SfAM ECS Tutorial

1. Bryn: The CLIMB portal

You can sign-up to CLIMB via bryn.climb.ac.uk but please note that the first user should be a principal investigator or independent investigator.

2. Launching a GVL server

3. Genomics Virtual Laboratory

The Genomics Virtual Laboratory is our standard ‘image’.

Launching a GVL server:

4. RStudio

RStudio is an online development environment for running R code:

  • RStudio Server provides access to RStudio and, by extension, R from within your browser.

  • Bring up GVL

  • Click on Rstudio

  • Input your “jupyter” log-in credentials

  • This should bring up an Rstudio interface.

From this interface you should be able to use R in a way that many of your will be familiar with.

Cars Plotting Example

Run these commands one-by-one. They will produce a number of different plots from the stock dataset cars.

The plots are produced by ggplot2 a powerful tool for plotting that uses the grammar of graphics.
This allows you to start with your base dataset and add plots layer by layer.

# Load the library

# Line plot
ggplot(cars, aes(speed, dist))+ geom_line()

# Barchart
ggplot(cars, aes(speed, dist))+ geom_bar(stat="identity")

# Line plot on top of the bar plot
ggplot(cars, aes(speed, dist))+ geom_bar(stat="identity") + geom_line()

Some more advanced plotting using qplot (This tutorial was taken from http://www.statmethods.net/advgraphs/ggplot2.html):

# ggplot2 examples

# create factors with value labels 
mtcars$gear <- factor(mtcars$gear,levels=c(3,4,5),
mtcars$am <- factor(mtcars$am,levels=c(0,1),
mtcars$cyl <- factor(mtcars$cyl,levels=c(4,6,8),

# Kernel density plots for mpg
# grouped by number of gears (indicated by color)
qplot(mpg, data=mtcars, geom="density", fill=gear, alpha=I(.5), 
   main="Distribution of Gas Milage", xlab="Miles Per Gallon", 

# Scatterplot of mpg vs. hp for each combination of gears and cylinders
# in each facet, transmittion type is represented by shape and color
qplot(hp, mpg, data=mtcars, shape=am, color=am, 
   facets=gear~cyl, size=I(3),
   xlab="Horsepower", ylab="Miles per Gallon") 

# Separate regressions of mpg on weight for each number of cylinders
qplot(wt, mpg, data=mtcars, geom=c("point", "smooth"), 
   method="lm", formula=y~x, color=cyl, 
   main="Regression of MPG on Weight", 
   xlab="Weight", ylab="Miles per Gallon")

# Boxplots of mpg by number of gears 
# observations (points) are overlayed and jittered
qplot(gear, mpg, data=mtcars, geom=c("boxplot", "jitter"), 
   fill=gear, main="Mileage by Gear Number",
   xlab="", ylab="Miles per Gallon")

5. VNC Remove Desktop

  • Click the VNC link on the GVL homepage and log in with your “researcher” credentials

  • Load a Terminal window (Start > Accessories > LXTerminal)

  • Run Artemis


You can load a genome directly from EBI: “Load from EBI - dbfetch”, enter accession CP000033 to load Lactobacillus acidophilus. Try finding your own accession to load from www.ebi.ac.uk

Things to try:

  • Search for your favourite gene
  • Get a GC% plot
  • Launch a BLAST search of a gene


EDGE is an integrated genomics environment for population genomics, metagenomics and 16S analysis.

It is available at http://edge.climb.ac.uk

7: EDGE - is there anthrax on the subway?


from the left menu.

8: EDGE: Metagenomics profiling

Look at the taxonomic assignment heatmap:

  • What are the likely sources of bacteria in this sample?
  • Is the causative agent of anthrax present?

Group discussion:

  • Is this expected? What might be going on here?
  • How would we prove whether B. anthracis is really in this dataset?

9: EDGE: Is anthrax really on the subway?

Full tutorial here:

8: EDGE - monkey genomics

Sion will give a quick overview of this project and you can use the remaining time going through his tutorial: