Installation and configuration: Amazon Web Services

Boot up an m4.xlarge machine from Amazon Web Services running Ubuntu 15.10 LTS (e.g. us-west AMI ami-05384865 or us-east ami-002f0f6a); increase the root volume size to ~100 GB. The m4.xlarge machines have 16 GB of RAM, and 4 CPUs, and will be enough to complete the assembly of the Nematostella data set. If you are using your own data, be aware of your space requirements and obtain an appropriately sized machine (“instance”) and storage (“volume”).

Install software

On the new machine, run the following commands to update the base software:

sudo apt-get update && \
sudo apt-get -y install screen git curl gcc make g++ python-dev unzip \
        default-jre pkg-config libncurses5-dev r-base-core r-cran-gplots \
        python-matplotlib python-pip python-virtualenv sysstat fastqc \
        trimmomatic bowtie samtools blast2 wget bowtie2 openjdk-8-jre \
        hmmer ruby

Install khmer from its source code.

cd ~/
python2.7 -m virtualenv pondenv
source pondenv/bin/activate
cd pondenv
pip install -U setuptools
git clone --branch v2.0
cd khmer
make install

The use of virtualenv allows us to install Python software without having root access. If you come back to this protocol in a different terminal session you will need to run:

source ~/pondenv/bin/activate

Installing Trinity

To install Trinity:

cd ${HOME}

wget \
 -O trinity.tar.gz
tar xzf trinity.tar.gz
cd trinityrnaseq*/
make |& tee trinity-build.log

Assuming it succeeds, modify the path appropriately in your virtualenv activation setup:

echo export PATH=$PATH:$(pwd) >> ~/pondenv/bin/activate
source ~/pondenv/bin/activate

You will also need to set the default Java version to 1.8

sudo update-alternatives --set java /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java

Install transrate

We use transrate to evaluate assemblies. Install!

curl -LO
tar -zxf transrate-1.0.3-linux-x86_64.tar.gz
echo 'export PATH=$PATH:"$HOME/transrate-1.0.3-linux-x86_64"' >> ~/pondenv/bin/activate
curl -LO
tar -zxf ncbi-blast-2.3.0+-x64-linux.tar.gz
echo 'export PATH="$HOME/ncbi-blast-2.3.0+/bin:$PATH"' >> ~/pondenv/bin/activate
source ~/pondenv/bin/activate

Install busco

Install stuff:

git clone
cd busco
echo "export PATH=$PATH:$(pwd)" >> ~/pondenv/bin/activate
curl -OL
curl -OL
tar -xzvf metazoa_odb9.tar.gz
tar -xzvf eukaryota_odb9.tar.gz
source ~/pondenv/bin/activate

Install salmon

We will use Salmon to quantify expression of transcripts. Salmon is a new breed of software for quantifying RNAseq reads that is both really fast and takes transcript length into consideration (Patro et al. 2015).

curl -LO
tar -xvzf Salmon-0.7.2_linux_x86_64.tar.gz
cd Salmon*/bin
echo export PATH=$PATH:$(pwd) >> ~/pondenv/bin/activate
source ~/pondenv/bin/activate

Load your data onto /mnt/data

Load your data into /mnt/work/data. You may need to make the /mnt/ directory writeable by doing

sudo chmod a+rwxt /mnt

first, and then creating the subdirectories

cd /mnt
mkdir -p work work/data
cd /mnt/work/data

Define your $PROJECT variable to be the location of your work directory; in this case, it will be /mnt/work:

export PROJECT=/mnt/work

Now load your data in!


If you want to try things out with a small test data set, you can use a subset of the Nematostella data from Tulin et al. (2013):

cd /mnt/work
curl -O
cd data
tar xvf ../mrnaseq-subset.tar

Check that your data is where it should be


ls $PROJECT/data

If you see all the files you think you should, good! Otherwise, debug.

If you’re using the Tulin et al. data provided in the snapshot above, you should see a bunch of files like:


Next: 1. Quality Trimming and Filtering Your Sequences

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