1. Quality Trimming and Filtering Your Sequences

Boot up an m1.xlarge machine from Amazon Web Services; this has about 15 GB of RAM, and 2 CPUs, and will be enough to complete the assembly of the example data set.

Note

This follows the NGS 2013 tutorial, Short-read quality evaluation, but for multiple files.

Note

The end results of this tutorial are available as public snapshot XXX on EC2/EBS.

Also see: Using ‘screen’.

Install software

Install screed:

pip install git+https://github.com/ged-lab/screed.git

Install the bleeding-edge version of khmer:

cd /usr/local/share
git clone https://github.com/ged-lab/khmer.git -b bleeding-edge
cd khmer
make

echo 'export PYTHONPATH=/usr/local/share/khmer/python' >> ~/.bashrc
source ~/.bashrc

Install Trimmomatic:

cd /root
curl -O http://www.usadellab.org/cms/uploads/supplementary/Trimmomatic/Trimmomatic-0.27.zip
unzip Trimmomatic-0.27.zip
cp Trimmomatic-0.27/trimmomatic-0.27.jar /usr/local/bin

Install libgtextutils and fastx:

cd /root
curl -O http://hannonlab.cshl.edu/fastx_toolkit/libgtextutils-0.6.1.tar.bz2
tar xjf libgtextutils-0.6.1.tar.bz2
cd libgtextutils-0.6.1/
./configure && make && make install

cd /root
curl -O http://hannonlab.cshl.edu/fastx_toolkit/fastx_toolkit-0.0.13.2.tar.bz2
tar xjf fastx_toolkit-0.0.13.2.tar.bz2
cd fastx_toolkit-0.0.13.2/
./configure && make && make install

In each of these cases, we’re downloading the software – you can use google to figure out what each package is and does if we don’t discuss it below. We’re then unpacking it, sometimes compiling it (which we can discuss later), and then installing it for general use.

Create a working directory

Let’s create a place to work:

cd /mnt
mkdir assembly
cd assembly

Trim and quality filter

Grab some Illumina adapters:

curl -O https://s3.amazonaws.com/public.ged.msu.edu/illuminaClipping.fa

Trim the first data set (~20 minutes):

mkdir trim
cd trim

java -jar /usr/local/bin/trimmomatic-0.27.jar PE ../SRR492065_?.fastq.gz s1_pe s1_se s2_pe s2_se ILLUMINACLIP:../illuminaClipping.fa:2:30:10

/usr/local/share/khmer/scripts/interleave-reads.py s?_pe > combined.fq

fastq_quality_filter -Q33 -q 30 -p 50 -i combined.fq > combined-trim.fq
fastq_quality_filter -Q33 -q 30 -p 50 -i s1_se > s1_se.trim
/usr/local/share/khmer/scripts/extract-paired-reads.py combined-trim.fq

gzip -9c combined-trim.fq.pe > ../SRR492065.pe.qc.fq.gz
gzip -9c combined-trim.fq.se s1_se > ../SRR492065.se.qc.fq.gz

cd ../
rm -fr trim

Trim the second data set (~20 minutes):

mkdir trim
cd trim

java -jar /usr/local/bin/trimmomatic-0.27.jar PE ../SRR492066_?.fastq.gz s1_pe s1_se s2_pe s2_se ILLUMINACLIP:../illuminaClipping.fa:2:30:10

/usr/local/share/khmer/scripts/interleave-reads.py s?_pe > combined.fq

fastq_quality_filter -Q33 -q 30 -p 50 -i combined.fq > combined-trim.fq
fastq_quality_filter -Q33 -q 30 -p 50 -i s1_se > s1_se.trim
/usr/local/share/khmer/scripts/extract-paired-reads.py combined-trim.fq

gzip -9c combined-trim.fq.pe > ../SRR492066.pe.qc.fq.gz
gzip -9c combined-trim.fq.se s1_se > ../SRR492066.se.qc.fq.gz

cd ../
rm -fr trim

Done! Now you have four files: SRR492065.pe.qc.fq.gz, SRR492065.se.qc.fq.gz, SRR492066.pe.qc.fq.gz, and SRR492066.se.qc.fq.gz.

The ‘.pe’ files are interleaved paired-end; you can take a look at them like so:

gunzip -c SRR492065.pe.qc.fq.gz | head

The other two are single-ended files, where the reads have been orphaned because we discarded stuff.

All four files are in FASTQ format.

Next: 2. Running digital normalization

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