have previously analysed. To make the count matrix, I used featureCounts.

First step in the process is to your RNA-seq count data. It can be done in tab or comma separated formats. …

You have probably seen word clouds before - but have you tried with gene expression data?

I used the following bash script to process a DESeq spreadsheet from a a previous RNA-seq post. The script extracts the gene name and p-value of the genes with differential expression. I used awk to separate the up and down regulated genes into different files. The score used to inform the font size is the exponent of the p-value. So this works best when there are a lot of statistically significant genes p-values. The data looks like this:

AC011899.9:60
CAMK1D:54
GNG4:44
CGNL1:41
APCDD1:37
HSD11B1:33

Now go to the "advanced" tab of the Wordle page and paste in the data. Experiment, with the colours, layouts and be sure to increase the "maximum words" to get a real appreciation for the number of changes in your experiment. Here is an example I made using both the up and down regulated gene sets showing the effect of azacytidine on AML3 cells. The result is pretty amazing.
Scrip…