 # 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.

## 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:

• 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.

``````
library(ggplot2)

# 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
library(ggplot2)

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

# 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",
ylab="Density")

# 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

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

• Run Artemis

`art`

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

## 6: EDGE

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

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

Choose

## 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: